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Members
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Athina Papadopoulou
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Francis Crawley
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Lauren Maxwell
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Shalini Kurapati
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Ryan O'Connor
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CJ Woodford
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Group Details Edit Group Details

Status: Pending Submission
Chair(s): Lauren Maxwell, CJ Woodford, Lars Eklund, LILi Zhang
Secretariat Liaison: Bridget Walker
Group Description:

The core objective of this WG is to create a Health Data Commons Global Open Research Commons (GORC) profile. Health data commons (HDCs) are the health-data-focused versions of data commons, which bring together health data infrastructures. HDCs have been or are being launched across a number of geographies to enable the reuse of participant-level health data at the national and cross-national levels. HDCs will enable machine learning and related artificial intelligence (AI)-based approaches to health data analysis and support the national and cross-national detection and response to epidemics. HDCs 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 principles are implemented affects how metadata and participant-level data can be reused across HDCs. 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 are limited crosswalks that link ontologies. Open collaboration on FAIR implementation also helps stakeholders reuse available resources and standards rather than developing new ones. 

Through this WG, we aim to bring together HDCs in all stages of planning or implementation to:

  1. identify what items in the GORC international model (IM) are relevant for country-level or transnational HDCs;
  2. identify additional metadata needed to understand different levels of interoperability and address sensitive data concerns (e.g., access conditions, data governance, benefit sharing) relevant to HDCs and not addressed in the GORC IM; 
  3. semantify the HDC GORC profile metadata;
  4. collect agreed-upon metadata from the HDC GORC profile to understand how HDCs are being implemented;
  5. produce a knowledge graph showing how different HDCs relate to one another and other types of Data Commons. 

The HDC metadata schema from this WG will support semantic and technical interoperability between HDCs and support FAIR HDCs, where health data has maximum citability, reusability, and interpretability while addressing local policy, ethics, and security concerns. The HDC GORC profile WG implementation profile and knowledge graph will also support the implementation map of the GORC model more generally and identify where and how organizations are currently addressing aspects of FAIR, where the gaps are, and the tools to address the gaps. 

The intent of this WG is not to solve FAIR for health or to provide best practice for implementing HDCs. Instead, we would like to support community development of FAIR metadata that HDCs and related stakeholders can use to understand what implementation decisions have been made by other HDCs or commons in other domains and to share their HDCs’ implementation decisions. Rather than being prescriptive, these metadata can identify gaps in cross-commons interoperability or the coverage of other global issues in the HDC space (e.g., indigenous health data sovereignty) and opportunities for semantic and technical interoperability with commons in other spaces (e.g., climate data, environmental exposures data, biodiversity data, oceans data).

We are currently in the process of finalizing our case statement and plan to submit to TAB for review on 1 August 2024, with our first meeting to hopefully occur in September 2024. We encourage community feedback on the case statement leading up to the formal RDA community review.

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