The BonaRes metadata schema for geospatial soil-agricultural research data – Merging INSPIRE and DataCite metadata schemes

Specka, Xenia; Gärtner, Philipp; Hoffmann, Carsten; Svoboda, Nikolai; Stecker, Markus; Einspanier, Udo; Senkler, Kristian; Heinrich, Uwe

doi:10.1016/j.cageo.2019.07.005

Cited by 20 publications

(12 citation statements)

References 10 publications

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“…A stark problem resided in the fact that to improve the algorithms, more data for testing would be necessary, which were often challenging to obtain [ 38 ]. Moreover, the final merging of the data sets was also problematic: mappings could be ambiguous [ 29 ], the corresponding elements differed in the obligation level [ 73 ], or the proposed mapping had flaws and therefore, could cause information misinterpretation [ 53 ]. The reviewed papers proposed focusing on improving schema matching to enable a broader understanding of schemes [ 26 ].…”

Section: Resultsmentioning

confidence: 99%

Understanding the Nature of Metadata: Systematic Review

Ulrich¹,

Kock-Schoppenhauer²,

Deppenwiese³

et al. 2022

J Med Internet Res

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Background Metadata are created to describe the corresponding data in a detailed and unambiguous way and is used for various applications in different research areas, for example, data identification and classification. However, a clear definition of metadata is crucial for further use. Unfortunately, extensive experience with the processing and management of metadata has shown that the term “metadata” and its use is not always unambiguous. Objective This study aimed to understand the definition of metadata and the challenges resulting from metadata reuse. Methods A systematic literature search was performed in this study following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for reporting on systematic reviews. Five research questions were identified to streamline the review process, addressing metadata characteristics, metadata standards, use cases, and problems encountered. This review was preceded by a harmonization process to achieve a general understanding of the terms used. Results The harmonization process resulted in a clear set of definitions for metadata processing focusing on data integration. The following literature review was conducted by 10 reviewers with different backgrounds and using the harmonized definitions. This study included 81 peer-reviewed papers from the last decade after applying various filtering steps to identify the most relevant papers. The 5 research questions could be answered, resulting in a broad overview of the standards, use cases, problems, and corresponding solutions for the application of metadata in different research areas. Conclusions Metadata can be a powerful tool for identifying, describing, and processing information, but its meaningful creation is costly and challenging. This review process uncovered many standards, use cases, problems, and solutions for dealing with metadata. The presented harmonized definitions and the new schema have the potential to improve the classification and generation of metadata by creating a shared understanding of metadata and its context.

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Section: Resultsmentioning

confidence: 99%

Understanding the Nature of Metadata: Systematic Review

Ulrich¹,

Kock-Schoppenhauer²,

Deppenwiese³

et al. 2022

J Med Internet Res

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show abstract

“…Die Geodatengemeinschaft hat in den letzten Jahren eine Reihe von Metadatenstandards etabliert, z. B. ISO 19115 Geographic information -Metadata, der durch die europäische INSPIRE-Richtlinie (Infrastructure for Spatial Information in Europe) erweitert wurde und zusätzliche Implementierungsregeln enthält, oder das BonaRes-Metadata-Schema [28].…”

Section: Metadaten Ontologien Und Standardsunclassified

FAIRagro: Ein Konsortium in der Nationalen Forschungsdateninfrastruktur (NFDI) für Forschungsdaten in der Agrosystemforschung

Specka

Martini

Weiland

et al. 2023

Informatik Spektrum

Self Cite

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ZusammenfassungFAIRagro ist ein Konsortium in der Nationalen Forschungsdateninfrastruktur (NFDI) in Deutschland um Forschungsdaten der Agrosystemforschung FAIR – d. h. auffindbar (F), zugänglich (A), interoperabel (I) und für andere Forschende domänenübergreifend nachnutzbar (R) zu machen. In der deutschen Forschungslandschaft rund um nachhaltige Agrosysteme werden heterogene Forschungsdaten erhoben und nur zum Teil in existierenden Forschungsdatenrepositorien veröffentlicht. Das Spektrum der Datenformate erstreckt sich beispielsweise von Laborergebnissen, über Satellitenbilder bis hin zu qualitativen Interviews mit Landwirt:innen. Um diese Daten zukünftig für Forschende verschiedener Disziplinen besser auffindbar und nachnutzbar zu machen, wird FAIRagro eine Forschungsdateninfrastruktur (FDI) für die Agrosystemforschung einrichten, in der disziplinäre Dateninfrastrukturen miteinander verknüpft werden. Spezifische Herausforderungen im Forschungsdatenmanagement (FDM) fachlicher Disziplinen wie Pflanzenzüchtung, integrierter Pflanzenschutz oder Agrarrobotik werden als Use Cases in FAIRagro adressiert und für diese Lösungen entwickelt. Darüber hinaus wird FAIRagro ein Netzwerk aus direkten Ansprechpersonen für Fragen zum Forschungsdatenmanagement in der Agrosystem-Community bereitstellen. In Übereinstimmung mit den Zielsetzungen der NFDI und der European Open Science Cloud ist FAIRagro aktiv an der konzeptionellen Implementierung eines interoperablen Datenraums beteiligt.

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“…Recently, several works have proposed metadata schemata to facilitate FAIR data management in different domains [21,22,23]. Specka et al have proposed BonaRes, which is a metadata schema for geospatial soil-agricultural research data [21]. Labropoulou et al have presented ELG-SHARE, which describes language resources and aims to improve the European Language Technology sector [22].…”

Section: Fair Guideline Principlesmentioning

confidence: 99%

DAMS: A Distributed Analytics Metadata Schema

et al. 2021

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In recent years, implementations enabling Distributed Analytics (DA) have gained considerable attention due to their ability to perform complex analysis tasks on decentralised data by bringing the analysis to the data. These concepts propose privacy-enhancing alternatives to data centralisation approaches, which have restricted applicability in case of sensitive data due to ethical, legal or social aspects. Nevertheless, the immanent problem of DA-enabling architectures is the black-box-alike behaviour of the highly distributed components originating from the lack of semantically enriched descriptions, particularly the absence of basic metadata for datasets or analysis tasks. To approach the mentioned problems, we propose a metadata schema for DA infrastructures, which provides a vocabulary to enrich the involved entities with descriptive semantics. We initially perform a requirement analysis with domain experts to reveal necessary metadata items, which represents the foundation of our schema. Afterwards, we transform the obtained domain expert knowledge into user stories and derive the most significant semantic content. In the final step, we enable machine-readability via RDF(S) and SHACL serialisations. We deploy our schema in a proof-of-concept monitoring dashboard to validate its contribution to the transparency of DA architectures. Additionally, we evaluate the schema’s compliance with the FAIR principles. The evaluation shows that the schema succeeds in increasing transparency while being compliant with most of the FAIR principles. Because a common metadata model is critical for enhancing the compatibility between multiple DA infrastructures, our work lowers data access and analysis barriers. It represents an initial and infrastructure-independent foundation for the FAIRification of DA and the underlying scientific data management.

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The BonaRes metadata schema for geospatial soil-agricultural research data – Merging INSPIRE and DataCite metadata schemes

Cited by 20 publications

References 10 publications

Understanding the Nature of Metadata: Systematic Review

Understanding the Nature of Metadata: Systematic Review

FAIRagro: Ein Konsortium in der Nationalen Forschungsdateninfrastruktur (NFDI) für Forschungsdaten in der Agrosystemforschung

DAMS: A Distributed Analytics Metadata Schema

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