178
views
0
recommends
+1 Recommend
2 collections
    1
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      PGP repository: a plant phenomics and genomics data publication infrastructure

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Plant genomics and phenomics represents the most promising tools for accelerating yield gains and overcoming emerging crop productivity bottlenecks. However, accessing this wealth of plant diversity requires the characterization of this material using state-of-the-art genomic, phenomic and molecular technologies and the release of subsequent research data via a long-term stable, open-access portal. Although several international consortia and public resource centres offer services for plant research data management, valuable digital assets remains unpublished and thus inaccessible to the scientific community. Recently, the Leibniz Institute of Plant Genetics and Crop Plant Research and the German Plant Phenotyping Network have jointly initiated the Plant Genomics and Phenomics Research Data Repository (PGP) as infrastructure to comprehensively publish plant research data. This covers in particular cross-domain datasets that are not being published in central repositories because of its volume or unsupported data scope, like image collections from plant phenotyping and microscopy, unfinished genomes, genotyping data, visualizations of morphological plant models, data from mass spectrometry as well as software and documents.

          The repository is hosted at Leibniz Institute of Plant Genetics and Crop Plant Research using e!DAL as software infrastructure and a Hierarchical Storage Management System as data archival backend. A novel developed data submission tool was made available for the consortium that features a high level of automation to lower the barriers of data publication. After an internal review process, data are published as citable digital object identifiers and a core set of technical metadata is registered at DataCite. The used e!DAL-embedded Web frontend generates for each dataset a landing page and supports an interactive exploration. PGP is registered as research data repository at BioSharing.org, re3data.org and OpenAIRE as valid EU Horizon 2020 open data archive. Above features, the programmatic interface and the support of standard metadata formats, enable PGP to fulfil the FAIR data principles—findable, accessible, interoperable, reusable.

          Database URL: http://edal.ipk-gatersleben.de/repos/pgp/

          Related collections

          Most cited references17

          • Record: found
          • Abstract: found
          • Article: not found

          Reproducible research in computational science.

          Roger Peng (2011)
          Computational science has led to exciting new developments, but the nature of the work has exposed limitations in our ability to evaluate published findings. Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Sharing Detailed Research Data Is Associated with Increased Citation Rate

            Background Sharing research data provides benefit to the general scientific community, but the benefit is less obvious for the investigator who makes his or her data available. Principal Findings We examined the citation history of 85 cancer microarray clinical trial publications with respect to the availability of their data. The 48% of trials with publicly available microarray data received 85% of the aggregate citations. Publicly available data was significantly (p = 0.006) associated with a 69% increase in citations, independently of journal impact factor, date of publication, and author country of origin using linear regression. Significance This correlation between publicly available data and increased literature impact may further motivate investigators to share their detailed research data.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The 1000 Genomes Project: data management and community access.

              The 1000 Genomes Project was launched as one of the largest distributed data collection and analysis projects ever undertaken in biology. In addition to the primary scientific goals of creating both a deep catalog of human genetic variation and extensive methods to accurately discover and characterize variation using new sequencing technologies, the project makes all of its data publicly available. Members of the project data coordination center have developed and deployed several tools to enable widespread data access.
                Bookmark

                Author and article information

                Journal
                Database (Oxford)
                Database (Oxford)
                databa
                databa
                Database: The Journal of Biological Databases and Curation
                Oxford University Press
                1758-0463
                2016
                16 April 2016
                16 April 2016
                : 2016
                : baw033
                Affiliations
                Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstraße 3, Stadt Seeland, 06466, Gatersleben, Germany
                Author notes
                [* ]Corresponding author: Tel: +49 39482 5842, Fax: +49 39482 5407, Email: arendd@ 123456ipk-gatersleben.de

                Citation details: Arend,D., Junker,A., Scholz,U. et al. PGP repository: a plant phenomics and genomics data publication infrastructure. Database (2016) Vol. 2016: article ID baw033; doi:10.1093/database/baw033

                Article
                baw033
                10.1093/database/baw033
                4834206
                27087305
                ab922c67-3dfd-4116-ae4c-437e5151fbd0
                © The Author(s) 2016. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 05 November 2015
                : 21 January 2016
                : 26 February 2016
                Page count
                Pages: 10
                Categories
                Original Article

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

                Comments

                Comment on this article