LibGuides: Research Data and Reproducibility: Quality and Ethics Frameworks

FAIR Data

The FAIR Principles were introduced in 2016 to improve how research data is managed and shared. FAIR stands for Findable, Accessible, Interoperable, and Reusable. These principles help data be easily discovered and reused—not only by people, but also by computers, which is increasingly important as data becomes larger and more complex.

Making data more FAIR can increase the visibility, impact, and reliability of research. It also supports reproducibility and helps researchers meet funder requirements, such as those in the NIH data management and sharing policy.

Use the links below to learn more about FAIR and how to put it into practice.

How to GO FAIR
A three-point framework providing practical how-to guidance for those seeking to make their data FAIR.
FAIRsharing
FAIRsharing is a community-driven resource with users and collaborators across all disciplines. They work together with our stakeholders to enable the FAIR Principles by promoting the value and the use of standards, databases and policies.
FAIR self-assessment tool
This tool from the Australian Research Data Commons helps assess the 'FAIRness' of a dataset and determine how to enhance it, where applicable.

Data Sharing and HIPAA

Research data that contain Protected Health Information (PHI) must be de-identified before sharing. Guidance on the HIPAA Privacy Rule in Research is linked below. More resources providing practical guidance for de-identifying data can be found in the Sharing Data section of this guide.

Guidance on the HIPAA Privacy Rule in Research
Details the HIPAA Privacy Rule, which outlines the circumstances under which covered entities may use or disclose protected health information for research.
Guidance Regarding Methods for De-identification of Protected Health Information in Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule

CARE Principles for Indigenous Data Governance

Open data and open science efforts often overlook the rights and interests of Indigenous Peoples. Principles that focus on making data easier to share don’t address important issues like power imbalances, historical context, or the need for Indigenous communities to control how their data and knowledge are used.

Indigenous Peoples are calling for greater control over their data based on Indigenous worldviews and for the benefit of their communities. The CARE Principles for Indigenous Data Governance were developed to support this. Unlike FAIR, which focuses on data, CARE focuses on people and purpose. These principles promote Indigenous self-determination and responsible data use.

CARE stands for:

Collective Benefit
Authority to Control
Responsibility
Ethics

These principles work alongside FAIR to ensure that open data practices respect Indigenous rights and values.

Visit the links below for more detailed information about the CARE Principles.

The CARE Principles for Indigenous Data Governance
This article in Data Science Journal is the first formal publication of the CARE Principles. It articulates their rationale, describes their relation to the FAIR Principles, and presents examples of their application.
Working with the CARE principles: operationalising Indigenous data governance
This post at the blog of the Ada Lovelace Institute discusses how the CARE Principles were developed.
Operationalizing the CARE and FAIR Principles for Indigenous data futures
This article in the journal Scientifc Data briefly introduces the CARE Principles and describes CARE in the context of scientific data, FAIR in the context of Indigenous data, how FAIR and CARE intersect, and implications and next steps for actionable change toward operationalizing FAIR with CARE.