UBC Wiki - User contributions [en]

Library:Technology Borrowing/Arduino

2026-09-30T02:34:00Z

Sarah: /* Arduino Prototyping Platform */

==== Arduino Prototyping Platform ====
Originally created in partnership with the Engineering Physics Department, three Arduino kits are available for a two-week loan to UBC students, faculty & staff. For up to date availability, please see our [https://resolve.library.ubc.ca/cgi-bin/catsearch?bid=6984295 holdings information page]
* Woodward Library Information Desk

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-07-15T19:42:46Z

Sarah: /* What is open data? */

== Open Research Data ==

== Big Ideas/Questions ==
If research data is saved on a device or in the cloud or both, how will others find it should they want to look at the data you have used to support your research outputs? As mentioned in the OSF module (''link to that section)'' research tranparency and reproducibility are dependent on open practices which includes open data. Open and shared data and code deposited in a data archive/repository using a metadata standard for discoverability facilitates ease of access for others to find and reuse in order to build on existing scholarship.

== Outcomes ==
* Understand the reasons for making research data open
* Recognize why some data may not be suitable for reuse
* Identify different types of metadata
* Produce a README file that enables others to interpret data
* Describe and deposit data in an open repository applying a license to the data for reuse
* Integrate archived data into an OSF project

== Why it Matters to You? ==
By making research data open and any code associated with the data it allows others to easily access, share and re-use the data, re-run the code and reproduce the research results and validate your research.

Research transparency is required and recommended from funding agencies who not only have requested authors to make copies of their research outputs open access, but are increasingly requesting that the associated data be made open whenever possible. When you open your data you are not only fulfilling funding requirements, but can demonstrate the impact of your data when it is cited.

Opening data encourages and requires good data management practices by establishing a workflow for each stage of the data life cycle so that you can find and interpret that data months and years after having collected it. People who find your data will be able to do so as well. Data deposit into an open repository in a non-proprietary format allows you to control how the data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

[https://www.dataone.org/data-life-cycle Visual of the data life cycle]

== Practice ==

=== What is open data? ===

==== '''Introduction - What is open data?''' ====
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. Open data is guided by the FAIR principles for sharing data meaning that it should be findable, accessible, interoperable and reusable.

''Read…''
* [http://opendatahandbook.org/guide/en/what-is-open-data/ Open Data Handbook definition]
* [https://www.go-fair.org/fair-principles/ FAIR principles]
''Or watch...''
* [https://youtu.be/K-kEvfaUJdA FAIR principles] (embed video into module?)

==== Privacy and open data ====
Open data should be governed by the FAIR principles to promote transparency and reproducibility, but not all data can or should be made open. Even though the FAIR principles provide guidance for sharing data it is with the understanding that ethical and contractual obligations will be upheld. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories can offer options to keep the data closed or embargoed. Even in this more closed scenario a best practice is to let others know the data exists by creating a description of your data and use a metadata standard. for researchers to find and request access to the data

==== '''Policies and open data''' ====
As mentioned open data may be required by granting agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (''Add to references - needs proper citatiion:'' <nowiki>https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html</nowiki>). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.

''Read more...''
* DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.
* [https://open.canada.ca/en/open-data-principles#toc92 Open Data in Canada]
''Mini reflection...''

Consider how the DRAFT Tri-Agency RDM policy might impact how you manage your data.

''Watch...''

[https://learn.scholarsportal.info/modules/portage/rdm-101-module-3/ Canadian Policy Review]

(''or use embed code:'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=13" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

=== Implementing Open Data ===
There are two key steps to making your data open:
# Deposit your data into a data repository that anyone can access
# Add a license to your data indicating how it can be reused
Even before taking these steps you'll need to plan how you're going to manage your data from its collection to analysis.

While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse and you should prepare that data to be deposited into a repository.

>> ''insert image of PDF'' - Data Curation from portage <nowiki>https://portagenetwork.ca/wp-content/uploads/2019/08/Brief_Guide_Data_Curation_AUGUST2019_EN.pdf</nowiki>

==== Data repositories ====
There are institutional and disciplinary repositories for data. An example of an institutional repository is UBC's Dataverse hosted by Scholars Portal. This repository is discipline agnostic where any researcher can create an account and deposit their data and associated files using the provided template to create the metadata required to ensure the data is findable. For disciplinary repositories, the Registry of Research Data Repositories is one example where you can search or browse by discipline to find a repository that will be more useful to researchers in your field (<nowiki>https://www.re3data.org</nowiki> ). Both options provide a persistent identifier such as a doi to help with discovery, sharing, and citing your data.

''Note:'' You might discover data being stored in the OSF. The OSF can be an option to house raw data while working with it or it might be one of the options to store your data temporarily, but it is not recommended for long term preservation and storage.

When depositing into a repository an important piece of making the data open is applying an open license. Public domain licenses can be applied or an open license that requires attribution, but will allow the user to download, reuse and repurpose the data.

''Read...''
* [https://opendatacommons.org/licenses/index.html Open Data Commons - Licenses]
* [https://opendefinition.org/guide/data/ Guide to Open Data Licensing]

==== Documentation ====
File naming and structure are important pieces of managing your data to make it easier for others to use. In keeping with the FAIR principles data formats should be non-proprietary, and unencrypted and uncompressed (''Reference needed here to MIT RDM'' )

''Read...''
* [https://researchdata.library.ubc.ca/plan/organize-your-data/ Guidelines for organizing your data]
* [https://dataoneorg.github.io/Education/bestpractices/assign-descriptive-file Assign descriptive file names]
* ''link back to open tools or same reading from that section)''

==== Metadata ====
Metadata is essential for data discovery, sharing and reuse. As it is “data about data” metadata provides a description of the study, files and variables answering the questions:
* Who created the data?
* What is the content of the data?
* When were the data created?
* Where is it geographically?
* How were the data developed?
* Why were the data developed? (''need citation DataOne L07 Metadata lesson)''
There are three levels of metadata:
# Descriptive: Provides information about the data that will help people understand what they will find in the dataset and the context. Project title, authors and collection methods are all types of descriptive metadata. Be sure to describe your variables giving them clear names (r''eference to Good Enough Research Data management)''. Name files with basic metadata file names (''provide example from RDM guide - UBC'')
# Administrative: What software is required to use the data? What is the license attached to the data?
# Structural: How do the data files relate to one another (citation needed here for RDM workshop)
An established metadata standard will provide common terms, definitions, and structure and may vary depending on the repository you select. Each repository will have their own standard, but will be consistent in common terminology, definitions, language and structure. Good metadata ensures that your files are human and machine readable.

It is recommended that you include a README plain text file with the data deposit that helps researchers interpret you data. You might also include a data dictionary, codebooks or other documentation, but this could be included in the README file as well.

''Read...''
* [https://www.dataone.org/sites/all/documents/education-modules/handouts/L07_DefiningMetadata_Handout.pdf Metadata]
*
''How to...''
* [https://go.library.ubc.ca/zxFJCb Creating a README for your dataset]
* [https://www.wikihow.com/Write-a-Read-Me How to write a read me]
''Activities...''
* Draft a README file to accompany a future or current data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''[https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf Creating a README for your dataset]''
* Find a data repository where you could deposit your data. You might consult the [https://www.re3data.org Registry of Research Data Repositories] that has aggregated disciplinary repositories. Search their database or browse by subject. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.
* Explore a data repository and find a dataset in your discipline. Is there a Data Dictionary, Codebook or README file that helps interpret the data?

==== Data Management Plans ====
Everything established up until now are all pieces you'll need to create a data management plan (DMP). Before even collecting your data, you need to plan. Each stage of the data life cycle should be considered when planning how you will manage your data and make it accessible. A DMP will address questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. There are templates such as [https://assistant.portagenetwork.ca/ Portage's DMP Assistant] that aid in preparing a plan guiding through each step. There are institutional instances of Portage's DMP Assistant including one at UBC. Similar to fulfiling open access and open data requirement, it is not uncommon for research funders to request a DMP to be submitted with the funding application demonstrating how the data will be handled at each stage of the data life cycle.

''Read...''
* [https://portagenetwork.ca/portage-training-resources/ DMP Exemplars] Look for this heading to see examples of DMPs
* [https://dataoneorg.github.io/Education/bp_step/plan/ Best practice: Plan]
''Watch...''
* [https://portagenetwork.ca/portage-training-resources/rdm-101/ Module 4: Steps Towards Good Research Data Managment]
(''or embed option: portage'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=16" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

''Tools...''
* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template.

''Reflection...''

With reference to your own dataset reflect on sections of the DMP and consider if your data is ready for reuse.
==== Additional activities using the OSF: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

=== Options to Explore ===

==== Workshops at UBC ====
UBC offers a number of introductory workshops throughout the year. These are a great place to get started, with the opportunity to get set up for the first time and ask questions. Registration, dates, titles and course materials can all be found here: <nowiki>https://researchcommons.library.ubc.ca/workshops/</nowiki> Topics covered include:
* Introduction to the Open Science Framework
* Research Data Management
** Managing Active Research Data
** Preserving and Reusing Research Data

==== '''Workshops outside of UBC''' ====
* CESSDA Data Management Expert Guide: https://www.cessda.eu/Training/Training-Resources/Library/Data-Management-Expert-Guide/6.-Archive-Publish/Data-publishing-routes
* Data One Webinars (previously recorded) https://www.dataone.org/previous-webinars/2017
* FOSTER Courses <nowiki>https://www.fosteropenscience.eu/courses</nowiki>. Several courses require that you register to access the course materials, but there may also be a free text version linked within the course and available from Zenodo. For an example see: FOSTER Consortium. (2018, November). Managing and Sharing Research Data (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.2630562
* Portage Events https://portagenetwork.ca/portage-training-resources/portage-events/'''.''' Portage aims “to coordinate and expand existing expertise, services, and infrastructure so that all academic researchers in Canada have access to the support they need for research data management.”

==== Case studies ====
* Data stories from DataONE https://www.dataone.org/data-stories

==== '''Open Data Reading and Viewing''' ====
* Perrier L, Blondal E, MacDonald H (2020) The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis. PLoS ONE 15(2): e0229182. https://doi.org/10.1371/journal.pone.0229182
* Wessels, B., Finn, R.L., Wadhwa, K., Sveinsdottir, T. (2017). ''Open data and the knowledge society.'' Amsterdam: Amsterdam University Press. doi:10.5117/9789462980181. Retrieved from: http://tinyurl.com/yabeh5hm
* FAIR Principles https://youtu.be/OvEHYCSmzCA
* Open data handbook http://opendatahandbook.org/
Open science, open data https://www.fosteropenscience.eu/content/open-science-open-data

==== '''Research Data Guides''' ====
* UBC Research Data Management Guide https://researchdata.library.ubc.ca/share/
* RDM Toolkit section for Arts, Humanities and Social Sciences data https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/
* Humanities Data Curation Guide https://guide.dhcuration.org/about/
* Journal of Open Humanities Data https://openhumanitiesdata.metajnl.com/
* Center for Open Data in the Humanities http://codh.rois.ac.jp/index.html.en
* Indigenous Data Sovereignty https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html

==== '''Data Repositories''' ====
* Scholars Portal Dataverse https://dataverse.scholarsportal.info/

* Dryad https://datadryad.org/stash
* Figshare https://figshare.com/
* Zenodo https://zenodo.org/

===== '''Search for Repositories''' =====
* Registry of Research Data Repositories https://www.re3data.org/

== References ==
* ''full reference list to come...''
* CESSDA Training Team (2017 - 2019). ''CESSDA Data Management Expert Guide.''
Bergen, Norway: CESSDA ERIC. Retrieved from <nowiki>https://www.cessda.eu/DMGuide</nowiki>
* DataONE Best Practices Working Group, DataONE (July 01, 2010) "Best Practice: Assign descriptive file names". Accessed through the Data Management Skillbuilding Hub at <nowiki>https://dataoneorg.github.io/Education/bestpractices/assign-descriptive-file</nowiki> on Jan 06, 2020

* UBC RDM workshop
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T20:09:41Z

Sarah: /* Metadata */

== Open Research Data ==

== Big Ideas/Questions ==
If research data is saved on a device or in the cloud or both, how will others find it should they want to look at the data you have used to support your research outputs? As mentioned in the OSF module research tranparency and reproducibility are dependent on open practices which includes open data. Open and shared data and code deposited in a data archive/repository using a metadata standard for discoverability facilitates ease of access for others to find and reuse in order to build on existing scholarship.

== Outcomes ==
* Understand the reasons for making research data open
* Recognize why some data may not be suitable for reuse
* Identify different types of metadata
* Produce a README file that enables others to interpret data
* Describe and deposit data in an open repository applying a license to the data for reuse
* Integrate archived data into an OSF project

== Why it Matters to You? ==
By making research data open and any code associated with the data it allows others to easily access, share and re-use the data, re-run the code and reproduce the research results and validate your research.

Research transparency is required and recommended from funding agencies who not only have requested authors to make copies of their research outputs open access, but are increasingly requesting that the associated data be made open whenever possible. When you open your data you are not only fulfilling funding requirements, but can demonstrate the impact of your data when it is cited.

Opening data encourages and requires good data management practices by establishing a workflow for each stage of the data life cycle so that you can find and interpret that data months and years after having collected it. People who find your data will be able to do so as well. Data deposit into an open repository in a non-proprietary format allows you to control how the data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

[https://www.dataone.org/data-life-cycle Visual of the data life cycle]

== Practice ==

=== What is open data? ===

==== '''Introduction - What is open data?''' ====
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. Open data is guided by the FAIR principles for sharing data meaning that it should be findable, accessible, interoperable and reusable.

''Read…''
* [http://opendatahandbook.org/guide/en/what-is-open-data/ Open Data Handbook definition]
* [https://www.go-fair.org/fair-principles/ FAIR principles]
''Or watch...''
* [https://youtu.be/K-kEvfaUJdA FAIR principles] (embed video into module?)

==== Privacy and open data ====
Open data should be governed by the FAIR principles to promote transparency and reproducibility, but not all data can or should be made open. Even though the FAIR principles provide guidance for sharing data it is with the understanding that ethical and contractual obligations will be upheld. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories can offer options to keep the data closed or embargoed. Even in this more closed scenario a best practice is to let others know the data exists by creating a description of your data and use a metadata standard. for researchers to find and request access to the data

==== '''Policies and open data''' ====
As mentioned open data may be required by granting agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (''Add to references - needs proper citatiion:'' <nowiki>https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html</nowiki>). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.

''Read more...''
* DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.
* [https://open.canada.ca/en/open-data-principles#toc92 Open Data in Canada]
''Mini reflection...''

Consider how the DRAFT Tri-Agency RDM policy might impact how you manage your data.

''Watch...''

[https://learn.scholarsportal.info/modules/portage/rdm-101-module-3/ Canadian Policy Review]

(''or use embed code:'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=13" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

=== Implementing Open Data ===
Before even collecting your data, you need to plan. Each stage of the data life cycle should be considered when planning how you will manage your data and make it accessible.

While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse and you should prepare that data to be deposited into a repository.

>> ''insert image of PDF'' - Data Curation from portage <nowiki>https://portagenetwork.ca/wp-content/uploads/2019/08/Brief_Guide_Data_Curation_AUGUST2019_EN.pdf</nowiki>

==== Data repositories ====
There are institutional and disciplinary repositories for data. An example of an institutional repository is UBC's Dataverse hosted by Scholars Portal. This repository is discipline agnostic where any researcher can create an account and deposit their data and associated files using the provided template to create the metadata required to ensure the data is findable. For disciplinary repositories, the Registry of Research Data Repositories is one example where you can search or browse by discipline to find a repository that will be more useful to researchers in your field (<nowiki>https://www.re3data.org</nowiki> ). Both options provide a persistent identifier such as a doi to help with discovery, sharing, and citing your data.

''Note:'' You might discover data being stored in the OSF. The OSF can be an option to house raw data while working with it or it might be one of the options to store your data temporarily, but it is not recommended for long term preservation and storage.

When depositing into a repository an important piece of making the data open is applying an open license. Public domain licenses can be applied or an open license that requires attribution, but will allow the user to download, reuse and repurpose the data.

''Read...''
* [https://opendatacommons.org/licenses/index.html Open Data Commons - Licenses]
* [https://opendefinition.org/guide/data/ Guide to Open Data Licensing]

==== Documentation ====
File naming and structure are important pieces of managing your data to make it easier for others to use. In keeping with the FAIR principles data formats should be non-proprietary, and unencrypted and uncompressed (''Reference needed here to MIT RDM'' )

''Read...''
* [https://researchdata.library.ubc.ca/plan/organize-your-data/ Guidelines for organizing your data]
* [https://dataoneorg.github.io/Education/bestpractices/assign-descriptive-file Assign descriptive file names]
* ''link back to open tools or same reading from that section)''

==== Metadata ====
Metadata is essential for data discovery, sharing and reuse. As it is “data about data” metadata provides a description of the study, files and variables answering the questions:
* Who created the data?
* What is the content of the data?
* When were the data created?
* Where is it geographically?
* How were the data developed?
* Why were the data developed? (''need citation DataOne L07 Metadata lesson)''
There are three levels of metadata:
# Descriptive: Provides information about the data that will help people understand what they will find in the dataset and the context. Project title, authors and collection methods are all types of descriptive metadata. Be sure to describe your variables giving them clear names (r''eference to Good Enough Research Data management)''. Name files with basic metadata file names (''provide example from RDM guide - UBC'')
# Administrative: What software is required to use the data? What is the license attached to the data?
# Structural: How do the data files relate to one another (citation needed here for RDM workshop)
An established metadata standard will provide common terms, definitions, and structure and may vary depending on the repository you select. Each repository will have their own standard, but will be consistent in common terminology, definitions, language and structure. Good metadata ensures that your files are human and machine readable.

It is recommended that you include a README plain text file with the data deposit that helps researchers interpret you data. You might also include a data dictionary, codebooks or other documentation, but this could be included in the README file as well.

''Read...''
* [https://www.dataone.org/sites/all/documents/education-modules/handouts/L07_DefiningMetadata_Handout.pdf Metadata]
*
''How to...''
* [https://go.library.ubc.ca/zxFJCb Creating a README for your dataset]
* [https://www.wikihow.com/Write-a-Read-Me How to write a read me]
''Activities...''
* Draft a README file to accompany a future or current data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''[https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf Creating a README for your dataset]''
* Find a data repository where you could deposit your data. You might consult the [https://www.re3data.org Registry of Research Data Repositories] that has aggregated disciplinary repositories. Search their database or browse by subject. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.
* Explore a data repository and find a dataset in your discipline. Is there a Data Dictionary, Codebook or README file that helps interpret the data?

==== Data Management Plans ====
Everything established up until now are all pieces you'll need to create a data management plan (DMP). A DMP will address questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. There are templates such as [https://assistant.portagenetwork.ca/ Portage's DMP Assistant] that aid in preparing a plan guiding through each step. There are institutional instances of Portage's DMP Assistant including one at UBC. Similar to fulfiling open access and open data requirement, it is not uncommon for research funders to request a DMP to be submitted with the funding application demonstrating how the data will be handled at each stage of the data life cycle.

''Read...''
* [https://portagenetwork.ca/portage-training-resources/ DMP Exemplars] Look for this heading to see examples of DMPs
* [https://dataoneorg.github.io/Education/bp_step/plan/ Best practice: Plan]
''Watch...''
* [https://portagenetwork.ca/portage-training-resources/rdm-101/ Module 4: Steps Towards Good Research Data Managment]
(''or embed option: portage'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=16" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

''Tools...''
* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template.

''Reflection...''

With reference to your own dataset reflect on sections of the DMP and consider if your data is ready for reuse.
==== Additional activities using the OSF: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

=== Options to Explore ===

==== Workshops at UBC ====
UBC offers a number of introductory workshops throughout the year. These are a great place to get started, with the opportunity to get set up for the first time and ask questions. Registration, dates, titles and course materials can all be found here: <nowiki>https://researchcommons.library.ubc.ca/workshops/</nowiki> Topics covered include:
* Introduction to the Open Science Framework
* Research Data Management
** Managing Active Research Data
** Preserving and Reusing Research Data

==== '''Workshops outside of UBC''' ====
* CESSDA Data Management Expert Guide: https://www.cessda.eu/Training/Training-Resources/Library/Data-Management-Expert-Guide/6.-Archive-Publish/Data-publishing-routes
* Data One Webinars (previously recorded) https://www.dataone.org/previous-webinars/2017
* FOSTER Courses <nowiki>https://www.fosteropenscience.eu/courses</nowiki>. Several courses require that you register to access the course materials, but there may also be a free text version linked within the course and available from Zenodo. For an example see: FOSTER Consortium. (2018, November). Managing and Sharing Research Data (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.2630562
* Portage Events https://portagenetwork.ca/portage-training-resources/portage-events/'''.''' Portage aims “to coordinate and expand existing expertise, services, and infrastructure so that all academic researchers in Canada have access to the support they need for research data management.”

==== Case studies ====
* Data stories from DataONE https://www.dataone.org/data-stories

==== '''Open Data Reading and Viewing''' ====
* Perrier L, Blondal E, MacDonald H (2020) The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis. PLoS ONE 15(2): e0229182. https://doi.org/10.1371/journal.pone.0229182
* Wessels, B., Finn, R.L., Wadhwa, K., Sveinsdottir, T. (2017). ''Open data and the knowledge society.'' Amsterdam: Amsterdam University Press. doi:10.5117/9789462980181. Retrieved from: http://tinyurl.com/yabeh5hm
* FAIR Principles https://youtu.be/OvEHYCSmzCA
* Open data handbook http://opendatahandbook.org/
Open science, open data https://www.fosteropenscience.eu/content/open-science-open-data

==== '''Research Data Guides''' ====
* UBC Research Data Management Guide https://researchdata.library.ubc.ca/share/
* RDM Toolkit section for Arts, Humanities and Social Sciences data https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/
* Humanities Data Curation Guide https://guide.dhcuration.org/about/
* Journal of Open Humanities Data https://openhumanitiesdata.metajnl.com/
* Center for Open Data in the Humanities http://codh.rois.ac.jp/index.html.en
* Indigenous Data Sovereignty https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html

==== '''Data Repositories''' ====
* Scholars Portal Dataverse https://dataverse.scholarsportal.info/

* Dryad https://datadryad.org/stash
* Figshare https://figshare.com/
* Zenodo https://zenodo.org/

===== '''Search for Repositories''' =====
* Registry of Research Data Repositories https://www.re3data.org/

== References ==
* ''full reference list to come...''
* CESSDA Training Team (2017 - 2019). ''CESSDA Data Management Expert Guide.''
Bergen, Norway: CESSDA ERIC. Retrieved from <nowiki>https://www.cessda.eu/DMGuide</nowiki>
* DataONE Best Practices Working Group, DataONE (July 01, 2010) "Best Practice: Assign descriptive file names". Accessed through the Data Management Skillbuilding Hub at <nowiki>https://dataoneorg.github.io/Education/bestpractices/assign-descriptive-file</nowiki> on Jan 06, 2020

* UBC RDM workshop
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T20:01:12Z

Sarah: /* Why it Matters to You? */

== Open Research Data ==

== Big Ideas/Questions ==
If research data is saved on a device or in the cloud or both, how will others find it should they want to look at the data you have used to support your research outputs? As mentioned in the OSF module research tranparency and reproducibility are dependent on open practices which includes open data. Open and shared data and code deposited in a data archive/repository using a metadata standard for discoverability facilitates ease of access for others to find and reuse in order to build on existing scholarship.

== Outcomes ==
* Understand the reasons for making research data open
* Recognize why some data may not be suitable for reuse
* Identify different types of metadata
* Produce a README file that enables others to interpret data
* Describe and deposit data in an open repository applying a license to the data for reuse
* Integrate archived data into an OSF project

== Why it Matters to You? ==
By making research data open and any code associated with the data it allows others to easily access, share and re-use the data, re-run the code and reproduce the research results and validate your research.

Research transparency is required and recommended from funding agencies who not only have requested authors to make copies of their research outputs open access, but are increasingly requesting that the associated data be made open whenever possible. When you open your data you are not only fulfilling funding requirements, but can demonstrate the impact of your data when it is cited.

Opening data encourages and requires good data management practices by establishing a workflow for each stage of the data life cycle so that you can find and interpret that data months and years after having collected it. People who find your data will be able to do so as well. Data deposit into an open repository in a non-proprietary format allows you to control how the data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

[https://www.dataone.org/data-life-cycle Visual of the data life cycle]

== Practice ==

=== What is open data? ===

==== '''Introduction - What is open data?''' ====
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. Open data is guided by the FAIR principles for sharing data meaning that it should be findable, accessible, interoperable and reusable.

''Read…''
* [http://opendatahandbook.org/guide/en/what-is-open-data/ Open Data Handbook definition]
* [https://www.go-fair.org/fair-principles/ FAIR principles]
''Or watch...''
* [https://youtu.be/K-kEvfaUJdA FAIR principles] (embed video into module?)

==== Privacy and open data ====
Open data should be governed by the FAIR principles to promote transparency and reproducibility, but not all data can or should be made open. Even though the FAIR principles provide guidance for sharing data it is with the understanding that ethical and contractual obligations will be upheld. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories can offer options to keep the data closed or embargoed. Even in this more closed scenario a best practice is to let others know the data exists by creating a description of your data and use a metadata standard. for researchers to find and request access to the data

==== '''Policies and open data''' ====
As mentioned open data may be required by granting agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (''Add to references - needs proper citatiion:'' <nowiki>https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html</nowiki>). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.

''Read more...''
* DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.
* [https://open.canada.ca/en/open-data-principles#toc92 Open Data in Canada]
''Mini reflection...''

Consider how the DRAFT Tri-Agency RDM policy might impact how you manage your data.

''Watch...''

[https://learn.scholarsportal.info/modules/portage/rdm-101-module-3/ Canadian Policy Review]

(''or use embed code:'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=13" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

=== Implementing Open Data ===
Before even collecting your data, you need to plan. Each stage of the data life cycle should be considered when planning how you will manage your data and make it accessible.

While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse and you should prepare that data to be deposited into a repository.

>> ''insert image of PDF'' - Data Curation from portage <nowiki>https://portagenetwork.ca/wp-content/uploads/2019/08/Brief_Guide_Data_Curation_AUGUST2019_EN.pdf</nowiki>

==== Data repositories ====
There are institutional and disciplinary repositories for data. An example of an institutional repository is UBC's Dataverse hosted by Scholars Portal. This repository is discipline agnostic where any researcher can create an account and deposit their data and associated files using the provided template to create the metadata required to ensure the data is findable. For disciplinary repositories, the Registry of Research Data Repositories is one example where you can search or browse by discipline to find a repository that will be more useful to researchers in your field (<nowiki>https://www.re3data.org</nowiki> ). Both options provide a persistent identifier such as a doi to help with discovery, sharing, and citing your data.

''Note:'' You might discover data being stored in the OSF. The OSF can be an option to house raw data while working with it or it might be one of the options to store your data temporarily, but it is not recommended for long term preservation and storage.

When depositing into a repository an important piece of making the data open is applying an open license. Public domain licenses can be applied or an open license that requires attribution, but will allow the user to download, reuse and repurpose the data.

''Read...''
* [https://opendatacommons.org/licenses/index.html Open Data Commons - Licenses]
* [https://opendefinition.org/guide/data/ Guide to Open Data Licensing]

==== Documentation ====
File naming and structure are important pieces of managing your data to make it easier for others to use. In keeping with the FAIR principles data formats should be non-proprietary, and unencrypted and uncompressed (''Reference needed here to MIT RDM'' )

''Read...''
* [https://researchdata.library.ubc.ca/plan/organize-your-data/ Guidelines for organizing your data]
* [https://dataoneorg.github.io/Education/bestpractices/assign-descriptive-file Assign descriptive file names]
* ''link back to open tools or same reading from that section)''

==== Metadata ====
Metadata is essential for data discovery, sharing and reuse. As it is “data about data” metadata provides a description of the study, files and variables. There are three levels of metadata:
# Descriptive: Provides information about the data that will help people understand what they will find in the dataset and the context. Project title, authors and collection methods are all types of descriptive metadata. Be sure to describe your variables giving them clear names (r''eference to Good Enough Research Data management)''. Name files with basic metadata file names (''provide example from RDM guide - UBC'')
# Administrative: What software is required to use the data? What is the license attached to the data?
# Structural: How do the data files relate to one another (citation needed here for RDM workshop)
The metadata standard you use will depend on the repository you select. Each repository will have their own standard.

It is recommended that you include a README plain text file with the data deposit that helps researchers interpret you data. You might also include a data dictionary, codebooks or other documentation, but this could be included in the README file as well.

''How to...''
* [https://go.library.ubc.ca/zxFJCb Creating a README for your dataset]
* [https://www.wikihow.com/Write-a-Read-Me How to write a read me]
''Activities...''
* Draft a README file to accompany a future or current data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''[https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf Creating a README for your dataset]''
* Find a data repository where you could deposit your data. You might consult the [https://www.re3data.org Registry of Research Data Repositories] that has aggregated disciplinary repositories. Search their database or browse by subject. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.
* Explore a data repository and find a dataset in your discipline. Is there a Data Dictionary, Codebook or README file that helps interpret the data?

==== Data Management Plans ====
Everything established up until now are all pieces you'll need to create a data management plan (DMP). A DMP will address questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. There are templates such as [https://assistant.portagenetwork.ca/ Portage's DMP Assistant] that aid in preparing a plan guiding through each step. There are institutional instances of Portage's DMP Assistant including one at UBC. Similar to fulfiling open access and open data requirement, it is not uncommon for research funders to request a DMP to be submitted with the funding application demonstrating how the data will be handled at each stage of the data life cycle.

''Read...''
* [https://portagenetwork.ca/portage-training-resources/ DMP Exemplars] Look for this heading to see examples of DMPs
* [https://dataoneorg.github.io/Education/bp_step/plan/ Best practice: Plan]
''Watch...''
* [https://portagenetwork.ca/portage-training-resources/rdm-101/ Module 4: Steps Towards Good Research Data Managment]
(''or embed option: portage'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=16" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

''Tools...''
* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template.

''Reflection...''

With reference to your own dataset reflect on sections of the DMP and consider if your data is ready for reuse.
==== Additional activities using the OSF: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

=== Options to Explore ===

==== Workshops at UBC ====
UBC offers a number of introductory workshops throughout the year. These are a great place to get started, with the opportunity to get set up for the first time and ask questions. Registration, dates, titles and course materials can all be found here: <nowiki>https://researchcommons.library.ubc.ca/workshops/</nowiki> Topics covered include:
* Introduction to the Open Science Framework
* Research Data Management
** Managing Active Research Data
** Preserving and Reusing Research Data

==== '''Workshops outside of UBC''' ====
* CESSDA Data Management Expert Guide: https://www.cessda.eu/Training/Training-Resources/Library/Data-Management-Expert-Guide/6.-Archive-Publish/Data-publishing-routes
* Data One Webinars (previously recorded) https://www.dataone.org/previous-webinars/2017
* FOSTER Courses <nowiki>https://www.fosteropenscience.eu/courses</nowiki>. Several courses require that you register to access the course materials, but there may also be a free text version linked within the course and available from Zenodo. For an example see: FOSTER Consortium. (2018, November). Managing and Sharing Research Data (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.2630562
* Portage Events https://portagenetwork.ca/portage-training-resources/portage-events/'''.''' Portage aims “to coordinate and expand existing expertise, services, and infrastructure so that all academic researchers in Canada have access to the support they need for research data management.”

==== Case studies ====
* Data stories from DataONE https://www.dataone.org/data-stories

==== '''Open Data Reading and Viewing''' ====
* Perrier L, Blondal E, MacDonald H (2020) The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis. PLoS ONE 15(2): e0229182. https://doi.org/10.1371/journal.pone.0229182
* Wessels, B., Finn, R.L., Wadhwa, K., Sveinsdottir, T. (2017). ''Open data and the knowledge society.'' Amsterdam: Amsterdam University Press. doi:10.5117/9789462980181. Retrieved from: http://tinyurl.com/yabeh5hm
* FAIR Principles https://youtu.be/OvEHYCSmzCA
* Open data handbook http://opendatahandbook.org/
Open science, open data https://www.fosteropenscience.eu/content/open-science-open-data

==== '''Research Data Guides''' ====
* UBC Research Data Management Guide https://researchdata.library.ubc.ca/share/
* RDM Toolkit section for Arts, Humanities and Social Sciences data https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/
* Humanities Data Curation Guide https://guide.dhcuration.org/about/
* Journal of Open Humanities Data https://openhumanitiesdata.metajnl.com/
* Center for Open Data in the Humanities http://codh.rois.ac.jp/index.html.en
* Indigenous Data Sovereignty https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html

==== '''Data Repositories''' ====
* Scholars Portal Dataverse https://dataverse.scholarsportal.info/

* Dryad https://datadryad.org/stash
* Figshare https://figshare.com/
* Zenodo https://zenodo.org/

===== '''Search for Repositories''' =====
* Registry of Research Data Repositories https://www.re3data.org/

== References ==
* ''full reference list to come...''
* CESSDA Training Team (2017 - 2019). ''CESSDA Data Management Expert Guide.''
Bergen, Norway: CESSDA ERIC. Retrieved from <nowiki>https://www.cessda.eu/DMGuide</nowiki>
* DataONE Best Practices Working Group, DataONE (July 01, 2010) "Best Practice: Assign descriptive file names". Accessed through the Data Management Skillbuilding Hub at <nowiki>https://dataoneorg.github.io/Education/bestpractices/assign-descriptive-file</nowiki> on Jan 06, 2020

* UBC RDM workshop
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:57:34Z

Sarah: /* Documentation */

== Open Research Data ==

== Big Ideas/Questions ==
If research data is saved on a device or in the cloud or both, how will others find it should they want to look at the data you have used to support your research outputs? As mentioned in the OSF module research tranparency and reproducibility are dependent on open practices which includes open data. Open and shared data and code deposited in a data archive/repository using a metadata standard for discoverability facilitates ease of access for others to find and reuse in order to build on existing scholarship.

== Outcomes ==
* Understand the reasons for making research data open
* Recognize why some data may not be suitable for reuse
* Identify different types of metadata
* Produce a README file that enables others to interpret data
* Describe and deposit data in an open repository applying a license to the data for reuse
* Integrate archived data into an OSF project

== Why it Matters to You? ==
By making research data open and any code associated with the data it allows others to easily access, share and re-use the data, re-run the code and reproduce the research results and validate your research.

Research transparency is required and recommended from funding agencies who not only have requested authors to make copies of their research outputs open access, but are increasingly requesting that the associated data be made open whenever possible. When you open your data you are not only fulfilling funding requirements, but can demonstrate the impact of your data when it is cited.

Opening data encourages and requires good data management practices by establishing a workflow for each stage of the data life cycle so that you can find and interpret that data months and years after having collected it. People who find your data will be able to do so as well. Data deposit into an open repository in a non-proprietary format allows you to control how the data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

[https://biblio.uottawa.ca/en/services/faculty/research-data-management/what-research-data-management Visual of the data life cycle]

== Practice ==

=== What is open data? ===

==== '''Introduction - What is open data?''' ====
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. Open data is guided by the FAIR principles for sharing data meaning that it should be findable, accessible, interoperable and reusable.

''Read…''
* [http://opendatahandbook.org/guide/en/what-is-open-data/ Open Data Handbook definition]
* [https://www.go-fair.org/fair-principles/ FAIR principles]
''Or watch...''
* [https://youtu.be/K-kEvfaUJdA FAIR principles] (embed video into module?)

