Health Data Commons: GORC international model profiling towards FAIR convergence

Submitted by: CJ Woodford, Lauren Maxwell

Meeting objectives

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. Health data ecosystems are especially important for enabling machine learning and related artificial intelligence (AI)-based approaches to health data analysis and for detecting and responding to epidemics. Health data commons can facilitate public health surveillance, observational clinical research, personalized medicine, and the design and conduct of randomized controlled trials (RCTs) by enabling country- and cross-country or global analyses of health data.

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.

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 International Model Working Group (GORC-WG) has analyzed a range of existing commons from different domains to collect and curate a set of attributes that will allow commons developers to compare features, as well as key performance indicators and metrics. From this analysis, the GORC-WG created a non-prescriptive commons model that provides a common language to describe all aspects of a commons with priority levels to guide staged adoption as the commons evolves. 

To enable FAIR convergence, stakeholders in the health data commons space must understand how FAIR has been implemented in similar initiatives, which resources (e.g., ontologies, data governance mechanisms) have been used, and why these choices have been made. Recognizing that there are additional barriers to adopting FAIR and limitations to openness specifically for health disciplines, 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 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.

This group will incorporate outputs from and work with related RDA groups to inform our outputs, including the Health Data Interest Group, Sensitive Data Interest Group, Life Science Data Infrastructures IG, Raising FAIRness in health data and health research performing organisations (HRPOs) WG, Artificial Intelligence and Data Visitation (AIDV) WG, and others affiliated with or working on health data and medical practise and research initiatives. We will also work with and foster participation from related groups external to RDA, including working groups such as EOSC’s FAIR metrics and Data Quality Task Force, CODATA’s GOSC “Sensitive data in population health” Case Study Group, and the UNESCO/CODATA Data Policy in Times of Crisis WG in addition to existing health data research infrastructures that may already be or intend to become health data commons, such as the NIH, ARDC People Research Data Commons, UK Health Data Research Innovation Gateway, ELIXIR, NFDI4Health, and GA4GH.

The intended outcome of this working group is to increase awareness and implementation of FAIR in health research and practice. We will do this by working with the health data community within and external to RDA, specifically currently operating health data commons, to develop a community-informed health data commons profile of the GORC international commons model. This profiling will include not only what health commons need to consider but also implementation recommendations on how they can achieve considerations in the profile and, thus a roadmap for FAIR convergence. We will also create a 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. A draft case statement is open and available for feedback prior to the plenary.

The objectives of this meeting are to:

Meeting agenda: 

Type of Meeting: Working meeting

Short introduction describing any previous activities: 

The Research Data Alliance Global Open Research Commons Interest Group (GORC-IG) is working to support coordination amongst national, pan-national and domain-specific organizations as they work to build interoperable resources necessary to enable researchers to address societal grand challenges.  The GORC-IG examined a range of existing Research commons architectures and synthesized a typology of the essential elements in a commons complete with definitions. The GORC-IG is working to develop a roadmap for global alignment to help set priorities for commons development and integration. The GORC WG International commons model was developed to support the development of the IG integration roadmap.

Adoption of the GORC International Commons model for domain agnostic, national commons has been ongoing (e.g. used in the REASON funding proposal). The development of a GORC vocabulary, knowledge graph, and formal adoption mechanism, including a maturity model are ongoing, all of which will support the development of a health commons profile and uptake in the health research community.

The first step of identifying national-level health data commons through a systematic review has been completed, and is currently being informally supplemented by snowball sampling in preparation for the endorsement of this WG.

Material for a landscape review to support this working group is being collected in a designated Zotero library.

BoF applicant serving as contact person: CJ Woodford, Lauren Maxwell

Meeting presenters: CJ Woodford

Avoid conflict with the following group (1): GORC International Model WG

Avoid conflict with the following group (2): Sensitive Data Interest Group

Contact for group (email): lauren.maxwell.us@gmail.com

Applicable Pathways: Life Science Data Issues, Data Infrastructures – Organisational to Environments

Driven by RDA Organisational Member: No

Please indicate at least (3) three breakout slots that would suit your meeting.: 

Please indicate a minimum of (3) three breakout slot (s) that would suit your repeat session in a different time zone.: 

Are you willing to host a second, repeat session in a different time zone?: Yes

Have you previously held a session at plenaries?: Yes