This glossary was created to introduce essential terminology associated with Research Data Management (RDM) to the RRC Polytech community and to help aid in the recall of terms you may have previously been introduced to.
In addition to this basic glossary of terms, we recommend the two following comprehensive glossaries of RDM terms (links open in new window):
Links open in new window.
Research data are the evidence that underpins the answer to the research question, and can be used to validate findings regardless of its form (e.g. print, digital, or physical). These might be quantitative information or qualitative statements collected by researchers in the course of their work by experimentation, observation, modelling, interview or other methods, or information derived from existing evidence. Data may be raw or primary (e.g. direct from measurement or collection) or derived from primary data for subsequent analysis or interpretation (e.g. cleaned up or as an extract from a larger dataset), or derived from existing sources where the rights may be held by others. Data may be defined as ‘relational’ or ‘functional’ components of research, thus signaling that their identification and value lies in whether and how researchers use them as evidence for claims.
The primary purpose of research data is to provide the information necessary to support or validate a research project’s observations, findings or outputs.
Source: UK Research and Innovation. (2020). Concordat on Open Research Data. https://www.ukri.org/wp-content/uploads/2020/10/UKRI-020920-ConcordatonOpenResearchData.pdf
The concept of data sovereignty ... is linked with Indigenous Peoples’ right to maintain, control, protect and develop their cultural heritage, traditional knowledge and traditional cultural expressions, as well as their right to maintain, control, protect and develop their intellectual property over these.
Source: Kukutai, T. & Taylor, J. (2016). Indigenous data sovereignty: Towards an agenda. Australian National University Press.
DMPs are living documents that can be modified to accommodate changes throughout the course of a research project. The content and length of DMPs depend on the research project, but all DMPs should describe:
DMPs also indicate who is responsible for managing the project’s data, describe the succession plans in place should that person leave the research team, and identify the data-related roles and responsibilities of other team members where appropriate. Finally, DMPs outline ethical, legal and commercial constraints the data are subject to, and methodological considerations that support or preclude data sharing.
Source: Government of Canada. (2021). Tri-Agency Research Data Management Policy. Innovation, Science and Economic Development Canada. https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html
Research Data Management (RDM) is one of the four key elements of Canada’s digital research infrastructure (DRI). It encompasses the processes applied throughout the lifecycle of a research project to guide the collection, documentation, storage, sharing, and preservation of research data, and allows researchers to find and access data.
Source: Digital Research Alliance of Canada. (2022). Research Data Management. https://alliancecan.ca/en/services/research-data-management