==== Privacy and open data ====
Open data should be governed by the FAIR principles to promote transparency and reproducibility, but not all data can or should be made open. Even though the FAIR principles provide guidance for sharing data it is with the understanding that ethical and contractual obligations will be upheld. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories can offer options to keep the data closed or embargoed. Even in this more closed scenario a best practice is to let others know the data exists by creating a description of your data and use a metadata standard. for researchers to find and request access to the data

==== '''Policies and open data''' ====
As mentioned open data may be required by granting agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (''Add to references - needs proper citatiion:'' <nowiki>https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html</nowiki>). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.

''Read more...''
* DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.
* [https://open.canada.ca/en/open-data-principles#toc92 Open Data in Canada]
''Mini reflection...''

Consider how the DRAFT Tri-Agency RDM policy might impact how you manage your data.

''Watch...''

[https://learn.scholarsportal.info/modules/portage/rdm-101-module-3/ Canadian Policy Review]

(''or use embed code:'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=13" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

=== Implementing Open Data ===
Before even collecting your data, you need to plan. Each stage of the data life cycle should be considered when planning how you will manage your data and make it accessible.

While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse and you should prepare that data to be deposited into a repository.

>> ''insert image of PDF'' - Data Curation from portage <nowiki>https://portagenetwork.ca/wp-content/uploads/2019/08/Brief_Guide_Data_Curation_AUGUST2019_EN.pdf</nowiki>

==== Data repositories ====
There are institutional and disciplinary repositories for data. An example of an institutional repository is UBC's Dataverse hosted by Scholars Portal. This repository is discipline agnostic where any researcher can create an account and deposit their data and associated files using the provided template to create the metadata required to ensure the data is findable. For disciplinary repositories, the Registry of Research Data Repositories is one example where you can search or browse by discipline to find a repository that will be more useful to researchers in your field (<nowiki>https://www.re3data.org</nowiki> ). Both options provide a persistent identifier such as a doi to help with discovery, sharing, and citing your data.

''Note:'' You might discover data being stored in the OSF. The OSF can be an option to house raw data while working with it or it might be one of the options to store your data temporarily, but it is not recommended for long term preservation and storage.

When depositing into a repository an important piece of making the data open is applying an open license. Public domain licenses can be applied or an open license that requires attribution, but will allow the user to download, reuse and repurpose the data.

''Read...''
* [https://opendatacommons.org/licenses/index.html Open Data Commons - Licenses]
* [https://opendefinition.org/guide/data/ Guide to Open Data Licensing]

==== Documentation ====
File naming and structure are important pieces of managing your data to make it easier for others to use. In keeping with the FAIR principles data formats should be non-proprietary, and unencrypted and uncompressed (''Reference needed here to MIT RDM'' )

''Read...''
* [https://researchdata.library.ubc.ca/plan/organize-your-data/ Guidelines for organizing your data]
* [https://dataoneorg.github.io/Education/bestpractices/assign-descriptive-file Assign descriptive file names]
* ''link back to open tools or same reading from that section)''

==== Metadata ====
Metadata is essential for data discovery, sharing and reuse. As it is “data about data” metadata provides a description of the study, files and variables. There are three levels of metadata:
# Descriptive: Provides information about the data that will help people understand what they will find in the dataset and the context. Project title, authors and collection methods are all types of descriptive metadata. Be sure to describe your variables giving them clear names (r''eference to Good Enough Research Data management)''. Name files with basic metadata file names (''provide example from RDM guide - UBC'')
# Administrative: What software is required to use the data? What is the license attached to the data?
# Structural: How do the data files relate to one another (citation needed here for RDM workshop)
The metadata standard you use will depend on the repository you select. Each repository will have their own standard.

It is recommended that you include a README plain text file with the data deposit that helps researchers interpret you data. You might also include a data dictionary, codebooks or other documentation, but this could be included in the README file as well.

''How to...''
* [https://go.library.ubc.ca/zxFJCb Creating a README for your dataset]
* [https://www.wikihow.com/Write-a-Read-Me How to write a read me]
''Activities...''
* Draft a README file to accompany a future or current data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''[https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf Creating a README for your dataset]''
* Find a data repository where you could deposit your data. You might consult the [https://www.re3data.org Registry of Research Data Repositories] that has aggregated disciplinary repositories. Search their database or browse by subject. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.
* Explore a data repository and find a dataset in your discipline. Is there a Data Dictionary, Codebook or README file that helps interpret the data?

==== Data Management Plans ====
Everything established up until now are all pieces you'll need to create a data management plan (DMP). A DMP will address questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. There are templates such as [https://assistant.portagenetwork.ca/ Portage's DMP Assistant] that aid in preparing a plan guiding through each step. There are institutional instances of Portage's DMP Assistant including one at UBC. Similar to fulfiling open access and open data requirement, it is not uncommon for research funders to request a DMP to be submitted with the funding application demonstrating how the data will be handled at each stage of the data life cycle.

''Read...''
* [https://portagenetwork.ca/portage-training-resources/ DMP Exemplars] Look for this heading to see examples of DMPs
* [https://dataoneorg.github.io/Education/bp_step/plan/ Best practice: Plan]
''Watch...''
* [https://portagenetwork.ca/portage-training-resources/rdm-101/ Module 4: Steps Towards Good Research Data Managment]
(''or embed option: portage'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=16" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

''Tools...''
* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template.

''Reflection...''

With reference to your own dataset reflect on sections of the DMP and consider if your data is ready for reuse.
==== Additional activities using the OSF: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

=== Options to Explore ===

==== Workshops at UBC ====
UBC offers a number of introductory workshops throughout the year. These are a great place to get started, with the opportunity to get set up for the first time and ask questions. Registration, dates, titles and course materials can all be found here: <nowiki>https://researchcommons.library.ubc.ca/workshops/</nowiki> Topics covered include:
* Introduction to the Open Science Framework
* Research Data Management
** Managing Active Research Data
** Preserving and Reusing Research Data

==== '''Workshops outside of UBC''' ====
* CESSDA Data Management Expert Guide: https://www.cessda.eu/Training/Training-Resources/Library/Data-Management-Expert-Guide/6.-Archive-Publish/Data-publishing-routes
* Data One Webinars (previously recorded) https://www.dataone.org/previous-webinars/2017
* FOSTER Courses <nowiki>https://www.fosteropenscience.eu/courses</nowiki>. Several courses require that you register to access the course materials, but there may also be a free text version linked within the course and available from Zenodo. For an example see: FOSTER Consortium. (2018, November). Managing and Sharing Research Data (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.2630562
* Portage Events https://portagenetwork.ca/portage-training-resources/portage-events/'''.''' Portage aims “to coordinate and expand existing expertise, services, and infrastructure so that all academic researchers in Canada have access to the support they need for research data management.”

==== Case studies ====
* Data stories from DataONE https://www.dataone.org/data-stories

==== '''Open Data Reading and Viewing''' ====
* Perrier L, Blondal E, MacDonald H (2020) The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis. PLoS ONE 15(2): e0229182. https://doi.org/10.1371/journal.pone.0229182
* Wessels, B., Finn, R.L., Wadhwa, K., Sveinsdottir, T. (2017). ''Open data and the knowledge society.'' Amsterdam: Amsterdam University Press. doi:10.5117/9789462980181. Retrieved from: http://tinyurl.com/yabeh5hm
* FAIR Principles https://youtu.be/OvEHYCSmzCA
* Open data handbook http://opendatahandbook.org/
Open science, open data https://www.fosteropenscience.eu/content/open-science-open-data

==== '''Research Data Guides''' ====
* UBC Research Data Management Guide https://researchdata.library.ubc.ca/share/
* RDM Toolkit section for Arts, Humanities and Social Sciences data https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/
* Humanities Data Curation Guide https://guide.dhcuration.org/about/
* Journal of Open Humanities Data https://openhumanitiesdata.metajnl.com/
* Center for Open Data in the Humanities http://codh.rois.ac.jp/index.html.en
* Indigenous Data Sovereignty https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html

==== '''Data Repositories''' ====
* Scholars Portal Dataverse https://dataverse.scholarsportal.info/

* Dryad https://datadryad.org/stash
* Figshare https://figshare.com/
* Zenodo https://zenodo.org/

===== '''Search for Repositories''' =====
* Registry of Research Data Repositories https://www.re3data.org/

== References ==
* ''full reference list to come...''
* CESSDA Training Team (2017 - 2019). ''CESSDA Data Management Expert Guide.''
Bergen, Norway: CESSDA ERIC. Retrieved from <nowiki>https://www.cessda.eu/DMGuide</nowiki>
* DataONE Best Practices Working Group, DataONE (July 01, 2010) "Best Practice: Assign descriptive file names". Accessed through the Data Management Skillbuilding Hub at <nowiki>https://dataoneorg.github.io/Education/bestpractices/assign-descriptive-file</nowiki> on Jan 06, 2020

* UBC RDM workshop
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:52:43Z

Sarah:

== Open Research Data ==

== Big Ideas/Questions ==
If research data is saved on a device or in the cloud or both, how will others find it should they want to look at the data you have used to support your research outputs? As mentioned in the OSF module research tranparency and reproducibility are dependent on open practices which includes open data. Open and shared data and code deposited in a data archive/repository using a metadata standard for discoverability facilitates ease of access for others to find and reuse in order to build on existing scholarship.

== Outcomes ==
* Understand the reasons for making research data open
* Recognize why some data may not be suitable for reuse
* Identify different types of metadata
* Produce a README file that enables others to interpret data
* Describe and deposit data in an open repository applying a license to the data for reuse
* Integrate archived data into an OSF project

== Why it Matters to You? ==
By making research data open and any code associated with the data it allows others to easily access, share and re-use the data, re-run the code and reproduce the research results and validate your research.

Research transparency is required and recommended from funding agencies who not only have requested authors to make copies of their research outputs open access, but are increasingly requesting that the associated data be made open whenever possible. When you open your data you are not only fulfilling funding requirements, but can demonstrate the impact of your data when it is cited.

Opening data encourages and requires good data management practices by establishing a workflow for each stage of the data life cycle so that you can find and interpret that data months and years after having collected it. People who find your data will be able to do so as well. Data deposit into an open repository in a non-proprietary format allows you to control how the data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

[https://biblio.uottawa.ca/en/services/faculty/research-data-management/what-research-data-management Visual of the data life cycle]

== Practice ==

=== What is open data? ===

==== '''Introduction - What is open data?''' ====
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. Open data is guided by the FAIR principles for sharing data meaning that it should be findable, accessible, interoperable and reusable.

''Read…''
* [http://opendatahandbook.org/guide/en/what-is-open-data/ Open Data Handbook definition]
* [https://www.go-fair.org/fair-principles/ FAIR principles]
''Or watch...''
* [https://youtu.be/K-kEvfaUJdA FAIR principles] (embed video into module?)

==== Privacy and open data ====
Open data should be governed by the FAIR principles to promote transparency and reproducibility, but not all data can or should be made open. Even though the FAIR principles provide guidance for sharing data it is with the understanding that ethical and contractual obligations will be upheld. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories can offer options to keep the data closed or embargoed. Even in this more closed scenario a best practice is to let others know the data exists by creating a description of your data and use a metadata standard. for researchers to find and request access to the data

==== '''Policies and open data''' ====
As mentioned open data may be required by granting agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (''Add to references - needs proper citatiion:'' <nowiki>https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html</nowiki>). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.

''Read more...''
* DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.
* [https://open.canada.ca/en/open-data-principles#toc92 Open Data in Canada]
''Mini reflection...''

Consider how the DRAFT Tri-Agency RDM policy might impact how you manage your data.

''Watch...''

[https://learn.scholarsportal.info/modules/portage/rdm-101-module-3/ Canadian Policy Review]

(''or use embed code:'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=13" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

=== Implementing Open Data ===
Before even collecting your data, you need to plan. Each stage of the data life cycle should be considered when planning how you will manage your data and make it accessible.

While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse and you should prepare that data to be deposited into a repository.

>> ''insert image of PDF'' - Data Curation from portage <nowiki>https://portagenetwork.ca/wp-content/uploads/2019/08/Brief_Guide_Data_Curation_AUGUST2019_EN.pdf</nowiki>

==== Data repositories ====
There are institutional and disciplinary repositories for data. An example of an institutional repository is UBC's Dataverse hosted by Scholars Portal. This repository is discipline agnostic where any researcher can create an account and deposit their data and associated files using the provided template to create the metadata required to ensure the data is findable. For disciplinary repositories, the Registry of Research Data Repositories is one example where you can search or browse by discipline to find a repository that will be more useful to researchers in your field (<nowiki>https://www.re3data.org</nowiki> ). Both options provide a persistent identifier such as a doi to help with discovery, sharing, and citing your data.

''Note:'' You might discover data being stored in the OSF. The OSF can be an option to house raw data while working with it or it might be one of the options to store your data temporarily, but it is not recommended for long term preservation and storage.

When depositing into a repository an important piece of making the data open is applying an open license. Public domain licenses can be applied or an open license that requires attribution, but will allow the user to download, reuse and repurpose the data.

''Read...''
* [https://opendatacommons.org/licenses/index.html Open Data Commons - Licenses]
* [https://opendefinition.org/guide/data/ Guide to Open Data Licensing]

==== Documentation ====
File naming and structure are important pieces of managing your data to make it easier for others to use. In keeping with the FAIR principles data formats should be non-proprietary, and unencrypted and uncompressed (''Reference needed here to MIT RDM'' )

''Read...''
* [https://researchdata.library.ubc.ca/plan/organize-your-data/ Guidelines for organizing your data]
* Assign descriptive file names https://dataoneorg.github.io/Education/bestpractices/assign-descriptive-file
* ''link back to open tools or same reading from that section)''

==== Metadata ====
Metadata is essential for data discovery, sharing and reuse. As it is “data about data” metadata provides a description of the study, files and variables. There are three levels of metadata:
# Descriptive: Provides information about the data that will help people understand what they will find in the dataset and the context. Project title, authors and collection methods are all types of descriptive metadata. Be sure to describe your variables giving them clear names (r''eference to Good Enough Research Data management)''. Name files with basic metadata file names (''provide example from RDM guide - UBC'')
# Administrative: What software is required to use the data? What is the license attached to the data?
# Structural: How do the data files relate to one another (citation needed here for RDM workshop)
The metadata standard you use will depend on the repository you select. Each repository will have their own standard.

It is recommended that you include a README plain text file with the data deposit that helps researchers interpret you data. You might also include a data dictionary, codebooks or other documentation, but this could be included in the README file as well.

''How to...''
* [https://go.library.ubc.ca/zxFJCb Creating a README for your dataset]
* [https://www.wikihow.com/Write-a-Read-Me How to write a read me]
''Activities...''
* Draft a README file to accompany a future or current data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''[https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf Creating a README for your dataset]''
* Find a data repository where you could deposit your data. You might consult the [https://www.re3data.org Registry of Research Data Repositories] that has aggregated disciplinary repositories. Search their database or browse by subject. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.
* Explore a data repository and find a dataset in your discipline. Is there a Data Dictionary, Codebook or README file that helps interpret the data?

==== Data Management Plans ====
Everything established up until now are all pieces you'll need to create a data management plan (DMP). A DMP will address questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. There are templates such as [https://assistant.portagenetwork.ca/ Portage's DMP Assistant] that aid in preparing a plan guiding through each step. There are institutional instances of Portage's DMP Assistant including one at UBC. Similar to fulfiling open access and open data requirement, it is not uncommon for research funders to request a DMP to be submitted with the funding application demonstrating how the data will be handled at each stage of the data life cycle.

''Read...''
* [https://portagenetwork.ca/portage-training-resources/ DMP Exemplars] Look for this heading to see examples of DMPs
''Watch...''
* [https://portagenetwork.ca/portage-training-resources/rdm-101/ Module 4: Steps Towards Good Research Data Managment]
(''or embed option: portage'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=16" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

''Tools...''
* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template.

''Reflection...''

With reference to your own dataset reflect on sections of the DMP and consider if your data is ready for reuse.
==== Additional activities using the OSF: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

=== Options to Explore ===

==== Workshops at UBC ====
UBC offers a number of introductory workshops throughout the year. These are a great place to get started, with the opportunity to get set up for the first time and ask questions. Registration, dates, titles and course materials can all be found here: <nowiki>https://researchcommons.library.ubc.ca/workshops/</nowiki> Topics covered include:
* Introduction to the Open Science Framework
* Research Data Management
** Managing Active Research Data
** Preserving and Reusing Research Data

==== '''Workshops outside of UBC''' ====
* CESSDA Data Management Expert Guide: https://www.cessda.eu/Training/Training-Resources/Library/Data-Management-Expert-Guide/6.-Archive-Publish/Data-publishing-routes
* Data One Webinars (previously recorded) https://www.dataone.org/previous-webinars/2017
* FOSTER Courses <nowiki>https://www.fosteropenscience.eu/courses</nowiki>. Several courses require that you register to access the course materials, but there may also be a free text version linked within the course and available from Zenodo. For an example see: FOSTER Consortium. (2018, November). Managing and Sharing Research Data (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.2630562
* Portage Events https://portagenetwork.ca/portage-training-resources/portage-events/'''.''' Portage aims “to coordinate and expand existing expertise, services, and infrastructure so that all academic researchers in Canada have access to the support they need for research data management.”

==== Case studies ====
* Data stories from DataONE https://www.dataone.org/data-stories

==== '''Open Data Reading and Viewing''' ====
* Perrier L, Blondal E, MacDonald H (2020) The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis. PLoS ONE 15(2): e0229182. https://doi.org/10.1371/journal.pone.0229182
* Wessels, B., Finn, R.L., Wadhwa, K., Sveinsdottir, T. (2017). ''Open data and the knowledge society.'' Amsterdam: Amsterdam University Press. doi:10.5117/9789462980181. Retrieved from: http://tinyurl.com/yabeh5hm
* FAIR Principles https://youtu.be/OvEHYCSmzCA
* Open data handbook http://opendatahandbook.org/
Open science, open data https://www.fosteropenscience.eu/content/open-science-open-data

==== '''Research Data Guides''' ====
* UBC Research Data Management Guide https://researchdata.library.ubc.ca/share/
* RDM Toolkit section for Arts, Humanities and Social Sciences data https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/
* Humanities Data Curation Guide https://guide.dhcuration.org/about/
* Journal of Open Humanities Data https://openhumanitiesdata.metajnl.com/
* Center for Open Data in the Humanities http://codh.rois.ac.jp/index.html.en
* Indigenous Data Sovereignty https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html

==== '''Data Repositories''' ====
* Scholars Portal Dataverse https://dataverse.scholarsportal.info/

* Dryad https://datadryad.org/stash
* Figshare https://figshare.com/
* Zenodo https://zenodo.org/

===== '''Search for Repositories''' =====
* Registry of Research Data Repositories https://www.re3data.org/

== References ==
* ''full reference list to come...''
* CESSDA Training Team (2017 - 2019). ''CESSDA Data Management Expert Guide.''
Bergen, Norway: CESSDA ERIC. Retrieved from <nowiki>https://www.cessda.eu/DMGuide</nowiki>
* DataONE Best Practices Working Group, DataONE (July 01, 2010) "Best Practice: Assign descriptive file names". Accessed through the Data Management Skillbuilding Hub at <nowiki>https://dataoneorg.github.io/Education/bestpractices/assign-descriptive-file</nowiki> on Jan 06, 2020

* UBC RDM workshop
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:47:48Z

Sarah: /* Options to Explore */

== Open Research Data ==

== Big Ideas/Questions ==
If research data is saved on a device or in the cloud or both, how will others find it should they want to look at the data you have used to support your research outputs? As mentioned in the OSF module research tranparency and reproducibility are dependent on open practices which includes open data. Open and shared data and code deposited in a data archive/repository using a metadata standard for discoverability facilitates ease of access for others to find and reuse in order to build on existing scholarship.

== Outcomes ==
* Understand the reasons for making research data open
* Recognize why some data may not be suitable for reuse
* Identify different types of metadata
* Produce a README file that enables others to interpret data
* Describe and deposit data in an open repository applying a license to the data for reuse
* Integrate archived data into an OSF project

== Why it Matters to You? ==
By making research data open and any code associated with the data it allows others to easily access, share and re-use the data, re-run the code and reproduce the research results and validate your research.

Research transparency is required and recommended from funding agencies who not only have requested authors to make copies of their research outputs open access, but are increasingly requesting that the associated data be made open whenever possible. When you open your data you are not only fulfilling funding requirements, but can demonstrate the impact of your data when it is cited.

Opening data encourages and requires good data management practices by establishing a workflow for each stage of the data life cycle so that you can find and interpret that data months and years after having collected it. People who find your data will be able to do so as well. Data deposit into an open repository in a non-proprietary format allows you to control how the data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

[https://biblio.uottawa.ca/en/services/faculty/research-data-management/what-research-data-management Visual of the data life cycle]

== Practice ==

=== What is open data? ===

==== '''Introduction - What is open data?''' ====
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. Open data is guided by the FAIR principles for sharing data meaning that it should be findable, accessible, interoperable and reusable.

''Read…''
* [http://opendatahandbook.org/guide/en/what-is-open-data/ Open Data Handbook definition]
* [https://www.go-fair.org/fair-principles/ FAIR principles]
''Or watch...''
* [https://youtu.be/K-kEvfaUJdA FAIR principles] (embed video into module?)

==== Privacy and open data ====
Open data should be governed by the FAIR principles to promote transparency and reproducibility, but not all data can or should be made open. Even though the FAIR principles provide guidance for sharing data it is with the understanding that ethical and contractual obligations will be upheld. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories can offer options to keep the data closed or embargoed. Even in this more closed scenario a best practice is to let others know the data exists by creating a description of your data and use a metadata standard. for researchers to find and request access to the data

==== '''Policies and open data''' ====
As mentioned open data may be required by granting agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (''Add to references - needs proper citatiion:'' <nowiki>https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html</nowiki>). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.

''Read more...''
* DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.
* [https://open.canada.ca/en/open-data-principles#toc92 Open Data in Canada]
''Mini reflection...''

Consider how the DRAFT Tri-Agency RDM policy might impact how you manage your data.

''Watch...''

[https://learn.scholarsportal.info/modules/portage/rdm-101-module-3/ Canadian Policy Review]

(''or use embed code:'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=13" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

=== Implementing Open Data ===
Before even collecting your data, you need to plan. Each stage of the data life cycle should be considered when planning how you will manage your data and make it accessible.

While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse and you should prepare that data to be deposited into a repository.

>> ''insert image of PDF'' - Data Curation from portage <nowiki>https://portagenetwork.ca/wp-content/uploads/2019/08/Brief_Guide_Data_Curation_AUGUST2019_EN.pdf</nowiki>

==== Data repositories ====
There are institutional and disciplinary repositories for data. An example of an institutional repository is UBC's Dataverse hosted by Scholars Portal. This repository is discipline agnostic where any researcher can create an account and deposit their data and associated files using the provided template to create the metadata required to ensure the data is findable. For disciplinary repositories, the Registry of Research Data Repositories is one example where you can search or browse by discipline to find a repository that will be more useful to researchers in your field (<nowiki>https://www.re3data.org</nowiki> ). Both options provide a persistent identifier such as a doi to help with discovery, sharing, and citing your data.

''Note:'' You might discover data being stored in the OSF. The OSF can be an option to house raw data while working with it or it might be one of the options to store your data temporarily, but it is not recommended for long term preservation and storage.

When depositing into a repository an important piece of making the data open is applying an open license. Public domain licenses can be applied or an open license that requires attribution, but will allow the user to download, reuse and repurpose the data.

''Read...''
* [https://opendatacommons.org/licenses/index.html Open Data Commons - Licenses]
* [https://opendefinition.org/guide/data/ Guide to Open Data Licensing]

==== Documentation ====
File naming and structure are important pieces of managing your data to make it easier for others to use. In keeping with the FAIR principles data formats should be non-proprietary, and unencrypted and uncompressed (''Reference needed here to MIT RDM'' )

''Read...''
* [https://researchdata.library.ubc.ca/plan/organize-your-data/ Guidelines for organizing your data]
* ''(link back to open tools or same reading from that section)''

==== Metadata ====
Metadata is essential for data discovery, sharing and reuse. As it is “data about data” metadata provides a description of the study, files and variables. There are three levels of metadata:
# Descriptive: Provides information about the data that will help people understand what they will find in the dataset and the context. Project title, authors and collection methods are all types of descriptive metadata. Be sure to describe your variables giving them clear names (r''eference to Good Enough Research Data management)''. Name files with basic metadata file names (''provide example from RDM guide - UBC'')
# Administrative: What software is required to use the data? What is the license attached to the data?
# Structural: How do the data files relate to one another (citation needed here for RDM workshop)
The metadata standard you use will depend on the repository you select. Each repository will have their own standard.

It is recommended that you include a README plain text file with the data deposit that helps researchers interpret you data. You might also include a data dictionary, codebooks or other documentation, but this could be included in the README file as well.

''How to...''
* [https://go.library.ubc.ca/zxFJCb Creating a README for your dataset]
* [https://www.wikihow.com/Write-a-Read-Me How to write a read me]
''Activities...''
* Draft a README file to accompany a future or current data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''[https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf Creating a README for your dataset]''
* Find a data repository where you could deposit your data. You might consult the [https://www.re3data.org Registry of Research Data Repositories] that has aggregated disciplinary repositories. Search their database or browse by subject. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.
* Explore a data repository and find a dataset in your discipline. Is there a Data Dictionary, Codebook or README file that helps interpret the data?

==== Data Management Plans ====
Everything established up until now are all pieces you'll need to create a data management plan (DMP). A DMP will address questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. There are templates such as [https://assistant.portagenetwork.ca/ Portage's DMP Assistant] that aid in preparing a plan guiding through each step. There are institutional instances of Portage's DMP Assistant including one at UBC. Similar to fulfiling open access and open data requirement, it is not uncommon for research funders to request a DMP to be submitted with the funding application demonstrating how the data will be handled at each stage of the data life cycle.

''Read...''
* [https://portagenetwork.ca/portage-training-resources/ DMP Exemplars] Look for this heading to see examples of DMPs
''Watch...''
* [https://portagenetwork.ca/portage-training-resources/rdm-101/ Module 4: Steps Towards Good Research Data Managment]
(''or embed option: portage'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=16" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

''Tools...''
* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template.

''Reflection...''

With reference to your own dataset reflect on sections of the DMP and consider if your data is ready for reuse.
==== Additional activities using the OSF: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

=== Options to Explore ===

==== Workshops at UBC ====
UBC offers a number of introductory workshops throughout the year. These are a great place to get started, with the opportunity to get set up for the first time and ask questions. Registration, dates, titles and course materials can all be found here: <nowiki>https://researchcommons.library.ubc.ca/workshops/</nowiki> Topics covered include:
* Introduction to the Open Science Framework
* Research Data Management
** Managing Active Research Data
** Preserving and Reusing Research Data

==== '''Workshops outside of UBC''' ====
* CESSDA Data Management Expert Guide: https://www.cessda.eu/Training/Training-Resources/Library/Data-Management-Expert-Guide/6.-Archive-Publish/Data-publishing-routes
* Data One Webinars (previously recorded) https://www.dataone.org/previous-webinars/2017
* FOSTER Courses <nowiki>https://www.fosteropenscience.eu/courses</nowiki>. Several courses require that you register to access the course materials, but there may also be a free text version linked within the course and available from Zenodo. For an example see: FOSTER Consortium. (2018, November). Managing and Sharing Research Data (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.2630562
* Portage Events https://portagenetwork.ca/portage-training-resources/portage-events/'''.''' Portage aims “to coordinate and expand existing expertise, services, and infrastructure so that all academic researchers in Canada have access to the support they need for research data management.”

==== Case studies ====
* Data stories from DataONE https://www.dataone.org/data-stories

==== '''Open Data Reading and Viewing''' ====
* Perrier L, Blondal E, MacDonald H (2020) The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis. PLoS ONE 15(2): e0229182. https://doi.org/10.1371/journal.pone.0229182
* Wessels, B., Finn, R.L., Wadhwa, K., Sveinsdottir, T. (2017). ''Open data and the knowledge society.'' Amsterdam: Amsterdam University Press. doi:10.5117/9789462980181. Retrieved from: http://tinyurl.com/yabeh5hm
* FAIR Principles https://youtu.be/OvEHYCSmzCA
* Open data handbook http://opendatahandbook.org/
Open science, open data https://www.fosteropenscience.eu/content/open-science-open-data

==== '''Research Data Guides''' ====
* UBC Research Data Management Guide https://researchdata.library.ubc.ca/share/
* RDM Toolkit section for Arts, Humanities and Social Sciences data https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/
* Humanities Data Curation Guide https://guide.dhcuration.org/about/
* Journal of Open Humanities Data https://openhumanitiesdata.metajnl.com/
* Center for Open Data in the Humanities http://codh.rois.ac.jp/index.html.en
* Indigenous Data Sovereignty https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html

==== '''Data Repositories''' ====
* Scholars Portal Dataverse https://dataverse.scholarsportal.info/

* Dryad https://datadryad.org/stash
* Figshare https://figshare.com/
* Zenodo https://zenodo.org/

===== '''Search for Repositories''' =====
* Registry of Research Data Repositories https://www.re3data.org/

== References ==
* ''full reference list to come...''
* CESSDA Training Team (2017 - 2019). ''CESSDA Data Management Expert Guide.''
Bergen, Norway: CESSDA ERIC. Retrieved from <nowiki>https://www.cessda.eu/DMGuide</nowiki>
* UBC RDM workshop
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:46:26Z

Sarah: /* Options to Explore */

== Open Research Data ==

== Big Ideas/Questions ==
If research data is saved on a device or in the cloud or both, how will others find it should they want to look at the data you have used to support your research outputs? As mentioned in the OSF module research tranparency and reproducibility are dependent on open practices which includes open data. Open and shared data and code deposited in a data archive/repository using a metadata standard for discoverability facilitates ease of access for others to find and reuse in order to build on existing scholarship.

== Outcomes ==
* Understand the reasons for making research data open
* Recognize why some data may not be suitable for reuse
* Identify different types of metadata
* Produce a README file that enables others to interpret data
* Describe and deposit data in an open repository applying a license to the data for reuse
* Integrate archived data into an OSF project

== Why it Matters to You? ==
By making research data open and any code associated with the data it allows others to easily access, share and re-use the data, re-run the code and reproduce the research results and validate your research.

Research transparency is required and recommended from funding agencies who not only have requested authors to make copies of their research outputs open access, but are increasingly requesting that the associated data be made open whenever possible. When you open your data you are not only fulfilling funding requirements, but can demonstrate the impact of your data when it is cited.

Opening data encourages and requires good data management practices by establishing a workflow for each stage of the data life cycle so that you can find and interpret that data months and years after having collected it. People who find your data will be able to do so as well. Data deposit into an open repository in a non-proprietary format allows you to control how the data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

[https://biblio.uottawa.ca/en/services/faculty/research-data-management/what-research-data-management Visual of the data life cycle]

== Practice ==

=== What is open data? ===

==== '''Introduction - What is open data?''' ====
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. Open data is guided by the FAIR principles for sharing data meaning that it should be findable, accessible, interoperable and reusable.

''Read…''
* [http://opendatahandbook.org/guide/en/what-is-open-data/ Open Data Handbook definition]
* [https://www.go-fair.org/fair-principles/ FAIR principles]
''Or watch...''
* [https://youtu.be/K-kEvfaUJdA FAIR principles] (embed video into module?)

==== Privacy and open data ====
Open data should be governed by the FAIR principles to promote transparency and reproducibility, but not all data can or should be made open. Even though the FAIR principles provide guidance for sharing data it is with the understanding that ethical and contractual obligations will be upheld. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories can offer options to keep the data closed or embargoed. Even in this more closed scenario a best practice is to let others know the data exists by creating a description of your data and use a metadata standard. for researchers to find and request access to the data

==== '''Policies and open data''' ====
As mentioned open data may be required by granting agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (''Add to references - needs proper citatiion:'' <nowiki>https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html</nowiki>). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.

''Read more...''
* DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.
* [https://open.canada.ca/en/open-data-principles#toc92 Open Data in Canada]
''Mini reflection...''

Consider how the DRAFT Tri-Agency RDM policy might impact how you manage your data.

''Watch...''

[https://learn.scholarsportal.info/modules/portage/rdm-101-module-3/ Canadian Policy Review]

(''or use embed code:'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=13" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

=== Implementing Open Data ===
Before even collecting your data, you need to plan. Each stage of the data life cycle should be considered when planning how you will manage your data and make it accessible.

While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse and you should prepare that data to be deposited into a repository.

>> ''insert image of PDF'' - Data Curation from portage <nowiki>https://portagenetwork.ca/wp-content/uploads/2019/08/Brief_Guide_Data_Curation_AUGUST2019_EN.pdf</nowiki>

==== Data repositories ====
There are institutional and disciplinary repositories for data. An example of an institutional repository is UBC's Dataverse hosted by Scholars Portal. This repository is discipline agnostic where any researcher can create an account and deposit their data and associated files using the provided template to create the metadata required to ensure the data is findable. For disciplinary repositories, the Registry of Research Data Repositories is one example where you can search or browse by discipline to find a repository that will be more useful to researchers in your field (<nowiki>https://www.re3data.org</nowiki> ). Both options provide a persistent identifier such as a doi to help with discovery, sharing, and citing your data.

''Note:'' You might discover data being stored in the OSF. The OSF can be an option to house raw data while working with it or it might be one of the options to store your data temporarily, but it is not recommended for long term preservation and storage.

When depositing into a repository an important piece of making the data open is applying an open license. Public domain licenses can be applied or an open license that requires attribution, but will allow the user to download, reuse and repurpose the data.

''Read...''
* [https://opendatacommons.org/licenses/index.html Open Data Commons - Licenses]
* [https://opendefinition.org/guide/data/ Guide to Open Data Licensing]

==== Documentation ====
File naming and structure are important pieces of managing your data to make it easier for others to use. In keeping with the FAIR principles data formats should be non-proprietary, and unencrypted and uncompressed (''Reference needed here to MIT RDM'' )

''Read...''
* [https://researchdata.library.ubc.ca/plan/organize-your-data/ Guidelines for organizing your data]
* ''(link back to open tools or same reading from that section)''

==== Metadata ====
Metadata is essential for data discovery, sharing and reuse. As it is “data about data” metadata provides a description of the study, files and variables. There are three levels of metadata:
# Descriptive: Provides information about the data that will help people understand what they will find in the dataset and the context. Project title, authors and collection methods are all types of descriptive metadata. Be sure to describe your variables giving them clear names (r''eference to Good Enough Research Data management)''. Name files with basic metadata file names (''provide example from RDM guide - UBC'')
# Administrative: What software is required to use the data? What is the license attached to the data?
# Structural: How do the data files relate to one another (citation needed here for RDM workshop)
The metadata standard you use will depend on the repository you select. Each repository will have their own standard.

It is recommended that you include a README plain text file with the data deposit that helps researchers interpret you data. You might also include a data dictionary, codebooks or other documentation, but this could be included in the README file as well.

''How to...''
* [https://go.library.ubc.ca/zxFJCb Creating a README for your dataset]
* [https://www.wikihow.com/Write-a-Read-Me How to write a read me]
''Activities...''
* Draft a README file to accompany a future or current data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''[https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf Creating a README for your dataset]''
* Find a data repository where you could deposit your data. You might consult the [https://www.re3data.org Registry of Research Data Repositories] that has aggregated disciplinary repositories. Search their database or browse by subject. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.
* Explore a data repository and find a dataset in your discipline. Is there a Data Dictionary, Codebook or README file that helps interpret the data?

