The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems

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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.

Graphical Abstract

Highlights

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FAIR agricultural data must use ontologies that are popular in the knowledge domain
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CGIAR Ontologies Community of Practice holds expertise for agricultural data annotation
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The Community selects innovative solutions to assist the data annotation with ontologies
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The Community develops multidisciplinary open-source ontologies for agricultural data

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.

Abstract

The deployment of digital technology in Agriculture and Food Science accelerates the production of large quantities of multidisciplinary data. The Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture harnesses the international ontology expertise that can guide teams managing multidisciplinary agricultural information platforms to increase the data interoperability and reusability. The CoP develops and promotes ontologies to support quality data labeling across domains, e.g., Agronomy Ontology, Crop Ontology, Environment Ontology, Plant Ontology, and Socio-Economic Ontology.

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Most cited references 34

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The FAIR Guiding Principles for scientific data management and stewardship

Mark D Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg … (2016)

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

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The Gene Ontology Resource: 20 years and still GOing strong

Jurg Bahler (2019)

Abstract The Gene Ontology resource (GO; http://geneontology.org) provides structured, computable knowledge regarding the functions of genes and gene products. Founded in 1998, GO has become widely adopted in the life sciences, and its contents are under continual improvement, both in quantity and in quality. Here, we report the major developments of the GO resource during the past two years. Each monthly release of the GO resource is now packaged and given a unique identifier (DOI), enabling GO-based analyses on a specific release to be reproduced in the future. The molecular function ontology has been refactored to better represent the overall activities of gene products, with a focus on transcription regulator activities. Quality assurance efforts have been ramped up to address potentially out-of-date or inaccurate annotations. New evidence codes for high-throughput experiments now enable users to filter out annotations obtained from these sources. GO-CAM, a new framework for representing gene function that is more expressive than standard GO annotations, has been released, and users can now explore the growing repository of these models. We also provide the ‘GO ribbon’ widget for visualizing GO annotations to a gene; the widget can be easily embedded in any web page.

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Expansion of the Gene Ontology knowledgebase and resources

Jurg Bahler (2017)

The Gene Ontology (GO) is a comprehensive resource of computable knowledge regarding the functions of genes and gene products. As such, it is extensively used by the biomedical research community for the analysis of -omics and related data. Our continued focus is on improving the quality and utility of the GO resources, and we welcome and encourage input from researchers in all areas of biology. In this update, we summarize the current contents of the GO knowledgebase, and present several new features and improvements that have been made to the ontology, the annotations and the tools. Among the highlights are 1) developments that facilitate access to, and application of, the GO knowledgebase, and 2) extensions to the resource as well as increasing support for descriptions of causal models of biological systems and network biology. To learn more, visit http://geneontology.org/.

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Author and article information

Contributors

Elizabeth Arnaud

Journal

Journal ID (nlm-ta): Patterns (N Y)

Journal ID (iso-abbrev): Patterns (N Y)

Title: Patterns

Publisher: Elsevier

ISSN (Electronic): 2666-3899

Publication date PMC-release: 25 September 2020

Publication date Collection: 09 October 2020

Publication date (Electronic): 25 September 2020

Volume: 1

Issue: 7

Electronic Location Identifier: 100105

Affiliations

[1 ]Digital Solutions Team, Digital Inclusion Lever, Bioversity International, Montpellier Office, Montpellier, France

[2 ]Markets, Trade and Institutions Division (MTID), International Food Policy Research Institute (IFPRI), Washington, DC, USA

[3 ]Department of Sociology, Philosophy and Anthropology & Exeter Centre for the Study of the Life Sciences (Egenis), University of Exeter, Exeter, UK

[4 ]Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA

[5 ]Socio-Economics Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, State of México, Mexico

[6 ]Helmholtz Metadata Collaboration, GEOMAR Helmholtz Centre for Ocean Research, Kiel, Germany

[7 ]Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

[8 ]Integrated Breeding Platform, Texcoco, State of México, Mexico

[9 ]Cassava Breeding Program, International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria

[10 ]Aquaculture and Fisheries Sciences, Worldfish, Penang, Malaysia

[11 ]Agrifood Policy Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines

[12 ]Research Informatics Unit (RIU), International Potato Center (CIP), Lima, Peru

[13 ]Statistics, Bioinformatics & Data Management (SBDM) Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, Telangana, India

[14 ]Biometrics Unit, International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria

[15 ]Mueller Bioinformatics Laboratory, Boyce Thompson Institute for Plant Research, Ithaca, NY, USA

[16 ]Unité Délégation à l’Information Scientifique et Technique - DIST, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Versailles, France

[17 ]Digital Biology, Earlham Institute, Norwich, Norfolk, UK

[18 ]Monitoring, Evaluation and Learning Team, International Center for Agricultural Research in the Dry Areas (ICARDA), Beirut, Lebanon

[19 ]Research Methods Group (RMG), World Agroforestry (ICRAF), Nairobi, Kenya

[20 ]UPR AIDA, The French Agricultural Research Centre for International Development (CIRAD), Sainte-Clotilde, Réunion, France

[21 ]GEMS Informatics Initiative, University of Minnesota, St. Paul, USA

[22 ]CP RDIT, Syngenta, St Sauveur, France

[23 ]Bayer Crop Science SA-NV, Diegem, Belgium

[24 ]Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

[25 ]CGIAR Platform for Big Data in Agriculture, International Center for Tropical Agriculture (CIAT), Cali, Colombia

[26 ]BioinfOmics, Plant Bioinformatics Facility, Université Paris-Saclay, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Versailles, France

[27 ]Performance, Innovation and Strategic Analysis, International Center for Tropical Agriculture (CIAT), Regional Office for Africa, Nairobi, Kenya

[28 ]Bioinformatics Cluster, Strategic Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines

[29 ]Data Management Section, International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria

[30 ]Genetic Resources Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, State of México, México

[31 ]Environment and Production Technology Division (EPTD), International Food Policy Research Institute (IFPRI), Washington, DC, USA

[32 ]Université de Montpellier, Montpellier, France

Author notes

[∗ ]Corresponding author e.arnaud@ 123456cgiar.org

[33]

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Article

Publisher Item ID: S2666-3899(20)30139-2 Publisher ID: 100105

DOI: 10.1016/j.patter.2020.100105

PMC ID: 7660444

PubMed ID: 33205138

SO-VID: 718271ba-caff-4575-b5a9-06d822a7c482

License:

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

History

Date received : 6 March 2020

Date revision received : 28 May 2020

Date accepted : 24 August 2020

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The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems

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Summary

Graphical Abstract

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The Bigger Picture

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Big Data Privacy

Most cited references 34

The FAIR Guiding Principles for scientific data management and stewardship

The Gene Ontology Resource: 20 years and still GOing strong

Expansion of the Gene Ontology knowledgebase and resources

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Cited by 20

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