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FAIRagro - A FAIR Data Infrastructure for Agrosystems (proposal)
FAIRagro - A FAIR Data Infrastructure for Agrosystems (proposal)
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This document is the proposal for the consortium FAIRagro in the framework of the National Research Data Infrastructure (NFDI) in Germany. The proposal was submitted to the German Research Foundation (DFG) in Nov 2021. The proposal was finally accepted with a revised working program in Feb 2023. All financial resources (personnel, material, direct project costs, in-kind contributions) were removed from the public version.n
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Role of Semantic Resources in Infrastructures for Data Management in Agrosystems and Landscape Research
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Role of Semantic Resources in Infrastructures for Data Management in Agrosystems and Landscape ResearchIn 2023, the FAIRagro project started its work as a consortium of almost 30 partnering organizations active in agrosystems research in Germany. FAIRagro is part of the National Research Data Infrastructure (NFDI) initiative with its goal to set up a cross-domain research data infrastructure (RDI) conforming to the FAIR principles. The main objective is to establish an interoperable and scalable RDI by connecting available repositories to facilitate combined data analyses. Partly, existing agricultural data repositories involved in FAIRagro already use semantic resources like vocabularies and thesauri. For example, the Smart Rural Area Data Infrastructure (SRADI) developed and maintained by the Technical University of Munich leverages metadata vocabularies like DCAT and Dublin Core, but also strives for compatibility with well established geospatial data standards as provided by the Open Geospatial Consortium. The BonaRes Repository, as another example, was launched in 2017 at the Leibniz Centre for Agricultural Landscape Research (ZALF) and manages soil-agricultural data from research projects. To address FAIR data principles, numerous open community standards, vocabularies and PIDs, e.g. INSPIRE and DataCite for metadata description, AGROVOC for keyword annotation and ORCID for author annotation are used. Within that context, there are challenges especially regarding cross-linking of data sets and repositories and a need for cross-standard interoperability. Large Linked Data resources like AGROVOC can provide bridges through URI-based metadata annotations. For practical purposes, this however requires consistent use according to best practices, mappings and alignments. The presentation will illustrate how the FAIRagro project addresses these issues within its activities.
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PDF
Languages
English
Media Type
Presentation
Publication Date
2023-08-29
License
CCBY
Size
3.8 MB
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Keywords
#FAIR#NFDI#research data management#ontology#AGROVOC#agriculture#agrosystems#semantics
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Overview of research data management in chemistry
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Overview of research data management in chemistryOverview of research data management in chemistry is a 90 minute presentation that provides a basic introduction into research data management (RDM, what are research data, why are they useful), and the FAIR principles (Findable, Accessible, Interoperable, Reusable). The presentation guides through the research data life cycle to explain the benefits of RDM as a part of good scientific practices. It also provides useful links and introductions to NFDI4Chem services.
File Format
PPTX, PDF
Languages
English
Media Type
Presentation
Publication Date
2023-03-14
License
CCBY
Size
9.7 MB
Authors
Keywords
#FAIR#NFDI#research data management#DMP#RDM#NFDI4Chem#data life cycle#data management plan#ELN#electronic lab notebook#DLC
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Knowledge Graphs - Working Group Charter (NFDI section-metadata)
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Knowledge Graphs - Working Group Charter (NFDI section-metadata)Knowledge Graphs are a key technology for implementing the FAIR principles in data infrastructures by ensuring interoperability for both humans and machines. The Working Group "Knowledge Graphs" in Section "(Meta)data, Terminologies, Provenance" of the German National Research Data Infrastructure (Nationale Forschungsdateninfrastruktur (NFDI) e.V.) aims to promote the use of knowledge graphs in all NFDI consortia, to facilitate cross-domain data interlinking and federation following the FAIR principles, and to contribute to the joint development of tools and technologies that enable transformation of structured and unstructured data into semantically reusable knowledge across different domains.
Learning Resource Type
Article
File Format
PDF
Languages
English
Media Type
Text
Publication Date
2023-04-05
License
CCBY
Size
214.9 KB
Authors
Keywords
#NFDI#research data management#Base4NFDI#FAIR principles#knowledge graph#knowledge graphs
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DAPHNE4NFDI. DAta from PHoton and Neutron Experiments
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DAPHNE4NFDI. DAta from PHoton and Neutron ExperimentsDAPHNE4NFDI is the first (inter-)national attempt to bring together users and large-scale research facilities to create a comprehensive infrastructure to process research DAta from PHoton and Neutron Experiments (DAPHNE) according to the FAIR principles. Our community faces a common need for high-level, rapid data analysis and the challenge of implementing research data management for increasingly large and complex datasets. All this involves not only a broad range of scientific disciplines and stakeholders, but also the connection to complex instrumentation and IT. Within the talk, Lisa Amelung would like to inform about the DAPHNE4NFDI consortium and discuss (current) challenges and future tasks.
File Format
PDF
Languages
English
Media Type
Presentation
Publication Date
2022-11-18
License
CCBY
Size
4.9 MB
Authors
Keywords
#NFDI#research data management#RDM#FAIR principles#Metadaten#metadata#Forschungsdatenmanagement#research data#Nationale Forschungsdateninfrastruktur#Forschungsdaten#data analysis#FAIR-Prinzipien#DAPHNE4NFDI#FDM#FAIR data#Infrastructure#Datenanalyse#FAIRe Daten#Infrastruktur#Metadatenerfassung#National Research Data#Neutronenforschung#Photonenforschung#metadata capture#neutron research#photon research
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