Operationalizing the CARE and FAIR Principles for Indigenous data futures

This discussion emerged from two joint, virtual conference sessions that integrated parallel processes at the Research Data Alliance (RDA): “Operationalising Be FAIR and CARE” ( https://www.rd-alliance.org/operationalising-be-fair-and-care ) and “Implementing the CARE Principles: The CARE-full Process” ( https://www.rd-alliance.org/implementing-care-principles-care-full-process ). The FAIR Data Maturity Model Working Group, hosted by the Research Data Alliance, was endorsed for activity in September 2018 with the primary objective to develop a core set of criteria that would be used across all the existing implementation methods for FAIR Data Principles 1 (see https://www.rd-alliance.org/group/fair-data-maturity-model-wg/case-statement/fair-data-maturity-model-wg-case-statement ). With this common set of criteria, outcomes of different assessment methods could be compared and provide a better understanding of the true status across the research community as we move towards meeting the criteria defined. Following a year-long public discussion and building on Indigenous Data Sovereignty network activities, the Global Indigenous Data Alliance released the CARE Principles for Indigenous Data Governance in September 2019 2 . After the launch of the CARE Principles, the RDA International Indigenous Data Sovereignty Interest Group, which produced the CARE Principles, and the RDA FAIR Data Maturity Model Working Group joined together to begin to explore how to operationalize FAIR with CARE. This manuscript 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.