Library:Research Data

From UBC Wiki

Research Data are the building blocks upon which knowledge is based. They can relate to various disciplines and be of myriad types, but the major categories are Observational, Computational, and Experimental. They are an important outcome of research and the importance of managing the data that are produced is becoming increasingly recognized.

This awareness of the significance of research data is reflected in the fact that some granting institutions, such as NSF, now require the applicant to submit a Data Management Plan (DMP) as part of their proposal. In Canada, the federal research granting agencies—the Social Sciences and Humanities Research Council (SSHRC), the Natural Sciences and Engineering Research Council (NSERC), the Canadian Institutes of Health Research (CIHR), as well as the Canada Foundation for Innovation (CFI), and in collaboration with Genome Canada—have joined forces to help address digital infrastructure challenges through the consultation document Toward a Policy Framework for Advancing Digital Scholarship in Canada.

Big Data and Data Sharing

Nowadays we are embracing more (and sometimes messier) data, which is having unprecedented implications on all fields. The success of a research project is measured increasingly by the data that is made available to the wider research community. This data makes the research verifiable and more visible, but can also contribute to other research projects. It can even be included in big science, which generally refers to large-scale scientific projects that are made possible through advancements in scientific processes and data sharing. By making use of datasets that have been created for other research projects, scientists can expand the scope of their research without having to go back and gather all of the smaller-scale data again.

The UK Data Archive outlines the value of sharing research data after a research project is over:

  • Encourages scientific enquiry and debate
  • Promotes innovation and potential new data uses
  • Leads to new collaborations between data users and data creators
  • Maximises transparency and accountability
  • Enables scrutiny of research findings
  • Encourages the improvement and validation of research methods
  • Reduces the cost of duplicating data collection
  • Increases the impact and visibility of research
  • Promotes the research that created the data and its outcomes
  • Can provide a direct credit to the researcher as a research output in its own right

This page last updated: 2014-03-12

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