Implementing a hierarchical data model into a repository platform - A feasibility study
Abstract
This poster was presented at the 2nd Conference on Research Data Infrastructure (CoRDI) in Aachen and gives an overview on the implementation of the institutional repository ReSeeD within the collaborative research center (CRC) 1280 „Extincting Learning“ at Ruhr University Bochum.
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Sub Sub M. Pacharra1, J. Frenzel2, W. Fiene2, P. Walk3, A. Ranganathan4,T. Otto5, N.O.C. Winter2 1University Library, Ruhr University Bochum, 2IT.SERVICES, Ruhr University Bochum, 3Antleaf, UK, 4Cottage Labs, UK, 5Cognitive Psychology, Ruhr University Bochum, Germany Implementing a hierarchical data model into a repository platform - A feasibility study •Development of data model -16 metadata fields, hierarchical folder structure - Mapping on DataCite and DublinCore •Development of GUI for metadata entry (MetaApp) as interim solution on local network drive •RDM Policy •Extensive Data Curation by INF Data Steward •Challenge: No specific NFDI consortium funded for Neuroscience Research and Participants •Handling (ingest, download) of large data (up to TB) •Data and metadata stored on local S3 storage •Internal data sharing, archiving for 10 years and data publication in the same system •Differentiated visibility of data → roles & permissions •Login for project partners via ORCID iD Campus-wide Features •about 80 researchers in 17 projects at 4 institutions •Neuroscience: biology, psychology, medicine, and computational neuroscience •Techniques: microscopy, single cell recording, magnetic resonance imaging, questionnaires •Large existing data sets (18 TB, 3.9 million files in 40,800 folders) CRC contribution to repository implementation Data structure on network drive Display in ReSeeD Subject level Experiment level External service provider Implementation CRC Features Research Data Management Group member Group manager CRC data steward CRC 1280 RUB Publication with DOI 10-year Preservation Publication manager 3-step review Organizational structure •CRC data model •Search across experiments (e.g. male between 30 and 39 years) + download of search results •Automated import of hierarchical data and metadata •Access to data restricted to CRC by default •3-step review workflow based on Hyrax (Samvera) •Definition of requirements for ReSeeD (CRC specific and campus-wide) in cooperation with RDM team •Participation of CRC in the project team (communication with service provider, coordination of beta tests) •Provision of data for testing during development •Preparation of data ingest into ReSeeD by ensuring compliance of all data with CRC data model Acknowledgement DFG grant 316803389, CRC “1280 Extinction Learning", INF project. Outlook Lessons learned •Cooperation with scientific use case led to innovative approach for the implementation of an institutional repository, stimulating also campus-wide provision of features •Implementation of hierarchical data model was more challenging than expected •Expectations from research regarding performance of the repository and the conduction of the implementation process were high → management of expectations necessary •Extensive data curation was crucial for the effective bulk ingest of data •Implementation of data model for plasma physics •Operation of repository for two partner universities within the UA Ruhr •Evaluation of the use of the repository •Workflow for data ingest from electronic lab notebook eLabFTW CRC data model with inheritance of metadata