LibGuides: Research Data and Reproducibility: Quality and Ethics Frameworks

FAIR Data

The FAIR Guiding Principles for scientific data management and stewardship were first published in 2016. They are intended to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The FAIR principles emphasize machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.

Making research data more FAIR can provide a range of benefits to researchers, research communities, research infrastructure facilities, and research organizations, such as increasing research impact, visibility, citations, reproducibility, and reliability. The Principles are also referenced as a quality and stewardship standard within many data management and sharing policies, including the NIH data management and sharing policy.

Visit the links below to learn more about the FAIR Principles and how to implement them.

FAIR Principles at GO FAIR
How to GO FAIR
A three-point framework providing practical how-to guidance for those seeking to make their data FAIR.
The FAIR Guiding Principles for scientific data management and stewardship
The FAIR Principles were first defined in this paper published in the journal Scientific Data.
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.
OpenAIRE Guides for Researchers: How to make your data FAIR
This guide provides an in-depth treatment to making data FAIR.

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

The current movement toward open data and open science does not fully engage with Indigenous Peoples' rights and interests. Existing principles within the open data movement (e.g., FAIR: findable, accessible, interoperable, reusable) primarily focus on data characteristics that will facilitate increased data sharing among entities while ignoring power differentials and historical contexts. The emphasis on greater data sharing alone creates tension for Indigenous Peoples who are also asserting greater control over the application and use of Indigenous data and Indigenous Knowledge for collective benefit.

This includes the right to create value from Indigenous data in ways that are grounded in Indigenous worldviews and realize opportunities within the knowledge economy. The CARE Principles for Indigenous Data Governance are people and purpose-oriented, reflecting the crucial role of data in advancing Indigenous innovation and self-determination. These principles complement the existing FAIR principles encouraging open and other data movements to consider both people and purpose in their advocacy and pursuits.

CARE stands for:

Collective Benefit
Authority to Control
Responsibility
Ethics

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

CARE Principles for Indigenous Data Governance - Summary
CARE Principles for Indigenous Data Governance - Full
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.