FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3089193614> ?p ?o ?g. }

• W3089193614 endingPage "100105" @default.
• W3089193614 startingPage "100105" @default.
• W3089193614 abstract "•FAIR agricultural data must use ontologies that are popular in the knowledge domain•CGIAR Ontologies Community of Practice holds expertise for agricultural data annotation•The Community selects innovative solutions to assist the data annotation with ontologies•The Community develops multidisciplinary open-source ontologies for agricultural data 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. 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. 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. The increasing application to agrifood research data of the FAIR (findable, accessible, interoperable, and reusable) principles1Wilkinson M.D. Dumontier M. Aalbersberg Ij.J. Appleton G. Axton M. Baak A. Blomberg N. Boiten J.-W. da Silva Santos L.B. Bourne P.E. et al.The FAIR guiding principles for scientific data management and stewardship.Sci. Data. 2016; 3: 160018Crossref PubMed Scopus (5372) Google Scholar has led to the research community's growing interest in using ontologies. FAIR principles indeed recommend that data must be described with commonly used, controlled vocabularies structured in thesauri and semantically rich ontologies. An ontology is a representation of a domain of knowledge where key concepts, as well as the relationships between those concepts, are defined.2Gruber T. Ontology.in: Liu L. Tamer Özsu M. Encyclopedia of Database Systems. Springer-Verlag, 2009Crossref Google Scholar By providing standardized definitions for the terms used by scientists along with defined logical relationships among these terms, ontologies compile information about the content of a dataset that can be explicitly used by computers.3Walls R.L. Athreya B. Cooper L. Elser J. Gandolfo M.A. Jaiswal P. Mungall C.J. Preece J. Rensing S. Smith B. et al.Ontologies as integrative tools for plant science.Am. J. Bot. 2012; 99: 1263-1275Crossref PubMed Scopus (68) Google Scholar Each concept has a Uniform Resource Identifier (URI) that uniquely identifies it as a web resource, accessible by anyone for data labeling, to efficiently support consistent use of ontology terms within and across disciplines and domains. Therefore, annotating data with quality and widely used ontologies increases the findability, interoperability, and reusability of data. Despite the existence of robust ontologies in the Life Sciences, no agreed set of quality ontologies covering all agrifood research disciplines exists, because it is not easy to identify which ones are representative of community standards, what best practices exist for using ontologies, and how we can collectively fill domain gaps.4Leonelli S. Data-Centric Biology: A Philosophical Study. Chicago University Press, 2016Crossref Google Scholar Within this scenario, data managers often create their own customized controlled vocabularies, which fragment the global semantic framework and keep data in silos. In 2013, the Interest Group on Agricultural Data (IGAD) (https://www.rd-alliance.org/groups/agriculture-data-interest-group-igad.html) was created within the Research Data Alliance to facilitate discussions on all aspects of agricultural information management. IGAD's Wheat Data Interoperability Working Group published guidelines recommending a set of standards and ontologies applicable to genetic, genomic, and phenotypic data (http://datastandards.wheatis.org) for wheat,5Dzale Yeumo E. Alaux M. Arnaud E. Aubin S. Baumann U. Buche P. Cooper L. Ćwiek-Kupczyńska H. Davey R.P. Fulss et al.Developing data interoperability using standards: a wheat community use case.F1000Res. 2017; 6: 1843Crossref PubMed Google Scholar while its Agrisemantics Working Group conducted a scoping study from which it produced list of global recommendations for the development, maintenance, and use of semantic resources in agriculture (https://rd-alliance.org/group/agrisemantics-wg/outcomes/39-hints-facilitate-use-semantics-data-agriculture-and-nutrition). IGAD does not directly engage in ontology development related to agriculture. The CGIAR (https://www.cgiar.org/), the world's largest global agricultural innovation network dedicated to reducing poverty, enhancing food security, and improving natural resources, launched the Platform for Big Data in Agriculture (https://bigdata.cgiar.org/) in 2017. The aim is to increase the impact of agricultural research and development by turning FAIR data into a powerful tool for discovery, while integrating principles of responsible and ethical data use. Through the Platform on Big Data, CGIAR's primary objective is to annotate multidisciplinary research data with the appropriate ontologies for publishing on the GARDIAN platform (https://gardian.bigdata.cgiar.org/), CGIAR's metadata repository, and stimulate the ontology content gap filling rather than developing complete new ontologies.6Leonelli S. Global data for local science: assessing the scale of data infrastructures in biological and biomedical research.BioSocieties. 