Preprint Technical Note Version 1 Preserved in Portico This version is not peer-reviewed

The SPHN Ecosystem Towards FAIR Data

Version 1 : Received: 27 September 2021 / Approved: 29 September 2021 / Online: 29 September 2021 (17:32:40 CEST)

How to cite: Österle, S.; Touré, V.; Crameri, K. The SPHN Ecosystem Towards FAIR Data. Preprints 2021, 2021090505 (doi: 10.20944/preprints202109.0505.v1). Österle, S.; Touré, V.; Crameri, K. The SPHN Ecosystem Towards FAIR Data. Preprints 2021, 2021090505 (doi: 10.20944/preprints202109.0505.v1).

Abstract

Health-related data originating from diverse sources are commonly stored in manifold databases and formats, making it difficult to find, access and gather data for research purposes. In addition, so-called secondary use scenarios for health data are usually hindered by local data codes, missing dictionaries and the lack of metadata and context descriptions. Following the FAIR principles (Findable, Accessible, Interoperable and Reusable), we developed a decentralized infrastructure to overcome these hurdles and enable collaborative research by making the meaning of health-related data understandable to both, humans and machines. This infrastructure is currently being implemented in the realm of the Swiss Personalized Health Network (SPHN), a research infrastructure initiative for enabling the use and exchange of health-related data for research in Switzerland. The SPHN ecosystem for FAIR data consists of the SPHN Dataset (semantic definitions), the SPHN RDF Schema (linkage and transport of the semantics in a machine-readable format), a project RDF template, extensive guidelines and conventions on how to generate SPHN RDF schema, a Terminology Service (converter of clinical terminologies in RDF), and a Quality Assurance Framework (automated data validation with SHACLs and SPARQLs). The SPHN ecosystem has been built in a way that it can easily be adapted and extended by any SPHN project to fit individual needs. By providing such a national ecosystem, SPHN supports researchers in generating, processing and sharing FAIR data.

Keywords

Semantics; standards; clinical research infrastructure; terminology; graph data; data-driven medicine

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