001038382 001__ 1038382
001038382 005__ 20250203103330.0
001038382 0247_ $$2doi$$a10.37044/osf.io/7y2jh
001038382 037__ $$aFZJ-2025-01379
001038382 1001_ $$0P:(DE-Juel1)191149$$aBeier, Sebastian$$b0
001038382 245__ $$aBioHackEU23 report: Enabling continuous RDM using Annotated Research Contexts with RO-Crate profiles for ISA
001038382 260__ $$c2024
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001038382 3367_ $$2ORCID$$aWORKING_PAPER
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001038382 3367_ $$2BibTeX$$aARTICLE
001038382 3367_ $$2DataCite$$aOutput Types/Working Paper
001038382 520__ $$aA prevailing paradigm in Research Data Management (RDM) is to publish research datasets in designated archives upon conclusion of a research process. However, it is beneficial to abandon the notion of final or static data artifacts and instead adopt a continuous approach towards working with research data, where data is constantly shared, versioned, and updated. This immutable yet evolving perspective allows for the application of existing technologies and processes from software engineering, such as continuous integration, release practices, and version management backed by decades of experience, and adaptable to RDM.To facilitate this, we propose the Annotated Research Context (ARC), a data and metadata layout convention based on the well-established ISA model for metadata annotation and implemented using Git repositories. ARCs are amenable towards frequent, lightweight data management operations, such as (meta)data validation and transformation. The Omnipy Python library is designed to help develop stepwise validated (meta)data transformations as scalable data flows that can be incrementally designed, updated, and rerun as requirements or data evolve.To demonstrate the concept of continuous RDM we will use Omnipy to define and orchestrate Git-backed CI/CD (Continuous Integration/Continuous Delivery) data flows to convert ISA metadata present in ARCs into validated RO-Crate representations adhering to the Bioschemas convention. A RO-Crate package combines the actual research data with its metadata description. Downstream, this allows semantic interpretation by Galaxy for e.g. workflow execution as well as machine-readable data access and data harvesting for search engines such as FAIDARE.
001038382 536__ $$0G:(DE-HGF)POF4-2171$$a2171 - Biological and environmental resources for sustainable use (POF4-217)$$cPOF4-217$$fPOF IV$$x0
001038382 536__ $$0G:(GEPRIS)442077441$$aDFG project G:(GEPRIS)442077441 - DataPLANT – Daten in Pflanzen-Grundlagenforschung (442077441)$$c442077441$$x1
001038382 588__ $$aDataset connected to CrossRef
001038382 7001_ $$0P:(DE-HGF)0$$aMühlhaus, Timo$$b1
001038382 7001_ $$00000-0002-9040-8733$$aPommier, Cyril$$b2
001038382 7001_ $$0P:(DE-HGF)0$$aOwen, Stuart$$b3
001038382 7001_ $$00000-0001-9021-3197$$aBrilhaus, Dominik$$b4
001038382 7001_ $$0P:(DE-HGF)0$$aWeil, Heinrich Lukas$$b5
001038382 7001_ $$0P:(DE-HGF)0$$aWetzels, Florian$$b6
001038382 7001_ $$0P:(DE-HGF)0$$aChait, Gavin$$b7
001038382 7001_ $$00000-0002-2455-5938$$aArend, Daniel$$b8
001038382 7001_ $$00000-0001-6546-1818$$aFeser, Manuel$$b9
001038382 7001_ $$0P:(DE-HGF)0$$aDoniparthi, Gajendra$$b10
001038382 7001_ $$0P:(DE-HGF)0$$aBauer, Jonathan$$b11
001038382 7001_ $$00000-0001-9888-7954$$aGundersen, Sveinung$$b12
001038382 7001_ $$0P:(DE-HGF)0$$aVázquez, Pável$$b13
001038382 773__ $$tBioHackrXiv$$y2024
001038382 909CO $$ooai:juser.fz-juelich.de:1038382$$pVDB
001038382 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)191149$$aForschungszentrum Jülich$$b0$$kFZJ
001038382 9131_ $$0G:(DE-HGF)POF4-217$$1G:(DE-HGF)POF4-210$$2G:(DE-HGF)POF4-200$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-2171$$aDE-HGF$$bForschungsbereich Erde und Umwelt$$lErde im Wandel – Unsere Zukunft nachhaltig gestalten$$vFür eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten$$x0
001038382 9141_ $$y2024
001038382 920__ $$lyes
001038382 9201_ $$0I:(DE-Juel1)IBG-4-20200403$$kIBG-4$$lBioinformatik$$x0
001038382 980__ $$apreprint
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001038382 980__ $$aUNRESTRICTED