dc.contributor.author | Arnaud, E. |
dc.contributor.author | Laporte, M.A. |
dc.contributor.author | Kim, S. |
dc.contributor.author | Aubert, C. |
dc.contributor.author | Leonelli, S. |
dc.contributor.author | Miro, B. |
dc.contributor.author | Cooper, L. |
dc.contributor.author | Jaiswal, P. |
dc.contributor.author | Kruseman, G. |
dc.contributor.author | Shrestha, R. |
dc.contributor.author | Buttigieg, P.L. |
dc.contributor.author | Mungall, C.J. |
dc.contributor.author | Pietragalla, J. |
dc.contributor.author | Agbona, A. |
dc.contributor.author | Muliro, J. |
dc.contributor.author | Detras, J. |
dc.contributor.author | Hualla, V. |
dc.contributor.author | Rathore, A. |
dc.contributor.author | Das, R.R. |
dc.contributor.author | Dieng, I. |
dc.contributor.author | Bauchet, G.J. |
dc.contributor.author | Menda, N. |
dc.contributor.author | Pommier, C. |
dc.contributor.author | Shaw, F. |
dc.contributor.author | Lyon, D. |
dc.contributor.author | Mwanzia, L. |
dc.contributor.author | Juarez, H. |
dc.contributor.author | Bonaiuti, E. |
dc.contributor.author | Chiputwa, B. |
dc.contributor.author | Obileye, O. |
dc.contributor.author | Auzoux, S. |
dc.contributor.author | Yeumo, E.D. |
dc.contributor.author | Mueller, L. |
dc.contributor.author | Silverstein, K. |
dc.contributor.author | Lafargue, A. |
dc.contributor.author | Antezana, E. |
dc.contributor.author | Devare, M. |
dc.contributor.author | King, B. |
dc.date.accessioned | 2022-09-16T07:48:37Z |
dc.date.available | 2022-09-16T07:48:37Z |
dc.date.issued | 2020-10-09 |
dc.identifier.citation | Arnaud, E., Laporte, M.A., Kim, S., Aubert, C., Leonelli, S., Miro, B., ... & King, B. (2020). The ontologies community of practice: a CGIAR initiative for big data in agrifood systems. Patterns, 1(7): 100105, 1-12. |
dc.identifier.issn | 2666-3899 |
dc.identifier.uri | https://hdl.handle.net/20.500.12478/7757 |
dc.description.abstract | The Bigger Picture Digital technology use in agriculture and agrifood systems research accelerates the production of multidisciplinary data, which spans genetics, environment, agroecology, biology, and socio-economics. Quality labeling of data secures its online findability, reusability, interoperability, and reliable interpretation, through controlled vocabularies organized into meaningful and computer-readable knowledge domains called ontologies. There is currently no full set of recommended ontologies for agricultural research, so data scientists, data managers, and database developers struggle to find validated terminology. The Ontologies Community of Practice of the CGIAR Platform for Big Data in Agriculture harnesses international expertise in knowledge representation and ontology development to produce missing ontologies, identifies best practices, and guides data labeling by teams managing multidisciplinary information platforms to release the FAIR data underpinning the evidence of research impact. Summary Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams. |
dc.description.sponsorship | CGIAR Trust Fund |
dc.description.sponsorship | UK Aid |
dc.description.sponsorship | National Science Foundation |
dc.description.sponsorship | Frontiers in Arctic Marine Monitoring |
dc.description.sponsorship | BBSRC Core Strategic Program |
dc.description.sponsorship | Alan Turing Institute |
dc.description.sponsorship | European Commission |
dc.format.extent | 1-12 |
dc.language.iso | en |
dc.subject | Ontology |
dc.subject | Agriculture |
dc.subject | Agrifood Systems |
dc.subject | Crop Production |
dc.subject | Breeding |
dc.subject | Phenotypes |
dc.subject | Genotypes |
dc.title | The ontologies community of practice: a CGIAR initiative for big data in agrifood systems |
dc.type | Journal Article |
cg.contributor.affiliation | Bioversity International |
cg.contributor.affiliation | International Food Policy Research Institute |
cg.contributor.affiliation | University of Exeter |
cg.contributor.affiliation | Oregon State University |
cg.contributor.affiliation | International Maize and Wheat Improvement Center |
cg.contributor.affiliation | GEOMAR Helmholtz Centre for Ocean Research, Germany |
cg.contributor.affiliation | Lawrence Berkeley National Laboratory |
cg.contributor.affiliation | Integrated Breeding Platform, Mexico |
cg.contributor.affiliation | International Institute of Tropical Agriculture |
cg.contributor.affiliation | WorldFish |
cg.contributor.affiliation | International Rice Research Institute |
cg.contributor.affiliation | International Potato Center |
cg.contributor.affiliation | International Crops Research Institute for the Semi-Arid Tropics |
cg.contributor.affiliation | Boyce Thompson Institute for Plant Research |
cg.contributor.affiliation | Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement, France |
cg.contributor.affiliation | Earlham Institute |
cg.contributor.affiliation | International Center for Agricultural Research in the Dry Areas |
cg.contributor.affiliation | World Agroforestry Centre |
cg.contributor.affiliation | The French Agricultural Research Centre for International Development |
cg.contributor.affiliation | University of Minnesota |
cg.contributor.affiliation | CP RDIT, Syngenta, France |
cg.contributor.affiliation | Bayer Crop Science SA-NV, Belgium |
cg.contributor.affiliation | Norwegian University of Science and Technology |
cg.contributor.affiliation | International Center for Tropical Agriculture |
cg.contributor.affiliation | Université Paris-Saclay |
cg.contributor.affiliation | Université de Montpellier |
cg.coverage.hub | Headquarters and Western Africa Hub |
cg.identifier.bibtexciteid | ARNAUD:2020 |
cg.authorship.types | CGIAR and advanced research institute |
cg.iitasubject | Agronomy |
cg.iitasubject | Knowledge Management |
cg.iitasubject | Plant Breeding |
cg.iitasubject | Plant Production |
cg.journal | Patterns |
cg.notes | Open Access Article; Published online: 25 Sept, 2020 |
cg.accessibilitystatus | Open Access |
cg.reviewstatus | Peer Review |
cg.usagerightslicense | Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 (CC BY-NC-ND 4.0) |
cg.targetaudience | Scientists |
cg.identifier.doi | https://dx.doi.org/10.1016/j.patter.2020.100105 |
cg.futureupdate.required | No |
cg.identifier.issue | 7 |
cg.identifier.volume | 1 |