Appendix A: Definitions

Data (Tri-Agency Data Governance)

Facts, measurements, recordings, records, or observations about the world collected by researchers and others, with a minimum of contextual interpretation. Data may be any format or medium taking the form of writings, notes, numbers, symbols, text, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, work flow charts, equipment descriptions, data files, data processing algorithms, or statistical records. 1


Data (Indigenous Data Governance)

“We consider Indigenous data to be “any past, present and future facts, knowledges, or information about an Indigenous nation and its citizens, lands, resources, cultures, and communities. This can include information about populations such as community profiles or graduation rates, land use management, maps of sacred lands and place names, songs, cultural teachings, digital media, and social media activities,” (Rainie et al. 2017b, p. 1) among others.

The definition encompasses both collective and individual level data. It also highlights that the concrete boundaries between data, information, and knowledge as defined in Western contexts are more fluid in an Indigenous context; Indigenous data extend far beyond bits and bytes (De Beer 2016) and have implications for the governance of both data born digital and that which emerges from knowledge, language, and information.” 2


Data Lifecycle

Refers to all the stages in the existence of digital information from creation to destruction. A lifecycle view is used to enable active management of the data objects and resources over time, thus maintaining accessibility and usability. 3


Data Stewardship

“The process of Data Stewardship involves ensuring effective control and use of data assets and can include creating and managing metadata, applying standards, managing data quality and integrity, and additional data governance activities related to data curation. It also may include creating educational materials, policies, and guidelines around data at an institution.” 4


Research Data Management (RDM)

RDM refers to the processes applied throughout the lifecycle of a research project to guide the collection, documentation, storage, sharing, and preservation of research data. 5



An undertaking intended to extend knowledge through a disciplined inquiry or systematic investigation. 6



Anyone who conducts research. 6


Data classification

Data classification, in the context of information security, is the classification of data based on its level of sensitivity and the impact on the University should that data be disclosed, altered, or destroyed without authorization. Data classification helps determine what baseline security controls are appropriate for safeguarding that data. 7 



  1. Government of Canada. 2021. “Frequently Asked Questions Tri-Agency Research Data Management Policy.” Retrieved February 22, 2022 (
  2. Carroll, Stephanie Russo, Desi Rodriguez-Lonebear, and Andrew Martinez. 2019. “Indigenous Data Governance: Strategies from United States Native Nations.” Data Science Journal, 18, 31.
  3. Committee on Data International Science Council. (n.d.) “Data lifecycle.” Retrieved December 20, 2022 (
  4. National Library of Medicine. (n.d.) “Data Stewardship.” Network of the National Library of Medicine. Retrieved June 30, 2022 (
  5. Digital Research Alliance of Canada. 2022. “Research Data Management.” Retrieved December 20, 2022 (
  6. Government of Canada. 2022. “Tri-Agency Framework: Responsible Conduct of Research (2021).” Retrieved December 20, 2022 (
  7. Carnegie Melon University Office of the CIO. “Guidelines for Data Classification.” Retrieved January 24, 2023 (