2013; 8: 449-465Crossref Scopus (40) Google Scholar The Ontologies Community of Practice (CoP) was created to harness in-house and external expertise in the development of ontologies and support the five other CGIAR Platform CoPs (Agronomy Data, Crop Modeling, Geospatial Data, Livestock Data, and Socio-Economic Data) toward finding adequate ontologies for data description. The Ontologies CoP, hereafter referred to as “The CoP,” was also developed as a means to include data generated by the latest technologies (e.g., remote sensors) and expand beyond crops to encompass data on fisheries and aquaculture, livestock, socio-economics, water management, and agroecology (agroecology includes social, economic, and environmental aspects of the food production systems http://www.fao.org/agroecology/knowledge/definitions/en/). The Ontologies CoP's thematic working groups currently develop ontologies, such as the Crop Ontology (CO) (http://www.cropontology.org), the Agronomy Ontology (ArgO) (https://bigdata.cgiar.org/resources/agronomy-ontology/), and the Socio-Economic Ontology (SEOnt) (https://github.com/AgriculturalSemantics/SEOnt). The CoP provides the ideal forum for co-learning and knowledge exchange on ontologies and for guiding consistent data annotation, as well as the deployment of quality ontologies in databases and repositories. The CoP stimulates exchanges between domain experts and experts in ontology design, knowledge modeling, ontology-driven applications, and semantic web technologies. While IGAD and the Ontologies CoP have members in common, only the Ontologies CoP aims to directly contribute to ontology development to ensure the quality of data mobilized by the CGIAR Platform for Big Data in Agriculture, its partners, as well as new players within the domains it covers. It includes researchers, modelers, information specialists, data managers, and ontology experts from the CGIAR research network, academia, and the private sector, thus creating a critical mass of expertise to tackle the major issues related to semantics for FAIR data in agrifood science. Currently, the Ontologies CoP newsletter has 353 subscribers and a LinkedIn group “CGIAR Big Data-Ontologies CoP” (https://www.linkedin.com/groups/13707155/) with 144 active members: 35 from universities, 61 from public research institutes, and 48 from the private sector. We regularly organize webinars, which are recorded to build a public channel of online reference videos (https://www.youtube.com/c/OntologiesInAgriculture) and to which we have 118 subscribers. The CoP webpage (https://bigdata.cgiar.org/communities-of-practice/ontologies/) provides access to its objectives and yearly workplan developed with members' input. In this paper we provide information on the ontology products that were developed by the CoP members, as well as the necessary perspectives to extend and cover all relevant domains for research on agriculture and food systems. We explain how the CoP supports and fosters the proper use of quality ontologies, the submission of missing terms by users, and collaboratively explore solutions to solving the complexity of data annotation. Finally, we stress the importance of partnering with industry in agriculture and food systems. The Ontologies CoP members play a direct role in ontology development and filling content gaps by compiling controlled vocabularies and requesting or mapping new terms to existing ontologies. Collaborative development of ontologies is a slow process but is a guarantee for quality and adoption. Currently, four thematic ontology working groups have been created for Agronomy, Fish and Fisheries, Plant phenotypes, and Socio-Economy. The CoP has begun to explore the use of new technologies in machine learning to create or improve ontologies and, in return, provides quality ontologies to support text mining. However, the use of artificial intelligence in the development of ontologies lags behind, largely due to the breadth and heterogeneous sets of expertise involved in quality assessment of the results. CGIAR currently has eight agrifood research programs (https://www.cgiar.org/research/research-portfolio/) focused