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ORINOCO: Building a self-hostable research information infrastructure

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This project delivers self-hostable tools to collect, organize, validate, visualize, and serve structured research (meta)data. It centers on a [LinkML schema toolkit](https://concepts.datalad.org) that enables the expression of arbitrary metadata with the ability to align with any number of generic and domain-specific vocabularies. Importantly, the availability of suitable ontologies is *not* a requirement for capturing metadata, and semantic alignment can be a parallel task, focused on high-value assets. From a central schema, such as the [research information demonstrator](https://concepts.datalad.org/s/demo-research-information) both metadata edit browser UIs and REST API can be generated automatically, using [shacl-vue](https://www.psychoinformatics.de/instruments/shacl-vue) and [dump-things](https://www.psychoinformatics.de/instruments/dump-things), respectively. Combined, these tools enable the operation of a "knowledge pool", a collaborative instrument for metadata collection and curation (see the [deployment of the TRR379](https://pool.trr379.de), or that of the [Psychoinformatics group](https://pool.psychoinformatics.de) as examples). This comprehensive approach to project-related metadata capture, spanning the continuum from research to administrative information, enables the automatic generation of complete project websites. The [TRR379 main website](https://www.trr379.de), and the [Psychoinformatics group website](https://www.psychoinformatics.de) are two examples that are continuously and programatically generated from knowledge pool queries. The ability to support public outreach and science communication with machine-assisted, metadata-driven project self-representations is an incentive to gather comprehensive, and valid metadata that far exceeds the scope of typical research data annotations towards more comprehensive information on their provenance in the context of wider research activities.

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Sujets associés

Research Data Management PracticesBiomedical Text Mining and OntologiesScientific Computing and Data Management

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