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Research Data Management: What and Why

What is Research Data Management?

Research Data Management (RDM) is the intentional process, throughout the entire lifecycle of a research project, of keeping data organized. This extends through:

  • planning the method(s) of data collection
  • to collecting and describing the data in a transparent structure
  • to ensuring its secure storage
  • to preparing it for analysis
  • to recording the decisions you make in analyzing the dataset
  • to sharing both the findings and the data itself
  • and then ensuring the data's long term storage upon completion of the research project.

Why is Research Data Management Important?

  Proper data management will help you:

  • ensure that your data is complete, documented, and accessible to you and to future researchers
  • satisfy grant, journal, or research ethics board requirements. For example the Tri-Agency has signaled they are getting more serious about enforcing the adherence to data management plans
  • raise the profile of your research
  • meet the data sharing expectations of your research community

While some data may be too sensitive to share, or sharing it may contravene ethics approvals, following proper data management practices can ensure you are methodical as you gather and manage your data. It can also either facilitate the sharing of those elements of your datasets which are not sensitive, or allow you to share fuller datasets in tightly controlled and mediated ways.

What Counts as Research Data?

Research Data are the recorded observations created, collected or observed in the process of conducting an original research project.  Research data can be either qualitative or quantitative in nature and may come in a wide range of formats, among them:

  • Qualitative Data (text; e.g. TXT, DOC, PDF, RTF, HTML, XML)
  • Quantitative Data (numerical data; e.g. CSV, MAT, XLS, SPSS)
  • Spatial Data (e.g. raster, vector, grid)
  • Multimedia – (e.g. JPEG, TIFF, MPEG, MP3, Quicktime, Bitmap)
  • Models – (e.g. 3D, statistical, similitude, macroeconomic, causal)
  • Software – (e.g. Scripts written in Java, C, Perl, Python, Ruby, PHP, R, GIS)
  • Instrument specific – (e.g. field surveying equipment)
  • Others (all other digital or non digital content have the potential to become Research Data)

Borrowed from the Portage Primer

A 4 1/2 minute cautionary Tale

Data Management Dictionary

Confused by some of the language around research data? You are not alone. This glossary will shed some light on the terminology around research data management.

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Will Meredith
Royal Roads University
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