on crop breeding, aimed at producing innovative technologies, such as improved crop varieties and advisory services to farmers. Producing FAIR data on plant genotypes and phenotypes, their environment, field management practices, and socio-economy is crucial to provide support information for the development and use of these technologies. For several years, CGIAR and its partners have contributed to ontology development for plant phenotype studies and field management practices. The ontologies developed by the CoP provide validated concepts and formatted variables for direct integration in the design of field or lab books, thus supporting data aggregation into multidisciplinary platforms or use by analytical and modeling tools. The CoP provides wider communication and a formal framework for this work, stimulating new members' contributions, as in the case of PepsiCo Inc. and NIAB (a UK crop science organization) to the Oat Ontology development (https://www.cropontology.org/ontology/CO_350/Oat; https://bigdata.cgiar.org/blog-post/agricultural-ontologies-in-use-new-crops-and-traits-in-the-crop-ontology/) or interactions with other CoPs, such as the Data-driven Agronomy and the Socio-Economic Data (SED) CoPs. Crop breeding relies on collecting data on the desired traits for a new crop variety by testing it in multiple locations and diverse environments, linking phenotypes to genotypes, and drawing conclusions from meta-analyses. In addition, information produced by agronomic trials for field management practices applied by farmers is key to understanding how the significant differences in the practices underpin the performance of the variety. The quality and consistency of data collected during field trials are improved by the use of electronic field books and require the use of ontologies validated by end users.7Shrestha R. Matteis L. Skofic M. Portugal A. McLaren G. Hyman G. Arnaud E. Bridging the phenotypic and genetic data useful for integrated breeding through a data annotation using the Crop Ontology developed by the crop communities of practice.Front. Plant Physiol. 2012; 3 (Article 326)https://doi.org/10.3389/fphys.2012.00326, ISSN: 1664-042XCrossref PubMed Google Scholar,8Devare M., Aubert C., Laporte M.-A., Valette L., Arnaud E., Buttigieg P.L., (2016). Data-driven agricultural research for development: a need for data harmonization via semantics. International Conference on Biomedical Ontologies (ICBO), 2016.Google Scholar In 2008, CGIAR initiated the development of the CO (http://www.cropontology.org) in response to the need of breeding data management systems and field books to have access to valid lists of defined breeders' traits and variables. Currently, the CO comprises 4,235 traits and 6,151 variables for 31 plant species. By providing descriptions of agronomic, morphological, physiological, quality, and stress traits along with a standard for composing the variables, the CO enables digital capture and aggregation of crop trait data, as well as comparison across projects and locations.7Shrestha R. Matteis L. Skofic M. Portugal A. McLaren G. Hyman G. Arnaud E. Bridging the phenotypic and genetic data useful for integrated breeding through a data annotation using the Crop Ontology developed by the crop communities of practice.Front. Plant Physiol. 2012; 3 (Article 326)https://doi.org/10.3389/fphys.2012.00326, ISSN: 1664-042XCrossref PubMed Google Scholar The CO was integrated into the Planteome's ontology project funded by the National Science Foundation, US (IOS:1340112 award; http://planteome.org) and was successfully adopted by the CGIAR Integrated Breeding Platform (https://www.integratedbreeding.net/) and by the Boyce Thompson Institute's Breedbase (https://breedbase.org/), both of which are comprehensive breeding management systems and analysis software, and by national databases, such as GnpIS (https://urgi.versailles.inra.fr/Tools/GnpIS)9Pommier C. Michotey C. Cornut G. Roumet P. Duchêne E. Flores R. Lebreton A. Alaux M. Durand S. Kimmel E. et al.Applying FAIR principles to plant phenotypic data management in GnpIS.Plant Phenom. 2019; 2019 (1671403): 15Crossref Scopus (24) Google Scholar in France, or international projects, such as Emphasis (European Plant Phenotyping Infrastructures; https://emphasis.plant-phenotyping.eu/). Both the Minimum Information About a Plant Phenotype Experiment (https://www.miappe.org/) metadata schema (MIAPPE),10Ćwiek-Kupczyńska H. Altmann T. Arend D. Arnaud E. Chen D. Cornut G. Fiorani F. Frohmberg W. Junker A. Klukas C. et al.Measures for interoperability of phenotypic data: minimum information requirements and formatting.Plant Methods. 