==== Data Management Plans ====
Everything established up until now are all pieces you'll need to create a data management plan (DMP). A DMP will address questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. There are templates such as [https://assistant.portagenetwork.ca/ Portage's DMP Assistant] that aid in preparing a plan guiding through each step. There are institutional instances of Portage's DMP Assistant including one at UBC. Similar to fulfiling open access and open data requirement, it is not uncommon for research funders to request a DMP to be submitted with the funding application demonstrating how the data will be handled at each stage of the data life cycle.

''Read...''
* [https://portagenetwork.ca/portage-training-resources/ DMP Exemplars] Look for this heading to see examples of DMPs
''Watch...''
* [https://portagenetwork.ca/portage-training-resources/rdm-101/ Module 4: Steps Towards Good Research Data Managment]
(''or embed option: portage'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=16" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

''Tools...''
* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template.

''Reflection...''

With reference to your own dataset reflect on sections of the DMP and consider if your data is ready for reuse.
==== Additional activities using the OSF: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

=== Options to Explore ===

==== Workshops at UBC ====
UBC offers a number of introductory workshops throughout the year. These are a great place to get started, with the opportunity to get set up for the first time and ask questions. Registration, dates, titles and course materials can all be found here: <nowiki>https://researchcommons.library.ubc.ca/workshops/</nowiki> Topics covered include:
* Introduction to the Open Science Framework
* Research Data Management
** Managing Active Research Data
** Preserving and Reusing Research Data

==== '''Workshops outside of UBC''' ====
* CESSDA Data Management Expert Guide: https://www.cessda.eu/Training/Training-Resources/Library/Data-Management-Expert-Guide/6.-Archive-Publish/Data-publishing-routes
* Data One Webinars (previously recorded) https://www.dataone.org/previous-webinars/2017
* FOSTER Courses <nowiki>https://www.fosteropenscience.eu/courses</nowiki>. Several courses require that you register to access the course materials, but there may also be a free text version linked within the course and available from Zenodo. For an example see: FOSTER Consortium. (2018, November). Managing and Sharing Research Data (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.2630562
* Portage Events https://portagenetwork.ca/portage-training-resources/portage-events/'''.''' Portage aims “to coordinate and expand existing expertise, services, and infrastructure so that all academic researchers in Canada have access to the support they need for research data management.”

==== '''Open Data Reading and Viewing''' ====
* Perrier L, Blondal E, MacDonald H (2020) The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis. PLoS ONE 15(2): e0229182. https://doi.org/10.1371/journal.pone.0229182
* Wessels, B., Finn, R.L., Wadhwa, K., Sveinsdottir, T. (2017). ''Open data and the knowledge society.'' Amsterdam: Amsterdam University Press. doi:10.5117/9789462980181. Retrieved from: http://tinyurl.com/yabeh5hm
* FAIR Principles https://youtu.be/OvEHYCSmzCA
* Open data handbook http://opendatahandbook.org/
Open science, open data https://www.fosteropenscience.eu/content/open-science-open-data

==== '''Research Data Guides''' ====
* UBC Research Data Management Guide https://researchdata.library.ubc.ca/share/
* RDM Toolkit section for Arts, Humanities and Social Sciences data https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/
* Humanities Data Curation Guide https://guide.dhcuration.org/about/
* Journal of Open Humanities Data https://openhumanitiesdata.metajnl.com/
* Center for Open Data in the Humanities http://codh.rois.ac.jp/index.html.en
* Indigenous Data Sovereignty https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html

==== '''Data Repositories''' ====
* Scholars Portal Dataverse https://dataverse.scholarsportal.info/

* Dryad https://datadryad.org/stash
* Figshare https://figshare.com/
* Zenodo https://zenodo.org/

===== '''Search for Repositories''' =====
* Registry of Research Data Repositories https://www.re3data.org/

== References ==
* ''full reference list to come...''
* CESSDA Training Team (2017 - 2019). ''CESSDA Data Management Expert Guide.''
Bergen, Norway: CESSDA ERIC. Retrieved from <nowiki>https://www.cessda.eu/DMGuide</nowiki>
* UBC RDM workshop
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:44:57Z

Sarah:

== Open Research Data ==

== Big Ideas/Questions ==
If research data is saved on a device or in the cloud or both, how will others find it should they want to look at the data you have used to support your research outputs? As mentioned in the OSF module research tranparency and reproducibility are dependent on open practices which includes open data. Open and shared data and code deposited in a data archive/repository using a metadata standard for discoverability facilitates ease of access for others to find and reuse in order to build on existing scholarship.

== Outcomes ==
* Understand the reasons for making research data open
* Recognize why some data may not be suitable for reuse
* Identify different types of metadata
* Produce a README file that enables others to interpret data
* Describe and deposit data in an open repository applying a license to the data for reuse
* Integrate archived data into an OSF project

== Why it Matters to You? ==
By making research data open and any code associated with the data it allows others to easily access, share and re-use the data, re-run the code and reproduce the research results and validate your research.

Research transparency is required and recommended from funding agencies who not only have requested authors to make copies of their research outputs open access, but are increasingly requesting that the associated data be made open whenever possible. When you open your data you are not only fulfilling funding requirements, but can demonstrate the impact of your data when it is cited.

Opening data encourages and requires good data management practices by establishing a workflow for each stage of the data life cycle so that you can find and interpret that data months and years after having collected it. People who find your data will be able to do so as well. Data deposit into an open repository in a non-proprietary format allows you to control how the data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

[https://biblio.uottawa.ca/en/services/faculty/research-data-management/what-research-data-management Visual of the data life cycle]

== Practice ==

=== What is open data? ===

==== '''Introduction - What is open data?''' ====
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. Open data is guided by the FAIR principles for sharing data meaning that it should be findable, accessible, interoperable and reusable.

''Read…''
* [http://opendatahandbook.org/guide/en/what-is-open-data/ Open Data Handbook definition]
* [https://www.go-fair.org/fair-principles/ FAIR principles]
''Or watch...''
* [https://youtu.be/K-kEvfaUJdA FAIR principles] (embed video into module?)

==== Privacy and open data ====
Open data should be governed by the FAIR principles to promote transparency and reproducibility, but not all data can or should be made open. Even though the FAIR principles provide guidance for sharing data it is with the understanding that ethical and contractual obligations will be upheld. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories can offer options to keep the data closed or embargoed. Even in this more closed scenario a best practice is to let others know the data exists by creating a description of your data and use a metadata standard. for researchers to find and request access to the data

==== '''Policies and open data''' ====
As mentioned open data may be required by granting agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (''Add to references - needs proper citatiion:'' <nowiki>https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html</nowiki>). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.

''Read more...''
* DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.
* [https://open.canada.ca/en/open-data-principles#toc92 Open Data in Canada]
''Mini reflection...''

Consider how the DRAFT Tri-Agency RDM policy might impact how you manage your data.

''Watch...''

[https://learn.scholarsportal.info/modules/portage/rdm-101-module-3/ Canadian Policy Review]

(''or use embed code:'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=13" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

=== Implementing Open Data ===
Before even collecting your data, you need to plan. Each stage of the data life cycle should be considered when planning how you will manage your data and make it accessible.

While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse and you should prepare that data to be deposited into a repository.

>> ''insert image of PDF'' - Data Curation from portage <nowiki>https://portagenetwork.ca/wp-content/uploads/2019/08/Brief_Guide_Data_Curation_AUGUST2019_EN.pdf</nowiki>

==== Data repositories ====
There are institutional and disciplinary repositories for data. An example of an institutional repository is UBC's Dataverse hosted by Scholars Portal. This repository is discipline agnostic where any researcher can create an account and deposit their data and associated files using the provided template to create the metadata required to ensure the data is findable. For disciplinary repositories, the Registry of Research Data Repositories is one example where you can search or browse by discipline to find a repository that will be more useful to researchers in your field (<nowiki>https://www.re3data.org</nowiki> ). Both options provide a persistent identifier such as a doi to help with discovery, sharing, and citing your data.

''Note:'' You might discover data being stored in the OSF. The OSF can be an option to house raw data while working with it or it might be one of the options to store your data temporarily, but it is not recommended for long term preservation and storage.

When depositing into a repository an important piece of making the data open is applying an open license. Public domain licenses can be applied or an open license that requires attribution, but will allow the user to download, reuse and repurpose the data.

''Read...''
* [https://opendatacommons.org/licenses/index.html Open Data Commons - Licenses]
* [https://opendefinition.org/guide/data/ Guide to Open Data Licensing]

==== Documentation ====
File naming and structure are important pieces of managing your data to make it easier for others to use. In keeping with the FAIR principles data formats should be non-proprietary, and unencrypted and uncompressed (''Reference needed here to MIT RDM'' )

''Read...''
* [https://researchdata.library.ubc.ca/plan/organize-your-data/ Guidelines for organizing your data]
* ''(link back to open tools or same reading from that section)''

==== Metadata ====
Metadata is essential for data discovery, sharing and reuse. As it is “data about data” metadata provides a description of the study, files and variables. There are three levels of metadata:
# Descriptive: Provides information about the data that will help people understand what they will find in the dataset and the context. Project title, authors and collection methods are all types of descriptive metadata. Be sure to describe your variables giving them clear names (r''eference to Good Enough Research Data management)''. Name files with basic metadata file names (''provide example from RDM guide - UBC'')
# Administrative: What software is required to use the data? What is the license attached to the data?
# Structural: How do the data files relate to one another (citation needed here for RDM workshop)
The metadata standard you use will depend on the repository you select. Each repository will have their own standard.

It is recommended that you include a README plain text file with the data deposit that helps researchers interpret you data. You might also include a data dictionary, codebooks or other documentation, but this could be included in the README file as well.

''How to...''
* [https://go.library.ubc.ca/zxFJCb Creating a README for your dataset]
* [https://www.wikihow.com/Write-a-Read-Me How to write a read me]
''Activities...''
* Draft a README file to accompany a future or current data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''[https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf Creating a README for your dataset]''
* Find a data repository where you could deposit your data. You might consult the [https://www.re3data.org Registry of Research Data Repositories] that has aggregated disciplinary repositories. Search their database or browse by subject. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.
* Explore a data repository and find a dataset in your discipline. Is there a Data Dictionary, Codebook or README file that helps interpret the data?

==== Data Management Plans ====
Everything established up until now are all pieces you'll need to create a data management plan (DMP). A DMP will address questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. There are templates such as [https://assistant.portagenetwork.ca/ Portage's DMP Assistant] that aid in preparing a plan guiding through each step. There are institutional instances of Portage's DMP Assistant including one at UBC. Similar to fulfiling open access and open data requirement, it is not uncommon for research funders to request a DMP to be submitted with the funding application demonstrating how the data will be handled at each stage of the data life cycle.

''Read...''
* [https://portagenetwork.ca/portage-training-resources/ DMP Exemplars] Look for this heading to see examples of DMPs
''Watch...''
* [https://portagenetwork.ca/portage-training-resources/rdm-101/ Module 4: Steps Towards Good Research Data Managment]
(''or embed option: portage'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=16" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

''Tools...''
* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template.

''Reflection...''

With reference to your own dataset reflect on sections of the DMP and consider if your data is ready for reuse.
==== Additional activities using the OSF: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

==== Options to Explore ====

===== Workshops at UBC =====
UBC offers a number of introductory workshops throughout the year. These are a great place to get started, with the opportunity to get set up for the first time and ask questions. Registration, dates, titles and course materials can all be found here: <nowiki>https://researchcommons.library.ubc.ca/workshops/</nowiki> Topics covered include:
* Introduction to the Open Science Framework
* Research Data Management
** Managing Active Research Data
** Preserving and Reusing Research Data

===== '''Workshops outside of UBC''' =====
* CESSDA Data Management Expert Guide: https://www.cessda.eu/Training/Training-Resources/Library/Data-Management-Expert-Guide/6.-Archive-Publish/Data-publishing-routes
* Data One Webinars (previously recorded) https://www.dataone.org/previous-webinars/2017
* FOSTER Courses <nowiki>https://www.fosteropenscience.eu/courses</nowiki>. Several courses require that you register to access the course materials, but there may also be a free text version linked within the course and available from Zenodo. For an example see: FOSTER Consortium. (2018, November). Managing and Sharing Research Data (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.2630562
* Portage Events https://portagenetwork.ca/portage-training-resources/portage-events/'''.''' Portage aims “to coordinate and expand existing expertise, services, and infrastructure so that all academic researchers in Canada have access to the support they need for research data management.”

===== '''Open Data Reading and Viewing''' =====
* Perrier L, Blondal E, MacDonald H (2020) The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis. PLoS ONE 15(2): e0229182. https://doi.org/10.1371/journal.pone.0229182
* Wessels, B., Finn, R.L., Wadhwa, K., Sveinsdottir, T. (2017). ''Open data and the knowledge society.'' Amsterdam: Amsterdam University Press. doi:10.5117/9789462980181. Retrieved from: http://tinyurl.com/yabeh5hm
* FAIR Principles https://youtu.be/OvEHYCSmzCA
* Open data handbook http://opendatahandbook.org/
Open science, open data https://www.fosteropenscience.eu/content/open-science-open-data

===== '''Research Data Guides''' =====
* UBC Research Data Management Guide https://researchdata.library.ubc.ca/share/
* RDM Toolkit section for Arts, Humanities and Social Sciences data https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/
* Humanities Data Curation Guide https://guide.dhcuration.org/about/
* Journal of Open Humanities Data https://openhumanitiesdata.metajnl.com/
* Center for Open Data in the Humanities http://codh.rois.ac.jp/index.html.en
* Indigenous Data Sovereignty https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html

===== '''Data Repositories''' =====
* Scholars Portal Dataverse https://dataverse.scholarsportal.info/
* Dryad https://datadryad.org/stash
* Figshare https://figshare.com/
* Zenodo https://zenodo.org/

===== '''Search for Repositories''' =====
* Registry of Research Data Repositories https://www.re3data.org/

== References ==
* ''full reference list to come...''
* CESSDA Training Team (2017 - 2019). ''CESSDA Data Management Expert Guide.''
Bergen, Norway: CESSDA ERIC. Retrieved from <nowiki>https://www.cessda.eu/DMGuide</nowiki>
* UBC RDM workshop
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:41:51Z

Sarah: /* Implementing Open Data */

== Open Research Data ==

== Big Ideas/Questions ==
If research data is saved on a device or in the cloud or both, how will others find it should they want to look at the data you have used to support your research outputs? As mentioned in the OSF module research tranparency and reproducibility are dependent on open practices which includes open data. Open and shared data and code deposited in a data archive/repository using a metadata standard for discoverability facilitates ease of access for others to find and reuse in order to build on existing scholarship.

== Outcomes ==
* Understand the reasons for making research data open
* Recognize why some data may not be suitable for reuse
* Identify different types of metadata
* Produce a README file that enables others to interpret data
* Describe and deposit data in an open repository applying a license to the data for reuse
* Integrate archived data into an OSF project

== Why it Matters to You? ==
By making research data open and any code associated with the data it allows others to easily access, share and re-use the data, re-run the code and reproduce the research results and validate your research.

Research transparency is required and recommended from funding agencies who not only have requested authors to make copies of their research outputs open access, but are increasingly requesting that the associated data be made open whenever possible. When you open your data you are not only fulfilling funding requirements, but can demonstrate the impact of your data when it is cited.

Opening data encourages and requires good data management practices by establishing a workflow for each stage of the data life cycle so that you can find and interpret that data months and years after having collected it. People who find your data will be able to do so as well. Data deposit into an open repository in a non-proprietary format allows you to control how the data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

[https://biblio.uottawa.ca/en/services/faculty/research-data-management/what-research-data-management Visual of the data life cycle]

== Practice ==

=== What is open data? ===

==== '''Introduction - What is open data?''' ====
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. Open data is guided by the FAIR principles for sharing data meaning that it should be findable, accessible, interoperable and reusable.

''Read…''
* [http://opendatahandbook.org/guide/en/what-is-open-data/ Open Data Handbook definition]
* [https://www.go-fair.org/fair-principles/ FAIR principles]
''Or watch...''
* [https://youtu.be/K-kEvfaUJdA FAIR principles] (embed video into module?)

==== Privacy and open data ====
Open data should be governed by the FAIR principles to promote transparency and reproducibility, but not all data can or should be made open. Even though the FAIR principles provide guidance for sharing data it is with the understanding that ethical and contractual obligations will be upheld. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories can offer options to keep the data closed or embargoed. Even in this more closed scenario a best practice is to let others know the data exists by creating a description of your data and use a metadata standard. for researchers to find and request access to the data

==== '''Policies and open data''' ====
As mentioned open data may be required by granting agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (''Add to references - needs proper citatiion:'' <nowiki>https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html</nowiki>). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.

''Read more...''
* DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.
* [https://open.canada.ca/en/open-data-principles#toc92 Open Data in Canada]
''Mini reflection...''

Consider how the DRAFT Tri-Agency RDM policy might impact how you manage your data.

''Watch...''

[https://learn.scholarsportal.info/modules/portage/rdm-101-module-3/ Canadian Policy Review]

(''or use embed code:'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=13" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

=== Implementing Open Data ===
Before even collecting your data, you need to plan. Each stage of the data life cycle should be considered when planning how you will manage your data and make it accessible.

While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse and you should prepare that data to be deposited into a repository.

>> ''insert image of PDF'' - Data Curation from portage <nowiki>https://portagenetwork.ca/wp-content/uploads/2019/08/Brief_Guide_Data_Curation_AUGUST2019_EN.pdf</nowiki>

==== Data repositories ====
There are institutional and disciplinary repositories for data. An example of an institutional repository is UBC's Dataverse hosted by Scholars Portal. This repository is discipline agnostic where any researcher can create an account and deposit their data and associated files using the provided template to create the metadata required to ensure the data is findable. For disciplinary repositories, the Registry of Research Data Repositories is one example where you can search or browse by discipline to find a repository that will be more useful to researchers in your field (<nowiki>https://www.re3data.org</nowiki> ). Both options provide a persistent identifier such as a doi to help with discovery, sharing, and citing your data.

''Note:'' You might discover data being stored in the OSF. The OSF can be an option to house raw data while working with it or it might be one of the options to store your data temporarily, but it is not recommended for long term preservation and storage.

When depositing into a repository an important piece of making the data open is applying an open license. Public domain licenses can be applied or an open license that requires attribution, but will allow the user to download, reuse and repurpose the data.

''Read...''
* [https://opendatacommons.org/licenses/index.html Open Data Commons - Licenses]
* [https://opendefinition.org/guide/data/ Guide to Open Data Licensing]

==== Documentation ====
File naming and structure are important pieces of managing your data to make it easier for others to use. In keeping with the FAIR principles data formats should be non-proprietary, and unencrypted and uncompressed (''Reference needed here to MIT RDM'' )

''Read...''
* [https://researchdata.library.ubc.ca/plan/organize-your-data/ Guidelines for organizing your data]
* ''(link back to open tools or same reading from that section)''

==== Metadata ====
Metadata is essential for data discovery, sharing and reuse. As it is “data about data” metadata provides a description of the study, files and variables. There are three levels of metadata:
# Descriptive: Provides information about the data that will help people understand what they will find in the dataset and the context. Project title, authors and collection methods are all types of descriptive metadata. Be sure to describe your variables giving them clear names (r''eference to Good Enough Research Data management)''. Name files with basic metadata file names (''provide example from RDM guide - UBC'')
# Administrative: What software is required to use the data? What is the license attached to the data?
# Structural: How do the data files relate to one another (citation needed here for RDM workshop)
The metadata standard you use will depend on the repository you select. Each repository will have their own standard.

It is recommended that you include a README plain text file with the data deposit that helps researchers interpret you data. You might also include a data dictionary, codebooks or other documentation, but this could be included in the README file as well.

''How to...''
* [https://go.library.ubc.ca/zxFJCb Creating a README for your dataset]
* [https://www.wikihow.com/Write-a-Read-Me How to write a read me]
''Activities...''
* Draft a README file to accompany a future or current data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''[https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf Creating a README for your dataset]''
* Find a data repository where you could deposit your data. You might consult the [https://www.re3data.org Registry of Research Data Repositories] that has aggregated disciplinary repositories. Search their database or browse by subject. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.
* Explore a data repository and find a dataset in your discipline. Is there a Data Dictionary, Codebook or README file that helps interpret the data?

==== Data Management Plans ====
Everything established up until now are all pieces you'll need to create a data management plan (DMP). A DMP will address questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. There are templates such as [https://assistant.portagenetwork.ca/ Portage's DMP Assistant] that aid in preparing a plan guiding through each step. There are institutional instances of Portage's DMP Assistant including one at UBC. Similar to fulfiling open access and open data requirement, it is not uncommon for research funders to request a DMP to be submitted with the funding application demonstrating how the data will be handled at each stage of the data life cycle.

''Read...''
* [https://portagenetwork.ca/portage-training-resources/ DMP Exemplars] Look for this heading to see examples of DMPs
''Watch...''
* [https://portagenetwork.ca/portage-training-resources/rdm-101/ Module 4: Steps Towards Good Research Data Managment]
(''or embed option: portage'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=16" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

''Tools...''
* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template.

''Reflection...''

With reference to your own dataset reflect on sections of the DMP and consider if your data is ready for reuse.
==== Additional activities using the OSF: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

==== Options to Explore ====

===== Workshops at UBC =====
UBC offers a number of introductory workshops throughout the year. These are a great place to get started, with the opportunity to get set up for the first time and ask questions. Registration, dates, titles and course materials can all be found here: <nowiki>https://researchcommons.library.ubc.ca/workshops/</nowiki> Topics covered include:
* Introduction to the Open Science Framework
* Research Data Management
** Managing Active Research Data
** Preserving and Reusing Research Data

===== '''Workshops outside of UBC''' =====
* CESSDA Data Management Expert Guide: <nowiki>https://www.cessda.eu/Training/Training-Resources/Library/Data-Management-Expert-Guide/6.-Archive-Publish/Data-publishing-routes</nowiki>
* Data One Webinars (previously recorded) <nowiki>https://www.dataone.org/previous-webinars/2017</nowiki>
* FOSTER Courses <nowiki>https://www.fosteropenscience.eu/courses</nowiki>. Several courses require that you register to access the course materials, but there may also be a free text version linked within the course and available from Zenodo. For an example see: FOSTER Consortium. (2018, November). Managing and Sharing Research Data (Version 1.0). Zenodo. <nowiki>http://doi.org/10.5281/zenodo.2630562</nowiki>
* Portage Events <nowiki>https://portagenetwork.ca/portage-training-resources/portage-events/</nowiki>'''.''' Portage aims “to coordinate and expand existing expertise, services, and infrastructure so that all academic researchers in Canada have access to the support they need for research data management.”

===== '''Open Data Reading and Viewing''' =====
* Perrier L, Blondal E, MacDonald H (2020) The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis. PLoS ONE 15(2): e0229182. <nowiki>https://doi.org/10.1371/journal.pone.0229182</nowiki>
* Wessels, B., Finn, R.L., Wadhwa, K., Sveinsdottir, T. (2017). ''Open data and the knowledge society.'' Amsterdam: Amsterdam University Press. doi:10.5117/9789462980181. Retrieved from: <nowiki>http://tinyurl.com/yabeh5hm</nowiki>
* FAIR Principles <nowiki>https://youtu.be/OvEHYCSmzCA</nowiki>
* Open data handbook <nowiki>http://opendatahandbook.org/</nowiki>
Open science, open data <nowiki>https://www.fosteropenscience.eu/content/open-science-open-data</nowiki>

===== '''Research Data Guides''' =====
* UBC Research Data Management Guide <nowiki>https://researchdata.library.ubc.ca/share/</nowiki>
* RDM Toolkit section for Arts, Humanities and Social Sciences data <nowiki>https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/</nowiki>
* Humanities Data Curation Guide <nowiki>https://guide.dhcuration.org/about/</nowiki>
* Journal of Open Humanities Data <nowiki>https://openhumanitiesdata.metajnl.com/</nowiki>
* Center for Open Data in the Humanities <nowiki>http://codh.rois.ac.jp/index.html.en</nowiki>
* Indigenous Data Sovereignty <nowiki>https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html</nowiki>

===== '''Data Repositories''' =====
* Scholars Portal Dataverse <nowiki>https://dataverse.scholarsportal.info/</nowiki>
* Dryad <nowiki>https://datadryad.org/stash</nowiki>
* Figshare <nowiki>https://figshare.com/</nowiki>
* Zenodo <nowiki>https://zenodo.org/</nowiki>

===== '''Search for Repositories''' =====
* Registry of Research Data Repositories <nowiki>https://www.re3data.org/</nowiki>

=== References ===
* CESSDA Training Team (2017 - 2019). ''CESSDA Data Management Expert Guide.''
Bergen, Norway: CESSDA ERIC. Retrieved from <nowiki>https://www.cessda.eu/DMGuide</nowiki>
* UBC RDM workshop
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:38:42Z

Sarah: /* Data Management Plans */

== Open Research Data ==

== Big Ideas/Questions ==
If research data is saved on a device or in the cloud or both, how will others find it should they want to look at the data you have used to support your research outputs? As mentioned in the OSF module research tranparency and reproducibility are dependent on open practices which includes open data. Open and shared data and code deposited in a data archive/repository using a metadata standard for discoverability facilitates ease of access for others to find and reuse in order to build on existing scholarship.

== Outcomes ==
* Understand the reasons for making research data open
* Recognize why some data may not be suitable for reuse
* Identify different types of metadata
* Produce a README file that enables others to interpret data
* Describe and deposit data in an open repository applying a license to the data for reuse
* Integrate archived data into an OSF project

== Why it Matters to You? ==
By making research data open and any code associated with the data it allows others to easily access, share and re-use the data, re-run the code and reproduce the research results and validate your research.

Research transparency is required and recommended from funding agencies who not only have requested authors to make copies of their research outputs open access, but are increasingly requesting that the associated data be made open whenever possible. When you open your data you are not only fulfilling funding requirements, but can demonstrate the impact of your data when it is cited.

Opening data encourages and requires good data management practices by establishing a workflow for each stage of the data life cycle so that you can find and interpret that data months and years after having collected it. People who find your data will be able to do so as well. Data deposit into an open repository in a non-proprietary format allows you to control how the data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

[https://biblio.uottawa.ca/en/services/faculty/research-data-management/what-research-data-management Visual of the data life cycle]

== Practice ==

=== What is open data? ===

==== '''Introduction - What is open data?''' ====
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. Open data is guided by the FAIR principles for sharing data meaning that it should be findable, accessible, interoperable and reusable.

''Read…''
* [http://opendatahandbook.org/guide/en/what-is-open-data/ Open Data Handbook definition]
* [https://www.go-fair.org/fair-principles/ FAIR principles]
''Or watch...''
* [https://youtu.be/K-kEvfaUJdA FAIR principles] (embed video into module?)

==== Privacy and open data ====
Open data should be governed by the FAIR principles to promote transparency and reproducibility, but not all data can or should be made open. Even though the FAIR principles provide guidance for sharing data it is with the understanding that ethical and contractual obligations will be upheld. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories can offer options to keep the data closed or embargoed. Even in this more closed scenario a best practice is to let others know the data exists by creating a description of your data and use a metadata standard. for researchers to find and request access to the data

==== '''Policies and open data''' ====
As mentioned open data may be required by granting agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (''Add to references - needs proper citatiion:'' <nowiki>https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html</nowiki>). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.

''Read more...''
* DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.
* [https://open.canada.ca/en/open-data-principles#toc92 Open Data in Canada]
''Mini reflection...''

Consider how the DRAFT Tri-Agency RDM policy might impact how you manage your data.

''Watch...''

[https://learn.scholarsportal.info/modules/portage/rdm-101-module-3/ Canadian Policy Review]

(''or use embed code:'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=13" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

=== Implementing Open Data ===
Before even collecting your data, you need to plan. Each stage of the data life cycle should be considered when planning how you will manage your data and make it accessible.

While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse and you should prepare that data to be deposited into a repository.

>> ''insert image of PDF'' - Data Curation from portage <nowiki>https://portagenetwork.ca/wp-content/uploads/2019/08/Brief_Guide_Data_Curation_AUGUST2019_EN.pdf</nowiki>

==== Data repositories ====
There are institutional and disciplinary repositories for data. An example of an institutional repository is UBC's Dataverse hosted by Scholars Portal. This repository is discipline agnostic where any researcher can create an account and deposit their data and associated files using the provided template to create the metadata required to ensure the data is findable. For disciplinary repositories, the Registry of Research Data Repositories is one example where you can search or browse by discipline to find a repository that will be more useful to researchers in your field (<nowiki>https://www.re3data.org</nowiki> ). Both options provide a persistent identifier such as a doi to help with discovery, sharing, and citing your data.

''Note:'' You might discover data being stored in the OSF. The OSF can be an option to house raw data while working with it or it might be one of the options to store your data temporarily, but it is not recommended for long term preservation and storage.

When depositing into a repository an important piece of making the data open is applying an open license. Public domain licenses can be applied or an open license that requires attribution, but will allow the user to download, reuse and repurpose the data.

''Read...''
* [https://opendatacommons.org/licenses/index.html Open Data Commons - Licenses]
* [https://opendefinition.org/guide/data/ Guide to Open Data Licensing]

==== Documentation ====
File naming and structure are important pieces of managing your data to make it easier for others to use. In keeping with the FAIR principles data formats should be non-proprietary, and unencrypted and uncompressed (''Reference needed here to MIT RDM'' )

''Read...''
* [https://researchdata.library.ubc.ca/plan/organize-your-data/ Guidelines for organizing your data]
* ''(link back to open tools or same reading from that section)''

==== Metadata ====
Metadata is essential for data discovery, sharing and reuse. As it is “data about data” metadata provides a description of the study, files and variables. There are three levels of metadata:
# Descriptive: Provides information about the data that will help people understand what they will find in the dataset and the context. Project title, authors and collection methods are all types of descriptive metadata. Be sure to describe your variables giving them clear names (r''eference to Good Enough Research Data management)''. Name files with basic metadata file names (''provide example from RDM guide - UBC'')
# Administrative: What software is required to use the data? What is the license attached to the data?
# Structural: How do the data files relate to one another (citation needed here for RDM workshop)
The metadata standard you use will depend on the repository you select. Each repository will have their own standard.

It is recommended that you include a README plain text file with the data deposit that helps researchers interpret you data. You might also include a data dictionary, codebooks or other documentation, but this could be included in the README file as well.

''How to...''
* [https://go.library.ubc.ca/zxFJCb Creating a README for your dataset]
* [https://www.wikihow.com/Write-a-Read-Me How to write a read me]
''Activities...''
* Draft a README file to accompany a future or current data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''[https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf Creating a README for your dataset]''
* Find a data repository where you could deposit your data. You might consult the [https://www.re3data.org Registry of Research Data Repositories] that has aggregated disciplinary repositories. Search their database or browse by subject. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.
* Explore a data repository and find a dataset in your discipline. Is there a Data Dictionary, Codebook or README file that helps interpret the data?

==== Data Management Plans ====
Everything established up until now are all pieces you'll need to create a data management plan (DMP). A DMP will address questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. There are templates such as [https://assistant.portagenetwork.ca/ Portage's DMP Assistant] that aid in preparing a plan guiding through each step. There are institutional instances of Portage's DMP Assistant including one at UBC. Similar to fulfiling open access and open data requirement, it is not uncommon for research funders to request a DMP to be submitted with the funding application demonstrating how the data will be handled at each stage of the data life cycle.

''Read...''
* [https://portagenetwork.ca/portage-training-resources/ DMP Exemplars] Look for this heading to see examples of DMPs
''Watch...''
* [https://portagenetwork.ca/portage-training-resources/rdm-101/ Module 4: Steps Towards Good Research Data Managment]
(''or embed option: portage'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=16" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

''Tools...''
* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template.

''Reflection...''

With reference to your own dataset reflect on sections of the DMP and consider if your data is ready for reuse.
==== Additional activities using the OSF: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

=== Read ===
{| class="wikitable"
|+
!Description
!Link
|-
|FAIR Principles
|<nowiki>https://www.go-fair.org/fair-principles/</nowiki>
|-
|UBC Research Data Management Guide
|<nowiki>https://researchdata.library.ubc.ca/share/</nowiki>
|-
|RDM Toolkit section for Arts, Humanities and Social Sciences data
|<nowiki>https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/</nowiki>
|-
|Humanities Data Curation Guide
|<nowiki>https://guide.dhcuration.org/about/</nowiki>
|-
|Journal of Open Humanities Data
|<nowiki>https://openhumanitiesdata.metajnl.com/</nowiki>
|-
|Center for Open Data in the Humanities
|<nowiki>http://codh.rois.ac.jp/index.html.en</nowiki>
|-
|Indigenous Data Sovereignty
|<nowiki>https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html</nowiki>
|}

=== Watch ===
{| class="wikitable"
|+
!Description
!Link
|-
|Software carpentry
|<nowiki>https://tibhannover.github.io/2018-07-09-FAIR-Data-and-Software/</nowiki>
|-
|Data one webinars
|<nowiki>https://www.dataone.org/previous-webinars/2017</nowiki>
|}

=== Attend ===
{| class="wikitable"
|+
!Description
!Link
|-
|Research Data Management Workshop
|<nowiki>https://libcal.library.ubc.ca/calendar/vancouver/?t=g&q=&cid=7544&cal=7544</nowiki>
|-
|
|
|}
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:33:40Z

Sarah: /* Implementing Open Data */

== Open Research Data ==

== Big Ideas/Questions ==
If research data is saved on a device or in the cloud or both, how will others find it should they want to look at the data you have used to support your research outputs? As mentioned in the OSF module research tranparency and reproducibility are dependent on open practices which includes open data. Open and shared data and code deposited in a data archive/repository using a metadata standard for discoverability facilitates ease of access for others to find and reuse in order to build on existing scholarship.

== Outcomes ==
* Understand the reasons for making research data open
* Recognize why some data may not be suitable for reuse
* Identify different types of metadata
* Produce a README file that enables others to interpret data
* Describe and deposit data in an open repository applying a license to the data for reuse
* Integrate archived data into an OSF project

== Why it Matters to You? ==
By making research data open and any code associated with the data it allows others to easily access, share and re-use the data, re-run the code and reproduce the research results and validate your research.

Research transparency is required and recommended from funding agencies who not only have requested authors to make copies of their research outputs open access, but are increasingly requesting that the associated data be made open whenever possible. When you open your data you are not only fulfilling funding requirements, but can demonstrate the impact of your data when it is cited.

Opening data encourages and requires good data management practices by establishing a workflow for each stage of the data life cycle so that you can find and interpret that data months and years after having collected it. People who find your data will be able to do so as well. Data deposit into an open repository in a non-proprietary format allows you to control how the data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

[https://biblio.uottawa.ca/en/services/faculty/research-data-management/what-research-data-management Visual of the data life cycle]

== Practice ==

=== What is open data? ===

==== '''Introduction - What is open data?''' ====
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. Open data is guided by the FAIR principles for sharing data meaning that it should be findable, accessible, interoperable and reusable.

''Read…''
* [http://opendatahandbook.org/guide/en/what-is-open-data/ Open Data Handbook definition]
* [https://www.go-fair.org/fair-principles/ FAIR principles]
''Or watch...''
* [https://youtu.be/K-kEvfaUJdA FAIR principles] (embed video into module?)

==== Privacy and open data ====
Open data should be governed by the FAIR principles to promote transparency and reproducibility, but not all data can or should be made open. Even though the FAIR principles provide guidance for sharing data it is with the understanding that ethical and contractual obligations will be upheld. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories can offer options to keep the data closed or embargoed. Even in this more closed scenario a best practice is to let others know the data exists by creating a description of your data and use a metadata standard. for researchers to find and request access to the data

==== '''Policies and open data''' ====
As mentioned open data may be required by granting agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (''Add to references - needs proper citatiion:'' <nowiki>https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html</nowiki>). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.

