Research Data Management

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

• planning your data collection method(s)
• to clearly and completely documenting how you collect, label, clean, and prepare your data for analysis
• to ensuring secure data storage during the research project
• to recording your decisions made during analysis
• to sharing both the findings and the data itself (as appropriate, depending on the project)
• to ensuring the data's long term preservation upon completion of the research project

  Proper data helps:

• ensure your data is complete, documented, and accessible to you and/or to future researchers
• satisfy grant, journal, or funder requirements.
• raise the profile of your research
• meet the data sharing expectations of your research community

Following proper data management practices can ensure you are methodical as you gather and manage your 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

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.