Making marine image data FAIR.

Autor: Schoening T; GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany. tschoening@geomar.de., Durden JM; National Oceanography Centre, European Way, Southampton, SO14 3ZH, UK., Faber C; GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany., Felden J; MARUM - Center for Marine Environmental Sciences, University of Bremen, Leobener Str. 8, D-28359, Bremen, Germany.; Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany., Heger K; GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany., Hoving HT; GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany., Kiko R; Laboratoire d'Océanographie de Villefranche, Sorbonne Université, 06230, Villefranche-sur-Mer, France., Köser K; GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany., Krämmer C; Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany., Kwasnitschka T; GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany., Möller KO; Helmholtz-Zentrum Hereon, Institute of Carbon Cycles, Geesthacht, Germany., Nakath D; GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany., Naß A; DLR/Institute of Planetary Research, Planetary Geology, Berlin, Germany., Nattkemper TW; Biodata Mining Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany., Purser A; Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany., Zurowietz M; Biodata Mining Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany.
Jazyk: angličtina
Zdroj: Scientific data [Sci Data] 2022 Jul 15; Vol. 9 (1), pp. 414. Date of Electronic Publication: 2022 Jul 15.
DOI: 10.1038/s41597-022-01491-3
Abstrakt: Underwater images are used to explore and monitor ocean habitats, generating huge datasets with unusual data characteristics that preclude traditional data management strategies. Due to the lack of universally adopted data standards, image data collected from the marine environment are increasing in heterogeneity, preventing objective comparison. The extraction of actionable information thus remains challenging, particularly for researchers not directly involved with the image data collection. Standardized formats and procedures are needed to enable sustainable image analysis and processing tools, as are solutions for image publication in long-term repositories to ascertain reuse of data. The FAIR principles (Findable, Accessible, Interoperable, Reusable) provide a framework for such data management goals. We propose the use of image FAIR Digital Objects (iFDOs) and present an infrastructure environment to create and exploit such FAIR digital objects. We show how these iFDOs can be created, validated, managed and stored, and which data associated with imagery should be curated. The goal is to reduce image management overheads while simultaneously creating visibility for image acquisition and publication efforts.
(© 2022. The Author(s).)
Databáze: MEDLINE
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