''Read more...''
* DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.
* [https://open.canada.ca/en/open-data-principles#toc92 Open Data in Canada]
''Mini reflection...''

Consider how the DRAFT Tri-Agency RDM policy might impact how you manage your data.

''Watch...''

[https://learn.scholarsportal.info/modules/portage/rdm-101-module-3/ Canadian Policy Review]

(''or use embed code:'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=13" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

=== Implementing Open Data ===
Before even collecting your data, you need to plan. Each stage of the data life cycle should be considered when planning how you will manage your data and make it accessible.

While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse and you should prepare that data to be deposited into a repository.

>> ''insert image of PDF'' - Data Curation from portage <nowiki>https://portagenetwork.ca/wp-content/uploads/2019/08/Brief_Guide_Data_Curation_AUGUST2019_EN.pdf</nowiki>

==== Data repositories ====
There are institutional and disciplinary repositories for data. An example of an institutional repository is UBC's Dataverse hosted by Scholars Portal. This repository is discipline agnostic where any researcher can create an account and deposit their data and associated files using the provided template to create the metadata required to ensure the data is findable. For disciplinary repositories, the Registry of Research Data Repositories is one example where you can search or browse by discipline to find a repository that will be more useful to researchers in your field (<nowiki>https://www.re3data.org</nowiki> ). Both options provide a persistent identifier such as a doi to help with discovery, sharing, and citing your data.

''Note:'' You might discover data being stored in the OSF. The OSF can be an option to house raw data while working with it or it might be one of the options to store your data temporarily, but it is not recommended for long term preservation and storage.

When depositing into a repository an important piece of making the data open is applying an open license. Public domain licenses can be applied or an open license that requires attribution, but will allow the user to download, reuse and repurpose the data.

''Read...''
* [https://opendatacommons.org/licenses/index.html Open Data Commons - Licenses]
* [https://opendefinition.org/guide/data/ Guide to Open Data Licensing]

==== Documentation ====
File naming and structure are important pieces of managing your data to make it easier for others to use. In keeping with the FAIR principles data formats should be non-proprietary, and unencrypted and uncompressed (''Reference needed here to MIT RDM'' )

''Read...''
* [https://researchdata.library.ubc.ca/plan/organize-your-data/ Guidelines for organizing your data]
* ''(link back to open tools or same reading from that section)''

==== Metadata ====
Metadata is essential for data discovery, sharing and reuse. As it is “data about data” metadata provides a description of the study, files and variables. There are three levels of metadata:
# Descriptive: Provides information about the data that will help people understand what they will find in the dataset and the context. Project title, authors and collection methods are all types of descriptive metadata. Be sure to describe your variables giving them clear names (r''eference to Good Enough Research Data management)''. Name files with basic metadata file names (''provide example from RDM guide - UBC'')
# Administrative: What software is required to use the data? What is the license attached to the data?
# Structural: How do the data files relate to one another (citation needed here for RDM workshop)
The metadata standard you use will depend on the repository you select. Each repository will have their own standard.

It is recommended that you include a README plain text file with the data deposit that helps researchers interpret you data. You might also include a data dictionary, codebooks or other documentation, but this could be included in the README file as well.

''How to...''
* [https://go.library.ubc.ca/zxFJCb Creating a README for your dataset]
* [https://www.wikihow.com/Write-a-Read-Me How to write a read me]
''Activities...''
* Draft a README file to accompany a future or current data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''[https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf Creating a README for your dataset]''
* Find a data repository where you could deposit your data. You might consult the [https://www.re3data.org Registry of Research Data Repositories] that has aggregated disciplinary repositories. Search their database or browse by subject. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.
* Explore a data repository and find a dataset in your discipline. Is there a Data Dictionary, Codebook or README file that helps interpret the data?

==== Data Management Plans ====
Everything established up until now are all pieces you'll need to create a data management plan (DMP). A DMP will address questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. There are templates such as [https://assistant.portagenetwork.ca/ Portage's DMP Assistant] that aid in preparing a plan guiding through each step. There are institutional instances of Portage's DMP Assistant including one at UBC. Similar to fulfiling open access and open data requirement, it is not uncommon for research funders to request a DMP to be submitted with the funding application demonstrating how the data will be handled at each stage of the data life cycle.

''Read...''

''Examples...''
* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template.

''Reflection''

'''Reflection:''' With reference to your own dataset reflect on sections of the DMP and consider if your data is ready for reuse.

==== Additional activities using the OSF: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

== Additional Resources ==

=== Read ===
{| class="wikitable"
|+
!Description
!Link
|-
|FAIR Principles
|<nowiki>https://www.go-fair.org/fair-principles/</nowiki>
|-
|UBC Research Data Management Guide
|<nowiki>https://researchdata.library.ubc.ca/share/</nowiki>
|-
|RDM Toolkit section for Arts, Humanities and Social Sciences data
|<nowiki>https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/</nowiki>
|-
|Humanities Data Curation Guide
|<nowiki>https://guide.dhcuration.org/about/</nowiki>
|-
|Journal of Open Humanities Data
|<nowiki>https://openhumanitiesdata.metajnl.com/</nowiki>
|-
|Center for Open Data in the Humanities
|<nowiki>http://codh.rois.ac.jp/index.html.en</nowiki>
|-
|Indigenous Data Sovereignty
|<nowiki>https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html</nowiki>
|}

=== Watch ===
{| class="wikitable"
|+
!Description
!Link
|-
|Software carpentry
|<nowiki>https://tibhannover.github.io/2018-07-09-FAIR-Data-and-Software/</nowiki>
|-
|Data one webinars
|<nowiki>https://www.dataone.org/previous-webinars/2017</nowiki>
|}

=== Attend ===
{| class="wikitable"
|+
!Description
!Link
|-
|Research Data Management Workshop
|<nowiki>https://libcal.library.ubc.ca/calendar/vancouver/?t=g&q=&cid=7544&cal=7544</nowiki>
|-
|
|
|}
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:27:08Z

Sarah: /* Implementing Open Data */

== Open Research Data ==

== Big Ideas/Questions ==
If research data is saved on a device or in the cloud or both, how will others find it should they want to look at the data you have used to support your research outputs? As mentioned in the OSF module research tranparency and reproducibility are dependent on open practices which includes open data. Open and shared data and code deposited in a data archive/repository using a metadata standard for discoverability facilitates ease of access for others to find and reuse in order to build on existing scholarship.

== Outcomes ==
* Understand the reasons for making research data open
* Recognize why some data may not be suitable for reuse
* Identify different types of metadata
* Produce a README file that enables others to interpret data
* Describe and deposit data in an open repository applying a license to the data for reuse
* Integrate archived data into an OSF project

== Why it Matters to You? ==
By making research data open and any code associated with the data it allows others to easily access, share and re-use the data, re-run the code and reproduce the research results and validate your research.

Research transparency is required and recommended from funding agencies who not only have requested authors to make copies of their research outputs open access, but are increasingly requesting that the associated data be made open whenever possible. When you open your data you are not only fulfilling funding requirements, but can demonstrate the impact of your data when it is cited.

Opening data encourages and requires good data management practices by establishing a workflow for each stage of the data life cycle so that you can find and interpret that data months and years after having collected it. People who find your data will be able to do so as well. Data deposit into an open repository in a non-proprietary format allows you to control how the data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

[https://biblio.uottawa.ca/en/services/faculty/research-data-management/what-research-data-management Visual of the data life cycle]

== Practice ==

=== What is open data? ===

==== '''Introduction - What is open data?''' ====
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. Open data is guided by the FAIR principles for sharing data meaning that it should be findable, accessible, interoperable and reusable.

''Read…''
* [http://opendatahandbook.org/guide/en/what-is-open-data/ Open Data Handbook definition]
* [https://www.go-fair.org/fair-principles/ FAIR principles]
''Or watch...''
* [https://youtu.be/K-kEvfaUJdA FAIR principles] (embed video into module?)

==== Privacy and open data ====
Open data should be governed by the FAIR principles to promote transparency and reproducibility, but not all data can or should be made open. Even though the FAIR principles provide guidance for sharing data it is with the understanding that ethical and contractual obligations will be upheld. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories can offer options to keep the data closed or embargoed. Even in this more closed scenario a best practice is to let others know the data exists by creating a description of your data and use a metadata standard. for researchers to find and request access to the data

==== '''Policies and open data''' ====
As mentioned open data may be required by granting agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (''Add to references - needs proper citatiion:'' <nowiki>https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html</nowiki>). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.

''Read more...''
* DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.
* [https://open.canada.ca/en/open-data-principles#toc92 Open Data in Canada]
''Mini reflection...''

Consider how the DRAFT Tri-Agency RDM policy might impact how you manage your data.

''Watch...''

[https://learn.scholarsportal.info/modules/portage/rdm-101-module-3/ Canadian Policy Review]

(''or use embed code:'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=13" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

=== Implementing Open Data ===
Before even collecting your data, you need to plan. Each stage of the data life cycle should be considered when planning how you will manage your data and make it accessible.

While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse and you should prepare that data to be deposited into a repository.

>> ''insert image of PDF'' - Data Curation from portage <nowiki>https://portagenetwork.ca/wp-content/uploads/2019/08/Brief_Guide_Data_Curation_AUGUST2019_EN.pdf</nowiki>

==== Data repositories ====
There are institutional and disciplinary repositories for data. An example of an institutional repository is UBC's Dataverse hosted by Scholars Portal. This repository is discipline agnostic where any researcher can create an account and deposit their data and associated files using the provided template to create the metadata required to ensure the data is findable. For disciplinary repositories, the Registry of Research Data Repositories is one example where you can search or browse by discipline to find a repository that will be more useful to researchers in your field (<nowiki>https://www.re3data.org</nowiki> ). Both options provide a persistent identifier such as a doi to help with discovery, sharing, and citing your data.

''Note:'' You might discover data being stored in the OSF. The OSF can be an option to house raw data while working with it or it might be one of the options to store your data temporarily, but it is not recommended for long term preservation and storage.

When depositing into a repository an important piece of making the data open is applying an open license. Public domain licenses can be applied or an open license that requires attribution, but will allow the user to download, reuse and repurpose the data.

''Read...''
* [https://opendatacommons.org/licenses/index.html Open Data Commons - Licenses]
* [https://opendefinition.org/guide/data/ Guide to Open Data Licensing]

==== Documentation ====
File naming and structure are important pieces of managing your data to make it easier for others to use. In keeping with the FAIR principles data formats should be non-proprietary, and unencrypted and uncompressed (''Reference needed here to MIT RDM'' )

''Read...''
* [https://researchdata.library.ubc.ca/plan/organize-your-data/ Guidelines for organizing your data]
* ''(link back to open tools or same reading from that section)''

==== Metadata ====
Metadata is essential for data discovery, sharing and reuse. As it is “data about data” metadata provides a description of the study, files and variables. There are three levels of metadata:
# Descriptive: Provides information about the data that will help people understand what they will find in the dataset and the context. Project title, authors and collection methods are all types of descriptive metadata. Be sure to describe your variables giving them clear names (r''eference to Good Enough Research Data management)''. Name files with basic metadata file names (''provide example from RDM guide - UBC'')
# Administrative: What software is required to use the data? What is the license attached to the data?
# Structural: How do the data files relate to one another (citation needed here for RDM workshop)
The metadata standard you use will depend on the repository you select. Each repository will have their own standard.

It is recommended that you include a README plain text file with the data deposit that helps researchers interpret you data. You might also include a data dictionary, codebooks or other documentation, but this could be included in the README file as well.

''How to...''
* [https://go.library.ubc.ca/zxFJCb Creating a README for your dataset]
* [https://www.wikihow.com/Write-a-Read-Me How to write a read me]
''Activities...''
* Draft a README file to accompany a future or current data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''[https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf Creating a README for your dataset]''
* Find a data repository where you could deposit your data. You might consult the [https://www.re3data.org Registry of Research Data Repositories] that has aggregated disciplinary repositories. Search their database or browse by subject. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.
* Explore a data repository and find a dataset in your discipline. Is there a Data Dictionary, Codebook or README file that helps interpret the data?

* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template.

'''Reflection:''' With reference to your own dataset reflect on sections of the DMP and consider if your data is ready for reuse.

'''README files'''

A paragraph here about readme files with a link to a sample from dataverse and some guided questions.
* '''Activity:''' Draft a README file to accompany a data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''Creating a README for your dataset'' https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf to create your own README file.
'''Metadata'''

There are three types of metadata

'''Data repositories'''

Data repositories provide guidelines for licenses and include metadata standards that can be applied to the dataset so even if the data is closed the metadata can be discovered should someone want to contact you with questions or to request access to that data. With an open license, the data can be downloaded without needing to contact the data author(s). Institutions may have their own data repository such as UBC's Dataverse hosted by Scholars Portal. This is not the only option though, there are disciplinary repositories where data can be published and are in keeping with the FAIR principles. The Registry of Research Data Repositories is one example where you can search for a discipline specific repsitory to deposit your data (https://www.re3data.org ). The Open Science Framework (OSF) can also be an option to house data while working with the raw data, but it is not recommended for long term preservation and storage. A repository agrees to store and preserve your data applying a metadata standard and provides a persistent identifier (DOI) for referencing.
{| class="wikitable"
|+
!Repository
!Link
|-
|UBC Scholars Portal Dataverse
|https://dataverse.scholarsportal.info/
|-
|Registry of Research Data Repositories
|https://www.re3data.org/
|-
|Data Cite
|https://datacite.org
|-
|Open Science Framework (OSF)
|https://osf.io/
|-
|Zenodo
|https://zenodo.org/
|}
'''Activities'''
* Explore one data repository and find a dataset in your discipline. Is there a Data Dictionary or README file that helps interpret the data?
* Find an open data repository where you could deposit your data. Scholars Portal Dataverse houses institutional repositories, but you might also consult the Registry of Research Data Repositories that has aggregated disciplinary repositories. Search their database or browse by subject: https://www.re3data.org. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.

==== Additional activities using the OSF: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

== Key Points ==
These are key points to be taken from the module

== Additional Resources ==

=== Read ===
{| class="wikitable"
|+
!Description
!Link
|-
|FAIR Principles
|<nowiki>https://www.go-fair.org/fair-principles/</nowiki>
|-
|UBC Research Data Management Guide
|<nowiki>https://researchdata.library.ubc.ca/share/</nowiki>
|-
|RDM Toolkit section for Arts, Humanities and Social Sciences data
|<nowiki>https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/</nowiki>
|-
|Humanities Data Curation Guide
|<nowiki>https://guide.dhcuration.org/about/</nowiki>
|-
|Journal of Open Humanities Data
|<nowiki>https://openhumanitiesdata.metajnl.com/</nowiki>
|-
|Center for Open Data in the Humanities
|<nowiki>http://codh.rois.ac.jp/index.html.en</nowiki>
|-
|Indigenous Data Sovereignty
|<nowiki>https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html</nowiki>
|}

=== Watch ===
{| class="wikitable"
|+
!Description
!Link
|-
|Software carpentry
|<nowiki>https://tibhannover.github.io/2018-07-09-FAIR-Data-and-Software/</nowiki>
|-
|Data one webinars
|<nowiki>https://www.dataone.org/previous-webinars/2017</nowiki>
|}

=== Attend ===
{| class="wikitable"
|+
!Description
!Link
|-
|Research Data Management Workshop
|<nowiki>https://libcal.library.ubc.ca/calendar/vancouver/?t=g&q=&cid=7544&cal=7544</nowiki>
|-
|
|
|}
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:20:26Z

Sarah: /* Data repositories */

== Open Research Data ==

== Big Ideas/Questions ==
If research data is saved on a device or in the cloud or both, how will others find it should they want to look at the data you have used to support your research outputs? As mentioned in the OSF module research tranparency and reproducibility are dependent on open practices which includes open data. Open and shared data and code deposited in a data archive/repository using a metadata standard for discoverability facilitates ease of access for others to find and reuse in order to build on existing scholarship.

== Outcomes ==
* Understand the reasons for making research data open
* Recognize why some data may not be suitable for reuse
* Identify different types of metadata
* Produce a README file that enables others to interpret data
* Describe and deposit data in an open repository applying a license to the data for reuse
* Integrate archived data into an OSF project

== Why it Matters to You? ==
By making research data open and any code associated with the data it allows others to easily access, share and re-use the data, re-run the code and reproduce the research results and validate your research.

Research transparency is required and recommended from funding agencies who not only have requested authors to make copies of their research outputs open access, but are increasingly requesting that the associated data be made open whenever possible. When you open your data you are not only fulfilling funding requirements, but can demonstrate the impact of your data when it is cited.

Opening data encourages and requires good data management practices by establishing a workflow for each stage of the data life cycle so that you can find and interpret that data months and years after having collected it. People who find your data will be able to do so as well. Data deposit into an open repository in a non-proprietary format allows you to control how the data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

[https://biblio.uottawa.ca/en/services/faculty/research-data-management/what-research-data-management Visual of the data life cycle]

== Practice ==

=== What is open data? ===

==== '''Introduction - What is open data?''' ====
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. Open data is guided by the FAIR principles for sharing data meaning that it should be findable, accessible, interoperable and reusable.

''Read…''
* [http://opendatahandbook.org/guide/en/what-is-open-data/ Open Data Handbook definition]
* [https://www.go-fair.org/fair-principles/ FAIR principles]
''Or watch...''
* [https://youtu.be/K-kEvfaUJdA FAIR principles] (embed video into module?)

==== Privacy and open data ====
Open data should be governed by the FAIR principles to promote transparency and reproducibility, but not all data can or should be made open. Even though the FAIR principles provide guidance for sharing data it is with the understanding that ethical and contractual obligations will be upheld. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories can offer options to keep the data closed or embargoed. Even in this more closed scenario a best practice is to let others know the data exists by creating a description of your data and use a metadata standard. for researchers to find and request access to the data

==== '''Policies and open data''' ====
As mentioned open data may be required by granting agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (''Add to references - needs proper citatiion:'' <nowiki>https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html</nowiki>). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.

''Read more...''
* DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.
* [https://open.canada.ca/en/open-data-principles#toc92 Open Data in Canada]
''Mini reflection...''

Consider how the DRAFT Tri-Agency RDM policy might impact how you manage your data.

''Watch...''

[https://learn.scholarsportal.info/modules/portage/rdm-101-module-3/ Canadian Policy Review]

(''or use embed code:'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=13" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

=== Implementing Open Data ===
Before even collecting your data, you need to plan. Each stage of the data life cycle should be considered when planning how you will manage your data and make it accessible.

While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse and you should prepare that data to be deposited into a repository.

>> ''insert image of PDF'' - Data Curation from portage <nowiki>https://portagenetwork.ca/wp-content/uploads/2019/08/Brief_Guide_Data_Curation_AUGUST2019_EN.pdf</nowiki>

==== Data repositories ====
There are institutional and disciplinary repositories for data. An example of an institutional repository is UBC's Dataverse hosted by Scholars Portal. This repository is discipline agnostic where any researcher can create an account and deposit their data and associated files using the provided template to create the metadata required to ensure the data is findable. For disciplinary repositories, the Registry of Research Data Repositories is one example where you can search or browse by discipline to find a repository that will be more useful to researchers in your field (<nowiki>https://www.re3data.org</nowiki> ). Both options provide a persistent identifier such as a doi to help with discovery, sharing, and citing your data.

''Note:'' You might discover data being stored in the OSF. The OSF can be an option to house raw data while working with it or it might be one of the options to store your data temporarily, but it is not recommended for long term preservation and storage.

When depositing into a repository an important piece of making the data open is applying an open license. Public domain licenses can be applied or an open license that requires attribution, but will allow the user to download, reuse and repurpose the data.

''Read...''
* [https://opendatacommons.org/licenses/index.html Open Data Commons - Licenses]
* [https://opendefinition.org/guide/data/ Guide to Open Data Licensing]

==== Documentation ====
File naming and structure are important pieces of managing your data to make it easier for others to use. In keeping with the FAIR principles data formats should be non-proprietary, and unencrypted and uncompressed (''Reference needed here to MIT RDM'' )

''Read...''
* [https://researchdata.library.ubc.ca/plan/organize-your-data/ Guidelines for organizing your data]
* ''(link back to open tools or same reading from that section)''

'''Review one of the following:'''
* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template.

'''Reflection:''' With reference to your own dataset reflect on sections of the DMP and consider if your data is ready for reuse.

'''README files'''

A paragraph here about readme files with a link to a sample from dataverse and some guided questions.
* '''Activity:''' Draft a README file to accompany a data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''Creating a README for your dataset'' https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf to create your own README file.
'''Metadata'''

There are three types of metadata

'''Data repositories'''

Data repositories provide guidelines for licenses and include metadata standards that can be applied to the dataset so even if the data is closed the metadata can be discovered should someone want to contact you with questions or to request access to that data. With an open license, the data can be downloaded without needing to contact the data author(s). Institutions may have their own data repository such as UBC's Dataverse hosted by Scholars Portal. This is not the only option though, there are disciplinary repositories where data can be published and are in keeping with the FAIR principles. The Registry of Research Data Repositories is one example where you can search for a discipline specific repsitory to deposit your data (https://www.re3data.org ). The Open Science Framework (OSF) can also be an option to house data while working with the raw data, but it is not recommended for long term preservation and storage. A repository agrees to store and preserve your data applying a metadata standard and provides a persistent identifier (DOI) for referencing.
{| class="wikitable"
|+
!Repository
!Link
|-
|UBC Scholars Portal Dataverse
|https://dataverse.scholarsportal.info/
|-
|Registry of Research Data Repositories
|https://www.re3data.org/
|-
|Data Cite
|https://datacite.org
|-
|Open Science Framework (OSF)
|https://osf.io/
|-
|Zenodo
|https://zenodo.org/
|}
'''Activities'''
* Explore one data repository and find a dataset in your discipline. Is there a Data Dictionary or README file that helps interpret the data?
* Find an open data repository where you could deposit your data. Scholars Portal Dataverse houses institutional repositories, but you might also consult the Registry of Research Data Repositories that has aggregated disciplinary repositories. Search their database or browse by subject: https://www.re3data.org. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.

==== Additional activities using the OSF: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

== Key Points ==
These are key points to be taken from the module

== Additional Resources ==

=== Read ===
{| class="wikitable"
|+
!Description
!Link
|-
|FAIR Principles
|<nowiki>https://www.go-fair.org/fair-principles/</nowiki>
|-
|UBC Research Data Management Guide
|<nowiki>https://researchdata.library.ubc.ca/share/</nowiki>
|-
|RDM Toolkit section for Arts, Humanities and Social Sciences data
|<nowiki>https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/</nowiki>
|-
|Humanities Data Curation Guide
|<nowiki>https://guide.dhcuration.org/about/</nowiki>
|-
|Journal of Open Humanities Data
|<nowiki>https://openhumanitiesdata.metajnl.com/</nowiki>
|-
|Center for Open Data in the Humanities
|<nowiki>http://codh.rois.ac.jp/index.html.en</nowiki>
|-
|Indigenous Data Sovereignty
|<nowiki>https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html</nowiki>
|}

=== Watch ===
{| class="wikitable"
|+
!Description
!Link
|-
|Software carpentry
|<nowiki>https://tibhannover.github.io/2018-07-09-FAIR-Data-and-Software/</nowiki>
|-
|Data one webinars
|<nowiki>https://www.dataone.org/previous-webinars/2017</nowiki>
|}

=== Attend ===
{| class="wikitable"
|+
!Description
!Link
|-
|Research Data Management Workshop
|<nowiki>https://libcal.library.ubc.ca/calendar/vancouver/?t=g&q=&cid=7544&cal=7544</nowiki>
|-
|
|
|}
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:18:16Z

Sarah: /* Data repositories */

== Open Research Data ==

== Big Ideas/Questions ==
If research data is saved on a device or in the cloud or both, how will others find it should they want to look at the data you have used to support your research outputs? As mentioned in the OSF module research tranparency and reproducibility are dependent on open practices which includes open data. Open and shared data and code deposited in a data archive/repository using a metadata standard for discoverability facilitates ease of access for others to find and reuse in order to build on existing scholarship.

== Outcomes ==
* Understand the reasons for making research data open
* Recognize why some data may not be suitable for reuse
* Identify different types of metadata
* Produce a README file that enables others to interpret data
* Describe and deposit data in an open repository applying a license to the data for reuse
* Integrate archived data into an OSF project

== Why it Matters to You? ==
By making research data open and any code associated with the data it allows others to easily access, share and re-use the data, re-run the code and reproduce the research results and validate your research.

Research transparency is required and recommended from funding agencies who not only have requested authors to make copies of their research outputs open access, but are increasingly requesting that the associated data be made open whenever possible. When you open your data you are not only fulfilling funding requirements, but can demonstrate the impact of your data when it is cited.

Opening data encourages and requires good data management practices by establishing a workflow for each stage of the data life cycle so that you can find and interpret that data months and years after having collected it. People who find your data will be able to do so as well. Data deposit into an open repository in a non-proprietary format allows you to control how the data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

[https://biblio.uottawa.ca/en/services/faculty/research-data-management/what-research-data-management Visual of the data life cycle]

== Practice ==

=== What is open data? ===

==== '''Introduction - What is open data?''' ====
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. Open data is guided by the FAIR principles for sharing data meaning that it should be findable, accessible, interoperable and reusable.

''Read…''
* [http://opendatahandbook.org/guide/en/what-is-open-data/ Open Data Handbook definition]
* [https://www.go-fair.org/fair-principles/ FAIR principles]
''Or watch...''
* [https://youtu.be/K-kEvfaUJdA FAIR principles] (embed video into module?)

==== Privacy and open data ====
Open data should be governed by the FAIR principles to promote transparency and reproducibility, but not all data can or should be made open. Even though the FAIR principles provide guidance for sharing data it is with the understanding that ethical and contractual obligations will be upheld. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories can offer options to keep the data closed or embargoed. Even in this more closed scenario a best practice is to let others know the data exists by creating a description of your data and use a metadata standard. for researchers to find and request access to the data

==== '''Policies and open data''' ====
As mentioned open data may be required by granting agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (''Add to references - needs proper citatiion:'' <nowiki>https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html</nowiki>). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.

''Read more...''
* DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.
* [https://open.canada.ca/en/open-data-principles#toc92 Open Data in Canada]
''Mini reflection...''

Consider how the DRAFT Tri-Agency RDM policy might impact how you manage your data.

''Watch...''

[https://learn.scholarsportal.info/modules/portage/rdm-101-module-3/ Canadian Policy Review]

(''or use embed code:'')

<iframe src="https://learn.scholarsportal.info/wp-admin/admin-ajax.php?action=h5p_embed&id=13" width="625" height="377" frameborder="0" allowfullscreen="allowfullscreen"></iframe><script src="https://learn.scholarsportal.info/wp-content/plugins/h5p/h5p-php-library/js/h5p-resizer.js" charset="UTF-8"></script>

=== Implementing Open Data ===
Before even collecting your data, you need to plan. Each stage of the data life cycle should be considered when planning how you will manage your data and make it accessible.

While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse and you should prepare that data to be deposited into a repository.

>> ''insert image of PDF'' - Data Curation from portage <nowiki>https://portagenetwork.ca/wp-content/uploads/2019/08/Brief_Guide_Data_Curation_AUGUST2019_EN.pdf</nowiki>

==== Data repositories ====
There are institutional and disciplinary repositories for data. An example of an institutional repository is UBC's Dataverse hosted by Scholars Portal. This repository is discipline agnostic where any researcher can create an account and deposit their data and associated files using the provided template to create the metadata required to ensure the data is findable. For disciplinary repositories, the Registry of Research Data Repositories is one example where you can search or browse by discipline to find a repository that will be more useful to researchers in your field (<nowiki>https://www.re3data.org</nowiki> ). Both options provide a persistent identifier such as a doi to help with discovery, sharing, and citing your data.

''Note:'' You might discover data being stored in the OSF. The OSF can be an option to house raw data while working with it or it might be one of the options to store your data temporarily, but it is not recommended for long term preservation and storage.

When depositing into a repository an important piece of making the data open is applying an open license. Public domain licenses can be applied or an open license that requires attribution, but will allow the user to download, reuse and repurpose the data.

''Read...''
* [https://opendatacommons.org/licenses/index.html Open Data Commons - Licenses]
* [https://opendefinition.org/guide/data/ Guide to Open Data Licensing]

'''Review one of the following:'''
* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template.

'''Reflection:''' With reference to your own dataset reflect on sections of the DMP and consider if your data is ready for reuse.

'''README files'''

A paragraph here about readme files with a link to a sample from dataverse and some guided questions.
* '''Activity:''' Draft a README file to accompany a data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''Creating a README for your dataset'' https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf to create your own README file.
'''Metadata'''

There are three types of metadata

'''Data repositories'''

Data repositories provide guidelines for licenses and include metadata standards that can be applied to the dataset so even if the data is closed the metadata can be discovered should someone want to contact you with questions or to request access to that data. With an open license, the data can be downloaded without needing to contact the data author(s). Institutions may have their own data repository such as UBC's Dataverse hosted by Scholars Portal. This is not the only option though, there are disciplinary repositories where data can be published and are in keeping with the FAIR principles. The Registry of Research Data Repositories is one example where you can search for a discipline specific repsitory to deposit your data (https://www.re3data.org ). The Open Science Framework (OSF) can also be an option to house data while working with the raw data, but it is not recommended for long term preservation and storage. A repository agrees to store and preserve your data applying a metadata standard and provides a persistent identifier (DOI) for referencing.
{| class="wikitable"
|+
!Repository
!Link
|-
|UBC Scholars Portal Dataverse
|https://dataverse.scholarsportal.info/
|-
|Registry of Research Data Repositories
|https://www.re3data.org/
|-
|Data Cite
|https://datacite.org
|-
|Open Science Framework (OSF)
|https://osf.io/
|-
|Zenodo
|https://zenodo.org/
|}
'''Activities'''
* Explore one data repository and find a dataset in your discipline. Is there a Data Dictionary or README file that helps interpret the data?
* Find an open data repository where you could deposit your data. Scholars Portal Dataverse houses institutional repositories, but you might also consult the Registry of Research Data Repositories that has aggregated disciplinary repositories. Search their database or browse by subject: https://www.re3data.org. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.

==== Additional activities using the OSF: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

== Key Points ==
These are key points to be taken from the module

== Additional Resources ==

=== Read ===
{| class="wikitable"
|+
!Description
!Link
|-
|FAIR Principles
|<nowiki>https://www.go-fair.org/fair-principles/</nowiki>
|-
|UBC Research Data Management Guide
|<nowiki>https://researchdata.library.ubc.ca/share/</nowiki>
|-
|RDM Toolkit section for Arts, Humanities and Social Sciences data
|<nowiki>https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/</nowiki>
|-
|Humanities Data Curation Guide
|<nowiki>https://guide.dhcuration.org/about/</nowiki>
|-
|Journal of Open Humanities Data
|<nowiki>https://openhumanitiesdata.metajnl.com/</nowiki>
|-
|Center for Open Data in the Humanities
|<nowiki>http://codh.rois.ac.jp/index.html.en</nowiki>
|-
|Indigenous Data Sovereignty
|<nowiki>https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html</nowiki>
|}

=== Watch ===
{| class="wikitable"
|+
!Description
!Link
|-
|Software carpentry
|<nowiki>https://tibhannover.github.io/2018-07-09-FAIR-Data-and-Software/</nowiki>
|-
|Data one webinars
|<nowiki>https://www.dataone.org/previous-webinars/2017</nowiki>
|}

=== Attend ===
{| class="wikitable"
|+
!Description
!Link
|-
|Research Data Management Workshop
|<nowiki>https://libcal.library.ubc.ca/calendar/vancouver/?t=g&q=&cid=7544&cal=7544</nowiki>
|-
|
|
|}
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:15:21Z

Sarah: /* Making your data open */

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:13:36Z

Sarah: /* Introduction - What is open data? */

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:13:11Z

Sarah: /* Policies and open data */

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:12:46Z

Sarah: /* Policies and open data */

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:06:19Z

Sarah: /* Privacy and open data */

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:05:15Z

Sarah: /* What is open data? */

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:02:46Z

Sarah: /* Why it Matters to You? */

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:01:47Z

Sarah: /* Outcomes */

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-25T19:00:35Z

Sarah:

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-23T06:17:59Z

Sarah: /* Big Ideas/Questions */

== Open Data ==

== Big Ideas/Questions ==
Research transparency and reproducibility are dependent on open practices which includes open data. Open data shared in a data archive/repository using a metadata standard for discoverability facilitates ease of access to that data for others to find and reuse in order to build on existing scholarship. If data is saved on a device or in the cloud or both how will others find it should they want to look at the data used to support your research outputs?

== Outcomes ==
* Understand the reasons for making your data open
* Recognize why some data may not be suitable for reuse
* Identify different
* Produce a README file that enables others to interpret the data
* Describe and if possible deposit data in an open repository applying a license to the data for reuse
* Integrate archived data into an OSF project

== Why it Matters to You? ==
By making research data open and any code associated with the data it allows others to easily access, share and re-use the data, re-run the code and reproduce the research results and validate your research. Research transparency is required and recommended from funding agencies who not only have requested authors to make copies of their research outputs open access, but are increasingly requesting that the associated data be made open whenever possible. Opening data encourages and requires good data management practices by establishing a workflow for each stage of the data life cycle so that you can find and interpret that data months and years after having collected it. People who find your data will be able to do so as well. Data deposit into an open repository in a non-proprietary format allows you to control how the data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

== Practice ==

=== What is open data? ===
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. With open data the goal is to make your data findable, accessible, interoperable and reusable. These are the FAIR principles for sharing data.
* '''Read''' about the FAIR Principles: <nowiki>https://www.go-fair.org/fair-principles/</nowiki>
* Watch:
* '''(Optional) Read''' more about what is open data: <nowiki>http://opendatahandbook.org/guide/en/what-is-open-data/</nowiki>

=== Privacy and open data ===
While open data should be governed by the FAIR principles to promote transparency and reproducibility not all data can or should be made open. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal information or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories may offer options to keep the data closed or embargoed while still making it known that the data exists by using a metadata standard.
* Read:
* Watch:
'''Open data policy'''

As mentioned open data may also be a requirement of granting agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.
* '''Read''' the DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.
* Watch:

=== Making your data open ===
Ideally and in keeping with open science practices, open data is made available in a repository with an accompanying README file or data dictionary to interpret the data. At the time of deposit a metadata standard is used in order to make that data findable.

==== Data management ====
Before even collecting your data, you need to plan. Each stage of the data life cycle should be considered when planning how you will manage your data and make it accessible.

While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse. Believing that you have properly managed your data so you could share it with someone else should they request it does not make that data easily findable. A data management plan (DMP) will address questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. There are templates such as Portage's DMP Assistant (https://assistant.portagenetwork.ca/) that aid in preparing a plan guiding through each step. It is not uncommon for research funders to request a DMP to be submitted with the funding application to demonstrate how the data will be handled at each stage of the data life cycle.

'''Review one of the following:'''
* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template.

'''Reflection:''' With reference to your own dataset reflect on sections of the DMP and consider if your data is ready for reuse.