2016; 12: 44Crossref PubMed Scopus (89) Google Scholar,11Papoutsoglou E.A. Faria D. Arend D. Arnaud E. Athanasiadis I.N. Chaves I. Coppens F. Cornut G. Costa B.V. Ćwiek-Kupczyńska et al.Enabling reusability of plant phenomic datasets with MIAPPE 1.1.New Phytol. 2020; https://doi.org/10.1111/nph.16544Crossref PubMed Scopus (47) Google Scholar and the Breeding Application Programming Interface (BrAPI) (https://brapi.org/),12Selby P. Abbeloos R. Backlund J.E. Basterrechea Salido M. Bauchet G. Benites-Alfaro O.E. Birkett C. Calaminos V.C. Carceller P. Cornut et al.BrAPI consortium. BrAPI-an application programming interface for plant breeding applications.Bioinformatics. 2019; 35: 4147-4155Crossref PubMed Scopus (54) Google Scholar which enable the extraction of genotype and phenotype data across databases, are compliant with the CO format. At the time CGIAR launched the CO, the Plant Trait Ontology (TO)13Cooper L. Meier A. Laporte M.-A. Elser J.L. Mungall C.J. Sinn B.T. Cavaliere D. Carbon S. Dunn N.A. Smith B. et al.The Planteome database: an integrated resource for reference ontologies, plant genomics and phenomics.Nucleic Acids Res. 2018; 46: D1168-D1180Crossref PubMed Scopus (90) Google Scholar did not include traits and definitions required for breeding data on the CGIAR mandate crops. To remediate this situation and create the necessary upper-level connection between the species-specific ontologies, CO trait terms were mapped to terms, thus enabling searches of annotated data across species, using a single trait term.14Arnaud E. Cooper L. Shrestha R. Menda N. Nelson R.T. Matteis L. Skofic M. Bastow R. Jaiswal P. Mueller L. et al.Towards a reference Plant Trait Ontology for modeling knowledge of plant traits and phenotypes.in: Proceedings of the International Conference on Knowledge Engineering and Ontology Development. SciTePress, 2012: 220-225https://doi.org/10.5220/0004138302200225Google Scholar,15Laporte M.-A. Valette L. Cooper L. Mungall C. Meier A. Jaiswal P. Arnaud E. Comparison of ontology mapping techniques to map plant trait ontologies.in: Proceedings of the Joint International Conference on Biological Ontology and BioCreative. Oregon State University, 2016Google Scholar As a result, Planteome Release 3.0 includes ten species-specific trait ontologies developed by the CO for the crops: cassava (Manihot esculenta), maize (Zea mays), pigeonpea (Cajanus cajan), rice (Oryza sativa), sweet potato (Ipomoea batatas), soybean (Glycine max), wheat (Triticum aestivum), lentil (Lens culinaris), sorghum (Sorghum bicolor), and yam (Dioscorea sp.). These mappings can be automatically created but still require manual curation, making them difficult to maintain considering that ontologies evolve over time.15Laporte M.-A. Valette L. Cooper L. Mungall C. Meier A. Jaiswal P. Arnaud E. Comparison of ontology mapping techniques to map plant trait ontologies.in: Proceedings of the Joint International Conference on Biological Ontology and BioCreative. Oregon State University, 2016Google Scholar Planteome is developing a Plant Stress Ontology (https://github.com/Planteome/plant-stress-ontology) that will require support from the Ontologies CoP for content validation, particularly on the described pest and disease symptoms. In 2014, CGIAR began developing the AgrO to support the new Agronomy Field Information System (AgroFIMS) (https://apps.cipotato.org/hidapagrofims/),8Devare M., Aubert C., Laporte M.-A., Valette L., Arnaud E., Buttigieg P.L., (2016). Data-driven agricultural research for development: a need for data harmonization via semantics. International Conference on Biomedical Ontologies (ICBO), 2016.Google Scholar which enables scientists to create their electronic field book. AgrO describes agronomic practices and techniques, and integrates variables used in agronomic experiments by agronomists of the Data-driven Agronomy CoP and by the International Consortium for Agricultural Systems Applications. Applying the principles of the Open Biological and Biomedical Ontology (OBO) Foundry,16Smith B. Ashburner M. Rosse C. Bard J. Bug W. Ceusters W. Goldberg L.J. Eilbeck K. Ireland A. Mungall C.J. et al.The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration.Nat. Biotechnol. 