FAIR and CARE principles

The HRL Science Program commits to creating data assets that are open, reusable, and respectful of Tribal sovereignty and community priorities. The FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective Benefit, Authority to Control, Responsibility, Ethics) frameworks serve as the shared language for those commitments. This page translates the FAIR and CARE frameworks into practical expectations that Data Producers, the Central Data Team, and Synthesis Teams can apply across every phase of the HRL Data Lifecycle.

FAIR and CARE at a glance

FAIR

The FAIR Guiding Principles for scientific data management and stewardship were published in 2016 to improve the ability of both humans and machines to find, access, interoperate, and reuse scientific data.

Principle	Definition	HRL expectation
Findable	Data and metadata carry persistent identifiers and can be discovered through catalogs and search.	Register DOIs and expose them through the HRL data catalog and common repositories. Provide structured inventories and keyword-rich, semantically-meaningful metadata (e.g., spatial/temporal coverage, parameters, and data dictionaries) so catalogs, APIs, and portals harvest records automatically.
Accessible	Users can retrieve data via standard protocols with transparent documentation and notice of any limits.	Host datasets on repositories with stable API endpoints and open download options that serve users with varied levels of technical sophistication. Where relevant, document authentication steps and contact points and maintain access notes, embargoes with renewal dates, and Tribal review requirements. Update metadata when access pathways or restrictions change so downstream users and catalogs stay accurate.
Interoperable	Data use shared languages, vocabularies, and formats to integrate with other systems.	Publish tidy data using HRL-approved schemas, controlled vocabularies, and consistent spatial references. Distribute data dictionaries, JSON schemas, and frictionless packages so ingestion workflows can validate inputs. Encode units, qualifiers, and missing-value conventions in machine-readable formats aligned with HRL standards to avoid ambiguity.
Reusable	Data include clear provenance, licensing, and quality statements so others can reuse them responsibly.	Bundle QA/QC logs, provenance code, and uncertainty and use notes with each data publication package and synthesis release. Specify licenses and citation instructions so downstream users understand reuse terms. Record provenance (raw inputs, code versions, DOIs) and a contact for questions or clarifications.

CARE

The CARE Principles for Indigenous Data Governance were published in 2020 to ensure that scientific data practices respect Indigenous sovereignty and promote equitable benefits for Indigenous Peoples and local communities. The CARE Principles are motivated by the recognition that data about Indigenous Peoples and collected by them are frequently suppressed, stolen, misused, and mishandled in ways that perpetuate harm and extractive relationships.

Principle	Definition	HRL expectation
Collective Benefit	Data activities should generate measurable value for Indigenous Peoples and affected communities.	Co-design monitoring objectives and document expected Tribal and community outcomes in metadata and other research materials. Describe feedback loops (e.g., shared interpretation, co-developed indicators) before distributing data.
Authority to Control	Indigenous Peoples retain rights to govern the creation, stewardship, dissemination, and use of data about them and/or collected by them.	Capture formal data sharing and management agreements and archive them with publication packages. Apply sovereignty flags at collection and downstream processing steps so that storage, catalog, and reporting workflows enforce access controls.
Responsibility	Those working with Indigenous data have a responsibility to transparently uphold Indigenous Peoples’ self-determination and collective benefit.	Assign contacts responsible for stewardship, data sharing and management agreement renewal, and incident response. Log training requirements and provide channels for Tribes and communities to audit usage or request updates. Co-create mechanisms for determining and tracking Tribal benefits and programmatic adherence to CARE principles.
Ethics	Data practices must minimize harm, maximize Tribal benefits, and respect cultural protocols and context.	Require co-developed Tribal or community review checkpoints before publication or synthesis, in alignment with data sharing and management agreements. Adopt respectful language in metadata, dashboards, and reports and archive approvals with the dataset record.

Importance of FAIR and CARE

• FAIR ensures scientific utility. Metadata rigor, persistent identifiers, and open formats allow HRL data to be discovered, cited, and reused across agencies and the broader research community.
• CARE ensures ethical science and reciprocity. Sensitive observations, Indigenous Knowledge, and Tribal and community agreements are honored in metadata, management, access controls, and communications so that benefits and decision-making authority remain with source communities in order to redress harmful histories and counteract extractive tendencies that persist in public agencies and Western science.
• Both frameworks travel together. Collection protocols, static publication packages, ingestion pipelines, and synthesis reports must document how FAIR and CARE requirements were met and where exceptions were negotiated.

Implementation references

Use this table as a quick crosswalk for what FAIR and CARE look like at each point in the HRL data lifecycle and which artifacts demonstrate that the requirements were addressed. Program-level resources for HRL parties to develop overarching knowledge, capacity, and practices for upholding FAIR and CARE commitments will be developed by the HRL Science Committee and the Central Data Team.

Lifecycle stage	FAIR emphasis	CARE emphasis	Key artifacts
Collection	Field metadata Instrument calibration logs Quality assurance	Consent terms Sensitivity flags Contact points	Protocols Field forms Agreements
Static Publication	DOI registration Tidy data packages Complete metadata	Embargo tracking Access notes Benefit statements	Metadata XML/EML/JSON CARE publication checklist License text
Ingestion and Standardization	Schema validation tests Controlled vocabularies Provenance scripts	Propagation of restriction flags Audit logs for access requests	Validation pipelines Configuration files
Storage and Serving	Access/API documentation Data dictionaries Monitoring of uptime/download metrics	Role-based access controls User agreements	Catalog entries Access logs
Analysis and Synthesis	Workflow documentation Reproducible notebooks Citation of inputs	Required attribution of Tribal contributions Review gates before release	Scientific reports Release notes
Reporting and Communication	Public-friendly metadata Links from reporting products to data products	Co-developed messaging Acknowledgement statements	Fact sheets Presentation decks Visualization tools

Resources

• HRL Science Committee Charter Commitments – Overarching governance statements that FAIR and CARE workflows implement.
• Data Governance and Roles – Descriptions of Data Producers, Central Data Team, and Synthesis Teams.
• Metadata Standards – Templates and vocabularies for recording FAIR and CARE metadata fields.