General Dataset Curation Guide (GDCG) 3.0

cg.contactpietro.bartolini@cgmel.orgen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.funderInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.projectICARDA Corporate - Monitoring & Evaluationen_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.creator.idBartolini, Pietro: 0000-0002-5946-5772en_US
cg.creator.idJeitani, Asma: 0000-0002-2130-2994en_US
cg.creator.idBonaiuti, Enrico: 0000-0002-4010-4141en_US
cg.creator.idDe Col, Valentina: 0000-0003-0895-969Xen_US
cg.creator.idGraziano, Valerio: 0000-0002-2025-3449en_US
cg.creator.idJani, Sara: 0000-0003-2554-5026en_US
cg.subject.agrovocdata managementen_US
cg.subject.agrovocdata qualityen_US
dc.contributorJeitani, Asmaen_US
dc.contributorBonaiuti, Enricoen_US
dc.contributorDe Col, Valentinaen_US
dc.contributorGraziano, Valerioen_US
dc.contributorJani, Saraen_US
dc.creatorBartolini, Pietroen_US
dc.date.accessioned2023-10-13T16:20:32Z
dc.date.available2023-10-13T16:20:32Z
dc.description.abstractData collection and organization is one of the main tasks during research activities. In fact, most of the project's 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). It 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. Unfortunately, not-curated data can quickly become unusable if nobody report all relevant information and stores it in a stable format. “Data curation activities enable data discovery and retrieval, maintain data quality, add value, and provide for re-use over time” (Munoz, 2017). The present guide is targeted at the members of the DM sub-team and all ICARDA scientists interested in improving their data quality. It will be important for anyone to have basic knowledge of this subject to be able, during research activities, to create well curated datasets, and to ensure data are as open as possible, always FAIR (Findable, Accessible, Interoperable, and Reusable) and managed responsibly in compliance with the OFDA Policy.en_US
dc.formatPDFen_US
dc.identifierhttps://mel.cgiar.org/reporting/downloadmelspace/hash/09875535674bedad75162b38ce2f7297en_US
dc.identifier.citationPietro Bartolini, Asma Jeitani, Enrico Bonaiuti, Valentina De Col, Valerio Graziano, Sara Jani. (31/7/2023). General Dataset Curation Guide (GDCG) 3. 0.en_US
dc.identifier.statusOpen accessen_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/68739
dc.languageenen_US
dc.relationFrancesco Bonechi, Enrico Bonaiuti, Valerio Graziano, Elizabeth Poole. (30/10/2019). General Dataset Curation Guide (GDCG).en_US
dc.relationFrancesco Bonechi, Pietro Bartolini. (30/10/2019). Data Dictionary Standard Template.en_US
dc.relation.urihttps://hdl.handle.net/20.500.11766/9400en_US
dc.relation.urihttps://hdl.handle.net/20.500.11766/68740en_US
dc.rightsCC-BY-SA-4.0en_US
dc.subjectdata sharingen_US
dc.subjectdata curationen_US
dc.titleGeneral Dataset Curation Guide (GDCG) 3.0en_US
dc.typeManualen_US
dcterms.available2023-07-31en_US

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