'''README files'''

A paragraph here about readme files with a link to a sample from dataverse and some guided questions.
* '''Activity:''' Draft a README file to accompany a data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''Creating a README for your dataset'' https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf to create your own README file.
'''Metadata'''

There are three types of metadata

'''Data repositories'''

Data repositories provide guidelines for licenses and include metadata standards that can be applied to the dataset so even if the data is closed the metadata can be discovered should someone want to contact you with questions or to request access to that data. With an open license, the data can be downloaded without needing to contact the data author(s). Institutions may have their own data repository such as UBC's Dataverse hosted by Scholars Portal. This is not the only option though, there are disciplinary repositories where data can be published and are in keeping with the FAIR principles. The Registry of Research Data Repositories is one example where you can search for a discipline specific repsitory to deposit your data (https://www.re3data.org ). The Open Science Framework (OSF) can also be an option to house data while working with the raw data, but it is not recommended for long term preservation and storage. A repository agrees to store and preserve your data applying a metadata standard and provides a persistent identifier (DOI) for referencing.
{| class="wikitable"
|+
!Repository
!Link
|-
|UBC Scholars Portal Dataverse
|https://dataverse.scholarsportal.info/
|-
|Registry of Research Data Repositories
|https://www.re3data.org/
|-
|Data Cite
|https://datacite.org
|-
|Open Science Framework (OSF)
|https://osf.io/
|-
|Zenodo
|https://zenodo.org/
|}
'''Activities'''
* Explore one data repository and find a dataset in your discipline. Is there a Data Dictionary or README file that helps interpret the data?
* Find an open data repository where you could deposit your data. Scholars Portal Dataverse houses institutional repositories, but you might also consult the Registry of Research Data Repositories that has aggregated disciplinary repositories. Search their database or browse by subject: https://www.re3data.org. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.

==== Additional activities using the OSF: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

== Key Points ==
These are key points to be taken from the module

== Additional Resources ==

=== Read ===
{| class="wikitable"
|+
!Description
!Link
|-
|FAIR Principles
|<nowiki>https://www.go-fair.org/fair-principles/</nowiki>
|-
|UBC Research Data Management Guide
|<nowiki>https://researchdata.library.ubc.ca/share/</nowiki>
|-
|RDM Toolkit section for Arts, Humanities and Social Sciences data
|<nowiki>https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/</nowiki>
|-
|Humanities Data Curation Guide
|<nowiki>https://guide.dhcuration.org/about/</nowiki>
|-
|Journal of Open Humanities Data
|<nowiki>https://openhumanitiesdata.metajnl.com/</nowiki>
|-
|Center for Open Data in the Humanities
|<nowiki>http://codh.rois.ac.jp/index.html.en</nowiki>
|-
|Indigenous Data Sovereignty
|<nowiki>https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html</nowiki>
|}

=== Watch ===
{| class="wikitable"
|+
!Description
!Link
|-
|Software carpentry
|<nowiki>https://tibhannover.github.io/2018-07-09-FAIR-Data-and-Software/</nowiki>
|-
|Data one webinars
|<nowiki>https://www.dataone.org/previous-webinars/2017</nowiki>
|}

=== Attend ===
{| class="wikitable"
|+
!Description
!Link
|-
|Research Data Management Workshop
|<nowiki>https://libcal.library.ubc.ca/calendar/vancouver/?t=g&q=&cid=7544&cal=7544</nowiki>
|-
|
|
|}
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-22T16:57:06Z

Sarah: /* Outcomes */

== Open Data ==

== Big Ideas/Questions ==
Research transparency and reproducibility are dependent on open practices which includes open data. Open data facilitates ease of access to your data so others can find, access and reuse that data building on existing scholarship.

== Outcomes ==
* Understand the reasons for making your data open
* Recognize why some data may not be made open
* Produce a README file that enables others to interpret the data
* Describe and if possible deposit data in an open repository applying a license to the data for reuse
* Integrate archived data into an OSF project

== Why it Matters to You? ==
By making research data open it becomes more discoverable and allows others to easily access, share and re-use the data. Research funding is increasingly requesting that data be made open whenever possible. It encourages and requires good data management practices establishing a workflow to ensure you can find and interpret your data months and years after having collected that data. Data deposit into a repository in a non-proprietary format allows you to control how that data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

== Practice ==

=== What is open data? ===
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. With open data the goal is to make your data findable, accessible, interoperable and reusable. These are the FAIR principles for sharing data.
* '''Read''' about the FAIR Principles: <nowiki>https://www.go-fair.org/fair-principles/</nowiki>
* '''Read''' more about what is open data: <nowiki>http://opendatahandbook.org/guide/en/what-is-open-data/</nowiki>

==== Transparency ====
While open data should be governed by the FAIR principles to promote transparency and reproducibility not all data can or should be made open. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal information or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories may offer options to keep the data closed while still making the metadata findable should someone be interested in contacting the researcher about the dataset.

==== Making your data open ====
Ideally and in keeping with open science practices, open data is made available in a repository with an accompanying README file or data dictionary that uses a metadata standard to improve discoverability. Using non-proprietary file formats and applying an open license to indicate how the data can be reused are important steps to take. Data repositories provide guidelines for licenses and include metadata standards that can be applied to the dataset so even if the data is closed the metadata can be discovered should someone want to contact you with questions or to request access to that data. With an open license, the data is discoverable and downloadable without needing to contact the data author(s). Institutions may have their own data repository such as UBC's Dataverse hosted by Scholars Portal. This is not the only option though, there are disciplinary repositories where data can be published and are in keeping with the FAIR principles. The Registry of Research Data Repositories is one example where you can search for a discipline specific repsitory to deposit your data (https://www.re3data.org ). The Open Science Framework (OSF) can also be an option to house data while working with the raw data, but it is not recommended for long term preservation and storage. A repository agrees to store and preserve your data applying a metadata standard and provides a persistent identifier (DOI) for referencing.

'''README files'''

'''Metadata'''

'''Data Repositories'''
{| class="wikitable"
|+
!Repository
!Link
|-
|UBC Scholars Portal Dataverse
|https://dataverse.scholarsportal.info/
|-
|Registry of Research Data Repositories
|https://www.re3data.org/
|-
|Data Cite
|https://datacite.org
|-
|Open Science Framework (OSF)
|https://osf.io/
|-
|Zenodo
|https://zenodo.org/
|}
* Explore one data repository and find a dataset in your discipline. Is there a Data Dictionary or README file that helps interpret the data?

'''Funding'''

Open data may also be a requirement of funding agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.
* '''Read''' the DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.

==== Data management ====
While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse. Believing that you have properly managed your data so you could share it with someone else should they request it does not make that data easily findable. A data management plan (DMP) will address questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. There are templates such as Portage's DMP Assistant (https://assistant.portagenetwork.ca/) that aid in preparing a plan guiding through each step. It is not uncommon for research funders to request a DMP to be submitted with the funding application to demonstrate how the data will be handled at each stage of the data life cycle.

'''Review one of the following:'''
* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template. With reference to your own dataset reflect on sections of the DMP to consider if your dataset is ready for reuse.

'''Activities'''
* Draft a README file to accompany a data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''Creating a README for your dataset'' https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf to create your own README file.
* Find an open data repository where you could deposit your data. Scholars Portal Dataverse houses institutional repositories, but you might also consult the Registry of Research Data Repositories that has aggregated disciplinary repositories. Search their database or browse by subject: https://www.re3data.org. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.

==== Additional optional activities: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

== Key Points ==
These are key points to be taken from the module

== Additional Resources ==

=== Read ===
{| class="wikitable"
|+
!Description
!Link
|-
|FAIR Principles
|<nowiki>https://www.go-fair.org/fair-principles/</nowiki>
|-
|UBC Research Data Management Guide
|<nowiki>https://researchdata.library.ubc.ca/share/</nowiki>
|-
|RDM Toolkit section for Arts, Humanities and Social Sciences data
|<nowiki>https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/</nowiki>
|-
|Humanities Data Curation Guide
|<nowiki>https://guide.dhcuration.org/about/</nowiki>
|-
|Journal of Open Humanities Data
|<nowiki>https://openhumanitiesdata.metajnl.com/</nowiki>
|-
|Center for Open Data in the Humanities
|<nowiki>http://codh.rois.ac.jp/index.html.en</nowiki>
|-
|Indigenous Data Sovereignty
|<nowiki>https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html</nowiki>
|}

=== Watch ===
{| class="wikitable"
|+
!Description
!Link
|-
|Software carpentry
|<nowiki>https://tibhannover.github.io/2018-07-09-FAIR-Data-and-Software/</nowiki>
|-
|Data one webinars
|<nowiki>https://www.dataone.org/previous-webinars/2017</nowiki>
|}

=== Attend ===
{| class="wikitable"
|+
!Description
!Link
|-
|Research Data Management Workshop
|<nowiki>https://libcal.library.ubc.ca/calendar/vancouver/?t=g&q=&cid=7544&cal=7544</nowiki>
|-
|
|
|}
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-22T05:26:27Z

Sarah: /* Describing research data */

== Open Data ==

== Big Ideas/Questions ==
Research transparency and reproducibility are dependent on open practices which includes open data. Open data facilitates ease of access to your data so others can find, access and reuse that data building on existing scholarship.

== Outcomes ==
* Understand the reasons for making your data open
* Recognize the reasons why some data may not be made open
* Producing a README file that demonstrates the characteristics of a README enabling others to interpret the data
* Describe and if possible deposit data in an open repository applying a license to the data for reuse
* Integrate the archived data into an OSF project

== Why it Matters to You? ==
By making research data open it becomes more discoverable and allows others to easily access, share and re-use the data. Research funding is increasingly requesting that data be made open whenever possible. It encourages and requires good data management practices establishing a workflow to ensure you can find and interpret your data months and years after having collected that data. Data deposit into a repository in a non-proprietary format allows you to control how that data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

== Practice ==

=== What is open data? ===
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. With open data the goal is to make your data findable, accessible, interoperable and reusable. These are the FAIR principles for sharing data.
* '''Read''' about the FAIR Principles: <nowiki>https://www.go-fair.org/fair-principles/</nowiki>
* '''Read''' more about what is open data: <nowiki>http://opendatahandbook.org/guide/en/what-is-open-data/</nowiki>

==== Transparency ====
While open data should be governed by the FAIR principles to promote transparency and reproducibility not all data can or should be made open. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal information or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories may offer options to keep the data closed while still making the metadata findable should someone be interested in contacting the researcher about the dataset.

==== Making your data open ====
Ideally and in keeping with open science practices, open data is made available in a repository using a metadata standard to improve discoverability. Applying an open license can indicate how the data can be reused. Data repositories provide guidelines for licenses and metadata standards, and institutions may have their own data repository such as UBC's Dataverse hosted by Scholars Portal. This is not the only option though, there are disciplinary repositories where data can be published and are in keeping with the FAIR principles. The Registry of Research Data Repositories is one example where you can search for a discipline specific repsitory to deposit your data (https://www.re3data.org ). The Open Science Framework (OSF) can also be an option to house data while working with the raw data, but it is not recommended for long term preservation and storage. A repository agrees to store and preserve your data applying a metadata standard for discoverability and provides a persistent identifier (DOI) for referencing. It is recommended that you provide a README file

'''Data Repositories'''
{| class="wikitable"
|+
!Repository
!Link
|-
|UBC Scholars Portal Dataverse
|https://dataverse.scholarsportal.info/
|-
|Registry of Research Data Repositories
|https://www.re3data.org/
|-
|Data Cite
|https://datacite.org
|-
|Open Science Framework (OSF)
|https://osf.io/
|-
|Zenodo
|https://zenodo.org/
|}
* Explore one data repository and find a dataset in your discipline. Is there a Data Dictionary or README file that helps interpret the data?

'''Funding'''

Open data may also be a requirement of funding agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.
* '''Read''' the DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.

==== Data management ====
While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse. Believing that you have properly managed your data so you could share it with someone else should they request it does not make that data easily findable. A data management plan (DMP) will address questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. There are templates such as Portage's DMP Assistant (https://assistant.portagenetwork.ca/) that aid in preparing a plan guiding through each step. It is not uncommon for research funders to request a DMP to be submitted with the funding application to demonstrate how the data will be handled at each stage of the data life cycle.

'''Review one of the following:'''
* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template. With reference to your own dataset reflect on sections of the DMP to consider if your dataset is ready for reuse.

'''Activities'''
* Draft a README file to accompany a data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''Creating a README for your dataset'' https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf to create your own README file.
* Find an open data repository where you could deposit your data. Scholars Portal Dataverse houses institutional repositories, but you might also consult the Registry of Research Data Repositories that has aggregated disciplinary repositories. Search their database or browse by subject: https://www.re3data.org. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.

==== Additional optional activities: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

== Key Points ==
These are key points to be taken from the module

== Additional Resources ==

=== Read ===
{| class="wikitable"
|+
!Description
!Link
|-
|FAIR Principles
|<nowiki>https://www.go-fair.org/fair-principles/</nowiki>
|-
|UBC Research Data Management Guide
|<nowiki>https://researchdata.library.ubc.ca/share/</nowiki>
|-
|RDM Toolkit section for Arts, Humanities and Social Sciences data
|<nowiki>https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/</nowiki>
|-
|Humanities Data Curation Guide
|<nowiki>https://guide.dhcuration.org/about/</nowiki>
|-
|Journal of Open Humanities Data
|<nowiki>https://openhumanitiesdata.metajnl.com/</nowiki>
|-
|Center for Open Data in the Humanities
|<nowiki>http://codh.rois.ac.jp/index.html.en</nowiki>
|-
|Indigenous Data Sovereignty
|<nowiki>https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html</nowiki>
|}

=== Watch ===
{| class="wikitable"
|+
!Description
!Link
|-
|Software carpentry
|<nowiki>https://tibhannover.github.io/2018-07-09-FAIR-Data-and-Software/</nowiki>
|-
|Data one webinars
|<nowiki>https://www.dataone.org/previous-webinars/2017</nowiki>
|}

=== Attend ===
{| class="wikitable"
|+
!Description
!Link
|-
|Research Data Management Workshop
|<nowiki>https://libcal.library.ubc.ca/calendar/vancouver/?t=g&q=&cid=7544&cal=7544</nowiki>
|-
|
|
|}
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-22T05:22:08Z

Sarah: /* Why it Matters to You? */

== Open Data ==

== Big Ideas/Questions ==
Research transparency and reproducibility are dependent on open practices which includes open data. Open data facilitates ease of access to your data so others can find, access and reuse that data building on existing scholarship.

== Outcomes ==
* Understand the reasons for making your data open
* Recognize the reasons why some data may not be made open
* Producing a README file that demonstrates the characteristics of a README enabling others to interpret the data
* Describe and if possible deposit data in an open repository applying a license to the data for reuse
* Integrate the archived data into an OSF project

== Why it Matters to You? ==
By making research data open it becomes more discoverable and allows others to easily access, share and re-use the data. Research funding is increasingly requesting that data be made open whenever possible. It encourages and requires good data management practices establishing a workflow to ensure you can find and interpret your data months and years after having collected that data. Data deposit into a repository in a non-proprietary format allows you to control how that data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

== Practice ==

=== What is open data? ===
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. With open data the goal is to make your data findable, accessible, interoperable and reusable. These are the FAIR principles for sharing data.
* '''Read''' about the FAIR Principles: <nowiki>https://www.go-fair.org/fair-principles/</nowiki>
* '''Read''' more about what is open data: <nowiki>http://opendatahandbook.org/guide/en/what-is-open-data/</nowiki>

==== Transparency ====
While open data should be governed by the FAIR principles to promote transparency and reproducibility not all data can or should be made open. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal information or other sensitive information that should not be readily accessible. For example, open data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories may offer options to keep the data closed while still making the metadata findable should someone be interested in contacting the researcher about the dataset.

==== Making your data open ====
Ideally and in keeping with open science practices, open data is made available in a repository using a metadata standard to improve discoverability. Applying an open license can indicate how the data can be reused. Data repositories provide guidelines for licenses and metadata standards, and institutions may have their own data repository such as UBC's Dataverse hosted by Scholars Portal. This is not the only option though, there are disciplinary repositories where data can be published and are in keeping with the FAIR principles. The Registry of Research Data Repositories is one example where you can search for a discipline specific repsitory to deposit your data (https://www.re3data.org ). The Open Science Framework (OSF) can also be an option to house data while working with the raw data, but it is not recommended for long term preservation and storage. A repository agrees to store and preserve your data applying a metadata standard for discoverability and provides a persistent identifier (DOI) for referencing. It is recommended that you provide a README file

'''Data Repositories'''
{| class="wikitable"
|+
!Repository
!Link
|-
|UBC Scholars Portal Dataverse
|https://dataverse.scholarsportal.info/
|-
|Registry of Research Data Repositories
|https://www.re3data.org/
|-
|Data Cite
|https://datacite.org
|-
|Open Science Framework (OSF)
|https://osf.io/
|-
|Zenodo
|https://zenodo.org/
|}
* Explore one of the data repositories and find a dataset in your discipline.

'''Funding'''

Open data may also be a requirement of funding agencies or journals as part of their agreement for funding or publication. In Canada the Tri-Agency Council's Statement of Principles on Digital Data Management states that "research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others." (https://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html). A draft policy recommends that institutions have a strategy in place to support researchers fulfilling their data management requirements for funding and also will require a data deposit into a repository.
* '''Read''' the DRAFT Tri-Agency Research Data Management Policy For Consultation (https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html). Consider how this might impact how you manage your data.

==== Data management ====
While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse. Believing that you have properly managed your data so you could share it with someone else should they request it does not make that data easily findable. A data management plan (DMP) will address questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. There are templates such as Portage's DMP Assistant (https://assistant.portagenetwork.ca/) that aid in preparing a plan guiding through each step. It is not uncommon for research funders to request a DMP to be submitted with the funding application to demonstrate how the data will be handled at each stage of the data life cycle.

'''Review one of the following:'''
* DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool),
* DMP Assistant (https://assistant.portagenetwork.ca/)
* DMP Online (https://dmponline.dcc.ac.uk/).
You may have to create an account to access the template. With reference to your own dataset reflect on sections of the DMP to consider if your dataset is ready for reuse.

===== Describing research data =====
Metadata is data about data. Your metadata should be human-friendly but also machine readable. This is achieved by using a metadata standard.

{| class="wikitable"
|+
!Repository
!Link
|-
|UBC Scholars Portal Dataverse
|https://dataverse.scholarsportal.info/
|-
|Registry of Research Data Repositories
|https://www.re3data.org/
|-
|Open Science Framework (OSF)
|https://osf.io/
|-
|Zenodo
|https://zenodo.org/
|}
Activities
* Draft a README file to accompany a data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''Creating a README for your dataset'' https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf to create your own README file.
* Find an open data repository where you could deposit your data. Scholars Portal Dataverse houses institutional repositories, but you might also consult the Registry of Research Data Repositories that has aggregated disciplinary repositories. Search their database or browse by subject: https://www.re3data.org. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.

==== Additional optional activities: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

== In Practice: This means how to apply to your work ==
This is the section where you ask reflective questions to help learners apply their learning to a project that they are working on

== Key Points ==
These are key points to be taken from the module

== Additional Resources ==

=== Read ===
{| class="wikitable"
|+
!Description
!Link
|-
|FAIR Principles
|<nowiki>https://www.go-fair.org/fair-principles/</nowiki>
|-
|UBC Research Data Management Guide
|<nowiki>https://researchdata.library.ubc.ca/share/</nowiki>
|-
|RDM Toolkit section for Arts, Humanities and Social Sciences data
|<nowiki>https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/</nowiki>
|-
|Humanities Data Curation Guide
|<nowiki>https://guide.dhcuration.org/about/</nowiki>
|-
|Journal of Open Humanities Data
|<nowiki>https://openhumanitiesdata.metajnl.com/</nowiki>
|-
|Center for Open Data in the Humanities
|<nowiki>http://codh.rois.ac.jp/index.html.en</nowiki>
|-
|Indigenous Data Sovereignty
|<nowiki>https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html</nowiki>
|}

=== Watch ===
{| class="wikitable"
|+
!Description
!Link
|-
|Software carpentry
|<nowiki>https://tibhannover.github.io/2018-07-09-FAIR-Data-and-Software/</nowiki>
|-
|Data one webinars
|<nowiki>https://www.dataone.org/previous-webinars/2017</nowiki>
|}

=== Attend ===
{| class="wikitable"
|+
!Description
!Link
!
!
|-
|Research Data Management Workshop
|<nowiki>https://libcal.library.ubc.ca/calendar/vancouver/?t=g&q=&cid=7544&cal=7544</nowiki>
|
|
|}
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-22T03:42:19Z

Sarah: /* Introduction */

== Open Data ==

== Big Ideas/Questions ==
Research transparency and reproducibility are dependent on open practices which includes open data. Open data facilitates ease of access to your data so others can find, access and reuse that data building on existing scholarship.

== Outcomes ==
* Understand the reasons for making your data open
* Recognize the reasons why some data may not be made open
* Producing a README file that demonstrates the characteristics of a README enabling others to interpret the data
* Describe and if possible deposit data in an open repository applying a license to the data for reuse
* Integrate the archived data into an OSF project

== Why it Matters to You? ==
Research funding is increasingly requesting that data be made open whenever possible. By making research data open, the work becomes more discoverable and allows others to easily access, share and re-use the data. It encourages and requires good data management practices establishing a workflow to ensure you can find and interpret your data months and years after having collected that data. Data deposit into a repository in a non-proprietary format allows you to control how that data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

== Practice ==

=== What is open data? ===
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020. <nowiki>https://casrai.org/term/data/</nowiki>). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. With open data the goal is to make your data findable, accessible, interoperable and reusable. These are the FAIR principles for sharing data.

'''Read''' about the FAIR Principles: <nowiki>https://www.go-fair.org/fair-principles/</nowiki>

'''Read''' more about what is open data: <nowiki>http://opendatahandbook.org/guide/en/what-is-open-data/</nowiki>

==== Data management ====
While it may be relatively easy to put a dataset on a website or uploaded into a project in the OSF, messy or poorly managed data does not help with its reuse. The FAIR principles provide guidance on what needs to be considered at each stage of the data life cycle. How data will be managed affects how easy it is to make that data open and for others to reuse. A data management plan (DMP) addresses questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. There are templates such as Portage's DMP Assistant (https://assistant.portagenetwork.ca/) that aid in preparing a plan guiding through each step. It is not uncommon for research funders to request a DMP to be submitted with the funding application to demonstrate how the data will be handled at each stage of the data life cycle.

'''Review''' the DMP Tool (https://library.stanford.edu/research/data-management-services/data-management-plans/dmptool), DMP Assistant (https://assistant.portagenetwork.ca/) or DMP Online (https://dmponline.dcc.ac.uk/). You may have to create an account to access the template. Using a dataset of your own reflect on some sections of the DMP and if your dataset is ready for reuse.

==== Transparency ====
While open data should be governed by the FAIR principles to promote transparency, not all data can or should be made open. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal information or other sensitive information that should not be readily accessible. For example, opening up data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories may offer options to keep the data closed while still making the metadata findable should someone be interested in contacting the researcher about the dataset.

Open data allows for transparency and increases reproducibility. It may also be a requirement of funding agencies or journals as part of their agreement for funding or publication.In Canada the Tri-Agency Council...

"The agencies believe that research data collected with the use of public funds should be responsibly and effectively managed and belong, to the fullest extent possible, in the public domain and available for reuse by others." <nowiki>https://www.ic.gc.ca/eic/site/063.nsf/eng/h_97610.html</nowiki>

==== Making your data open ====
Ideally and in keeping with open science practices, open data is made available in a repository using a metadata standard to improve discoverability. With an open license you can indicate how the data can be reused. Data repositories can provide guidelines for licenses and metadata and institutions may have their own data repositories such as UBC's Scholars Portal Dataverse. There are other repositories where data can be published including discipline specific repositories. Data Cite is one example where you can search for a repository to deposit data (https://datacite.org<nowiki/>/). The Open Science Framework (OSF) can also be an option to house data, but it is not recommended for long term preservation.

===== File naming =====

===== Describing research data =====
Metadata is data about data. Your metadata should be human-friendly but also machine readable. This is achieved by using a metadata standard.

==== Data repositories ====
There are many data repositories available to deposit your data including discipline specific repositories. Consult with a subject or data librarian to find one or use one of the resources in the table below.
{| class="wikitable"
|+
!Repository
!Link
|-
|UBC Scholars Portal Dataverse
|https://dataverse.scholarsportal.info/
|-
|Registry of Research Data Repositories
|https://www.re3data.org/
|-
|Open Science Framework (OSF)
|https://osf.io/
|-
|Zenodo
|https://zenodo.org/
|}

== Practice ==
This section is where you link to learning challenges to apply what you have learned
* Draft a README file to accompany a data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''Creating a README for your dataset'' https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf to create your own README file.
* Find an open data repository where you could deposit your data. Scholars Portal Dataverse is one option, but the Registry of Research Data Repositories provides a longer list of options https://www.re3data.org. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.

==== Additional optional activities: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

== In Practice: This means how to apply to your work ==
This is the section where you ask reflective questions to help learners apply their learning to a project that they are working on

== Key Points ==
These are key points to be taken from the module

== Additional Resources ==

=== Read ===
{| class="wikitable"
|+
!Description
!Link
|-
|FAIR Principles
|<nowiki>https://www.go-fair.org/fair-principles/</nowiki>
|-
|UBC Research Data Management Guide
|<nowiki>https://researchdata.library.ubc.ca/share/</nowiki>
|-
|RDM Toolkit section for Arts, Humanities and Social Sciences data
|<nowiki>https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/</nowiki>
|-
|Humanities Data Curation Guide
|<nowiki>https://guide.dhcuration.org/about/</nowiki>
|-
|Journal of Open Humanities Data
|<nowiki>https://openhumanitiesdata.metajnl.com/</nowiki>
|-
|Center for Open Data in the Humanities
|<nowiki>http://codh.rois.ac.jp/index.html.en</nowiki>
|-
|Indigenous Data Sovereignty
|<nowiki>https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html</nowiki>
|}

=== Watch ===
{| class="wikitable"
|+
!Description
!Link
|-
|Software carpentry
|<nowiki>https://tibhannover.github.io/2018-07-09-FAIR-Data-and-Software/</nowiki>
|-
|Data one webinars
|<nowiki>https://www.dataone.org/previous-webinars/2017</nowiki>
|}

=== Attend ===
{| class="wikitable"
|+
!Description
!Link
!
!
|-
|Research Data Management Workshop
|<nowiki>https://libcal.library.ubc.ca/calendar/vancouver/?t=g&q=&cid=7544&cal=7544</nowiki>
|
|
|}
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-22T03:03:38Z

Sarah: /* Big Ideas/Questions */

== Open Data ==

== Big Ideas/Questions ==
Research transparency and reproducibility are dependent on open practices which includes open data. Open data facilitates ease of access to your data so others can find, access and reuse that data building on existing scholarship.

== Outcomes ==
* Understand the reasons for making your data open
* Recognize the reasons why some data may not be made open
* Producing a README file that demonstrates the characteristics of a README enabling others to interpret the data
* Describe and if possible deposit data in an open repository applying a license to the data for reuse
* Integrate the archived data into an OSF project

== Why it Matters to You? ==
Research funding is increasingly requesting that data be made open whenever possible. By making research data open, the work becomes more discoverable and allows others to easily access, share and re-use the data. It encourages and requires good data management practices establishing a workflow to ensure you can find and interpret your data months and years after having collected that data. Data deposit into a repository in a non-proprietary format allows you to control how that data is shared and reused and has the added benefit to increase citations, raise your researcher profile, and improve your chance of future funding.

== Content ==

=== Introduction ===

==== What is open data? ====
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open government and open source, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. As part of the research life cycle data management and how you make your data open is to be considered even before the data collection begins. A data management plan addresses questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. With open data the goal is to make your data findable, accessible, interoperable and reusable. These are the FAIR principles for sharing data.

==== Transparency ====
While open data should be governed by the FAIR principles to promote transparency, not all data can or should be made open. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal information or other sensitive information that should not be readily accessible. For example, opening up data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. Consider personal information that may be available when collecting the data and whether or not it is ethical to disseminate that information widely. Consultation with a Research Ethics Board may be necessary before making personal information open. There are methods to de-identify data and repositories may offer options to keep the data closed while still making the metadata findable should someone be interested in contacting the researcher about the dataset.

Open data allows for transparency and increases reproducibility. It may also be a requirement of funding agencies or journals as part of their agreement for funding or publication.In Canada the Tri-Agency Council...

"The agencies believe that research data collected with the use of public funds should be responsibly and effectively managed and belong, to the fullest extent possible, in the public domain and available for reuse by others." <nowiki>https://www.ic.gc.ca/eic/site/063.nsf/eng/h_97610.html</nowiki>

==== Making your data open ====
Ideally and in keeping with open science practices, open data is made available in a repository using a metadata standard to improve discoverability. With an open license you can indicate how the data can be reused. Data repositories can provide guidelines for licenses and metadata and institutions may have their own data repositories such as UBC's Scholars Portal Dataverse. There are other repositories where data can be published including discipline specific repositories. Data Cite is one example where you can search for a repository to deposit data (https://datacite.org<nowiki/>/). The Open Science Framework (OSF) can also be an option to house data, but it is not recommended for long term preservation.

===== File naming =====

===== Describing research data =====
Metadata is data about data. Your metadata should be human-friendly but also machine readable. This is achieved by using a metadata standard.

==== Data repositories ====
There are many data repositories available to deposit your data including discipline specific repositories. Consult with a subject or data librarian to find one or use one of the resources in the table below.
{| class="wikitable"
|+
!Repository
!Link
|-
|UBC Scholars Portal Dataverse
|https://dataverse.scholarsportal.info/
|-
|Registry of Research Data Repositories
|https://www.re3data.org/
|-
|Open Science Framework (OSF)
|https://osf.io/
|-
|Zenodo
|https://zenodo.org/
|}

== Practice ==
This section is where you link to learning challenges to apply what you have learned
* Draft a README file to accompany a data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''Creating a README for your dataset'' https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf to create your own README file.
* Find an open data repository where you could deposit your data. Scholars Portal Dataverse is one option, but the Registry of Research Data Repositories provides a longer list of options https://www.re3data.org. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.

==== Additional optional activities: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

== In Practice: This means how to apply to your work ==
This is the section where you ask reflective questions to help learners apply their learning to a project that they are working on

== Key Points ==
These are key points to be taken from the module

== Additional Resources ==

=== Read ===
{| class="wikitable"
|+
!Description
!Link
|-
|FAIR Principles
|<nowiki>https://www.go-fair.org/fair-principles/</nowiki>
|-
|UBC Research Data Management Guide
|<nowiki>https://researchdata.library.ubc.ca/share/</nowiki>
|-
|RDM Toolkit section for Arts, Humanities and Social Sciences data
|<nowiki>https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/</nowiki>
|-
|Humanities Data Curation Guide
|<nowiki>https://guide.dhcuration.org/about/</nowiki>
|-
|Journal of Open Humanities Data
|<nowiki>https://openhumanitiesdata.metajnl.com/</nowiki>
|-
|Center for Open Data in the Humanities
|<nowiki>http://codh.rois.ac.jp/index.html.en</nowiki>
|-
|Indigenous Data Sovereignty
|<nowiki>https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html</nowiki>
|}

=== Watch ===
{| class="wikitable"
|+
!Description
!Link
|-
|Software carpentry
|<nowiki>https://tibhannover.github.io/2018-07-09-FAIR-Data-and-Software/</nowiki>
|-
|Data one webinars
|<nowiki>https://www.dataone.org/previous-webinars/2017</nowiki>
|}

=== Attend ===
{| class="wikitable"
|+
!Description
!Link
!
!
|-
|Research Data Management Workshop
|<nowiki>https://libcal.library.ubc.ca/calendar/vancouver/?t=g&q=&cid=7544&cal=7544</nowiki>
|
|
|}
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-17T19:51:55Z

Sarah: /* Why it Matters to You? */

Open Data
* ''Learning activity'': Learn about Open Data
* ''Activity/Deliverable'': Arrange for well described and, if possible, deposit data in an open repository. Participants will produce a README file that addresses things like metadata standard used in their discipline as well as a reflection on all of the characteristics of a README necessary to enable others to find and reuse their data. (OSF as providing context)

* ''Related learning objective'': Demonstrate awareness of Open Data by developing a well curated data deposit.

== Big Ideas/Questions ==
Research transparency and reproducibility are dependent on open practices including open data. Open data facilitates ease of access to your data so others can find, access and reuse that data building on existing scholarship.

== Why it Matters to You? ==
Research funding increasingly requires that your data be made open whenever possible. By making your research data open, your work becomes more discoverable and allows others to easily access, share and re-use the data. It encourages and often requires good data management practices establishing a workflow to ensure you can find your data months and years after having collected that data. Data deposit into a repository allows you to control how that data is shared and reused and has the added benefit to increase you number of citations, raise your researcher profile, and improve your chance of future funding.

== Content ==

=== Introduction ===

==== What is open data? ====
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020). An established workflow for collecting, managing and storing that data is essential to making your data open.

Related to open access, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. As part of the research life cycle data management and how you make your data open is to be considered even before the data collection begins. A data management plan addresses questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. With open data the goal is to make your data findable, accessible, interoperable and reusable. These are the FAIR principles for sharing data.

==== Transparency ====
While open data should be governed by the FAIR principles to promote transparency, not all data can or should be made open. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal information or other sensitive information that should not be readily accessible. For example, opening up data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. There are methods to de-identify data as well as repositories where data can be stored and remain closed so you can still securely store your data for archiving ensuring best practices for research data management.

Open data allows for transparency and increases reproducibility. It may also be a requirement of funding agencies or journals as part of their agreement for funding or publication.In Canada the Tri-Agency Council...

"The agencies believe that research data collected with the use of public funds should be responsibly and effectively managed and belong, to the fullest extent possible, in the public domain and available for reuse by others." <nowiki>https://www.ic.gc.ca/eic/site/063.nsf/eng/h_97610.html</nowiki>

==== Making your data open ====
Ideally and in keeping with open science practices, open data is made available in a repository using a metadata standard to improve discoverability. With an open license you can indicate how the data can be reused. Data repositories can provide guidelines for licenses and metadata and institutions may have their own data repositories such as UBC's Scholars Portal Dataverse. There are other repositories where data can be published including discipline specific repositories. Data Cite is one example where you can search for a repository to deposit data (https://datacite.org<nowiki/>/). The Open Science Framework (OSF) can also be an option to house data, but it is not recommended for long term preservation.

===== File naming =====

===== Describing research data =====
Metadata is data about data. Your metadata should be human-friendly but also machine readable. This is achieved by using a metadata standard.

==== Data repositories ====
There are many data repositories available to deposit your data including discipline specific repositories. Consult with a subject or data librarian to find one or use one of the resources in the table below.
{| class="wikitable"
|+
!Repository
!Link
|-
|UBC Scholars Portal Dataverse
|https://dataverse.scholarsportal.info/
|-
|Registry of Research Data Repositories
|https://www.re3data.org/
|-
|Open Science Framework (OSF)
|https://osf.io/
|-
|Zenodo
|https://zenodo.org/
|}

== Practice ==
This section is where you link to learning challenges to apply what you have learned
* Draft a README file to accompany a data deposit. Your README file should be a plain text file and should include such things as title, contact information, file structure and file formats. Refer to UBC's ''Creating a README for your dataset'' https://researchdata-06oct2014.sites.olt.ubc.ca/files/2020/04/QuickGuide_UBC_readme_v1.0_20200427.pdf to create your own README file.
* Find an open data repository where you could deposit your data. Scholars Portal Dataverse is one option, but the Registry of Research Data Repositories provides a longer list of options https://www.re3data.org. If possible deposit your data into a selected repository. Make sure you have a readme .txt file to accompany your data deposit.

==== Additional optional activities: ====
* Create a component for your data in the OSF.
* Save the README .txt file to the data component in your OSF project.
* Connect your deposited data to the data component of your OSF project.

