Methods

Open data and materials

Open research resources make the evidence and procedures behind a claim inspectable and reusable when sharing is appropriate. Useful transparency includes documentation, analysis code, and materials, with justified restrictions where participant protection or rights require them.

Could another researcher understand what was done, rerun the analysis, or plan a new test from the resources provided?

How it works

  1. Prepare a coherent package rather than an unexplained upload. For a teaching exercise, include a variable dictionary, task instructions, scoring decisions, scripts, software information, and a readme connecting each file to the reported question and result.
  2. Use stable identifiers, meaningful metadata, clear licensing, and provenance. FAIR principles address findability, accessibility, interoperability, and reuse; their access provisions allow authentication and authorization where necessary. Being FAIR and being unrestricted are different properties.
  3. Check consent conditions, participant risks, intellectual-property permissions, and repository requirements before release. When the underlying records cannot be public, explain the restriction and share permissible materials, metadata, code, or carefully labeled synthetic demonstration data instead.

Evidence: (The FAIR Guiding Principles, n.d.); (Reproducibility and Replicability in Science: Summary, n.d.); (Protecting Privacy When Sharing Human Research Participant Data, n.d.)

A reusable teaching package

Teaching scenario

Synthetic teaching scenario: A team uploads a file named final-final.csv without explaining its columns. Another student cannot tell whether a value of 99 is a score or missing data. In revision, the team supplies a dictionary, a cleaning script, the questionnaire, and a readme showing the order of analysis. It uses fabricated records for a public tutorial and explicitly labels them synthetic. The restricted participant dataset has a separate access route. Students assess whether the package supports learning and inspection without confusing demonstration records with evidence from actual participants.

This example is constructed for learning. It does not report an observed research result.

What can you conclude?

It enables readers to inspect decisions, identify errors, reproduce computations, and adapt materials for a documented new study.

Evidence: (The FAIR Guiding Principles, n.d.); (Reproducibility and Replicability in Science: Summary, n.d.); (Protecting Privacy When Sharing Human Research Participant Data, n.d.)

Limits and common mistakes

  • Availability does not guarantee measurement quality, correct analyses, complete reporting, or reproducible execution on another computer.
  • Public release may be inappropriate even after removing direct identifiers; consent, contextual risks, law, and rights still matter.

A mistake to catch

Equating an accessible folder with reusable evidence while omitting the documentation needed to interpret its files.

Evidence: (The FAIR Guiding Principles, n.d.); (Reproducibility and Replicability in Science: Summary, n.d.); (Protecting Privacy When Sharing Human Research Participant Data, n.d.)

Sources and inspection notes

Links checked October 21, 2026. Inspection scope is stated for each source; an access date is not a publication date.

  1. Protecting privacy when sharing human research participant data. (n.d.). https://www.grants.nih.gov/grants/guide/notice-files/NOT-OD-22-213.html

    US NIH supplemental privacy guidance, NOT-OD-22-213 Nonbinding principles and best practices associated with the NIH data-sharing policy; not a global compliance manual. Checked 21 October 2026.

  2. Reproducibility and replicability in science: Summary. (n.d.). https://www.nationalacademies.org/read/25303/chapter/3

    National Academies consensus report, 2019 Definitions used here distinguish new-data replication from same-data computational reproduction; terminology varies by field. Checked 21 October 2026.

  3. The FAIR guiding principles. (n.d.). https://www.gofair.foundation/fair-principles

    GO FAIR Foundation stewardship principles Read identifiers, metadata, access authorization, provenance, and licensing; FAIR does not require unrestricted data access. Checked 21 October 2026.

Keep reading

Spielman, R. M., Jenkins, W. J., & Lovett, M. D. (2020). Psychology 2e. OpenStax. https://openstax.org/books/psychology-2e/pages/2-1-why-is-research-important

Nearest related OpenStax Psychology 2e reading. This is a course connection, not evidence for every specialist detail above.

Specialist reading: The FAIR guiding principles. (n.d.). https://www.gofair.foundation/fair-principles

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