SFB1479 Metadata schema

Research data annotation is a prerequisite for data finding and sharing in collaborative research centers, both internally and with external researchers. To obtain high-quality annotations the metadata schema needs to be well-defined and documented. Additionally, it is good practice to publish schemas for reuse in other consortia and to allow for mapping with other metadata systems. A complex metadata schema has been developed for annotating research data from the Collaborative Research Center (CRC) 1479, entitled "Oncogene-driven immune escape" (OncoEscape). It relates publications from within the CRC with the underlying datasets. For these, information about their storage location and accessibility are recorded, together with a detailed classification of contents covering source organisms, sample preparation and processing, interventions, machine readouts and details of methodology. Where possible, the terms of the metadata schema are mapped to elements from well-established research ontologies in order to clarify their meaning and facilitate mapping to other resources. To make the information even more accessible, the data model is presented as an RDF knowledge graph, demonstrating the relevance of metadata for the investigations they describe. This model largely follows the Ontology for Biomedical Investigations (OBI) in the representation of scientific research. The ontological terms used in the knowledge graph, both reused and newly introduced, are collected in an ontology based on the Information Artifact Ontology (IAO). This preliminary version contains specifications of the data items from the metadata schema. This information will be complemented with a documentation of the knowledge graph, information about semantic integration and mappings to other metadata schemas.

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Publication Details

Journal
FreiDATA
Published
2025-02-27
DOI
https://doi.org/10.60493/sem6z-pkr31
Citations
1
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
0.81
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article

SFB1479 Metadata schema

1 citations
FreiDATA
Biomedical Text Mining and Ontologies
0.81
article

SFB1479 Metadata schema

article en
1 citations

Abstract

Research data annotation is a prerequisite for data finding and sharing in collaborative research centers, both internally and with external researchers. To obtain high-quality annotations the metadata schema needs to be well-defined and documented. Additionally, it is good practice to publish schemas for reuse in other consortia and to allow for mapping with other metadata systems. A complex metadata schema has been developed for annotating research data from the Collaborative Research Center (CRC) 1479, entitled "Oncogene-driven immune escape" (OncoEscape). It relates publications from within the CRC with the underlying datasets. For these, information about their storage location and accessibility are recorded, together with a detailed classification of contents covering source organisms, sample preparation and processing, interventions, machine readouts and details of methodology. Where possible, the terms of the metadata schema are mapped to elements from well-established research ontologies in order to clarify their meaning and facilitate mapping to other resources. To make the information even more accessible, the data model is presented as an RDF knowledge graph, demonstrating the relevance of metadata for the investigations they describe. This model largely follows the Ontology for Biomedical Investigations (OBI) in the representation of scientific research. The ontological terms used in the knowledge graph, both reused and newly introduced, are collected in an ontology based on the Information Artifact Ontology (IAO). This preliminary version contains specifications of the data items from the metadata schema. This information will be complemented with a documentation of the knowledge graph, information about semantic integration and mappings to other metadata schemas.

FreiDATA
University of Freiburg (DE)
Partnerships for the goals
Openalex Percentile: Top 21%
Biomedical Text Mining and Ontologies
0.81
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