2007; 25: 1251-1255Crossref PubMed Scopus (1866) Google Scholar AgrO directly integrates terms and their original URIs taken from existing ontologies, such as the Environmental Ontology (ENVO) and the Chemical Ontology (ChEBI). For example, the definition of “tillage process” in AgrO uses the “soil” concept from ENVO in addition to AgrO's novel concept “tillage implement.” Missing terms or knowledge relevant to the agronomy domain were directly proposed to the ontologies. For instance, urea is a widely used fertilizer in agriculture, but the urea concept in ChEBI was not defined as having a fertilizer role. So, the missing link was requested by AgrO and added to ChEBI. More information about AgrO content can be found on the CGIAR Platform for Big Data in Agriculture Website (https://bigdata.cgiar.org/resources/agronomy-ontology/). CGIAR and its partners perform a large number of agricultural household surveys yielding important data and statistics on the socio-economic status, production and food systems, and environment of smallholders in the developing world. The SED CoP (https://bigdata.cgiar.org/communities-of-practice/socio-economic-data/) created the “100Q Working Group” that developed 100 core questions to be included in household surveys to collect consistent information on key socio-economic indicators. The set of questions consists of the following sections: household composition and characteristics, farm characteristics, land availability and use, livestock availability and use, income and assets, gender, food security and dietary diversity, and other aspects.17Van Wijk M. Alvarez C. Anupama G. Arnaud E. Azzarri C. Burra D. Caracciolo F. Coomes D. Garbero A. Gotor E. et al.Towards a core approach for cross-sectional farm household survey data collection: a tiered setup for quantifying key farm and livelihood indicators. Community of Practice on Socio-Economic Data Report COPSED-2019-001. CGIAR Platform for Big Data in Agriculture.https://cgspace.cgiar.org/handle/10568/105714Date: 2019Google Scholar The Ontologies and SED CoPs are working together to identify concepts from the survey questions and results, which will be used to form the new SEOnt. SEOnt will provide concepts and variables to the survey forms to annotate the data collected with the 100 questions, while taking into account the sensitive nature of the personal information. The first draft of SEOnt is available on GitHub (https://github.com/AgriculturalSemantics/SEOnt). The use of ontologies in making data interoperable is also enhanced when metadata schemas are adopted, such as the metadata schema being developed by the SED CoP, which relies heavily on the work of the Ontologies CoP. CGIAR research also aims to improve the sustainability, productivity, and resilience of fish agrifood systems and collects fish-related datasets, which include fish health, diseases, breeding, genetics, and catch data, among others. Harmonizing fish data annotation with an ontology will enable easier data aggregation and analysis. One available ontology, FISHO (https://bioportal.bioontology.org/ontologies/FISHO),18Ali N.M. Khan H.A. Then A.Y. Ving Ching C. Gaur M. Dhillon S.K. Fish Ontology framework for taxonomy-based fish recognition.PeerJ. 2017; 5: e3811Crossref PubMed Scopus (8) Google Scholar focuses on ichthyology, diversity, and adaptation. The Food and Agriculture Organization (FAO) of the United Nations initiated several fisheries ontologies, but the ontologies available remained drafts.19Caracciolo C. Heguiabehere J. Gangemi A. Baldassarre C. Keizer J. Taconet M. Knowledge management at FAO: a case study on network of ontologies in fisheries.in: Suárez-Figueroa M. Gómez-Pérez A. Motta E. Gangemi A. Ontology Engineering in a Networked World. Springer, 2012: 383-405Crossref Scopus (4) Google Scholar Therefore, in May 2019, CGIAR and relevant partners formed the Fish Ontology Working Group to compile, update, and contribute fishery terms to existing ontologies. The working group plans to collaborate with the other animal science partners toward developing and adopting animal ontologies within CGIAR. To enable the interoperability of data along the agricultural value chain, the Ontologies CoP members plan to foster a collaboration with the Food Ontology (https://foodon.org/) consortium, which aims at building a comprehensive global farm-to-fork ontology20Dooley D.M. Griffiths E.J. Gosal G.S. Buttigieg P.L. Hoehndorf R. Lange M.C. Schriml L.M. Brinkman F.S.L. Hsiao W.W.L. FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration.NPJ Sci. Food. 2018; 2: 23Crossref PubMed Scopus (115) Google Scholar by contributing concepts on tropical and subtropical production systems and food products. A specific value chain ontology will be developed indicating the actors and their roles in the chain. The CoP could use the terminology compiled by CGIAR's Research Program on Policies, Institutions, and Markets for the Value Chains platform (http://tools4valuechains.org) as a source