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Towards a FAIR sharing of scientific experiments: Improving discoverability and reusability of dielectric measurements of biological tissues

: Rezaul Karim, M.; Heinrichs, Matthias; Gleim, Lars C.; Cochez, Michael; Porter, Emily; Gioia, Alessandra la; Salahuddin, Saqib; O'Halloran, Martin; Decker, Stefan; Beyan, Oya

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Paschke, Adrian (Ed.); Burger, Albert (Ed.); Splendiani, Andrea (Ed.); Marshall, M. Scott (Ed.); Romano, Paolo (Ed.); Presutti, Valentina (Ed.):
10th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4LS 2017. Proceedings. Online resource : Rome, Italy, December 4-7, 2017
Aachen: RWTH, 2017 (CEUR Workshop Proceedings 2042)
URN: urn:nbn:de:0074-2042-3
Paper 11, 10 pp.
International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4LS) <10, 2017, Rome>
Conference Paper, Electronic Publication
Fraunhofer FIT ()
scientific data; dielectric measurements; Metadata Management; Semantic Web; FAIR Data Principles

Experiments on the dielectric properties of biological tissues generate data that characterizes the interaction of human tissues with electromagnetic fields. This data is vital for designing electromagnetic-based therapeutic and diagnostic technologies, and for assessing the safety of wireless devices. Despite the importance of the data, poor reporting and lack of metadata impede its reuse and forgo interoperability. Recently, the minimum information model for reporting Dielectric Measurements of Biological Tissues (MINDER) has been developed as a common framework. In this work, we have developed a metadata model and implemented a data sharing framework to improve findability and reproducibility of experimental data inspired by FAIR principles. We define a process for sharing the reported data and present tools to support rich metadata generation based on existing community standards. The developed system is evaluated against competency questions collected from data consumers, and thereby proven to help to interpret and compare data across studies.