The FAIR Wilkinson et al. (2016) and/or CARE Carroll et al. (2020) principles are frequently mandated in various contexts around the world, inclduing for outputs of the Australian Research Data Commons (ARDC).

These principles are not a DIY manual that can be simply adopted by researchers - to implement a system that supports CARE and FAIR research requires development of infrastructure and a governance frameworks. The RRKive principles and website are aimed at those undertaking these substantials tasks.

This document contains a copy of each set of principles with notes about how PILARS is designed to help Implement them.

CARE and FAIR principles (Annotated for PILARS) by Sefton et al is licensed under CC BY 4.0

Care Principles

CARE-C: Collective Benefit

Data ecosystems shall be designed and function in ways that enable Indigenous Peoples to derive benefit from the data.

  • CARE-C1: For inclusive development and innovation

Governments and institutions must actively support the use and reuse of data by Indigenous nations and communities by facilitating the establishment of the foundations for Indigenous innovation, value generation, and the promotion of local self-determined development processes.

  • CARE-C2: For improved governance and citizen engagement

    Data enrich the planning, implementation, and evaluation processes that support the service and policy needs of Indigenous communities. Data also enable better engagement between citizens, institutions, and governments to improve decision-making.

    Ethical use of open data has the capacity to improve transparency and decision-making by providing Indigenous nations and communities with a better understanding of their peoples, territories, and resources. It similarly can provide greater insight into third-party policies and programs affecting Indigenous Peoples.

  • CARE-C3: For equitable outcomes

    Indigenous data are grounded in community values, which extend to society at large. Any value created from Indigenous data should benefit Indigenous communities in an equitable manner and contribute to Indigenous aspirations for wellbeing.

CARE-A: Authority to Control

Indigenous Peoples’ rights and interests in Indigenous data must be recognised and their authority to control such data be empowered. Indigenous data governance enables Indigenous Peoples and governing bodies to determine how Indigenous Peoples, as well as Indigenous lands, territories, resources, knowledges and geographical indicators, are represented and identified within data.

  • CARE-A1: Recognizing rights and interests

Indigenous Peoples have rights and interests in both Indigenous Knowledge and Indigenous data. Indigenous Peoples have collective and individual rights to free, prior, and informed consent in the collection and use of such data, including the development of data policies and protocols for collection.

  • CARE-A2: Data for governance Indigenous

Peoples have the right to data that are relevant to their world views and empower self-determination and effective self-governance. Indigenous data must be made available and accessible to Indigenous nations and communities in order to support Indigenous governance.

  • CARE-A3: Governance of data

Indigenous Peoples have the right to develop cultural governance protocols for Indigenous data and be active leaders in the stewardship of, and access to, Indigenous data especially in the context of Indigenous Knowledge.

CARE-R: Responsibility

Those working with Indigenous data have a responsibility to share how those data are used to support Indigenous Peoples’ selfdetermination and collective benefit. Accountability requires meaningful and openly available evidence of these efforts and the benefits accruing to Indigenous Peoples.

  • CARE-R1: For positive relationships

Indigenous data use is unviable unless linked to relationships built on respect, reciprocity, trust, and mutual understanding, as defined by the Indigenous Peoples to whom those data relate. Those working with Indigenous data are responsible for ensuring that the creation, interpretation, and use of those data uphold, or are respectful of, the dignity of Indigenous nations and communities.

  • CARE-R2: For expanding capability and capacity

Use of Indigenous data invokes a reciprocal responsibility to enhance data literacy within Indigenous communities and to support the development of an Indigenous data workforce and digital infrastructure to enable the creation, collection, management, security, governance, and application of data.

  • CARE-R3: For Indigenous languages and worldviews

Resources must be provided to generate data grounded in the languages, worldviews, and lived experiences (including values and principles) of Indigenous Peoples.

CARE-E: Ethics

Indigenous Peoples’ rights and wellbeing should be the primary concern at all stages of the data life cycle and across the data ecosystem.

  • CARE-E1: For minimizing harm and maximizing benefit

Ethical data are data that do not stigmatize or portray Indigenous Peoples, cultures, or knowledges in terms of deficit. Ethical data are collected and used in ways that align with Indigenous ethical frameworks and with rights affirmed in UNDRIP. Assessing ethical benefits and harms should be done from the perspective of the Indigenous Peoples, nations, or communities to whom the data relate.

  • CARE-E2: For justice

Ethical processes address imbalances in power, resources, and how these affect the expression of Indigenous rights and human rights. Ethical processes must include representation from relevant Indigenous communities.

  • CARE-E3: For future use

Data governance should take into account the potential future use and future harm based on ethical frameworks grounded in the values and principles of the relevant Indigenous community. Metadata should acknowledge the provenance and purpose and any limitations or obligations in secondary use inclusive of issues of consent.

>## FAIR Principles > > > > >## FAIR-F: Findable > >To be Findable: > > > > >- FAIR-F1: (meta)data are assigned a globally unique and persistent identifier > > > > >- FAIR-F2: data are described with rich metadata (defined by R1 below) > > > > >- FAIR-F3: metadata clearly and explicitly include the identifier of the data it describes > > > > >- FAIR-F4: (meta)data are registered or indexed in a searchable resource

Notes

It’s easy to say the meta(data) should be assigned PIDs (Persistent IDs) but there are a lot of requirements that follow from this. Peersistent identifiers require persistent storage of the (meta)data, governance

FAIR-A: Accessible

To be Accessible:

  • FAIR-A1: (meta)data are retrievable by their identifier using a standardized communications protocol

  • FAIR-A1.1 the protocol is open, free, and universally implementable

  • FAIR-A1.2 the protocol allows for an authentication and authorization procedure, where necessary

  • FAIR-A2: metadata are accessible, even when the data are no longer available

Notes

If we’re going to observe the CARE principles then an authentication and authorization procedure is DEFINITELY needed – but this is not a trivial undertaking, particularly in observing the standardized communications protocol this is covered in

FAIR-I: Interoperable

To be Interoperable:

  • FAIR-I1: (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. (See )

  • FAIR-I2: (meta)data use vocabularies that follow FAIR principles

  • FAIR-I3: (meta)data include qualified references to other (meta)data (see Protocol 2.5)

FAIR Principles - R

To be Reusable:

  • FAIR-R1: meta(data) are richly described with a plurality of accurate and relevant attributes

  • FAIR-R1.1: (meta)data are released with a clear and accessible data usage license

  • FAIR-R1:2: (meta)data are associated with detailed provenance

  • FAIR-R1.3: (meta)data meet domain-relevant community standards

Notes

TODO

Carroll, Stephanie Russo, Ibrahim Garba, Oscar L. Figueroa-Rodríguez, Jarita Holbrook, Raymond Lovett, Simeon Materechera, Mark Parsons, et al. 2020. “The CARE Principles for Indigenous Data Governance.” https://doi.org/10.5334/dsj-2020-043.
Wilkinson, Mark D., Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, et al. 2016. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.” Scientific Data 3 (March): 160018. http://dx.doi.org/10.1038/sdata.2016.18.