of concepts and invite social scientists and economists to contribute to this work. Finally, CGIAR needs to demonstrate in a meaningful way the contribution of its research to the Sustainable Development Goals (SDGs). Integrating objectives, targets, and processes of the CGIAR Strategic Research Framework into the SDG Interface Ontology (SDGiO) (https://github.com/SDG-InterfaceOntology/sdgio), which is developed with the support of the United Nations Environment Program, will provide a new set of concepts to annotate data about agrifood innovations and their impact on stakeholders. In general, an increasing number of controlled vocabularies, structured taxonomies, and semantically rich ontologies are developed ex novo in an ad hoc manner to support research projects, often without drawing on concepts and definitions from existing ontologies. For example, the thematic repository AgroPortal (http://agroportal.lirmm.fr/),21Jonquet C. Toulet A. Arnaud E. Aubin S. Dzalé Yeumo E. Emonet V. Graybeal J. Laporte M.-A. Musen M.A. Pesce P. Larmande P. AgroPortal: a vocabulary and ontology repository for agronomy.Comput. Electron. Agric. 2018; 144: 126-143Crossref Scopus (78) Google Scholar developed by the Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, currently compiles 121 ontologies and thesauri only for plants, agriculture, food, and biodiversity. This situation has led to a growing number of incompatible domain-specific ontologies impeding desirable data integration and interoperability. Consequently, scientists and data managers require guidance to unambiguously select the proper ontology terms in order to annotate data. Taking a step closer toward identifying and agreeing upon the criteria that make an ontology a quality resource for data annotation, the Ontologies CoP organized a webinar with an Expert Panel (https://www.youtube.com/c/OntologiesInAgriculture) involving Christopher J. Mungall (Lawrence Berkeley National Laboratory) and Pier Luigi Buttigieg (Alfred Wegener Institute), who are both members of the OBO Foundry editorial board (http://www.obofoundry.org/docs/Membership.html), Pankaj Jaiswal, leader of the Planteome project (Oregon State University), and Alexandra Lafargue, Knowledge Manager (Syngenta). A list of 17 key criteria, inspired by the OBO Foundry principles (http://www.obofoundry.org/principles/fp-000-summary.html), was proposed by the Expert Panel (Table 1). CGIAR data managers and ontology curators were asked to rank the criteria to understand which were the most important to non-expert users and should therefore be documented as a priority to guide the selection for annotation.Table 1Criteria Established by CoP Experts to Characterize the Quality of Ontologies for Data AnnotationCriteria Classified by the Expert Panel1Adhere to the OBO Foundry guidelines2Represent a unique non-overlapping knowledge domain (also known as orthogonality)3Willingness to express and integrate multiple, evidence-based classification systems in the chosen domain4Logically structured with a well-defined scope5May contain relationships and dependencies to other reference ontologies6Represent accurate science supported by evidence7Open source and Creative Commons CC-BY or CC-0 license (https://creativecommons.org/)8Must be widely used in annotation and data capture9Support both inter- and intra-specific needs with species agnostic (core) and specific (extensions) resources that work together10Sustainable funding sources11Human resources to manage (i.e., curators, editors, and developers)12Established ontology management system, including roles and responsibility13Must be designed to answer both the computing and community needs14Must explicitly identify the communities of reference15Centralized maintenance of the validated content, and distributed contribution and access16Onto" @default.
• W3089193614 created "2020-10-01" @default.
• W3089193614 creator A5002872376 @default.
• W3089193614 creator A5004905948 @default.
• W3089193614 creator A5005511675 @default.
• W3089193614 creator A5006233757 @default.
• W3089193614 creator A5008590164 @default.
• W3089193614 creator A5009186560 @default.
• W3089193614 creator A5013640580 @default.
• W3089193614 creator A5014226494 @default.
• W3089193614 creator A5018498646 @default.
• W3089193614 creator A5026303816 @default.
• W3089193614 creator A5026782462 @default.
• W3089193614 creator A5027303084 @default.
• W3089193614 creator A5030657787 @default.
• W3089193614 creator A5036847815 @default.
• W3089193614 creator A5037917300 @default.
• W3089193614 creator A5039739059 @default.
• W3089193614 creator A5042134752 @default.
