foundingGIDE Deliverable D4.1 Report on the recommended ontologies for preclinical image datasets and technologies
Year: 2025
Type: article
Abstract: Preclinical imaging has emerged over the past two decades as a rapidly expanding field, playing a fundamental role in both basic research and the development of new therapies. The advent of new imaging modalities and increasingly sensitive and specific probes allow us to obtain in vivo information at the anatomical, molecular and functional levels. This exponential increase in the ability to generate image datasets has not gone hand in hand with the ability to make these datasets findable and reusable. One of the main limitations is related to the lack of a set of ontologies large enough to cover the multidisciplinarity of preclinical imaging. The aim of this deliverable is to identify the different domains in which preclinical images fall, to search and evaluate the different ontologies that can cover each of the various domains and to select the more useful ontologies based on a series of criteria. The final objective is to provide a set of recommended ontologies for improving the findability of preclinical image datasets. This version has been approved by the EC. We would like to add an additional contributor for this deliverable: Shuichi OnamiAffiliations: RIKEN Center for Biosystems Dynamics Research RIKEN Information R&D and Strategy Headquarters ORCID: https://orcid.org/0000-0002-8255-1724
Authors: Longo, Dario, Orosco Salinas, Tania
Institution: National Research Council
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: European Commission
OpenAlex ID: W6949573306
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