== In Practice: This means how to apply to your work ==
This is the section where you ask reflective questions to help learners apply their learning to a project that they are working on

== Key Points ==
These are key points to be taken from the module

== Additional Resources ==

=== Read ===
{| class="wikitable"
|+
!Description
!Link
|-
|FAIR Principles
|<nowiki>https://www.go-fair.org/fair-principles/</nowiki>
|-
|UBC Research Data Management Guide
|<nowiki>https://researchdata.library.ubc.ca/share/</nowiki>
|-
|RDM Toolkit section for Arts, Humanities and Social Sciences data
|<nowiki>https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/</nowiki>
|-
|Humanities Data Curation Guide
|<nowiki>https://guide.dhcuration.org/about/</nowiki>
|-
|Journal of Open Humanities Data
|<nowiki>https://openhumanitiesdata.metajnl.com/</nowiki>
|-
|Center for Open Data in the Humanities
|<nowiki>http://codh.rois.ac.jp/index.html.en</nowiki>
|-
|Indigenous Data Sovereignty
|<nowiki>https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html</nowiki>
|}

=== Watch ===
{| class="wikitable"
|+
!Description
!Link
|-
|Software carpentry
|<nowiki>https://tibhannover.github.io/2018-07-09-FAIR-Data-and-Software/</nowiki>
|-
|Data one webinars
|<nowiki>https://www.dataone.org/previous-webinars/2017</nowiki>
|}

=== Attend ===
{| class="wikitable"
|+
!Description
!Link
!
!
|-
|Research Data Management Workshop
|<nowiki>https://libcal.library.ubc.ca/calendar/vancouver/?t=g&q=&cid=7544&cal=7544</nowiki>
|
|
|}
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-06-17T18:56:55Z

Sarah: /* Big Ideas/Questions */

Open Data
* ''Learning activity'': Learn about Open Data (attend a workshop)
* ''Activity/Deliverable'': Arrange for well described and, if possible, deposit data in an open repository. Participants will produce a README file that addresses things like metadata standard used in their discipline as well as a reflection on all of the characteristics of a README necessary to enable others to find and reuse their data. (OSF as providing context)

* ''Related learning objective'': Demonstrate awareness of Open Data by developing a well curated data deposit.

== Big Ideas/Questions ==
Research transparency and reproducibility are dependent on open practices including open data. Open data facilitates ease of access to your data so others can find, access and reuse that data building on existing scholarship.

== Why it Matters to You? ==
Research funding increasingly requires that your data be made open whenever possible. By making your research data open, your work becomes more discoverable and allows others to easily access, share and re-use the data. It also encourages and often requires good data management practices establishing a workflow to ensure you can find your data months and years after having collected that data. Depositing your data into a repository allows you to control how that data is shared and reused and has the added benefit of increasing the number of citations, raising your researcher profile, and potentially improve your chance of future funding.

== Content ==

=== Introduction ===

==== What is open data? ====
Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020).

Related to open access, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. As part of the research life cycle data management and how to make your data open is to be considered even before the data collection begins. A data management plan addresses questions about the type of data to be collected and how that data will be stored, shared and preserved for future access. The goal is to make your data findable, accessible, interoperable and reusable. These are the FAIR Data Principles for sharing data.

===== ''Reading'' =====
* FAIR principles: https://www.go-fair.org/fair-principles/

==== Transparency ====
While open data should be governed by the FAIR guidelines to promote transparency, not all data can or should be made open. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal information or other sensitive information that should not be readily accessible. For example, opening up data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. There are methods to de-identify data as well as repositories where data can be stored and remain closed so you can still securely store your data for archiving ensuring best practices for research data management.

Open data allows for transparency and increases reproducibility. It may also be a requirement of funding agencies or journals as part of their agreement for funding or publication.In Canada the Tri-Agency Council...

"The agencies believe that research data collected with the use of public funds should be responsibly and effectively managed and belong, to the fullest extent possible, in the public domain and available for reuse by others." <nowiki>https://www.ic.gc.ca/eic/site/063.nsf/eng/h_97610.html</nowiki>

==== Making your data open ====
Ideally and in keeping with open science practices, open data is made available in a repository using a metadata standard to improve discoverability. With an open license you can indicate how the data can be reused. Data repositories can provide guidelines for licenses and metadata and institutions may have their own data repositories such as UBC's Scholars Portal Dataverse. There are other repositories where data can be published including discipline specific repositories. Data Cite is one example where you can search for a repository to deposit data (https://datacite.org<nowiki/>/). The Open Science Framework (OSF) can also be an option to house data, but it is not recommended for long term preservation.

==== Data repositories ====
There are many data repositories available to deposit your data including discipline specific repositories. Consult with a subject or data librarian to find one or use one of the resources in the table below.
{| class="wikitable"
|+
!Repository
!Link
|-
|UBC Scholars Portal Dataverse
|https://dataverse.scholarsportal.info/
|-
|Registry of Research Data Repositories
|https://www.re3data.org/
|-
|Open Science Framework (OSF)
|https://osf.io/
|-
|Zenodo
|https://zenodo.org/
|}

== Practice ==
This section is where you link to learning challenges to apply what you have learned

After reading the FAIR principles, in what ways might you incorporate these principles into your own work.

== In Practice: This means how to apply to your work ==
This is the section where you ask reflective questions to help learners apply their learning to a project that they are working on

== Key Points ==
These are key points to be taken from the module

== Additional Resources ==

=== Read ===
{| class="wikitable"
|+
!Description
!Link
|-
|FAIR Principles
|<nowiki>https://www.go-fair.org/fair-principles/</nowiki>
|-
|UBC Research Data Management Guide
|<nowiki>https://researchdata.library.ubc.ca/share/</nowiki>
|-
|RDM Toolkit section for Arts, Humanities and Social Sciences data
|<nowiki>https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/</nowiki>
|-
|Humanities Data Curation Guide
|<nowiki>https://guide.dhcuration.org/about/</nowiki>
|-
|Journal of Open Humanities Data
|<nowiki>https://openhumanitiesdata.metajnl.com/</nowiki>
|-
|Center for Open Data in the Humanities
|<nowiki>http://codh.rois.ac.jp/index.html.en</nowiki>
|-
|Indigenous Data Sovereignty
|<nowiki>https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html</nowiki>
|}

=== Watch ===
{| class="wikitable"
|+
!Description
!Link
|-
|Software carpentry
|<nowiki>https://tibhannover.github.io/2018-07-09-FAIR-Data-and-Software/</nowiki>
|-
|Data one webinars
|<nowiki>https://www.dataone.org/previous-webinars/2017</nowiki>
|}

=== Attend ===
{| class="wikitable"
|+
!Description
!Link
!
!
|-
|Research Data Management Workshop
|<nowiki>https://libcal.library.ubc.ca/calendar/vancouver/?t=g&q=&cid=7544&cal=7544</nowiki>
|
|
|}
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-05-14T21:58:32Z

Sarah:

Open Data
* ''Learning activity'': Learn about Open Data (attend a workshop)
* ''Activity/Deliverable'': Arrange for well described and, if possible, deposit data in an open repository. Participants will produce a README file that addresses things like metadata standard used in their discipline as well as a reflection on all of the characteristics of a README necessary to enable others to find and reuse their data. (OSF as providing context)

* ''Related learning objective'': Demonstrate awareness of Open Data by developing a well curated data deposit.

== Big Ideas/Questions ==

== Why it Matters to You? ==
Open data facilitates ease of access to raw data so others can access and reuse that data to build on existing research or use it in new research. It allows for transparency and scientific integrity as others can view and check your data. By making your research data open, your work becomes more discoverable and allows others to easily access, share and re-use the data. Sharing and opening your data can increase the number of citations, raise your researcher profile, and has the potential to increase your chance of funding.

== Content ==

=== Introduction ===

==== What is open data? ====
Open data can be government data, but it is also data generated from research. First let’s clarify what is meant by data. Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation.

Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020).

Related to open access, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. Open data allows for transparency and increases reproducibility. It may also be a requirement of funding agencies or journals as part of their agreement for funding or publication. As part of research data management, open data should be considered even before the data collection begins. A data management plan addresses questions about the type of data to be collected and how that data will be stored, shared and preserved for future access will ensure that your data is findable, accessible, interoperable and reusable. These are the FAIR Data Principles for sharing data. For a full reading of the FAIR principles see https://www.go-fair.org/fair-principles/

==== Transparency ====
While open data should be governed by the FAIR guidelines to promote transparency, not all data can or should be made open. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal information or other sensitive information that should not be readily accessible. For example, opening up data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. There are methods to de-identify data as well as repositories where data can be stored and remain closed so you can still securely store your data for archiving ensuring best practices for research data management.

==== Making your data open ====
Ideally and in keeping with open science practices, open data is made available in a repository using a metadata standard to improve discoverability. With an open license you can indicate how the data can be reused. Data repositories can provide guidelines for licenses and metadata and institutions may have their own data repositories such as UBC's Scholars Portal Dataverse. There are other repositories where data can be published including discipline specific repositories. Data Cite is one example where you can search for a repository to deposit data (https://datacite.org<nowiki/>/). The Open Science Framework (OSF) can also be an option to house data, but it is not recommended for long term preservation.

==== Data repositories ====
There are many data repositories available to deposit your data including discipline specific repositories. Consult with a subject or data librarian to find one or use one of the resources in the table below.
{| class="wikitable"
|+
!Repository
!Link
|-
|UBC Scholars Portal Dataverse
|https://dataverse.scholarsportal.info/
|-
|Registry of Research Data Repositories
|https://www.re3data.org/
|-
|Open Science Framework (OSF)
|https://osf.io/
|-
|Zenodo
|https://zenodo.org/
|}

== Practice ==
This section is where you link to learning challenges to apply what you have learned

== In Practice: This means how to apply to your work ==
This is the section where you ask reflective questions to help learners apply their learning to a project that they are working on

== Key Points ==
These are key points to be taken from the module

== Additional Resources ==

=== Read ===
{| class="wikitable"
|+
!Description
!Link
|-
|FAIR Principles
|<nowiki>https://www.go-fair.org/fair-principles/</nowiki>
|-
|UBC Research Data Management Guide
|<nowiki>https://researchdata.library.ubc.ca/share/</nowiki>
|-
|RDM Toolkit section for Arts, Humanities and Social Sciences data
|<nowiki>https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/</nowiki>
|-
|Humanities Data Curation Guide
|<nowiki>https://guide.dhcuration.org/about/</nowiki>
|-
|Journal of Open Humanities Data
|<nowiki>https://openhumanitiesdata.metajnl.com/</nowiki>
|-
|Center for Open Data in the Humanities
|<nowiki>http://codh.rois.ac.jp/index.html.en</nowiki>
|-
|Indigenous Data Sovereignty
|<nowiki>https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html</nowiki>
|}

=== Watch ===
{| class="wikitable"
|+
!Description
!Link
|-
|Software carpentry
|<nowiki>https://tibhannover.github.io/2018-07-09-FAIR-Data-and-Software/</nowiki>
|-
|Data one webinars
|<nowiki>https://www.dataone.org/previous-webinars/2017</nowiki>
|}

=== Attend ===
{| class="wikitable"
|+
!Description
!Link
!
!
|-
|Research Data Management Workshop
|<nowiki>https://libcal.library.ubc.ca/calendar/vancouver/?t=g&q=&cid=7544&cal=7544</nowiki>
|
|
|}
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-05-14T21:46:39Z

Sarah:

Open Data
* ''Learning activity'': Learn about Open Data (attend a workshop)
* ''Activity/Deliverable'': Arrange for well described and, if possible, deposit data in an open repository. Participants will produce a README file that addresses things like metadata standard used in their discipline as well as a reflection on all of the characteristics of a README necessary to enable others to find and reuse their data. (OSF as providing context)

* ''Related learning objective'': Demonstrate awareness of Open Data by developing a well curated data deposit.

== Big Ideas/Questions ==

== Why it Matters to You? ==
Open data supports transparency in research. By making your research data open research becomes more discoverable and allows others to easily access, share and re-use the data. Sharing and opening your data can increase the number of citations, raise your researcher profile, and has the potential to increase your chance of funding.

== Content ==

=== Introduction ===

==== What is open data? ====
Open data can be government data, but it is also data generated from research. First let’s clarify what is meant by data. Data is “[f]acts, measurements, recordings, records, or observations about the world collected by scientists and others, with a minimum of contextual interpretation.

Data may be in any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records.” (CASRAI Research Data Management Glossary, 2020).

Related to open access, open data ensures public access to data and should include details sufficient enough so that others know how the data can be reused or repurposed. Open data allows for transparency and increases reproducibility. It may also be a requirement of funding agencies or journals as part of their agreement for funding or publication. As part of research data management, open data should be considered even before the data collection begins. A data management plan addresses questions about the type of data to be collected and how that data will be stored, shared and preserved for future access will ensure that your data is findable, accessible, interoperable and reusable. These are the FAIR Data Principles for sharing data. For a full reading of the FAIR principles see https://www.go-fair.org/fair-principles/

==== Transparency ====
While open data should be governed by the FAIR guidelines to promote transparency, not all data can or should be made open. There may be privacy, ethical, or cultural issues to consider. Some data may contain personal information or other sensitive information that should not be readily accessible. For example, opening up data that contains the location for a rare plant or species at risk may further endanger that species if others are able to locate it. There are methods to de-identify data as well as repositories where data can be stored and remain closed so you can still securely store your data for archiving ensuring best practices for research data management.

==== Making your data open ====
Ideally and in keeping with open science practices, open data is made available in a repository using a metadata standard to improve discoverability. With an open license you can indicate how the data can be reused. Data repositories can provide guidelines for licenses and metadata and institutions may have their own data repositories such as UBC's Scholars Portal Dataverse. There are other repositories where data can be published including discipline specific repositories. Data Cite is one example where you can search for a repository to deposit data (https://datacite.org<nowiki/>/). The Open Science Framework (OSF) can also be an option to house data, but it is not recommended for long term preservation.

==== Data repositories ====
There are many data repositories available to deposit your data including discipline specific repositories. Consult with a subject or data librarian to find one or use one of the resources in the table below.
{| class="wikitable"
|+
!Repository
!Link
|-
|UBC Scholars Portal Dataverse
|https://dataverse.scholarsportal.info/
|-
|Registry of Research Data Repositories
|https://www.re3data.org/
|-
|Open Science Framework (OSF)
|https://osf.io/
|-
|Zenodo
|https://zenodo.org/
|}

== Practice ==
This section is where you link to learning challenges to apply what you have learned

== In Practice: This means how to apply to your work ==
This is the section where you ask reflective questions to help learners apply their learning to a project that they are working on

== Key Points ==
These are key points to be taken from the module

== Additional Resources ==

=== Read ===
{| class="wikitable"
|+
!Description
!Link
|-
|FAIR Principles
|<nowiki>https://www.go-fair.org/fair-principles/</nowiki>
|-
|UBC Research Data Management Guide
|<nowiki>https://researchdata.library.ubc.ca/share/</nowiki>
|-
|RDM Toolkit section for Arts, Humanities and Social Sciences data
|<nowiki>https://rdmtoolkit.jisc.ac.uk/plan-and-design/research-data-in-arts-humanities-and-social-sciences/</nowiki>
|-
|Humanities Data Curation Guide
|<nowiki>https://guide.dhcuration.org/about/</nowiki>
|-
|Journal of Open Humanities Data
|<nowiki>https://openhumanitiesdata.metajnl.com/</nowiki>
|-
|Center for Open Data in the Humanities
|<nowiki>http://codh.rois.ac.jp/index.html.en</nowiki>
|-
|Indigenous Data Sovereignty
|<nowiki>https://www.stateofopendata.od4d.net/chapters/issues/indigenous-data.html</nowiki>
|}

=== Watch ===
{| class="wikitable"
|+
!Description
!Link
|-
|Software carpentry
|<nowiki>https://tibhannover.github.io/2018-07-09-FAIR-Data-and-Software/</nowiki>
|-
|Data one webinars
|<nowiki>https://www.dataone.org/previous-webinars/2017</nowiki>
|}

=== Attend ===
{| class="wikitable"
|+
!Description
!Link
!
!
|-
|Research Data Management Workshop
|<nowiki>https://libcal.library.ubc.ca/calendar/vancouver/?t=g&q=&cid=7544&cal=7544</nowiki>
|
|
|}
[[Category:POSE]]

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-05-14T21:06:25Z

Sarah: /* What is open data? */

Sandbox:Sandbox:Open UBC/POSE/Open Research/Data

2020-05-14T15:57:22Z

Sarah: /* Why it Matters to You? */

Library:How to Cite/Major Style Guides

2020-01-22T23:45:18Z

Sarah: IEEE link was a dead link.

==Major Styles==

===ACS (American Chemical Society)===
ACS is the standard style used for Chemistry.

*[http://ezproxy.library.ubc.ca/login?url=http://pubs.acs.org/isbn/9780841239999 ACS Style Guide (ebook)]
*[http://resolve.library.ubc.ca/cgi-bin/catsearch?title=ACS+Style+Guide ACS Style Guide (available at UBC Library)]

{{Learning_Commons:Chapman_Learning_Commons/APA}}

===ASCE (American Society of Civil Engineers)===
*[http://www.asce.org/Content.aspx?id=29594 ASCE Author's Guide: Writing Style]

===Biology 140===
*[http://www.zoology.ubc.ca/bio1 UBC First Year Biology Website]

{{Learning_Commons:Chapman_Learning_Commons/Chicago_Style}}

===CSE (Council of Science Editors)===

*[http://resolve.library.ubc.ca/cgi-bin/catsearch?title=Scientific+Style+and+Format Scientific style and format: The CSE manual for authors, editors, and publishers]

===HARVARD===
A parenthetical style used most commonly in the UK and Australia.

*[http://guides.is.uwa.edu.au/harvard Harvard guide from the University of Western Australia]
*[http://libweb.anglia.ac.uk/referencing/harvard.htm Guide from Anglia Ruskin University]

===IEEE Style (Institute of Electrical and Electronics Engineers)===
IEEE Style is used primarily in Electrical and Computer Engineering

*[https://guides.lib.monash.edu/citing-referencing/ieee How to Cite References: IEEE Documentation Style]

===Legal===
Used primarily in Law
*[http://guides.library.ubc.ca/legalcitation Legal Citation Guide]

{{Learning_Commons:Chapman_Learning_Commons/MLA}}

===Vancouver Style / Uniform Requirements for Manuscripts Submitted to Biomedical Journals===
Commonly used in medical and scientific journals
*[https://web.library.uq.edu.au/files/26541/VancouverStyleGuideFinal2014.pdf Vancouver Style 'How To' Guide (University of Queensland Library)]
*[http://libguides.library.curtin.edu.au/ld.php?content_id=23580955 Vancouver Referencing (Curtain University Library)]
*[http://www.ncbi.nlm.nih.gov/books/NBK7256/ Citing Medicine: NLM Style Guide for Authors, Editors, and Publishers (National Library of Medicine)]
*[http://www.bcit.ca/files/library/pdf/bcit-vancouverstyle.pdf BCIT (Vancouver Style Guide)]

[[Category:Evaluating/Citing]]
[[Category:Tutorial]]

Library:Help for Graduate Students

2019-09-30T19:25:47Z

Sarah: /* Library Research Commons */

Download the [https://about.library.ubc.ca/files/2017/07/GradStudentGuide_web.pdf Graduate Student Guide].

=='''Getting Started'''==

Learning, knowledge, research, insight: welcome to [http://www.library.ubc.ca UBC Library], the second largest research library in Canada.The Library has more than ten branches and divisions, including on- and off-campus locations and its Okanagan campus location.The Library's collections include more than 6.5 million items, and it offers students, faculty and staff a wide range of services, including one-on-one research help, workshops, interlibrary loans, document delivery, off-campus access to ebooks and online articles, data services, a GIS lab, microform readers & scanners and much more.

===Library Services===

====Hours/Locations====

*[http://hours.library.ubc.ca/ Hours & Locations]
*[https://services.library.ubc.ca/facilities/guided-library-tours/ Guided Library Tours]
*[https://events.library.ubc.ca/ Workshops]

==== Library Research Commons====

The [http://guides.library.ubc.ca/library_research_commons Research Commons] provides support services produced by graduate students, for graduate students. Choose from a workshop or a one-on-one consultation to learn how to:

* [http://guides.library.ubc.ca/library_research_commons/thesis_formatting Format your dissertation or thesis ] to meet the requirements of Graduate and Postdoctoral Studies.
* Use any of three popular [http://guides.library.ubc.ca/library_research_commons/thesis_dissertations citation management tools]: RefWorks, Mendeley or Zotero to save your citations and format your works cited list.
* Perform quantitative analysis with [http://guides.library.ubc.ca/library_research_commons/SPSS SPSS] software.
* Use [http://guides.library.ubc.ca/library_research_commons/nvivo NVivo] software to perform qualitative analysis.

====Ask a Librarian====

Every branch of the Library has staff who can help you find what you need. Looking for articles, primary sources, data or statistics to support your research?

*Don’t hesitate to [https://help.library.ubc.ca/ask-colorbox ask us] for assistance at the reference desk, via email, and/or online chat.
*Or make an appointment with your [http://directory.library.ubc.ca/subjectlibrarians/ subject librarian], who can provide personalized help with your research.

====Where are?====
*[https://services.library.ubc.ca/computers-technology/public-computers/ Public Computers & Software]
*[https://services.library.ubc.ca/computers-technology/copy-print-scan/ Printers, Copiers and Scanners]
*[http://guides.library.ubc.ca/gis/labs Data/GIS Labs & Software]
*[https://services.library.ubc.ca/facilities/group-silent-study-space/ Group & Silent Study Areas]
*[https://services.library.ubc.ca/off-campus-access/connect-from-home/ Electronic Resources]: Current UBC students can access licensed Library resources with their valid Campus-Wide Login (CWL) or UBCcard barcode.

====How do I?====
*[https://services.library.ubc.ca/computers-technology/copy-print-scan/pay-for-print-students-faculty-staff/ Print from my laptop]
*[http://services.library.ubc.ca/library-facilities/study-space/ Book Group Study rooms]
*[http://services.library.ubc.ca/library-facilities/technology-borrowing/ Borrow a laptop, camcorder, or other equipment]

=='''Access & Borrowing'''==

{{Library:Help_for_Distance_Students/Library_from_Home/Connecting_from_Home}}

===Library Cards===

* Your [http://ubccard.ubc.ca/?login UBCcard] is your Library card.
* You can also apply for [http://services.library.ubc.ca/borrowing-services/reciprocal-borrowing/ubc-members/ Reciprocal Borrowing] privileges at many other Canadian and U.S. post-secondary institutions.

===Borrower Services===

*[https://services.library.ubc.ca/borrowing-services/library-account/ Log into your account] to:
**change your PIN
**renew items
**pay your fines online
**set up email reminders for item due dates

*[https://services.library.ubc.ca/borrowing-services/docdel/ Borrowing between UBC Campuses] (Document Delivery )
*[https://services.library.ubc.ca/borrowing-services/ill/ Borrowing from non-UBC Libraries] (Interlibrary Loan)
*[https://services.library.ubc.ca/facilities/disability-access-by-building/ Disabillity Access] (Information & services for people with disabilities)
*[https://services.library.ubc.ca/borrowing-services/using-course-reserves/#StudentResources-1 Access Course Reserves]

=='''Researching'''==

===Search Tools===

*[http://search.library.ubc.ca/#general Search Summon by Keyword]
*[http://search.library.ubc.ca/#databases Indexes & Databases and Articles]
*[https://open.library.ubc.ca/ Open Collections]
*[https://circle.ubc.ca/ cIRcle] (UBC's institutional repository)
**contains most of the theses & dissertations produced by UBC students since 1919
*[http://resources.library.ubc.ca/page.php?details=worldcat&id=34 WorldCat] (searches 10000+ Library catalogues)

===Citing & Bibliographies===

*[https://help.library.ubc.ca/evaluating-and-citing-sources/how-to-cite/ How to Cite]
*[https://events.library.ubc.ca/series/84 Thesis Formatting Workshops]
*[https://help.library.ubc.ca/evaluating-and-citing-sources/citation-management/ Citation Management]
**[http://guides.library.ubc.ca/refworks RefWorks]

===Contact a Librarian===

*[https://help.library.ubc.ca/ask-colorbox Chat with a Librarian]
*[http://directory.library.ubc.ca/subjectlibrarians/ Subject/Liaison Librarians]
*[https://about.library.ubc.ca/contact-us/suggest-a-book/ Suggest a Book or Other Material]

===Research Guides===

*[http://guides.library.ubc.ca/?b=s Research Guides]: Need articles for a project and don’t know where to start? Search our subject-specific guides for help with your research.
*[https://help.library.ubc.ca/finding-resources/primary-sources/ Primary Sources]
*[https://help.library.ubc.ca/finding-resources/government-publications/ Government Publications]
*[http://guides.library.ubc.ca/newspapers Newspapers and News Sources]

===Copyright===
Copyrighted materials are everywhere—you produce them and you use them every day. Be responsible in your research and studies: know what you can and can’t do under copyright law. Infringing copyright is a serious matter. Find information about copyright, UBC’s requirements and more at [https://copyright.ubc.ca/ Copyright at UBC].

=='''Writing'''==

===Your Thesis/Dissertation===

*[https://copyright.ubc.ca/ Copyright at UBC]: contains links to information about copyright law in Canada, resource guides, an FAQ and much more. Of particular interest:
**[http://guides.library.ubc.ca/c.php?g=698822&p=4965734 Image Sources]
**[http://guides.library.ubc.ca/c.php?g=698822&p=4965735 Image Citation]

*[https://www.grad.ubc.ca/handbook-graduate-supervision/graduate-thesis The Graduate Thesis]: your guide to find UBC's Faculty of Graduate Studies requirements for your thesis proposal, as well as research ethics and planning and defending your thesis. Of particular interest:
**[https://www.grad.ubc.ca/current-students/dissertation-thesis-preparation Dissertation & Thesis Preparation]: contains links to format requirements, a sample thesis and how to include published material in your work.

*[https://circle.ubc.ca/ cIRcle]: UBC’s digital repository for research and teaching materials created by the UBC community, openly accessible to anyone on the web. This is where you will ultimately submit your final thesis/dissertation.
**See the [https://www.grad.ubc.ca/current-students/final-dissertation-thesis-submission Final Dissertation & Thesis Submission] for more information.

* [http://learningcommons.ubc.ca/improve-your-writing/ Centre for Writing and Scholarly Communication] (Vancouver campus): supports graduate-level writing. Join the writing community to work on your projects every week with the support of your peers or book an appointment to see a graduate writing consultant.

* [http://library.ok.ubc.ca/wrs/csc/ Centre for Scholarly Communication] (Okanagan campus): provides workshops and one-on-one consultations about all aspects of scholarly communication, including writing support for theses, dissertations, journal articles, grant proposals, and conference presentations, as well as copyright, open access, and author rights.

====Looking for theses?====

*[http://guides.library.ubc.ca/theses Theses & Dissertations]: UBC, Canadian, North American and international theses.

===Your Research Data===

*[http://researchdata.library.ubc.ca/ Research Data Management]: website developed and maintained by UBC Library that provides valuable information and resources related to UBC's Research Data management strategy. If you are producing, reusing or interested in preserving and sharing your research data, please visit the site.

===Beyond the Thesis/Dissertation===

*[http://guides.library.ubc.ca/academic_profile Creating & Managing an Academic Profile]: guide to the skills and tools you need for discussing, interacting, presenting, writing, commenting, and finally publishing your research.

*[http://guides.library.ubc.ca/citationmetricsworkshop Citation Metrics Workshop]: explains how to use citation analysis tools to measure the impact of articles, books, journals and individual researchers.

*[https://scholcomm.ubc.ca/ Scholarly Communications @ UBC]: UBC's information portal for those interested and/or involved in scholarly authorship and publication.

=='''Teaching'''==

'''Online Course Reserves & Readings'''

*[http://services.library.ubc.ca/borrowing-services/course-reserves/ Using Course Reserves]: learn about managing course reserves & readings in Canvas and Library Online Course Reserves (LOCR) Standalone.

'''Your Teaching'''

*[https://ctlt.ubc.ca/programs/graduate-student-ta-programs/ Centre for Teaching, Learning and Technology]
*[https://services.library.ubc.ca/off-campus-access/guide-to-purls/ Guide to Finding Persistent URLs]: create persistent links to articles, books & other materials.
*[https://copyright.ubc.ca/guidelines-and-resources/faq/instructor-faq/ Copyright @UBC Instructor FAQs]

'''Media Services'''

*[https://services.library.ubc.ca/borrowing-services/videos-films/ Videos & Films]
*[http://mediabooking.library.ubc.ca/mediabooking/login.php UBC Library Media Booking System ]

<noinclude>
[[Category:Library_User_Guides]]</noinclude>

Library:How to Cite/Major Style Guides

2019-09-23T23:13:15Z

Sarah: updating link

==Major Styles==

===ACS (American Chemical Society)===
ACS is the standard style used for Chemistry.

*[http://ezproxy.library.ubc.ca/login?url=http://pubs.acs.org/isbn/9780841239999 ACS Style Guide (ebook)]
*[http://resolve.library.ubc.ca/cgi-bin/catsearch?title=ACS+Style+Guide ACS Style Guide (available at UBC Library)]

{{Learning_Commons:Chapman_Learning_Commons/APA}}

===ASCE (American Society of Civil Engineers)===
*[http://www.asce.org/Content.aspx?id=29594 ASCE Author's Guide: Writing Style]

===Biology 140===
*[http://www.zoology.ubc.ca/bio1 UBC First Year Biology Website]

{{Learning_Commons:Chapman_Learning_Commons/Chicago_Style}}

===CSE (Council of Science Editors)===

*[http://resolve.library.ubc.ca/cgi-bin/catsearch?title=Scientific+Style+and+Format Scientific style and format: The CSE manual for authors, editors, and publishers]

===HARVARD===
A parenthetical style used most commonly in the UK and Australia.

*[http://guides.is.uwa.edu.au/harvard Harvard guide from the University of Western Australia]
*[http://libweb.anglia.ac.uk/referencing/harvard.htm Guide from Anglia Ruskin University]

===IEEE Style (Institute of Electrical and Electronics Engineers)===
IEEE Style is used primarily in Electrical and Computer Engineering

*[https://guides.library.queensu.ca/c.php?g=501793&p=3436604 How to Cite References: IEEE Documentation Style]

===Legal===
Used primarily in Law
*[http://guides.library.ubc.ca/legalcitation Legal Citation Guide]

{{Learning_Commons:Chapman_Learning_Commons/MLA}}

===Vancouver Style / Uniform Requirements for Manuscripts Submitted to Biomedical Journals===
Commonly used in medical and scientific journals
*[https://web.library.uq.edu.au/files/26541/VancouverStyleGuideFinal2014.pdf Vancouver Style 'How To' Guide (University of Queensland Library)]
*[http://libguides.library.curtin.edu.au/ld.php?content_id=23580955 Vancouver Referencing (Curtain University Library)]
*[http://www.ncbi.nlm.nih.gov/books/NBK7256/ Citing Medicine: NLM Style Guide for Authors, Editors, and Publishers (National Library of Medicine)]
*[http://www.bcit.ca/files/library/pdf/bcit-vancouverstyle.pdf BCIT (Vancouver Style Guide)]

[[Category:Evaluating/Citing]]
[[Category:Tutorial]]

Library:How to Cite/Major Style Guides

2019-08-28T23:01:10Z

Sarah: updating link

==Major Styles==

===ACS (American Chemical Society)===
ACS is the standard style used for Chemistry.

*[http://ezproxy.library.ubc.ca/login?url=http://pubs.acs.org/isbn/9780841239999 ACS Style Guide (ebook)]
*[http://resolve.library.ubc.ca/cgi-bin/catsearch?title=ACS+Style+Guide ACS Style Guide (available at UBC Library)]

{{Learning_Commons:Chapman_Learning_Commons/APA}}

===ASCE (American Society of Civil Engineers)===
*[http://www.asce.org/Content.aspx?id=29594 ASCE Author's Guide: Writing Style]

===Biology 140===
*[http://www.zoology.ubc.ca/bio1 UBC First Year Biology Website]

{{Learning_Commons:Chapman_Learning_Commons/Chicago_Style}}

===CSE (Council of Science Editors)===

*[http://resolve.library.ubc.ca/cgi-bin/catsearch?title=Scientific+Style+and+Format Scientific style and format: The CSE manual for authors, editors, and publishers]

===HARVARD===
A parenthetical style used most commonly in the UK and Australia.

*[http://guides.is.uwa.edu.au/harvard Harvard guide from the University of Western Australia]
*[http://libweb.anglia.ac.uk/referencing/harvard.htm Guide from Anglia Ruskin University]

===IEEE Style (Institute of Electrical and Electronics Engineers)===
IEEE Style is used primarily in Electrical and Computer Engineering

*[https://ieee-dataport.org/help/how-cite-references-ieee-documentation-style How to Cite References: IEEE Documentation Style]

===Legal===
Used primarily in Law
*[http://guides.library.ubc.ca/legalcitation Legal Citation Guide]

{{Learning_Commons:Chapman_Learning_Commons/MLA}}

===Vancouver Style / Uniform Requirements for Manuscripts Submitted to Biomedical Journals===
Commonly used in medical and scientific journals
*[https://web.library.uq.edu.au/files/26541/VancouverStyleGuideFinal2014.pdf Vancouver Style 'How To' Guide (University of Queensland Library)]
*[http://libguides.library.curtin.edu.au/ld.php?content_id=23580955 Vancouver Referencing (Curtain University Library)]
*[http://www.ncbi.nlm.nih.gov/books/NBK7256/ Citing Medicine: NLM Style Guide for Authors, Editors, and Publishers (National Library of Medicine)]
*[http://www.bcit.ca/files/library/pdf/bcit-vancouverstyle.pdf BCIT (Vancouver Style Guide)]

[[Category:Evaluating/Citing]]
[[Category:Tutorial]]

Library:How to Cite/Major Style Guides

2019-08-28T22:57:29Z

Sarah: Updating dead link

==Major Styles==

===ACS (American Chemical Society)===
ACS is the standard style used for Chemistry.

*[http://ezproxy.library.ubc.ca/login?url=http://pubs.acs.org/isbn/9780841239999 ACS Style Guide (ebook)]
*[http://resolve.library.ubc.ca/cgi-bin/catsearch?title=ACS+Style+Guide ACS Style Guide (available at UBC Library)]

{{Learning_Commons:Chapman_Learning_Commons/APA}}

===ASCE (American Society of Civil Engineers)===
*[http://www.asce.org/Content.aspx?id=29594 ASCE Author's Guide: Writing Style]

===Biology 140===
*[http://www.zoology.ubc.ca/bio1 UBC First Year Biology Website]

{{Learning_Commons:Chapman_Learning_Commons/Chicago_Style}}

===CSE (Council of Science Editors)===

*[http://resolve.library.ubc.ca/cgi-bin/catsearch?title=Scientific+Style+and+Format Scientific style and format: The CSE manual for authors, editors, and publishers]

===HARVARD===
A parenthetical style used most commonly in the UK and Australia.

