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

Specka, Xenia; Martini, Daniel; Weiland, Claus; Arend, Daniel; Asseng, Senthold; Boehm, Franziska; Feike, Til; Fluck, Juliane; Gackstetter, David; Gonzales-Mellado, Aida; Hartmann, Thomas; Haunert, Jan‐Henrik; Hoedt, Florian; Hoffmann, Carsten; König, Patrick; Lange, Matthias; Lesch, Stephan; Lindstädt, Birte; Lischeid, Gunnar; Möller, Markus; Rascher, Uwe; Reif, Jochen C.; Markus, Schmalzl,; Senft, Matthias; Stahl, Ulrike; Svoboda, Nikolai; Usadel, Björn; Webber, Heidi; Ewert, Frank

doi:10.1007/s00287-022-01520-w

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…Also part of Elixir’s infrastructure, BioSamples contains results from biological samples stored in archives such as ArrayExpress ( https://www.ebi.ac.uk/biostudies/arrayexpress ; Kolesnikov et al., 2015 ), the European Nucleotide Archive (ENA) ( https://www.ebi.ac.uk/ena/browser/home ; Silvester et al., 2018 ) and the European Genome-phenome Archive (EGA) ( https://ega.crg.eu/ ; Lappalainen et al., 2015 ). Specifically for plant data, the NFDI funded consortiums FAIRagro ( https://fairagro.net/en/ , Specka et al., 2023 ) and DataPLANT have developed their own infrastructure for data curation and archiving. The FAIRagro infrastructure contains a number of data repositories, including e!DAL-PGP ( https://edal-pgp.ipk-gatersleben.de/ ; Arend et al., 2016 ), for the storage of data relating to agricultural research.…”

Section: Discussion and Perspectivesmentioning

confidence: 99%

Ontologies for increasing the FAIRness of plant research data

Dumschott,

Dörpholz,

Laporte

et al. 2023

Front. Plant Sci.

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The importance of improving the FAIRness (findability, accessibility, interoperability, reusability) of research data is undeniable, especially in the face of large, complex datasets currently being produced by omics technologies. Facilitating the integration of a dataset with other types of data increases the likelihood of reuse, and the potential of answering novel research questions. Ontologies are a useful tool for semantically tagging datasets as adding relevant metadata increases the understanding of how data was produced and increases its interoperability. Ontologies provide concepts for a particular domain as well as the relationships between concepts. By tagging data with ontology terms, data becomes both human- and machine- interpretable, allowing for increased reuse and interoperability. However, the task of identifying ontologies relevant to a particular research domain or technology is challenging, especially within the diverse realm of fundamental plant research. In this review, we outline the ontologies most relevant to the fundamental plant sciences and how they can be used to annotate data related to plant-specific experiments within metadata frameworks, such as Investigation-Study-Assay (ISA). We also outline repositories and platforms most useful for identifying applicable ontologies or finding ontology terms.

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Section: Discussion and Perspectivesmentioning

confidence: 99%

Ontologies for increasing the FAIRness of plant research data

Dumschott,

Dörpholz,

Laporte

et al. 2023

Front. Plant Sci.

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“…Initiatives such as DataPLANT, FAIRagro and ELIXIR [59][60][61][62][63] have developed numerous tools to help plant scientists overcome RDM challenges. As part of the German National Research Data Initiative (NFDI), the DataPLANT consortium [59] provides a research data infrastructure for the plant sciences with a user-centric approach.…”

Section: Figurementioning

confidence: 99%

DataPLAN: A Web-Based Data Management Plan Generator for the Plant Sciences

Zhou,

Beier,

Brilhaus

et al. 2023

Data

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Research data management (RDM) combines a set of practices for the organization, storage and preservation of data from research projects. The RDM strategy of a project is usually formalized as a data management plan (DMP)—a document that sets out procedures to ensure data findability, accessibility, interoperability and reusability (FAIR-ness). Many aspects of RDM are standardized across disciplines so that data and metadata are reusable, but the components of DMPs in the plant sciences are often disconnected. The inability to reuse plant-specific DMP content across projects and funding sources requires additional time and effort to write unique DMPs for different settings. To address this issue, we developed DataPLAN—an open-source tool incorporating prewritten DMP content for the plant sciences that can be used online or offline to prepare multiple DMPs. The current version of DataPLAN supports Horizon 2020 and Horizon Europe projects, as well as projects funded by the German Research Foundation (DFG). Furthermore, DataPLAN offers the option for users to customize their own templates. Additional templates to accommodate other funding schemes will be added in the future. DataPLAN reduces the workload needed to create or update DMPs in the plant sciences by presenting standardized RDM practices optimized for different funding contexts.

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A Repository for Formal Contexts

Hanika,

Jäschke

2024

Lecture Notes in Computer Science

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

Cited by 4 publications

References 34 publications

Ontologies for increasing the FAIRness of plant research data

Ontologies for increasing the FAIRness of plant research data

DataPLAN: A Web-Based Data Management Plan Generator for the Plant Sciences

A Repository for Formal Contexts

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