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      Applying FAIR Principles to Plant Phenotypic Data Management in GnpIS

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          Abstract

          GnpIS is a data repository for plant phenomics that stores whole field and greenhouse experimental data including environment measures. It allows long-term access to datasets following the FAIR principles: Findable, Accessible, Interoperable, and Reusable, by using a flexible and original approach. It is based on a generic and ontology driven data model and an innovative software architecture that uncouples data integration, storage, and querying. It takes advantage of international standards including the Crop Ontology, MIAPPE, and the Breeding API. GnpIS allows handling data for a wide range of species and experiment types, including multiannual perennial plants experimental network or annual plant trials with either raw data, i.e., direct measures, or computed traits. It also ensures the integration and the interoperability among phenotyping datasets and with genotyping data. This is achieved through a careful curation and annotation of the key resources conducted in close collaboration with the communities providing data. Our repository follows the Open Science data publication principles by ensuring citability of each dataset. Finally, GnpIS compliance with international standards enables its interoperability with other data repositories hence allowing data links between phenotype and other data types. GnpIS can therefore contribute to emerging international federations of information systems.

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            A relational model of data for large shared data banks

            E F Codd (1970)
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              Linked Data - The Story So Far

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                Author and article information

                Contributors
                Journal
                Plant Phenomics
                Plant Phenomics
                PLANTPHENOMICS
                Plant Phenomics
                AAAS
                2643-6515
                2019
                30 April 2019
                : 2019
                : 1671403
                Affiliations
                1URGI, INRA, Université Paris-Saclay, 78026 Versailles, France
                2AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France
                3UMR SVQV, 28 rue de Herrlisheim, B.P. 20507, 68021 Colmar, France
                4Bioversity International, parc Scientifique Agropolis II, 34397 Montpellier cedex 5, France
                Article
                10.34133/2019/1671403
                7718628
                33313522
                a03c22b4-d0e3-4d68-a5d1-190347bb5966
                Copyright © 2019 C. Pommier et al.

                Exclusive licensee Nanjing Agricultural University. Distributed under a Creative Commons Attribution License (CC BY 4.0).

                History
                : 8 January 2019
                : 8 April 2019
                Funding
                Funded by: INRA
                Funded by: Agence Nationale de la Recherche
                Award ID: ANR-11-INBS-0012
                Funded by: TransPLANT project
                Award ID: 283496
                Funded by: European Commission
                Award ID: 676559
                Funded by: “Investments for the Future programme” (PIA)
                Categories
                Research Article

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