Ontologies

Community of Practice

Welcome!

Community members gathered at the 2018 PhenoHarmonIS workshop, the biennial event hosted by the Ontologies Community of Practice.

The Ontologies Community of Practice is led by Bioversity International and has been launched as a part of the CGIAR Platform for Big Data in Agriculture.

Given the multi-disciplinary nature of the CGIAR Big Data Platform for Agriculture , the CoP Ontologies will span the knowledge domains that are relevant to the Platform with the objective of establishing best practices, and guidelines in the selection of ontologies of reference.

It will also focus on the use and application of semantics for data harmonization at the levels of collection and storage, and for data interoperability and data discovery following the FAIR principles. This space can be used as a discussion area, share and request relevant information and contribute towards building the community as a whole.

Ontologies are important to all data-driven research, and this group serves as an intermediary to assess common goals, needs ,and opportunities across all BIG DATA CoPs and working groups.

The CoP aims at bringing together experts in ontology design, ontology-driven applications (fieldbooks, databases, annotation tools, query tools, etc), semantic web technologies, specialists from 15 CGIAR Centers, collaborative organizations, and other BIG DATA CoPs, to create a critical mass of expertise needed to tackle major issues related to semantics for FAIR (Findable, Accessible, Interoperable, Reusable) data in agriculture. By promoting FAIR data principles, the Ontologies CoP fosters a culture that values data and enables its members to solve global grand challenges faster, better, and at greater scale.

Get in touch

CoP Lead

Elizabeth Arnaud | Email
Elizabeth leads the Ontologies CoP and is a scientist at Bioversity International in Montpellier, France

CoP Communication

Céline Aubert | Email
Bioversity International, France

Aman Sidhu | Email
Bioversity International, France

Visit the Ontologies CoP YouTube channel

Engage with the Community

BE INFORMED

Tune in to the latest CoP updates by subscribing to our newsletter.

ENGAGE

Interact with community members through our LinkedIn Group.

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CONTRIBUTE

Share your expertise in our working groups.

Working groups

Plant Phenotypes Ontologies

The Plant Phenotypes Ontologies WG develops and maintains the community-built Crop Ontology (CO) compiling traits and variables per crop species. CO fosters the standardization of data including data recorded on phenotyping platforms, collected during food processing, generated from consumers’ surveys and used to describe ecological interventions.

This work enriches the species-agnostic reference ontologies of the Planteome project:

Bioversity International leads the working group in collaboration with CGIAR centers, Boyce Thompson Institute, members of ELIXIR-EXCELERATE, Phenome Projects, and the Jaiswal Lab of the Oregon State University.

See Ontologies Development Leaders

See Ontologies Development Partners

See CGIAR Crop Ontology Curators

Guillaume Bauchet
Breeding Bioinformatician, Boyce Thompson Institute
Banana ontology

Jing Yu
Genome Database Curator, Washington State University
Cotton ontology

Vilma Rocio Hualla Mamani
Data Management Specialist, International Potato Center
Sweet potato and potato ontologies

Abhishek Rathore
Principal Scientist and Theme Leader, International Crops Research Institute for the Semi-Arid Tropics
Chickpea, finger millet, groundnut, pearl millet, pigeonpea, and sorghum crop ontologies

Roma Rani Das
Statistician, International Crops Research Institute for the Semi-Arid Tropics
Chickpea, finger millet, groundnut, pearl millet, pigeonpea, and sorghum crop ontologies

Praveen Reddy Teegala
Programmer-cum-Systems Analyst, International Crops Research Institute for the Semi-Arid Tropics
Chickpea, finger millet, groundnut, pearl millet, pigeonpea, and sorghum crop ontologies

Afolabi Agbona
Data Manager, IITA
Cassava and yam ontologies

Noel Madalla
Bioversity International
Banana ontology

Rosemary Shrestha
Data Coordinator, CIMMYT
Wheat and maize ontologies

Marie Angélique Laporte
Associate Scientist, Bioversity International
Global Curator

Julian Pietragalla
Regional Deployment Manager, Latin America, Integrated Breeding Platform
Global Curator

Fish Ontology

The Fish Ontology Working Group aims to enable easy aggregation and analysis of data. This will be done by describing the fisheries terminologies that enhance data quality, thus ensuring that fisheries-related datasets can be integrated and used in more efficient ways.

The Fish Ontology Working Group was formed in April 2019 and is currently led by WorldFish with the support of Bioversity International.

Agronomy Ontology

AgrO, the Agronomy Ontology, provides semantically described agronomic terms to facilitate the standards-annotated collection, storage and use of agronomic data, enabling easy aggregation and analysis. AgrO is being built using traits identified by agronomists, the ICASA variables, and other existing ontologies such as ENVO, UO, and PATO. A key use case for AgrO is the Agronomy Field Information Management System (AgroFIMS) that enables digital collection of agronomic data that is semantically described a priori with agronomic terms from AgrO. This effort involves multiple centers and is lead by IFPRI and Bioversity International.

SociO!

A socio-economic ontology of controlled vocabularies, classifications, and concordances that allow standardization of key indicators. The key is that in SociO! Is a ‘living’ ontology, since the high variety of socio-economic data implies that it will need to evolve continuously.

