Data may be in any format or medium taking the form of text, numbers, symbols, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, workflows, equipment descriptions, data files, data processing algorithms, software, programming languages, code, or statistical records. Data are facts, measurements, recordings, records, or observations collected by researchers and others, with a minimum of contextual interpretation.

What is considered relevant research data is often highly contextual and determining what counts as such should be guided by disciplinary norms. Research data may be experimental data, observational data, operational data, third party data, public sector data, monitoring data, processed data, or repurposed data. Research data are used as primary sources to support research, scholarship, or creative practice and are used as evidence in the research process and/or are commonly accepted in the research community as necessary to validate research findings and results.

RDM is essential throughout the data lifecycle-from data creation, processing, analysis, preservation, storage and access, to sharing and reuse (where appropriate), at which point the cycle begins again. Data management should be practiced over the entire lifecycle of the data, including planning the investigation, conducting the research, backing up data as it is created and used, disseminating data and preserving data for the long term after the research investigation has concluded.

A ‘data management plan’ (DMP) is “a living document, typically associated with an individual research project or program that consists of the practices, processes and strategies that pertain to a set of specified topics related to data management and curation.

DMPs should be modified throughout the course of a research project to reflect changes in project design, methods, or other considerations. DMPs guide researchers in articulating their plans for managing data; they do not necessarily compel researchers to manage data differently” (Tri-Agency Research Data Management Policy, Frequently Asked Questions, Government of Canada 2021).

Data deposit refers to when the research data collected as part of a research project are transferred to a research data repository.

The repository should have easily accessible policies describing deposit and user licenses, access control, preservation procedures, storage and backup practices and sustainability and succession plans. The deposit of research data into appropriate repositories supports ongoing data retention and, where appropriate, access to the data.

Ideally, data deposits will include accompanying documentation, source code, software, metadata and any supplementary materials that provide additional information about the data, including the context in which it was collected and used to inform the research project. This additional information facilitates the curation, discoverability, accessibility and reuse of the data.