General Dataset Curation Guide (GDCG)

cg.contactfrancesco.bonechi@cgmel.orgen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerInternational Livestock Research Institute - ILRIen_US
cg.contributor.crpCGIAR Research Program on Livestock Agri-Food Systems - Livestocken_US
cg.contributor.crpCGIAR Research Program 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.projectCGIAR Platform for Big Data in Agricultureen_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.coverage.end-date2019-01-17en_US
cg.coverage.start-date2018-09-01en_US
cg.creator.idBonaiuti, Enrico: 0000-0002-4010-4141en_US
cg.creator.idGraziano, Valerio: 0000-0002-2025-3449en_US
cg.creator.idPoole, Elizabeth: 0000-0002-8570-794Xen_US
cg.subject.agrovocdata managementen_US
cg.subject.agrovocdata qualityen_US
dc.contributorBonaiuti, Enricoen_US
dc.contributorGraziano, Valerioen_US
dc.contributorPoole, Elizabethen_US
dc.creatorBonechi, Francescoen_US
dc.date.accessioned2019-01-29T12:07:03Z
dc.date.available2019-01-29T12:07:03Z
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.identifierhttps://mel.cgiar.org/reporting/downloadmelspace/hash/71e697853145c232d26c2707fee81696/v/571a3c97fa94783e49410e7a1a397f6ben_US
dc.identifier.citationFrancesco Bonechi, Enrico Bonaiuti, Valerio Graziano, Elizabeth Poole. (30/10/2019). General Dataset Curation Guide (GDCG).en_US
dc.identifier.statusOpen accessen_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/9400
dc.languageenen_US
dc.rightsCC-BY-NC-SA-4.0en_US
dc.subjectdata sharingen_US
dc.subjectdata curationen_US
dc.titleGeneral Dataset Curation Guide (GDCG)en_US
dc.typeManualen_US
dcterms.available2019-10-30en_US
mel.funder.grant#International Center for Tropical Agriculture - CIAT :C-109-17en_US
mel.project.openhttp://bigdata.cgiar.org/en_US

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