*[http://guides.is.uwa.edu.au/harvard Harvard guide from the University of Western Australia]
*[http://libweb.anglia.ac.uk/referencing/harvard.htm Guide from Anglia Ruskin University]

===IEEE Style (Institute of Electrical and Electronics Engineers)===
IEEE Style is used primarily in Electrical and Computer Engineering

*[https://libguides.murdoch.edu.au/IEEE/home Murdoch University IEEE Referencing Guide]

===Legal===
Used primarily in Law
*[http://guides.library.ubc.ca/legalcitation Legal Citation Guide]

{{Learning_Commons:Chapman_Learning_Commons/MLA}}

===Vancouver Style / Uniform Requirements for Manuscripts Submitted to Biomedical Journals===
Commonly used in medical and scientific journals
*[https://web.library.uq.edu.au/files/26541/VancouverStyleGuideFinal2014.pdf Vancouver Style 'How To' Guide (University of Queensland Library)]
*[http://libguides.library.curtin.edu.au/ld.php?content_id=23580955 Vancouver Referencing (Curtain University Library)]
*[http://www.ncbi.nlm.nih.gov/books/NBK7256/ Citing Medicine: NLM Style Guide for Authors, Editors, and Publishers (National Library of Medicine)]
*[http://www.bcit.ca/files/library/pdf/bcit-vancouverstyle.pdf BCIT (Vancouver Style Guide)]

[[Category:Evaluating/Citing]]
[[Category:Tutorial]]

Open UBC Working Group/Blog Signup

2019-07-15T21:08:54Z

Sarah: Added name to sign up

=== Open UBC Blog Post Sign-up ===
The new Open UBC website will have blogging functionality to allow for more regular updates on all things open at UBC. We hope to begin blog posts for September. Please sign-up for one or more months to be our guest blogger on any area related to open that interests you. The posts will be due the first day of the month assigned.

If you know of anyone else who may be interested in blogging, please let us know.

{| class="wikitable"
|'''Month'''
|'''Sign-up'''
|-
|September 2019
|Rie
|-
|October 2019
|Erin
|-
|November 2019
|Lucas
|-
|December 2019
|Will
|-
|January 2020
|Steph
|-
|February 2020
|Christina
|-
|March 2020
|Leonora
|-
|April 2020
|Sarah
|-
|May 2020
|
|-
|June 2020
|
|-
|July 2020
|
|-
|August 2020
|
|}

=== How to Add a Blog Post to the Site ===
# If you do not have access to the [https://open.ubc.ca Open UBC site], email Rie ([mailto:rie.namba@ubc.ca rie.namba@ubc.ca]) to gain access to the Open UBC site.
# Go to https://open.ubc.ca/wp-admin/ and login to the site with your CWL.
# When you are logged in, '''Go to Dashboard>Posts>Add New''' to add a new blog post.
# Add a title to the blog post, and add content of your blog.
# Under Category, tick '''Updates''' to categorize your blog post as Updates. By categorizing the blog post as Updates, your blog post will show up on the [https://open.ubc.ca/updates/ Updates page]
# Under Publish, press '''publish''' to make your blog post publicly visible.
# You're done! Your blog post is now published on the Open UBC site.

==== How to feature your blog post to the slider ====
If you want to highlight your blog post that you have written, you might want to feature your blog post to the big slider on the Open UBC . In order to do that :
# Edit the post you would like to add to the slider
# Under Category, tick '''Slider''' to categorize your blog post as a slider. By categorizing the blog post as a Slider, the slider will show up on your home page.
# You will then an image for the slider. Find a big image (ideally '''1200 X 500''' pixel but it can slightly be bigger or smaller). Once you find an image for the slider, under '''Featured Image''', click on '''set Featured Image'''. Then upload the image you want to add to the slider.
# You will also need an excerpt for your blog post. The excerpt will be displayed on the home page slider's "text". Under '''excerpt''' (if you don't see excerpt, go to the screen option on top of the page, and tick "excerpt") , type a short summary on what the blog post is about (ideally 1 sentence. ).
# Press '''Update''' and then now your blog post is featured on the slider.
# Your'e done! Your blog post is now featured on the slider.
[[Category:Open UBC]]

Learning Commons:Chapman Learning Commons/Tools

2019-01-02T23:52:29Z

Sarah:

==Citation Management Tools==

Citation management tools can help you collect, organize, store, share, and format citations. There are many different citation management tools and each has different features. A few of the most popular tools include RefWorks, Mendeley, Zotero, Endnote, and Papers. UBC Library officially licenses and supports Refworks for all UBC students, faculty, and alumni.

{| class="wikitable" style="color: black; background-color: #EFF8FB; border-width: 3px"
|-
| Citation management tools and databases that allow you to copy and paste formatted citations into your work sometimes produce errors. Regardless of what tool you decide to use it is still your responsibility to check and make sure that the citation has all the required information and is properly formatted.
|}

'''RefWorks'''

[http://guides.library.ubc.ca/refworks RefWorks] is the citation management tool officially supported by UBC Library and is free to use for UBC students, faculty, and alumni. Refworks has an online interface that can be used to collect and organize your citations and a plugin for Microsoft Word that helps you format your citations in any of hundreds of styles and easily integrate the citation into your work. Need help? See the Library's [http://guides.library.ubc.ca/refworks Refworks guide] or attend a [http://elred.library.ubc.ca/libs/date//search/Refworks Refworks Workshop].

'''Zotero'''

[http://www.zotero.org/ Zotero] [zoh-TAIR-oh] is a free open-source tool that aims to help you "collect, organize, cite, and share your research sources." Zotero includes both desktop and browser-based interfaces along with plugins for Microsoft Word and OpenOffice.

'''Mendeley'''

[http://www.mendeley.com Mendeley] is a free tool with both web-based and desktop components that includes PDF markup and social networking functionalities. Mendeley also includes plugins for Microsoft Word and OpenOffice.

'''EndNote'''

[http://www.endnote.com/ EndNote] is a popular paid citation management tool. The full version of EndNote costs money, but there is a free, web-based version within the [http://resources.library.ubc.ca/277 Web of Science] database, called My EndNote Web. My EndNote Web has fewer features than EndNote.

'''EasyBib'''

[http://www.easybib.com/ EasyBib] allows you to create bibliographies in a variety of different citation styles, including MLA and APA. Visitors can just type in the item they need to cite, and EasyBib will provide the correct citation for each entry.

'''Citation Builder'''

[http://www.lib.ncsu.edu/lobo2/citationbuilder/index.php Citation Builder] allows you to build citations for a variety of information sources in MLA or APA. A free tool from NCSU Libraries.

'''LaTeX'''

[http://www.latex-project.org/ LaTeX] is a document preparation system often used by mathematicians, scientists, and engineers to automatically format documents that comply with thesis and journal formatting requirements. LaTeX has a steep learning curve. A few resources on LaTeX include:
*[http://gw2jh3xr2c.search.serialssolutions.com/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=book&rft.title=Learning+LaTeX&rft.au=David+F.+Griffiths&rft.au=Desmond+J.+Higham&rft.date=1997-01-01&rft.pub=Society+for+Industrial+and+Applied+Math&rft.isbn=9780898713831&rft.externalDBID=n%2Fa&rft.externalDocID=9418 Learning LaTeX by David F. Griffiths, Desmond J. Higham]
*[https://github.com/briandealwis/ubcdiss UBC Dissertation Template for LaTeX by Brian de Alwis]
*[http://www.phys.washington.edu/users/mforbes/projects/ubcthesis/ LaTeX Class by Michael McNeil Forbes]

[[category: CLC Resource Guides]]

Learning Commons:Chapman Learning Commons/Tools

2019-01-02T23:51:59Z

Sarah:

==Citation Management Tools==

Citation management tools can help you collect, organize, store, share, and format citations. There are many different citation management tools and each has different features. A few of the most popular tools include RefWorks, Mendeley, Zotero, Endnote, and Papers. UBC Library officially licenses and supports Refworks for all UBC students, faculty, and alumni.

[[File:wiki.ubc.ca/File:Comparison_Table_Oct-2017-Final.pdf|thumb|Citation Management Comparison Table]]

{| class="wikitable" style="color: black; background-color: #EFF8FB; border-width: 3px"
|-
| Citation management tools and databases that allow you to copy and paste formatted citations into your work sometimes produce errors. Regardless of what tool you decide to use it is still your responsibility to check and make sure that the citation has all the required information and is properly formatted.
|}

'''RefWorks'''

[http://guides.library.ubc.ca/refworks RefWorks] is the citation management tool officially supported by UBC Library and is free to use for UBC students, faculty, and alumni. Refworks has an online interface that can be used to collect and organize your citations and a plugin for Microsoft Word that helps you format your citations in any of hundreds of styles and easily integrate the citation into your work. Need help? See the Library's [http://guides.library.ubc.ca/refworks Refworks guide] or attend a [http://elred.library.ubc.ca/libs/date//search/Refworks Refworks Workshop].

'''Zotero'''

[http://www.zotero.org/ Zotero] [zoh-TAIR-oh] is a free open-source tool that aims to help you "collect, organize, cite, and share your research sources." Zotero includes both desktop and browser-based interfaces along with plugins for Microsoft Word and OpenOffice.

'''Mendeley'''

[http://www.mendeley.com Mendeley] is a free tool with both web-based and desktop components that includes PDF markup and social networking functionalities. Mendeley also includes plugins for Microsoft Word and OpenOffice.

'''EndNote'''

[http://www.endnote.com/ EndNote] is a popular paid citation management tool. The full version of EndNote costs money, but there is a free, web-based version within the [http://resources.library.ubc.ca/277 Web of Science] database, called My EndNote Web. My EndNote Web has fewer features than EndNote.

'''EasyBib'''

[http://www.easybib.com/ EasyBib] allows you to create bibliographies in a variety of different citation styles, including MLA and APA. Visitors can just type in the item they need to cite, and EasyBib will provide the correct citation for each entry.

'''Citation Builder'''

[http://www.lib.ncsu.edu/lobo2/citationbuilder/index.php Citation Builder] allows you to build citations for a variety of information sources in MLA or APA. A free tool from NCSU Libraries.

'''LaTeX'''

[http://www.latex-project.org/ LaTeX] is a document preparation system often used by mathematicians, scientists, and engineers to automatically format documents that comply with thesis and journal formatting requirements. LaTeX has a steep learning curve. A few resources on LaTeX include:
*[http://gw2jh3xr2c.search.serialssolutions.com/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=book&rft.title=Learning+LaTeX&rft.au=David+F.+Griffiths&rft.au=Desmond+J.+Higham&rft.date=1997-01-01&rft.pub=Society+for+Industrial+and+Applied+Math&rft.isbn=9780898713831&rft.externalDBID=n%2Fa&rft.externalDocID=9418 Learning LaTeX by David F. Griffiths, Desmond J. Higham]
*[https://github.com/briandealwis/ubcdiss UBC Dissertation Template for LaTeX by Brian de Alwis]
*[http://www.phys.washington.edu/users/mforbes/projects/ubcthesis/ LaTeX Class by Michael McNeil Forbes]

[[category: CLC Resource Guides]]

Learning Commons:Chapman Learning Commons/Tools

2019-01-02T23:51:06Z

Sarah: Adding comparison table

==Citation Management Tools==

Citation management tools can help you collect, organize, store, share, and format citations. There are many different citation management tools and each has different features. A few of the most popular tools include RefWorks, Mendeley, Zotero, Endnote, and Papers. UBC Library officially licenses and supports Refworks for all UBC students, faculty, and alumni.

[[File:Comparison Table Oct-2017-Final.pdf|thumb|Citation Management Comparison Table]]

{| class="wikitable" style="color: black; background-color: #EFF8FB; border-width: 3px"
|-
| Citation management tools and databases that allow you to copy and paste formatted citations into your work sometimes produce errors. Regardless of what tool you decide to use it is still your responsibility to check and make sure that the citation has all the required information and is properly formatted.
|}

'''RefWorks'''

[http://guides.library.ubc.ca/refworks RefWorks] is the citation management tool officially supported by UBC Library and is free to use for UBC students, faculty, and alumni. Refworks has an online interface that can be used to collect and organize your citations and a plugin for Microsoft Word that helps you format your citations in any of hundreds of styles and easily integrate the citation into your work. Need help? See the Library's [http://guides.library.ubc.ca/refworks Refworks guide] or attend a [http://elred.library.ubc.ca/libs/date//search/Refworks Refworks Workshop].

'''Zotero'''

[http://www.zotero.org/ Zotero] [zoh-TAIR-oh] is a free open-source tool that aims to help you "collect, organize, cite, and share your research sources." Zotero includes both desktop and browser-based interfaces along with plugins for Microsoft Word and OpenOffice.

'''Mendeley'''

[http://www.mendeley.com Mendeley] is a free tool with both web-based and desktop components that includes PDF markup and social networking functionalities. Mendeley also includes plugins for Microsoft Word and OpenOffice.

'''EndNote'''

[http://www.endnote.com/ EndNote] is a popular paid citation management tool. The full version of EndNote costs money, but there is a free, web-based version within the [http://resources.library.ubc.ca/277 Web of Science] database, called My EndNote Web. My EndNote Web has fewer features than EndNote.

'''EasyBib'''

[http://www.easybib.com/ EasyBib] allows you to create bibliographies in a variety of different citation styles, including MLA and APA. Visitors can just type in the item they need to cite, and EasyBib will provide the correct citation for each entry.

'''Citation Builder'''

[http://www.lib.ncsu.edu/lobo2/citationbuilder/index.php Citation Builder] allows you to build citations for a variety of information sources in MLA or APA. A free tool from NCSU Libraries.

'''LaTeX'''

[http://www.latex-project.org/ LaTeX] is a document preparation system often used by mathematicians, scientists, and engineers to automatically format documents that comply with thesis and journal formatting requirements. LaTeX has a steep learning curve. A few resources on LaTeX include:
*[http://gw2jh3xr2c.search.serialssolutions.com/?ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info:sid/summon.serialssolutions.com&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=book&rft.title=Learning+LaTeX&rft.au=David+F.+Griffiths&rft.au=Desmond+J.+Higham&rft.date=1997-01-01&rft.pub=Society+for+Industrial+and+Applied+Math&rft.isbn=9780898713831&rft.externalDBID=n%2Fa&rft.externalDocID=9418 Learning LaTeX by David F. Griffiths, Desmond J. Higham]
*[https://github.com/briandealwis/ubcdiss UBC Dissertation Template for LaTeX by Brian de Alwis]
*[http://www.phys.washington.edu/users/mforbes/projects/ubcthesis/ LaTeX Class by Michael McNeil Forbes]

[[category: CLC Resource Guides]]

Documentation:Open UBC/2019 Open UBC Website Redesign

2018-12-13T17:46:32Z

Sarah: /* Prosed Open UBC Website Architecture Idea */

=== Purpose/Objective to the website: ===
* To incorporate Open Scholarly component to the Open UBC site
* Use the feedback from the UX section to improve the architecture of the site. [https://docs.google.com/document/d/1bKn7_Am3Gbr6W65tkYXzLinRmmWrfWZqoFU_5EAIeV0/edit See Open UBC Website Review summary] for the result for the UX.

=== Proposed Mock up ===

==== Home page ====
[[File:Open_ubc_homepage_mockup.png|thumb|open ubc homepage mockup with new architecture|center]]

http://open-2019.sites.olt.ubc.ca/

==== Landing Page ====
http://open-2019.sites.olt.ubc.ca/access/

==== Toolkit/Get Started page structure ====
http://open-2019.sites.olt.ubc.ca/education/toolkits-education/teach-in-the-open/

=== '''Feedbacks on the proposed mockup''' ===
* We can have landing pages for “examples”
* Open 101 on the Home page can be changed to “Getting Started” instead
* The template for the toolkit can be applied to “Get Started” as well
* Landing page can be icon +description, the secondary landing page can have the picture background like in the live open UBC site
* Having an Open textbook database on wiki, and inviting site visitors to contribute to the database (with criteria)

== Prosed Open UBC Website Architecture Idea ==
* Goal of this activity was to sort out existing pages to the new architecture – and see if there are any gaps to it.
* In the end of the activity, we combined some pages, added resources from the UBC library and indicated gap in resources:
*<span style="color:green"> Dark green = new pages that can be added to the new open UBC site </span><span style="color:orange"> Orange= Resources from the Library </span> Black = Existing page on the Open UBC site
{| class="wikitable"
|'''Access'''
|'''Data'''
|'''Research'''
|'''Education'''
|'''What’s missing/What doesn’t fit'''
|-
|'''Get started: '''
*<span style="color:green"> What is OA
* Policy Statement at UBC</span>
'''Toolkit:'''
* Finding Open Textbook
* Finding Open Educational Resources
* OER Accessibility Toolkit
*<span style="color:green"> Open Textbook</span>
* <span style="color:green"> Stats Space UBC</span>
'''Example:'''
|'''Get Started:'''
* <span style="color:orange">Research Data Management</span>
* Draft Tri-Agency Research Data management Policy for Consultation
'''Toolkit:'''
* <span style="color:orange">Finding Open Data</span>
* Sharing Data
* Finding Systematic Review Protocols
* <span style="color:orange">Creating @ DOI</span>
'''Example:'''
* FRDR. Find and Share Canadian Research Data.
* Dataverse
* OSF
|'''Get Started:'''
* <span style="color:green"> Getting Started Guide for Instructors</span>
* <span style="color:orange">Tri-agency policies on Open Ac</span>
'''Toolkit'''
* <span style="color:orange">How to make work OA?</span>
* <span style="color:orange">Find OA Journals</span>
* <span style="color:orange">Open Source Frameworks</span>
'''Example:'''
|'''Get Started:'''
* <span style="color:green"> Open pedagogy toolkit on GS (Getting Started)</span>
'''Toolkits:'''
* Creating Open Educational Resources
* Open Licensing for Instructors/Students
* Open learning Teaching and Tools( teaching with wiki, canvas, blogs)
* Why + How +What is learn in the Open combined
* Why +How+What is teach in the Open combined
'''Example:'''
|
* Open Technology/ Learning Workshop archive
* <span style="color:green">OA on CV</span>
* <span style="color:green">Tenure + Promotion Doc</span>
* <span style="color:green">Policy?</span>
* <span style="color:green">Academic profiles for open content</span>
|}

==== Proposed Schedule/Timeline ====
[https://docs.google.com/spreadsheets/d/1NK0qfEMWQVs0JoI3dOzpLudk3WrkRhHoM1E6NUdObrA/edit#gid=0 See Open UBC website renewal timeline]

===== NEXT STEP =====
* Rie will set up a clone for the Open UBC site, and re-arrange the pages according to the card sorting activity. Rie will send an invitation email for the new(clone) Open UBC site once it is done.
* Erin will communicate with the Library to get feedback on the proposed Architecture

Documentation:Open UBC/2019 Open UBC Website Redesign

2018-12-13T17:10:36Z

Sarah: /* Prosed Open UBC Website Architecture Idea */

=== Purpose/Objective to the website: ===
* To incorporate Open Scholarly component to the Open UBC site
* Use the feedback from the UX section to improve the architecture of the site. [https://docs.google.com/document/d/1bKn7_Am3Gbr6W65tkYXzLinRmmWrfWZqoFU_5EAIeV0/edit See Open UBC Website Review summary] for the result for the UX.

=== Proposed Mock up ===

==== Home page ====
[[File:Open_ubc_homepage_mockup.png|thumb|open ubc homepage mockup with new architecture|center]]

http://open-2019.sites.olt.ubc.ca/

==== Landing Page ====
http://open-2019.sites.olt.ubc.ca/access/

==== Toolkit/Get Started page structure ====
http://open-2019.sites.olt.ubc.ca/education/toolkits-education/teach-in-the-open/

=== '''Feedbacks on the proposed mockup''' ===
* We can have landing pages for “examples”
* Open 101 on the Home page can be changed to “Getting Started” instead
* The template for the toolkit can be applied to “Get Started” as well
* Landing page can be icon +description, the secondary landing page can have the picture background like in the live open UBC site
* Having an Open textbook database on wiki, and inviting site visitors to contribute to the database (with criteria)

== Prosed Open UBC Website Architecture Idea ==
* Goal of this activity was to sort out existing pages to the new architecture – and see if there are any gaps to it.
* In the end of the activity, we combined some pages, added resources from the UBC library and indicated gap in resources:
*<span style="color:green"> Dark green = new pages that can be added to the new open UBC site </span><span style="color:orange"> Orange= Resources from the Library </span> Black = Existing page on the Open UBC site
{| class="wikitable"
|'''Access'''
|'''Data'''
|'''Research'''
|'''Education'''
|'''What’s missing/What doesn’t fit'''
|-
|'''Get started: '''
*<span style="color:green"> What is OA
* Policy Statement at UBC</span>
'''Toolkit:'''
* Finding Open Textbook
* Finding Open Educational Resources
* OER Accessibility Toolkit
*<span style="color:green"> Open Textbook</span>
* <span style="color:green"> Stats Space UBC</span>
'''Example:'''
|'''Get Started:'''
* <span style="color:orange">Research Data Management</span>
'''Toolkit:'''
* <span style="color:orange">Finding Open Data</span>
* <span style="color:orange">Creating @ DOI</span>
'''Example:'''
|'''Get Started:'''
* <span style="color:green"> Getting Started Guide for Instructors</span>
* <span style="color:orange">Tri-agency policies on Open Ac</span>
'''Toolkit'''
* <span style="color:orange">How to make work OA?</span>
* <span style="color:orange">Find OA Journals</span>
* <span style="color:orange">Open Source Frameworks</span>
'''Example:'''
|'''Get Started:'''
* <span style="color:green"> Open pedagogy toolkit on GS (Getting Started)</span>
'''Toolkits:'''
* Creating Open Educational Resources
* Open Licensing for Instructors/Students
* Open learning Teaching and Tools( teaching with wiki, canvas, blogs)
* Why + How +What is learn in the Open combined
* Why +How+What is teach in the Open combined
'''Example:'''
|
* Open Technology/ Learning Workshop archive
* <span style="color:green">OA on CV</span>
* <span style="color:green">Tenure + Promotion Doc</span>
* <span style="color:green">Policy?</span>
* <span style="color:green">Academic profiles for open content</span>
|}

==== Proposed Schedule/Timeline ====
[https://docs.google.com/spreadsheets/d/1NK0qfEMWQVs0JoI3dOzpLudk3WrkRhHoM1E6NUdObrA/edit#gid=0 See Open UBC website renewal timeline]

===== NEXT STEP =====
* Rie will set up a clone for the Open UBC site, and re-arrange the pages according to the card sorting activity. Rie will send an invitation email for the new(clone) Open UBC site once it is done.
* Erin will communicate with the Library to get feedback on the proposed Architecture

Library:Research Skills For Engineering Students

2018-11-09T00:20:56Z

Sarah: /* Editing This Tutorial */

<div>[[File:EngineeringBanner.png|1434 px|Library Research Skills for Engineering Students]]</div>
<div style="border:1px solid #ffd5bf; background:#fff3ff; width:99%; padding:4px; margin-bottom:10px">
<div style="border:1px solid #ffd5bf; background:#f8f6ff; width:99%; padding:4px">
<big>'''Tutorial home page''' for: Library Research Skills for Engineering Students</big>
__TOC__
</div>

<div style="border:1px solid #fff3ff; background:#f8f6ff; width:99%; padding:4px">
==Editing This Tutorial==
Please consult with [http://directory.library.ubc.ca/people/view/1096 Sarah Parker] prior to making changes to these tutorial pages.

You are very welcome to reuse this content via [http://en.wikipedia.org/wiki/Wikipedia:Section#Sections_vs._separate_pages_vs._transclusion transclusion] or to copy the content.'''</p>

==All Tutorial Files==
[[:Category:Research Skills For Engineering Students/All-Pages]]

==Introduction==
'''Table of Contents'''
#Research Skills For Engineering Students/Introduction/Page 01 Learning objectives
#Research Skills For Engineering Students/Introduction/Page 02 Why is research important to your education & career?

[[Category:Research Skills For Engineering Students/Introduction]]
<dpl>
category = Research Skills For Engineering Students/Introduction
columns = 2
shownamespace = false
</dpl>
<dpl>
category = Research Skills For Engineering Students/Introduction
columns = 2
namespace = File
mode = userformat
format = «gallery»,%PAGE%¦%TITLE%\n,,«/gallery»
</dpl>

==Module 1: Thinking about your research problem==
'''Table of Contents'''
#Research Skills For Engineering Students/Module 01/Page 01 Introduction
#Research Skills For Engineering Students/Module 01/Page 02 The problem
#Research Skills For Engineering Students/Module 01/Page 03 Brainstorming concepts & keywords
#Research Skills For Engineering Students/Module 01/Page 04 Conclusion

[[Category:Research Skills For Engineering Students/Module 01]]
<dpl>
category = Research Skills For Engineering Students/Module 01
columns = 2
shownamespace = false
</dpl>
<dpl>
category = Research Skills For Engineering Students/Module 01
columns = 2
namespace = File
mode = userformat
format = «gallery»,%PAGE%¦%TITLE%\n,,«/gallery»
</dpl><br>'''List of included YouTube Videos:'''
<br>{{#widget:YouTube|id=R2DiNLGKexE|height=260|width=380}}

==Module 2: Types of engineering information==
'''Table of Contents'''
#Research Skills For Engineering Students/Module 02/Page 01 Introduction
#Research Skills For Engineering Students/Module 02/Page 02 Evaluating information
#Research Skills For Engineering Students/Module 02/Page 03 Popular vs. scholarly sources
#Research Skills For Engineering Students/Module 02/Page 04 Conclusion
#Research Skills For Engineering Students/Module 02/Page 05 Introduction
#Research Skills For Engineering Students/Module 02/Page 06 Primary & secondary sources
#Research Skills For Engineering Students/Module 02/Page 07 The research cycle
#Research Skills For Engineering Students/Module 02/Page 08 Peer review
#Research Skills For Engineering Students/Module 02/Page 09 Conclusion

<dpl>
category = Research Skills For Engineering Students/Module 02
columns = 2
shownamespace = false
</dpl>
<dpl>
category = Research Skills For Engineering Students/Module 02
columns = 2
namespace = File
mode = userformat
format = «gallery»,%PAGE%¦%TITLE%\n,,«/gallery»
</dpl><br>'''List of included YouTube Videos:'''
<br>{{#widget:YouTube|id=8SBdiWqsKsU|height=260|width=380}} {{#widget:YouTube|id=i_jFERTtw2w|height=260|width=380}}

==Module 3: Google & Google Scholar==
'''Table of Contents'''
#Research Skills For Engineering Students/Module 03/Page 01 Introduction
#Research Skills For Engineering Students/Module 03/Page 02 What is Google good at?
#Research Skills For Engineering Students/Module 03/Page 03 What about Google Scholar?
#Research Skills For Engineering Students/Module 03/Page 04 Google operators
#Research Skills For Engineering Students/Module 03/Page 05 Conclusion

[[:Category:Research Skills For Engineering Students/Module 03]]
<dpl>
category = Research Skills For Engineering Students/Module 03
columns = 2
shownamespace = false
</dpl>
<dpl>
category = Research Skills For Engineering Students/Module 03
columns = 2
namespace = File
mode = userformat
format = «gallery»,%PAGE%¦%TITLE%\n,,«/gallery»
</dpl><br>'''List of included YouTube Videos:'''
<br>{{#widget:YouTube|id=qyxzTuFium4|height=260|width=380}}

==Module 4: Summon==
'''Table of Contents'''
#Research Skills For Engineering Students/Module 04/Page 01 Introduction
#Research Skills For Engineering Students/Module 04/Page 02 Why use Summon?
#Research Skills For Engineering Students/Module 04/Page 03 Searching & applying limits
#Research Skills For Engineering Students/Module 04/Page 04 Limitations of Summon
#Research Skills For Engineering Students/Module 04/Page 05 Conclusion

[[:Category:Research Skills For Engineering Students/Module 04]]
<dpl>
category = Research Skills For Engineering Students/Module 04
columns = 2
shownamespace = false
</dpl>
<dpl>
category = Research Skills For Engineering Students/Module 04
columns = 2
namespace = File
mode = userformat
format = «gallery»,%PAGE%¦%TITLE%\n,,«/gallery»
</dpl><br>'''List of included YouTube Videos:'''
<br>{{#widget:YouTube|id=hPRzC0Ri6DE|height=260|width=380}}

==Module 5: Academic Databases==
'''Table of Contents'''
#Research Skills For Engineering Students/Module 05/Page 01 Introduction
#Research Skills For Engineering Students/Module 05/Page 02 What’s an academic database?
#Research Skills For Engineering Students/Module 05/Page 03 Database operators & tools
#Research Skills For Engineering Students/Module 05/Page 04 Conclusion
#Research Skills For Engineering Students/Module 05/Page 05 Introduction
#Research Skills For Engineering Students/Module 05/Page 06 Compendex
#Research Skills For Engineering Students/Module 05/Page 07 Accessing an article
#Research Skills For Engineering Students/Module 05/Page 08 Conclusion
#Research Skills For Engineering Students/Module 05/Page 09 Introduction
#Research Skills For Engineering Students/Module 05/Page 10 Web of Science
#Research Skills For Engineering Students/Module 05/Page 11 Conclusion
#Research Skills For Engineering Students/Module 05/Page 12 Introduction
#Research Skills For Engineering Students/Module 05/Page 13 UBC Library research guides
#Research Skills For Engineering Students/Module 05/Page 14 Conclusion

[[:Category:Research Skills For Engineering Students/Module 05]]
<dpl>
category = Research Skills For Engineering Students/Module 05
columns = 2
shownamespace = false
</dpl>
<dpl>
category = Research Skills For Engineering Students/Module 05
columns = 2
namespace = File
mode = userformat
format = «gallery»,%PAGE%¦%TITLE%\n,,«/gallery»
</dpl><br>'''List of included YouTube Videos:'''
<br>{{#widget:YouTube|id=S-g2c8_B6Jw|height=260|width=380}} {{#widget:YouTube|id=JQIcY-AcXog|height=260|width=380}} {{#widget:YouTube|id=0-lwox_LB18|height=260|width=380}} {{#widget:YouTube|id=iATLDLxaS8Y|height=260|width=380}}

==Module 6: Standards & Patents==
'''Table of Contents'''
#Research Skills For Engineering Students/Module 06/Page 01 Introduction
#Research Skills For Engineering Students/Module 06/Page 02 Standards
#Research Skills For Engineering Students/Module 06/Page 03 Patents
#Research Skills For Engineering Students/Module 06/Page 04 Finding standards & patents
#Research Skills For Engineering Students/Module 06/Page 05 Conclusion

[[:Category:Research Skills For Engineering Students/Module 06]]
<dpl>
category = Research Skills For Engineering Students/Module 06
columns = 2
shownamespace = false
</dpl>
<dpl>
category = Research Skills For Engineering Students/Module 06
columns = 2
namespace = File
mode = userformat
format = «gallery»,%PAGE%¦%TITLE%\n,,«/gallery»
</dpl><br>'''List of included YouTube Videos:'''
<br>{{#widget:YouTube|id=S3sg_LbfKww|height=260|width=380}}

==Module 7: Citing your information==
'''Table of Contents'''
#Research Skills For Engineering Students/Module 07/Page 01 Introduction
#Research Skills For Engineering Students/Module 07/Page 02 Why do you cite?
#Research Skills For Engineering Students/Module 07/Page 03 Plagiarism
#Research Skills For Engineering Students/Module 07/Page 04 When/what do you need to cite?
#Research Skills For Engineering Students/Module 07/Page 05 Conclusion
#Research Skills For Engineering Students/Module 07/Page 06 Introduction
#Research Skills For Engineering Students/Module 07/Page 07 How do you cite?
#Research Skills For Engineering Students/Module 07/Page 08 Citation styles
#Research Skills For Engineering Students/Module 07/Page 09 Conclusion

[[:Category:Research Skills For Engineering Students/Module 07]]
<dpl>
category = Research Skills For Engineering Students/Module 07
columns = 2
shownamespace = false
</dpl>
<dpl>
category = Research Skills For Engineering Students/Module 07
columns = 2
namespace = File
mode = userformat
format = «gallery»,%PAGE%¦%TITLE%\n,,«/gallery»
</dpl><br>'''List of included YouTube Videos:'''
<br>{{#widget:YouTube|id=ss2d-VdRMLs|height=260|width=380}} {{#widget:YouTube|id=4YIqduDRpjc|height=260|width=380}}

==Conclusion==
'''Table of Contents'''
#Research Skills For Engineering Students/Conclusion/Page 01 Summary

[[Category:Research Skills For Engineering Students/Conclusion]]
<dpl>
category = Research Skills For Engineering Students/Conclusion
columns = 2
shownamespace = false
</dpl>
<dpl>
category = Research Skills For Engineering Students/Conclusion
columns = 2
namespace = File
mode = userformat
format = «gallery»,%PAGE%¦%TITLE%\n,,«/gallery»
</dpl>

==Need Help?==
[[:Category:Library Research Skills For Engineering Students/Need Help]]
<dpl>
category = Library Research Skills For Engineering Students/Need Help
columns = 2
shownamespace = false
</dpl>

==References and Credits Files==
[[:Category:Research Skills For Engineering Students/Credits]]
<dpl>
category = Research Skills For Engineering Students/Credits
columns = 2
shownamespace = false
</dpl>

=The Structure for Storing this Content:=
#On the library instruction space: [http://wiki.ubc.ca/Library:Instructional_Materials http://wiki.ubc.ca/Library:Instructional_Materials], we will post only one page (a home page to this tutorial) under the category '''Library Tutorials''' http://wiki.ubc.ca/Library:Instructional_Materials#Library_Tutorials
#The ''tutorial home page'' is a simple list of the module home pages. This structure will facilitate easily finding all of the pages in each module. (This page is the tutorial home page for the "Library Research Skills for Land & Food Systems Tutorial".)
#The ''module home page'' will include the pages of only the one module. Example, [http://wiki.ubc.ca/Category:Library_Research_Skills_For_Land_and_Food_Systems/Module_01 http://wiki.ubc.ca/Category:Library_Research_Skills_For_Land_and_Food_Systems/Module_01]
#''All-Pages page '' in the tutorial. To facilitate maintenance of this tutorial, we will add the category "All-Pages" to each page in the tutorial.

=History of this Tutorial=
The ''Library Research Skills for Land and Food Systems tutorial'' began as the tutorial ''Library Research Skills for Biologists'' co-written by Sally Taylor and Katherine Kalsbeek assisted by Web Designer, Suzan Zagar in 2000. In the fall of 2000, Sally Taylor in collaboration with Director of First Year Biology, Carol Pollock designed a marked library assignment that included a research tutorial originally created in WebCT, then migrated to Vista. In November 2011, we worked on the migration of the content in the Biology Research Skills tutorial to UBC wiki with the presentation into Connect. In July 2013, the content of Biology Research Skills was used to form in part the tutorial for ''Library Research Skills for Land and Food Systems'' on Blackboard.

This [http://urls.bccampus.ca/lq tutorial] was licensed under Creative Commons License Attribution, Share Alike and donated to [http://www.eln.bc.ca/link/ ALPS LINK] in 2008. The creators, Katherine Kalsbeek, Sally Taylor and Suzan Zagar were recognized by a panel of their peers and awarded the [http://sharelibraryresources.pbworks.com/w/page/16137719/Uploading%20Contest%3A%20Prize%20Winners 2008 Laud a Librarian Award]. Since 2008, it has been updated and maintained by Katherine Miller and Suzan Zagar.

In the 2011-2012 academic year, the Library Research Skills for Biologists tutorial was used by 1377 BIOL 140 students (88% of the students registered in the class) who reviewed 5 or more pages in the tutorial.

In 2014, based on assessment with students (card sorting exercise) and a survey of faculty members in the Faculty of Land and Food Systems, the tutorial was completely restructured and there was a major rewriting of the content. This restructuring and rewriting was co-authored by Katherine Miller and Megan Brown. Suzan Zagar was the key technical creator, making changes to UBC wiki content and Blackboard Learn.

