National health data commons, ecosystems, spaces, clouds, or virtual research environments (VREs) have been or are being launched across a number of geographies to enable the reuse of participant-level health data at the country and cross-national levels. These Health Data Commons can both enable the use of advanced analysis techniques such as machine learning and other artificial intelligence (AI) methods and leverage existing data flows for public health surveillance, observational clinical research, personalized medicine, and the design and conduct of randomized controlled trials (RCTs).

Whether and how the FAIR (findable, accessible, interoperable, reusable) principles are implemented affects how metadata and participant-level data can be reused across health data commons. FAIR convergence, as when related stakeholders take the same or similar routes to implementing FAIR, can help foster interoperability in the health data space where there are many standards for the capture or exchange of health data and for which there is limited cross-ontology interoperability. Open collaboration on FAIR implementation also helps stakeholders reuse available resources and standards rather than developing new ones. A similar approach can be considered regarding other relevant principles such as the TRUST (Transparency, Responsibility, User focus, Sustainability and Technology) principles for management and operations of data systems and the CARE (Collective benefit, Authority to control, Responsibility, and Ethics) principles regarding data and resource governance and privacy.

The goal of interoperability in and FAIRness for health data commons aligns with the overarching vision of the Global Open Research Commons (GORC), which calls for frictionless access to all research artifacts to everyone, everywhere, at all times, with the appropriate infrastructure, protocols, and support in place. The GORC IM WG (https://www.rd-alliance.org/groups/gorc-international-model-wg/) created a non-prescriptive commons model (https://doi.org/10.15497/RDA00099) that provides a common language to describe all aspects of a commons with priority levels to guide staged adoption as the commons evolves and was developed with significant consideration of the FAIR, TRUST, and CARE principles..

This working group will create a health data commons profiling of the GORC International Commons Model that can then be used and expanded upon in the health data space to address FAIR and other relevant principles as well as to compare features. By generating machine actionable metadata that describes commons in a consistent, structured way, we will make visible the choices communities of practice make when implementing FAIR across health data commons.

The intended outcome of this working group is to make visible implementation decisions by country-level or cross-national Data Commons to support cross-commons interoperability in the health data space and beyond. Understanding how health data Commons operationalize data access conditions, the FAIR and other relevant principles, and health ethical concerns will help understand what types of investments need to be made to enable cross-commons federated data reuse within the health data space (e.g., as for epidemic response) or between the health and other data spaces (e.g., as for understanding the impact of climate change on health).

We will extend the GORC IM to the specific needs of health data commons by working with the health data community within and external to RDA, including groups developing or operating health data commons. The community-informed health data commons profile of the GORC international commons model will provide recommendations for structured FAIR metadata that can help developing commons understand what related health data commons are doing. These metadata will be used to develop a structured network graph illustrating current and intended future uses of the profile in practice. This working group will then assess the health commons profiling of the GORC model and its practical implementations with respect to analyzing and contributing to a FAIR assessment tool specifically for health data commons.

We anticipate that this WG will be endorsed by September 2024, and work on a landscape review, contacting and engaging with HDCs, and the creation of a questionnaire to gather semi-structured information about HDCs and their needs to be underway. It is expected that a speaker series will begin in December 2024 or January 2025.

The objectives of this meeting are to:
1. Present and discuss the methodology and prepared tools for engaging with HDCs
2. Present and discuss the findings from the on-going scoping review and snowball sampling
3. Gather feedback and identify interest in analyzing gathered information into a draft profile
4. Potential alignment for a global HDCs network

1. Welcome and icebreakers (5 minutes)
2. Introduction to the topic and previous work (10 minutes)
a) FAIR for Health Data Commons (5 min)
b) GORC international commons model (5 min)
3. Open discussion on methodology and workplan (60 min)
a) Presentation of current work plan and timeline (10 min)
b) Presentation of questionnaire for health data commons engagement and speaker series (5 min)
c) Discussion and feedback for questionnaire and speaker series mechanism (20 min)
d) Discussion of Health Data Commons identified to participate in completing questionnaire, participating in speaker series (10 min)
e) Presentation and discussion of landscape review status and findings (15 min)
4. Call for members, engagement in health data commons analysis of questionnaires and literature review (10 minutes)
a) Express of interests for further engagement
b) Ways to fulfill such objectives
c) Possible timeline
5. Next steps and wrap up (5 minutes)

RDA community members interested in joining the working group, Research infrastructure providers and data services active in delivering Research / Data Commons initiatives in health and health-related fields