
The Implementation Network for Sharing Population Information from Research Entities (INSPIRE) project is assembling technologies and standards in support of a data hub that facilitates federated and/or shared research capable of interoperating across often-neglected low-resource settings: it aims to provide a platform-as-a-service, which can make data of disparate types available to many different styles of analysis, among which AI systems are increasingly prominent.
INSPIRE uses OMOP, a common data model that is becoming the gold standard for systematically integrating health data from disparate sources and conducting observational research at scale using routine clinical care data. However, OMOP is not completely FAIR29 and further work is needed to improve the ability to integrate diverse sources of data. This case study team will improve the interoperation of OMOP with other standards to enable machine-actionable descriptions of data structure and provenance (e.g., DDI-CDI, PROV-O, SDTL); the composition of measurements focused on the objects of research (e.g., I-ADOPT); record linkage modeling for creating and evaluating bridges that connect domains, vocabularies (e.g., SKOS); and data discovery (e.g., Schema.org, DCAT). This suite of standards forms the basis of an ‘AI-Ready’ description of data suitable for use across domain and institutional boundaries.
Partner(s)
LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE ROYAL CHARTER(LSHTM)
The London School of Hygiene & Tropical Medicine is a world leading centre for research and postgraduate education in public and global health.
Keep readingWork Package leads
wp7 featured output

Population Health Data Implementation Guide
This implementation guide describes the way all aspects of the data are made available for use, both within and from outside the INSPIRE Network community, using standard metadata to describe the data. This is an exploration of how generic standards can be used to express the agreed community metadata set.
Events
Population Health Data Implementation Guide (Deliverable 7.1)
Gregory, Arofan; Todd, Jim; Amadi, David; Greenfield, Jay; Muyingo, Sylvia; Tomlin, Keith One of the key requirements for FAIR data reuse is that the user of a FAIR data resource understands the exact nature of the data. The FAIR…
Keep readingWorldFAIR Project webinar series announced
The WorldFAIR Project is launching a webinar series aiming to promote and discuss all published and upcoming deliverables and project outputs. The webinars will run from May 2023 to May 2024. They are free to attend. Please note all…
Keep readingDagstuhl Workshop “Interoperability for Cross-Domain Research: Machine-Actionability & Scalability”
The “Interoperability for Cross-Domain Research: Machine-Actionability & Scalability” workshop was held at Schloss Dagstuhl from August 28 to September 2, 2022 (https://www.dagstuhl.de/22353). The workshop was the fourth in a series, starting in 2018, exploring the role of metadata standards…
Keep reading
You must be logged in to post a comment.