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dc.contributorBonaiuti, Enricoen_US
dc.contributorGraziano, Valerioen_US
dc.creatorBonechi, Francescoen_US
dc.date2019-01-25en_US
dc.date.accessioned2019-01-29T12:07:03Z
dc.date.available2019-01-29T12:07:03Z
dc.identifierhttps://mel.cgiar.org/reporting/download/hash/71e697853145c232d26c2707fee81696en_US
dc.identifier.citationFrancesco Bonechi, Enrico Bonaiuti, Valerio Graziano. (25/1/2019). General Dataset Curation Guide (GDCG).en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/9400
dc.description.abstractData collection and organization is one of the main tasks during research activities. In fact, most of projects results depend on the good management of data. However, “the long-term value of data can be affected, for better or worse, by how well those data are curated. Unfortunately, many valuable datasets are poorly curated, which contributes to errors, redundant effort, and obstacles to replication and use” (Ruggles, 2018). This, because is common to organize data in spreadsheets in a way which makes them easily understandable for the dataset author at that time, without following the machine-readable standards or considering any next research use. Due to this, there is then the need to review and adjust these datasets. This is one of the data curation roles. “Data curation activities enable data discovery and retrieval, maintain data quality, add value, and provide for re-use over time” (DH Curation Guide, 2017). Nowadays specific jobs related to data curation responsibilities are increasing (with title like “data curator” or “data curation specialist”) demonstrating the current importance of these skills. Thus, it will be important for anyone to have basic knowledge of this subject to be able during research activities, creating autonomously well curated datasets and this guide will support doing so.en_US
dc.formatPDFen_US
dc.languageenen_US
dc.rightsCC-BY-NC-4.0en_US
dc.subjectdata qualityen_US
dc.subjectdata sharingen_US
dc.subjectdata curationen_US
dc.titleGeneral Dataset Curation Guide (GDCG)en_US
dc.typeManualen_US
cg.creator.ID0000-0002-4010-4141en_US
cg.creator.ID0000-0002-2025-3449en_US
cg.creator.ID-typeORCIDen_US
cg.creator.ID-typeORCIDen_US
cg.subject.agrovocdata managementen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.crpCRP on Livestock Agri-Food Systems - LAFSen_US
cg.contributor.crpCRP on Grain Legumes and Dryland Cereals - GLDCen_US
cg.contributor.crpBig Data in Agriculture - BDAen_US
cg.contributor.funderInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.project-lead-instituteUniversity of Florence - UniFien_US
cg.coverage.regionGlobalen_US
cg.coverage.start-date2018-09-01en_US
cg.coverage.end-date2019-01-17en_US
cg.contactfrancesco.bonechi@cgmel.orgen_US
dc.identifier.statusOpen accessen_US


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