• W3089193614 creator A5045485800 @default.
• W3089193614 creator A5049418039 @default.
• W3089193614 creator A5049958749 @default.
• W3089193614 creator A5051211037 @default.
• W3089193614 creator A5051738321 @default.
• W3089193614 creator A5053379004 @default.
• W3089193614 creator A5056354760 @default.
• W3089193614 creator A5057229448 @default.
• W3089193614 creator A5058556739 @default.
• W3089193614 creator A5059590876 @default.
• W3089193614 creator A5062866399 @default.
• W3089193614 creator A5069712642 @default.
• W3089193614 creator A5070120654 @default.
• W3089193614 creator A5073234001 @default.
• W3089193614 creator A5073436487 @default.
• W3089193614 creator A5077893289 @default.
• W3089193614 creator A5079997069 @default.
• W3089193614 creator A5081844388 @default.
• W3089193614 creator A5084045686 @default.
• W3089193614 creator A5088003532 @default.
• W3089193614 creator A5091806083 @default.
• W3089193614 date "2020-10-01" @default.
• W3089193614 modified "2023-10-18" @default.
• W3089193614 title "The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems" @default.
• W3089193614 cites W2080458637 @default.
• W3089193614 cites W2097877306 @default.
• W3089193614 cites W2112589398 @default.
• W3089193614 cites W2113142309 @default.
• W3089193614 cites W2117499974 @default.
• W3089193614 cites W2120534674 @default.
• W3089193614 cites W2142678031 @default.
• W3089193614 cites W2143666974 @default.
• W3089193614 cites W2148084209 @default.
• W3089193614 cites W2302501749 @default.
• W3089193614 cites W2521644033 @default.
• W3089193614 cites W2549618915 @default.
• W3089193614 cites W2604808360 @default.
• W3089193614 cites W2622241117 @default.
• W3089193614 cites W2627040512 @default.
• W3089193614 cites W2765375876 @default.
• W3089193614 cites W2769436351 @default.
• W3089193614 cites W2772877400 @default.
• W3089193614 cites W2891678595 @default.
• W3089193614 cites W2900353268 @default.
• W3089193614 cites W2903298154 @default.
• W3089193614 cites W2945510383 @default.
• W3089193614 cites W2946315735 @default.
• W3089193614 cites W2948474279 @default.
• W3089193614 cites W2950595506 @default.
• W3089193614 cites W3011662685 @default.
• W3089193614 cites W3033408540 @default.
• W3089193614 cites W4244026550 @default.
• W3089193614 doi "https://doi.org/10.1016/j.patter.2020.100105" @default.
• W3089193614 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7660444" @default.
• W3089193614 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33205138" @default.
• W3089193614 hasPublicationYear "2020" @default.
• W3089193614 type Work @default.
• W3089193614 sameAs 3089193614 @default.
• W3089193614 citedByCount "47" @default.
• W3089193614 countsByYear W30891936142021 @default.
• W3089193614 countsByYear W30891936142022 @default.
• W3089193614 countsByYear W30891936142023 @default.
• W3089193614 crossrefType "journal-article" @default.
• W3089193614 hasAuthorship W3089193614A5002872376 @default.
• W3089193614 hasAuthorship W3089193614A5004905948 @default.
• W3089193614 hasAuthorship W3089193614A5005511675 @default.
• W3089193614 hasAuthorship W3089193614A5006233757 @default.
• W3089193614 hasAuthorship W3089193614A5008590164 @default.
• W3089193614 hasAuthorship W3089193614A5009186560 @default.
• W3089193614 hasAuthorship W3089193614A5013640580 @default.
• W3089193614 hasAuthorship W3089193614A5014226494 @default.
• W3089193614 hasAuthorship W3089193614A5018498646 @default.
• W3089193614 hasAuthorship W3089193614A5026303816 @default.
• W3089193614 hasAuthorship W3089193614A5026782462 @default.
• W3089193614 hasAuthorship W3089193614A5027303084 @default.
• W3089193614 hasAuthorship W3089193614A5030657787 @default.
• W3089193614 hasAuthorship W3089193614A5036847815 @default.
• W3089193614 hasAuthorship W3089193614A5037917300 @default.
• W3089193614 hasAuthorship W3089193614A5039739059 @default.

• next