=How to Direct Link to this Tutorial=
{{Elearning:NewLMS/Creating_Direct_Links}}

:'''Problems getting this to work? Note these Details''':
#Click on the drop down arrow next to the page title. The instruction in step one mentions to copy the URL. The URL you use is found by clicking on the drop down arrow next to the page title. In this example, I want a direct link to module 3 page 5 that I can share.<br>[[File:Direct link 1.jpg|500px|click on drop down arrow]] <br>
#Click on Metadata<br>[[File:Direct link 2.jpg|500px|Metadata]]<br>
#Select Resource Location URL. For step two in the instructions above, note that you need to encode the Resource Location URL.<br>[[File:Direct link 3.jpg|500px|Resource Location]]

=Populating the Tutorial in Courses=
To have students from a course added automatically into one of the UBC Library's tutorials in Connect, after confirming permission from the course instructor, contact [https://www.directory.ubc.ca/index.cfm?d=%403I%3E%3CR_*%23J\B_FYUS%3ENW2!9\5QMR^0%3D3%29!TSBS4OJ0P%40%20%0A Yvonne Chan] before the term starts.
Note: in courses where there is a T1 section, the library populates the courses into the second term too, by default.
In Connect, you may not see a specific course showing as part of either 20XX1-2 or 20XXW2 classes because the course is by default grouped under 20XXW1. This grouping occurs during the course creation process, when it was required that the course be associated with a primary section and Yvonne will pick one section. For some courses, there is no section that spans term 1 and 2, otherwise, she will select that section and make the course shows up under the grouping, 20XXW1-2. With this in mind, you can then assume that all Connect Library tutorials that are labelled "20XXW" are enrolled with users from all sections from both Term 1 and Term 2 by default.
(This information is summarized from an email from Yvonne dated Dec 8, 2014.)

</div>
<center>2015 | THE UNIVERSITY OF BRITISH COLUMBIA | WOODWARD LIBRARY</center>

[[Category:Research Skills For Engineering Students/All-Pages]]
[[Category:Researching at the Library]] [[Category:Woodward Library]]
[[Category:Library_Tutorials]]
[[Category:Suzan Zagar]]

Library:Truncation and Wildcard Symbols

2018-09-26T23:29:17Z

Sarah: /* NOTES AND EXPLANATIONS */

{| width="100%%" border="1" cellspacing="0" cellpadding="4" align="center" valign="top"
| rowspan="2" valign="top" bgcolor="#000000" | <span style="font-size: x-large; color:#FFFFFF;">'''SOURCE'''</span>
| rowspan="2" valign="top" bgcolor="#000000" | <span style="font-size: x-large; color:#FFFFFF;">'''TRUNCATION'''</span>
| colspan="3" align="center" bgcolor="#000000" | <span style="font-size: x-large; color:#FFFFFF;">'''WILDCARD'''</span>
| rowspan="2" valign="top" bgcolor="#000000" | <span style="font-size: x-large; color:#FFFFFF;">'''PHRASE'''</span>
| rowspan="2" valign="top" bgcolor="#000000" | <span style="font-size: x-large; color:#FFFFFF;">'''PROXIMITY'''</span>
| rowspan="2" valign="top" bgcolor="#000000" | <span style="font-size: x-large; color:#FFFFFF;">'''Other<br />Notes'''</span>
|- align="center"
| bgcolor="#EEEEEE" | Stands in for 0 or 1 characters
| bgcolor="#EEEEEE" | Stands in for 0 or more characters
| bgcolor="#EEEEEE" | Stands in for exactly 1 character
|- valign="top"
| align="left" |'''OVID databases:'''
*[http://resources.library.ubc.ca/139 Medline]
*[http://resources.library.ubc.ca/129 Embase]
*[http://resources.library.ubc.ca/644 EBM reviews]<br/>(incl. Cochrane)
*[http://resources.library.ubc.ca/253 HaPI]
*[http://resources.library.ubc.ca/112 AGRICOLA]
*[http://resources.library.ubc.ca/132 FSTA]
| align="left" | <center><span style="font-size: xx-large; color:#0000FF;"><nowiki>*</nowiki></span>   or  <span style="font-size: xx-large; color:#0000FF;"><nowiki>$</nowiki></span>   or  <span style="font-size: xx-large; color:#0000FF;"><nowiki>:</nowiki></span></center><br/>'''Example:<br />'''disease$<br/><br/><br/><br/><br/><br/>''Note:'' [[#Ovid1|[i]]]
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;"><nowiki>?</nowiki></span></center><br/>'''Example:'''<br />flavo?r<br/><br/><br/><br/><br/><br/>''Note:'' [[#Ovid2|[ii]]]
| style="color:black; align="center" |
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;"><nowiki>#</nowiki></span></center><br/>'''Example:'''<br />wom#n<br /><br /><br /><br /><br /><br />''Note:'' [[#Ovid3|[iii]]]
| valign="top" align="left"|
*No quotation marks needed for phrase searching
*Stop words will be removed
| align="left" |<center><span style="font-size: x-large; color:#0000FF;">'''adj/n'''</span><br />(where "n" is a number)</center><br/>'''Example:'''<br />natural adj20 childbirth<br/><br/><br /><br />''Note:'' [[#Ovid4|[iv]]]
|  
|-
| align="left" valign="top" |[http://resources.library.ubc.ca/321 '''PubMed''']
| align="left" |<br/><center><span style="font-size: xx-large; color:#0000FF;"><nowiki>*</nowiki></span></center><br/>''Note:'' [[#PubMed1|[v]]]
| style="color:black; align="center" |
| style="color:black; align="center" |
| style="color:black; align="center" |
| <center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span><br />Phrase searching prevents subject mapping
|  
|  
|-
| align="left" valign="top"|
'''PROQUEST databases:'''
*[http://resources.library.ubc.ca/811 LLBA]
| align="left" |<br /><center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span><br /></center><br /><br /><br /><br /><br />''Note:'' [[#ProQuest1|[vi]]]
| style="color:black; align="left" |<center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span></center><br /><br /><br /><br /><br />
| style="color:black; align="left" |
| style="color:black; align="left" |<br /><center><span style="font-size: xx-large; color:#0000FF;">'''?'''</span></center><br /><br /><br /><br /><br />''Note:'' [[#ProQuest2|[vii]]]
| <center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span><br /><br /><br /><br /><br /><br />
| align="left" | <center><span style="font-size: x-large; color:#0000FF;">'''NEAR/n'''</span><br/>or <br/><span style="font-size: x-large; color:#0000FF;">'''N/n'''</span><br /><br />(where "n" is a number)</center><br/>''Note:'' [[#ProQuest3|[viii]]]
| align="left" | <center><span style="font-size: x-large; color:#0000FF;">'''PRE/n'''</span><br/>or <br/><span style="font-size: x-large; color:#0000FF;">'''P/n'''</span><br /><br /></center><br /><br /><br />'Note:'' [[#ProQuest4|[ix]]]
|-
| align="left" |
'''EBSCOHOST databases:'''
*[http://resources.library.ubc.ca/196 Academic Search Premier]
*[http://resources.library.ubc.ca/92 Cinahl]
*[http://resources.library.ubc.ca/1042 Communication and Mass Media Complete]
*[http://resources.library.ubc.ca/576 Mental Measurement Yearbook (Buros]
*[http://resources.library.ubc.ca/159 PsycInfo]
*[http://resources.library.ubc.ca/118 SPORTDiscus]
| <center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span></center><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br />
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''#'''</span></center><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br />''Note:'' [[#EBSCOHOST1|[x]]]
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span></center><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br />
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''?'''</span></center><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br />''Note:'' [[#EBSCOHOST2|[xi]]]
| align="left" |<center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span></center><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br />
| align="left" | <center><span style="font-size: x-large; color:#0000FF;">'''N/n'''</span><br/>or <br/> <span style="font-size: x-large; color:#0000FF;">'''W/n'''</span><br /><br />(where "n" is a number)</center><br/><br /><br /><br /><br /><br /><br />''Note:'' [[#EBSCOHOST3|[xii]]]
|  
|-
| align="left" valign="top"|
'''Engineering Village'''<br />
*[http://resources.library.ubc.ca/715 Compendex]
| align="left" |<center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span><br />'''Automatic Stemming'''</center><br />''Note:'' [[#EngineeringV1|[xiii]]]
| style="color:black; align="left" |
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span><br /><br /><br /><br /><br />
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''?'''</span><br /><br /><br /><br /><br />
| align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span><br/>or <br/> <span style="font-size: xx-large; color:#0000FF;">'''{xxx}'''</span></center><br /><br />
| <center><span style="font-size: x-large; color:#0000FF;">'''NEAR/n'''</span><br/>or <br/> <span style="font-size:x-large; color:#0000FF;">'''ONEAR/n'''</span></center><br />''Note:'' [[#EngineeringV2|[xiv]]]
| align="left" | '''Proximity searching does not work with:''' truncation, wildcards, parentheses, quotation marks, or braces.
|- align="left" valign="top" |
|
'''Web of Science Core Collection'''<br />
*[http://resources.library.ubc.ca/277 Web of Science]
| align="left" |<center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span></center><br /><br /><br /><br />''Note:'' [[#WebOfKnowledge1|[xv]]]
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''$'''</span><br /></center><br /><br /><br /><br />''Note:'' [[#WebOfKnowledge2|[xvi]]]
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span><br /></center>
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''?'''</span><br /></center>
| <center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span><br /></center>
| <center><span style="font-size: x-large; color:#0000FF;">'''NEAR/n'''</span><br />(where "n" is a number)</center>
| When the word "near" appears in the title of a source item such as the title of a journal, book, or proceeding - enclose in parentheses (near)
|- align="left" valign="top" |
| [http://resources.library.ubc.ca/1529 '''PEDro''']
| '''Automatic stemming.'''<br /><br />Use truncation character '''*''' only at the beginning of a word<br /><br />''Note:'' [[#PEDro1|[xvii]]]
| style="color:black; align="left" |
| style="color:black; align="left" |
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''@'''</span><br /></center>
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span><br /></center>
| style="color:black; align="left" |  
| style="color:black; align="left" |
|- align="left" valign="top" |
| [http://resources.library.ubc.ca/219 '''CAB Direct''']
| style="color:black; align="center" |<center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span></center><br />or<br />'''Automatic Stemming'''<br /><br />Use quotation marks "XXX" to turn off Auto-stemming
| style="color:black; align="left" |
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span></center>
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''?'''</span></center>
| <center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span></center>
|  
|  
|- align="left" valign="top" |
| [http://search.library.ubc.ca/#catalogue '''UBC Library Catalogue''']
| style="color:black; align="left" |<center><span style="font-size: xx-large; color:#0000FF;">'''?'''</span></center>
| style="color:black; align="left" |
| style="color:black; align="left" |
| style="color:black; align="left" |
| style="color:black; align="left" |<center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span></center>
| style="color:black; align="left" | 
| style="color:black; align="left" | 
|- align="left" valign="top" |
| [http://search.library.ubc.ca/#general '''UBC Library - Summon Search''']
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span></center>
| style="color:black; align="left" |  
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span></center>
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''?'''</span></center>
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span></center>
| style="color:black; align="left" |  
| style="color:black; align="left" | Wildcards cannot be used as the first character in a search
|- align="left" valign="top" |
| '''[http://resources.library.ubc.ca/943 Google/Google Scholar]'''
| style="color:black; align="left" |  
| style="color:black; align="left" |  
| style="color:black; align="left" |  
| style="color:black; align="left" |  
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span></center>
| style="color:black; align="left" |  
| style="color:black; align="left" | <br />''Notes:'' [[#Google1|[xviii]]]
|}

===NOTES AND EXPLANATIONS===

<references/>
'''Ovid databases'''

<div id="Ovid1"></div>[[#Ovid1|[i]]] In OVID databases, you must turn off Subject mapping for truncation to work. You may add a number after your truncation symbol (i.e., *5) to restrict the search to a certain number of characters. For instance, the search: fish*4 would find fish, fishes, fishing, etc., but would not find longer results, such as “fish'''eries'''”.<br /><br />
<div id="Ovid2"></div>[[#Ovid2|[ii]]] In OVID databases, use the '''?''' wildcard symbol to find variations of spelling for the term. The '''?''' symbol is used inside or at the end of the search word. For instance colo?r would find all results for "color", and all results for "colour". And bird? Would find results for "bird" and "birds".<br /><br />
<div id="Ovid3"></div>[[#Ovid3|[iii]]] In OVID databases, use the '''#''' wildcard symbol to find variant spellings of a word. It can be placed inside or at the end of a search word. The '''#''' symbol must replace exactly one character (i.e., wom#n). So if you searched for: colo#r it would only find results for “colour”, but not color.<br /><br />
<div id="Ovid4"></div>[[#Ovid4|[iv]]] In OVID databases, you may broaden or narrow your search by using the proximity searching operator Adj. e.g. adj3 searches for terms within 3 words of each other.

'''PubMed'''

<div id="PubMed1"></div>[[#PubMed1|[v]]] In PubMed using the '''*''' truncation symbol will prevent subject mapping of your search term. It will find the first 600 possible results and then stop searching. It will also cause a keyword search to search as a phrase (i.e., the keyword search: fetus infection* will search for "fetus infection" as a phrase, instead of truncating the term "infection" on its own).

'''PROQUEST databases'''

<div id="ProQuest1"></div>[[#Proquest1|[vi]]] In PROQUEST databases, you may add a number after your truncation symbol (i.e., *5) to restrict the search to a certain number of characters. For instance, the search: fish*4 would find fish, fishes, fishing, etc., but would not find longer results, such as "fish'''eries'''".<br /><br />
<div id="ProQuest2"></div>[[#Proquest2|[vii]]] In PROQUEST databases, use the '''?''' wildcard symbol to replace any single character, either inside or at the end of the search word. Multiple wildcards can be used to represent multiple characters. For example: nurse? would find: nurses, nursed, but not nurse; sm?th would find: smith and smyth; ad??? would find: added, adult, adopt.<br /><br />
<div id="ProQuest3"></div>[[#Proquest3|[viii]]] In PROQUEST databases, use '''NEAR/n''' or '''N/n''' to look for documents that contain two search terms, in any order, within "n" specified number of words apart. Replace "n" with a number. For example: nursing NEAR/3 education; media N/3 women.<br /><br />
<div id="ProQuest4"></div>[[#Proquest4|[ix]]] Use '''PRE''' or '''P''' to search for a term within specified number of words before a second term. '''Eg:''' nursing PRE/4 education

'''EBSCOHOST databases'''

<div id="EBSCOHOST1"></div>[[#EBSCOHOST1|[x]]] Use '''#''' for alternate spellings. For example, colo#r finds – color/colour.<br /><br />
<div id="EBSCOHOST2"></div>[[#EBSCOHOST2|[xi]]] In EBSCOHOST databases, use the '''?''' wildcard to find an unknown character. For example, type '''ne?t''' to find all citations containing '''neat''', '''nest''' or '''next'''. EBSCOhost does not find '''net''' because the wildcard replaces a single character.<br /><br />
<div id="EBSCOHOST3"></div>[[#EBSCOHOST3|[xii]]] In EBSCOHOST databases The '''N5''' proximity search finds the words if they are within ''five words'' of one another regardless of the order in which they appear. The '''W5''' proximity search finds the words if they are within ''five words'' of one another and in the order in which you entered them.

'''Engineering Village'''

<div id="EngineeringV1"></div>[[#EngineeringV1|[xiii]]] In Engineering Village, Inspec database, search term stemming is automatic. Using truncation or wildcard characters automatically turns the “auto-truncation” off. You may also turn it off by selecting “auto-stemming>off”. To turn truncation off when not using a truncation or wild card character, put search terms in between quotation marks or braces {} – note that truncation characters and wildcards will not work within quotation marks or braces. You may use the * truncation character at the beginning or end of a search term. In addition, if you precede a word with the stemming character '''$''', you can pick up all forms of the word.<br />'''Ex:''' $management , picks up manager, managed, etc. <br /><br />
<div id="EngineeringV2"></div>[[#EngineeringV2|[xiv]]] '''ONEAR''' finds words in proximity to one another, in '''exact order''' entered.

'''Web of Science'''

<div id="WebOfKnowledge1"></div>[[#WebOfKnowledge1|[xv]]] In Web of Science databases you can use truncation in Title or Topic searches. You must enter at least three characters before and after a wildcard when using either left-hand truncation or right-hand truncation.<br /><br />
<div id="WebOfKnowledge1"></div>[[#WebOfKnowledge1|[xvi]]] The asterisk (*) represents any group of characters, including no character.The question mark (?) represents any single character.The dollar sign ($) represents zero or one character.

'''PEDro'''

<div id="PEDro1"></div>[[#PEDro1|[xvii]]]
*In PEDro, truncation at the end of a word is automatic.<br /><br />
*If you would like to truncate the beginning of the word - for example, if you wanted to find papers on edema, oedema, lymphedema or lymphoedema, type *edema in a text field. Note, that if you put an asterisk * at the beginning of a word, PEDro will not also find variants at the end of the word (that is, PEDro can’t simultaneously search for variants at the beginning and end of the same word). <br /><br />

'''Google/Google Scholar'''

<div id="Google1"></div>[[#Google1|[xviii]]]
In Google, there are some additional wild cards you can use:
# Search for specific types of files, such as PDFs, PPTs, or XLS, by adding '''filetype:''' and the 3-letter file abbreviation.
# Add a tilde sign ('''~''') immediately in front of a word to search for that word as well as even more synonyms.
# Use the '''related:''' operator to find pages that have similar content by typing '''related:''' followed by the website address. For instance, if you find a website you like, try using '''related:[insert URL]''' to locate similar websites.
# If you want to search for pages that may have just one of several words, include '''OR''' (capitalized) between the words. Without the OR, your results would typically show only pages that match both terms. You can also use the '''|''' symbol between words for the same effect.
# Include '''site:''' to search for information within a single website like all mentions of "Olympics" on the New York Times website. [ Olympics site:nytimes.com ]<br />'''Tip:''' Also search within a specific top-level domain like .org or .edu or country top-level domain like .de or .jp.
# Add a dash ('''-''') before a word to exclude all results that include that word. This is especially useful for synonyms like Jaguar the car brand and jaguar the animal.[ jaguar speed -car ].

<span style="color:#0000FF;">'''Information in this guide is based on database and search engine help pages.'''</span>

[[File:Library_Truncation_and_wildcards_handout_2013.pdf‎ ]]

----
Credits: <ref>Beck, Charlotte and Longley, Kate, UBC Library. 2012 [online]. Truncation and Wildcard Symbols. Available from http://wiki.ubc.ca/Library:Truncation_and_Wildcard_Symbols [accessed on 4 September 2012].
This work '''Truncation and Wildcard Symbols''', by Charlotte Beck and Kate Longley, identified by [http://www.library.ubc.ca UBC Library], is free of known copyright restrictions.
<br /> <br /> [[File:CreativeCommonLogo.jpg|88px]]
<br /> [http://creativecommons.org/publicdomain/mark/1.0/ Creative commons license]</ref>

<references/>

[[Category:Woodward Library]]
[[Category:Library Tutorials]]
[[Category:Woodward Library]]
[[Category:Charlotte Beck]]

Library:Truncation and Wildcard Symbols

2018-09-26T23:27:57Z

Sarah: Web of Knowledge to Web of Science so that it's current.

{| width="100%%" border="1" cellspacing="0" cellpadding="4" align="center" valign="top"
| rowspan="2" valign="top" bgcolor="#000000" | <span style="font-size: x-large; color:#FFFFFF;">'''SOURCE'''</span>
| rowspan="2" valign="top" bgcolor="#000000" | <span style="font-size: x-large; color:#FFFFFF;">'''TRUNCATION'''</span>
| colspan="3" align="center" bgcolor="#000000" | <span style="font-size: x-large; color:#FFFFFF;">'''WILDCARD'''</span>
| rowspan="2" valign="top" bgcolor="#000000" | <span style="font-size: x-large; color:#FFFFFF;">'''PHRASE'''</span>
| rowspan="2" valign="top" bgcolor="#000000" | <span style="font-size: x-large; color:#FFFFFF;">'''PROXIMITY'''</span>
| rowspan="2" valign="top" bgcolor="#000000" | <span style="font-size: x-large; color:#FFFFFF;">'''Other<br />Notes'''</span>
|- align="center"
| bgcolor="#EEEEEE" | Stands in for 0 or 1 characters
| bgcolor="#EEEEEE" | Stands in for 0 or more characters
| bgcolor="#EEEEEE" | Stands in for exactly 1 character
|- valign="top"
| align="left" |'''OVID databases:'''
*[http://resources.library.ubc.ca/139 Medline]
*[http://resources.library.ubc.ca/129 Embase]
*[http://resources.library.ubc.ca/644 EBM reviews]<br/>(incl. Cochrane)
*[http://resources.library.ubc.ca/253 HaPI]
*[http://resources.library.ubc.ca/112 AGRICOLA]
*[http://resources.library.ubc.ca/132 FSTA]
| align="left" | <center><span style="font-size: xx-large; color:#0000FF;"><nowiki>*</nowiki></span>   or  <span style="font-size: xx-large; color:#0000FF;"><nowiki>$</nowiki></span>   or  <span style="font-size: xx-large; color:#0000FF;"><nowiki>:</nowiki></span></center><br/>'''Example:<br />'''disease$<br/><br/><br/><br/><br/><br/>''Note:'' [[#Ovid1|[i]]]
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;"><nowiki>?</nowiki></span></center><br/>'''Example:'''<br />flavo?r<br/><br/><br/><br/><br/><br/>''Note:'' [[#Ovid2|[ii]]]
| style="color:black; align="center" |
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;"><nowiki>#</nowiki></span></center><br/>'''Example:'''<br />wom#n<br /><br /><br /><br /><br /><br />''Note:'' [[#Ovid3|[iii]]]
| valign="top" align="left"|
*No quotation marks needed for phrase searching
*Stop words will be removed
| align="left" |<center><span style="font-size: x-large; color:#0000FF;">'''adj/n'''</span><br />(where "n" is a number)</center><br/>'''Example:'''<br />natural adj20 childbirth<br/><br/><br /><br />''Note:'' [[#Ovid4|[iv]]]
|  
|-
| align="left" valign="top" |[http://resources.library.ubc.ca/321 '''PubMed''']
| align="left" |<br/><center><span style="font-size: xx-large; color:#0000FF;"><nowiki>*</nowiki></span></center><br/>''Note:'' [[#PubMed1|[v]]]
| style="color:black; align="center" |
| style="color:black; align="center" |
| style="color:black; align="center" |
| <center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span><br />Phrase searching prevents subject mapping
|  
|  
|-
| align="left" valign="top"|
'''PROQUEST databases:'''
*[http://resources.library.ubc.ca/811 LLBA]
| align="left" |<br /><center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span><br /></center><br /><br /><br /><br /><br />''Note:'' [[#ProQuest1|[vi]]]
| style="color:black; align="left" |<center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span></center><br /><br /><br /><br /><br />
| style="color:black; align="left" |
| style="color:black; align="left" |<br /><center><span style="font-size: xx-large; color:#0000FF;">'''?'''</span></center><br /><br /><br /><br /><br />''Note:'' [[#ProQuest2|[vii]]]
| <center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span><br /><br /><br /><br /><br /><br />
| align="left" | <center><span style="font-size: x-large; color:#0000FF;">'''NEAR/n'''</span><br/>or <br/><span style="font-size: x-large; color:#0000FF;">'''N/n'''</span><br /><br />(where "n" is a number)</center><br/>''Note:'' [[#ProQuest3|[viii]]]
| align="left" | <center><span style="font-size: x-large; color:#0000FF;">'''PRE/n'''</span><br/>or <br/><span style="font-size: x-large; color:#0000FF;">'''P/n'''</span><br /><br /></center><br /><br /><br />'Note:'' [[#ProQuest4|[ix]]]
|-
| align="left" |
'''EBSCOHOST databases:'''
*[http://resources.library.ubc.ca/196 Academic Search Premier]
*[http://resources.library.ubc.ca/92 Cinahl]
*[http://resources.library.ubc.ca/1042 Communication and Mass Media Complete]
*[http://resources.library.ubc.ca/576 Mental Measurement Yearbook (Buros]
*[http://resources.library.ubc.ca/159 PsycInfo]
*[http://resources.library.ubc.ca/118 SPORTDiscus]
| <center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span></center><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br />
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''#'''</span></center><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br />''Note:'' [[#EBSCOHOST1|[x]]]
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span></center><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br />
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''?'''</span></center><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br />''Note:'' [[#EBSCOHOST2|[xi]]]
| align="left" |<center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span></center><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br />
| align="left" | <center><span style="font-size: x-large; color:#0000FF;">'''N/n'''</span><br/>or <br/> <span style="font-size: x-large; color:#0000FF;">'''W/n'''</span><br /><br />(where "n" is a number)</center><br/><br /><br /><br /><br /><br /><br />''Note:'' [[#EBSCOHOST3|[xii]]]
|  
|-
| align="left" valign="top"|
'''Engineering Village'''<br />
*[http://resources.library.ubc.ca/715 Compendex]
| align="left" |<center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span><br />'''Automatic Stemming'''</center><br />''Note:'' [[#EngineeringV1|[xiii]]]
| style="color:black; align="left" |
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span><br /><br /><br /><br /><br />
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''?'''</span><br /><br /><br /><br /><br />
| align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span><br/>or <br/> <span style="font-size: xx-large; color:#0000FF;">'''{xxx}'''</span></center><br /><br />
| <center><span style="font-size: x-large; color:#0000FF;">'''NEAR/n'''</span><br/>or <br/> <span style="font-size:x-large; color:#0000FF;">'''ONEAR/n'''</span></center><br />''Note:'' [[#EngineeringV2|[xiv]]]
| align="left" | '''Proximity searching does not work with:''' truncation, wildcards, parentheses, quotation marks, or braces.
|- align="left" valign="top" |
|
'''Web of Science Core Collection'''<br />
*[http://resources.library.ubc.ca/277 Web of Science]
| align="left" |<center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span></center><br /><br /><br /><br />''Note:'' [[#WebOfKnowledge1|[xv]]]
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''$'''</span><br /></center><br /><br /><br /><br />''Note:'' [[#WebOfKnowledge2|[xvi]]]
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span><br /></center>
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''?'''</span><br /></center>
| <center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span><br /></center>
| <center><span style="font-size: x-large; color:#0000FF;">'''NEAR/n'''</span><br />(where "n" is a number)</center>
| When the word "near" appears in the title of a source item such as the title of a journal, book, or proceeding - enclose in parentheses (near)
|- align="left" valign="top" |
| [http://resources.library.ubc.ca/1529 '''PEDro''']
| '''Automatic stemming.'''<br /><br />Use truncation character '''*''' only at the beginning of a word<br /><br />''Note:'' [[#PEDro1|[xvii]]]
| style="color:black; align="left" |
| style="color:black; align="left" |
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''@'''</span><br /></center>
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span><br /></center>
| style="color:black; align="left" |  
| style="color:black; align="left" |
|- align="left" valign="top" |
| [http://resources.library.ubc.ca/219 '''CAB Direct''']
| style="color:black; align="center" |<center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span></center><br />or<br />'''Automatic Stemming'''<br /><br />Use quotation marks "XXX" to turn off Auto-stemming
| style="color:black; align="left" |
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span></center>
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''?'''</span></center>
| <center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span></center>
|  
|  
|- align="left" valign="top" |
| [http://search.library.ubc.ca/#catalogue '''UBC Library Catalogue''']
| style="color:black; align="left" |<center><span style="font-size: xx-large; color:#0000FF;">'''?'''</span></center>
| style="color:black; align="left" |
| style="color:black; align="left" |
| style="color:black; align="left" |
| style="color:black; align="left" |<center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span></center>
| style="color:black; align="left" | 
| style="color:black; align="left" | 
|- align="left" valign="top" |
| [http://search.library.ubc.ca/#general '''UBC Library - Summon Search''']
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span></center>
| style="color:black; align="left" |  
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''*'''</span></center>
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''?'''</span></center>
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span></center>
| style="color:black; align="left" |  
| style="color:black; align="left" | Wildcards cannot be used as the first character in a search
|- align="left" valign="top" |
| '''[http://resources.library.ubc.ca/943 Google/Google Scholar]'''
| style="color:black; align="left" |  
| style="color:black; align="left" |  
| style="color:black; align="left" |  
| style="color:black; align="left" |  
| style="color:black; align="left" | <center><span style="font-size: xx-large; color:#0000FF;">'''xxx'''</span></center>
| style="color:black; align="left" |  
| style="color:black; align="left" | <br />''Notes:'' [[#Google1|[xviii]]]
|}

===NOTES AND EXPLANATIONS===

<references/>
'''Ovid databases'''

<div id="Ovid1"></div>[[#Ovid1|[i]]] In OVID databases, you must turn off Subject mapping for truncation to work. You may add a number after your truncation symbol (i.e., *5) to restrict the search to a certain number of characters. For instance, the search: fish*4 would find fish, fishes, fishing, etc., but would not find longer results, such as “fish'''eries'''”.<br /><br />
<div id="Ovid2"></div>[[#Ovid2|[ii]]] In OVID databases, use the '''?''' wildcard symbol to find variations of spelling for the term. The '''?''' symbol is used inside or at the end of the search word. For instance colo?r would find all results for "color", and all results for "colour". And bird? Would find results for "bird" and "birds".<br /><br />
<div id="Ovid3"></div>[[#Ovid3|[iii]]] In OVID databases, use the '''#''' wildcard symbol to find variant spellings of a word. It can be placed inside or at the end of a search word. The '''#''' symbol must replace exactly one character (i.e., wom#n). So if you searched for: colo#r it would only find results for “colour”, but not color.<br /><br />
<div id="Ovid4"></div>[[#Ovid4|[iv]]] In OVID databases, you may broaden or narrow your search by using the proximity searching operator Adj. e.g. adj3 searches for terms within 3 words of each other.

'''PubMed'''

<div id="PubMed1"></div>[[#PubMed1|[v]]] In PubMed using the '''*''' truncation symbol will prevent subject mapping of your search term. It will find the first 600 possible results and then stop searching. It will also cause a keyword search to search as a phrase (i.e., the keyword search: fetus infection* will search for "fetus infection" as a phrase, instead of truncating the term "infection" on its own).

'''PROQUEST databases'''

<div id="ProQuest1"></div>[[#Proquest1|[vi]]] In PROQUEST databases, you may add a number after your truncation symbol (i.e., *5) to restrict the search to a certain number of characters. For instance, the search: fish*4 would find fish, fishes, fishing, etc., but would not find longer results, such as "fish'''eries'''".<br /><br />
<div id="ProQuest2"></div>[[#Proquest2|[vii]]] In PROQUEST databases, use the '''?''' wildcard symbol to replace any single character, either inside or at the end of the search word. Multiple wildcards can be used to represent multiple characters. For example: nurse? would find: nurses, nursed, but not nurse; sm?th would find: smith and smyth; ad??? would find: added, adult, adopt.<br /><br />
<div id="ProQuest3"></div>[[#Proquest3|[viii]]] In PROQUEST databases, use '''NEAR/n''' or '''N/n''' to look for documents that contain two search terms, in any order, within "n" specified number of words apart. Replace "n" with a number. For example: nursing NEAR/3 education; media N/3 women.<br /><br />
<div id="ProQuest4"></div>[[#Proquest4|[ix]]] Use '''PRE''' or '''P''' to search for a term within specified number of words before a second term. '''Eg:''' nursing PRE/4 education

'''EBSCOHOST databases'''

<div id="EBSCOHOST1"></div>[[#EBSCOHOST1|[x]]] Use '''#''' for alternate spellings. For example, colo#r finds – color/colour.<br /><br />
<div id="EBSCOHOST2"></div>[[#EBSCOHOST2|[xi]]] In EBSCOHOST databases, use the '''?''' wildcard to find an unknown character. For example, type '''ne?t''' to find all citations containing '''neat''', '''nest''' or '''next'''. EBSCOhost does not find '''net''' because the wildcard replaces a single character.<br /><br />
<div id="EBSCOHOST3"></div>[[#EBSCOHOST3|[xii]]] In EBSCOHOST databases The '''N5''' proximity search finds the words if they are within ''five words'' of one another regardless of the order in which they appear. The '''W5''' proximity search finds the words if they are within ''five words'' of one another and in the order in which you entered them.

'''Engineering Village'''

<div id="EngineeringV1"></div>[[#EngineeringV1|[xiii]]] In Engineering Village, Inspec database, search term stemming is automatic. Using truncation or wildcard characters automatically turns the “auto-truncation” off. You may also turn it off by selecting “auto-stemming>off”. To turn truncation off when not using a truncation or wild card character, put search terms in between quotation marks or braces {} – note that truncation characters and wildcards will not work within quotation marks or braces. You may use the * truncation character at the beginning or end of a search term. In addition, if you precede a word with the stemming character '''$''', you can pick up all forms of the word.<br />'''Ex:''' $management , picks up manager, managed, etc. <br /><br />
<div id="EngineeringV2"></div>[[#EngineeringV2|[xiv]]] '''ONEAR''' finds words in proximity to one another, in '''exact order''' entered.

'''Web of Knowledge'''

<div id="WebOfKnowledge1"></div>[[#WebOfKnowledge1|[xv]]] In Web of Knowledge databases you can use truncation in Title or Topic searches. You must enter at least three characters before and after a wildcard when using either left-hand truncation or right-hand truncation.<br /><br />
<div id="WebOfKnowledge1"></div>[[#WebOfKnowledge1|[xvi]]] The asterisk (*) represents any group of characters, including no character.The question mark (?) represents any single character.The dollar sign ($) represents zero or one character.

'''PEDro'''

<div id="PEDro1"></div>[[#PEDro1|[xvii]]]
*In PEDro, truncation at the end of a word is automatic.<br /><br />
*If you would like to truncate the beginning of the word - for example, if you wanted to find papers on edema, oedema, lymphedema or lymphoedema, type *edema in a text field. Note, that if you put an asterisk * at the beginning of a word, PEDro will not also find variants at the end of the word (that is, PEDro can’t simultaneously search for variants at the beginning and end of the same word). <br /><br />

'''Google/Google Scholar'''

<div id="Google1"></div>[[#Google1|[xviii]]]
In Google, there are some additional wild cards you can use:
# Search for specific types of files, such as PDFs, PPTs, or XLS, by adding '''filetype:''' and the 3-letter file abbreviation.
# Add a tilde sign ('''~''') immediately in front of a word to search for that word as well as even more synonyms.
# Use the '''related:''' operator to find pages that have similar content by typing '''related:''' followed by the website address. For instance, if you find a website you like, try using '''related:[insert URL]''' to locate similar websites.
# If you want to search for pages that may have just one of several words, include '''OR''' (capitalized) between the words. Without the OR, your results would typically show only pages that match both terms. You can also use the '''|''' symbol between words for the same effect.
# Include '''site:''' to search for information within a single website like all mentions of "Olympics" on the New York Times website. [ Olympics site:nytimes.com ]<br />'''Tip:''' Also search within a specific top-level domain like .org or .edu or country top-level domain like .de or .jp.
# Add a dash ('''-''') before a word to exclude all results that include that word. This is especially useful for synonyms like Jaguar the car brand and jaguar the animal.[ jaguar speed -car ].

<span style="color:#0000FF;">'''Information in this guide is based on database and search engine help pages.'''</span>

[[File:Library_Truncation_and_wildcards_handout_2013.pdf‎ ]]

----
Credits: <ref>Beck, Charlotte and Longley, Kate, UBC Library. 2012 [online]. Truncation and Wildcard Symbols. Available from http://wiki.ubc.ca/Library:Truncation_and_Wildcard_Symbols [accessed on 4 September 2012].
This work '''Truncation and Wildcard Symbols''', by Charlotte Beck and Kate Longley, identified by [http://www.library.ubc.ca UBC Library], is free of known copyright restrictions.
<br /> <br /> [[File:CreativeCommonLogo.jpg|88px]]
<br /> [http://creativecommons.org/publicdomain/mark/1.0/ Creative commons license]</ref>

<references/>

[[Category:Woodward Library]]
[[Category:Library Tutorials]]
[[Category:Woodward Library]]
[[Category:Charlotte Beck]]