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Research Data Management

Numbers and more

"Many people think of data-driven research as something that (only) happens in the sciences....(resulting in) a spreadsheet filled with numbers. Both of these beliefs are incorrect. Research data are collected and used in scholarship across all academic disciplines and, while it can consist of numbers in a spreadsheet, it also takes many different formats..."(Macalester College Library. Defining Research Data).

Examples of Research Data


As noted above, researchers in every discipline can generate research data and those data are not limited to numbers in a spreadsheet.  Depending on the context any of the following formats may be examples of research data:

  • videos
  • data extraction forms
  • precise search strategies employed (e.g., during scoping or systematic reviews)
  • images
  • artifacts
  • diaries
  • audio recordings
  • text files
  • finding aids
  • poetry, sketchbooks and other artistic outputs
  • prototypes
  • specimens
  • algorithms & scripts
  • archival metadata
  • interview notes and more.  

FAIR Principles & Indigenous Data Governance

FAIR Principles


In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data.  Since then the notion that publicly funded research needs to be FAIR has been widely embraced and adopted by the Academy.  FAIR data are:

  • Findable: e.g., your data should be easy for others to find - e.g., by means of a permalink,  DOI or similar persistent/permanent identifier.
  • Accessible: e.g., it should be straightforward for others to access your data -   Authentication protocols can and should be established where needed.
    • NOTE: The GoFAIR Initiative acknowledges that "there may be legitimate reasons to shield data and services generated with public funding from public access. These include personal privacy, national security, and competitiveness" (What is the difference between "FAIR Data" and "Open Data?").
    • "Accessible" chiefly refers to ensuring that it's clear by what means the data may be accessed - and if not available for access - why not. Data are considered FAIR even if registration and authentication are required for access.
  • Interoperable: e.g., your data should be able to "interoperate with applications or workflows for analysis, storage, and processing." 
    • A major key to interoperability is using metadata - the information fields that describe your various data categories.

  • Re-usable: e.g., "metadata and data should be well-described so that they can be replicated and/or combined in different settings" (Go FAIR. FAIR Principles).


For more information about the FAIR Principles see GO FAIR: How to Go FAIR


Indigenous Data Governance

"Existing principles within the open data movement (e.g. FAIR: findable, accessible, interoperable, reusable) primarily focus on characteristics of data that will facilitate increased data sharing among entities while ignoring power differentials and historical contexts. The emphasis on greater data sharing alone creates a tension for Indigenous Peoples who are also asserting greater control over the application and use of Indigenous data and Indigenous Knowledge for collective benefit" (Global Indigenous Data Alliance. CARE Principles for Indigenous Data Governance).


The CARE Principles for Indigenous Data Governance are designed to complement the FAIR principles and take into account the current and historic power imbalances between researchers and Indigenous communities.


To learn more about the CARE Principles, and other important guidance for those engaging in research for, by, about, and/or with Indigenous individuals, communities, territories or Nations see the Indigenous Data page of this guide.

Primary vs. Secondary Data [2:11]