The ontology has been developed by CGIAR researchers from the International Food Policy Research Institute (IFPRI), Bioversity International, and the International Maize and Wheat Improvement Centre (CIMMYT). The working group on the socio-economic ontology Socio! works in close collaboration with the SED CoP led by Gideon Kruseman. The Socio! working group is led by Soonho Kim of IFPRI.

Interested in contributing to the development of these ontologies?

Community News

Agricultural Ontologies in Use: The Agronomy Ontology

February 26, 2020 / Hannah Craig

The Ontologies Community of Practice is engaged in the development of ontologies for agricultural research. In a series of blog ...

Webinar – The Crop Ontology, the Integrated Breeding Platform, and Breedbase: Collaboration for harmonizing breeding data

February 25, 2020 / Hannah Craig

Webinar organized by the Ontologies Community of Practice of the CGIAR Platform for Big Data in Agriculture ...

Webinar summary – The Crop Ontology, the Integrated Breeding Platform, and Breedbase: Collaboration for harmonizing breeding data

February 25, 2020 / Hannah Craig

In this new webinar series, "All about our products and their uses," the Ontologies Community of Practice presents and explores ...

Agricultural Ontologies in Use: The MIAPPE Standard

January 27, 2020 / Hannah Craig

The Ontologies Community of Practice is engaged in the development of ontologies for agricultural research. In a series of blog ...

Webinar – Knowledge graphs: Lost in translation?

December 12, 2019 / Hannah Craig

CGIAR Webinar by the Platform for Big Data in Agriculture’s Community of Practice on Ontologies – Knowledge graphs: Lost in ...

Agricultural Ontologies in Use: New Crops and Traits in the Crop Ontology

October 10, 2019 / Hannah Craig

The Ontologies Community of Practice is engaged in the development of ontologies for agricultural research. In a series of blog ...

Resources

Tools

Ontology registries

Agroportal offers a reference ontology repository for agronomy.
Ontobee is aimed to facilitate ontology data sharing, visualization, query, integration, and analysis.
Ontology Look up Service of the European Bioinformatics Institute (EBI-EMBL)

Ontologies developed/curated by the CoP

Agronomy Ontology provides standardized terms to describe agronomic practices, techniques, and variables used in agronomic experiments.
Crop Ontology Curation Tool enables you to browse the Crop Ontology, search for specific terms and access the definition, as well as additional information.
Planteome provides a suite of reference and species-specific ontologies for plants and annotations to genes and phenotypes.

View tools

Fairsharing is a curated, informative and educational resource on data and metadata standards, inter-related to databases and data policies.
Obo-Foundry has the objective to develop a family of interoperable ontologies that are both logically well-formed and scientifically accurate.
Wheat Data Interoperability Guidelines provides a common framework for describing, representing, linking and publishing wheat data with respect to open standards.

Documents

2017 Work Plan – Detailed description of expected outcomes and deliverables for 2017
2018 Work Plan – Detailed description of expected outcomes and deliverables for 2018
2019 Work Plan – Detailed description of expected outcomes and deliverables for 2019
2020 Work Plan – Detailed description of expected outcomes and deliverables for 2020
Crop Ontology leaflet
Dimensions to consider when identifying an ontology for data

Workshops

Webinars

Webinar – The Crop Ontology, the Integrated Breeding Platform, and Breedba...

Webinar – Knowledge graphs: Lost in translation?

Webinar summary – Semantic annotation of images in the FAIR data era

Webinar – Semantic annotation of images in the FAIR data era

Webinar – Can machine learning technologies be useful to create or complet...

Events

[WEBINAR] FAIR Guidelines and Assessment in GARDIAN

June 25, 2019 – June 25, 2019 | Online

[WEBINAR] Ask Gerrit and Cheryl Anything about Decision Support System for Agrotechnology Transfer

August 29, 2019 – August 29, 2019 | Online

[WEBINAR] Semantic Annotation of Images in the FAIR Data Era

September 3, 2019 – September 3, 2019 | Online

Communities

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Interested in joining our community of practice?

Become a youth in data partner

Submit an initiative!

AgroFIMS: Your new companion for easy standardization of data collection and description

The Agronomy Field Information Management System (AgroFIMS) has been developed on CGIAR’s HiDAP (Highly-interactive Data Analysis Platform created by CGIAR’s International Potato Center, CIP). AgroFIMS draws fully on ontologies, particularly the Agronomy Ontology and the Crop Ontology. It consists of modules that represent the typical cycle of operations in agronomic trial management, and enables the creation of data collection sheets and digital data collection using the same ontology-based set of variables, terminology, units and protocols. AgroFIMS therefore:

Standardizes data collection and description for easy aggregation and inter-linking across disparate datasets;
Allows easy integration with HiDAP breeding data, or any other ontology-based datasets;
Functions as a data staging repository, allowing data uploads with view/edit permissions;
Enables data quality checks, statistical analysis of the data collected, and the generation of sophisticated statistics reports;
Aligns a priori with CGIAR’s CG Core metadata schema;
Enables easy upload to the institutional repositories, and much more.

Funding for AgroFIMS was provided by the Bill and Melinda Gates Foundation’s Open Access, Open Data Initiative, and the CGIAR Big Data Platform. Two user-testing workshops have been organized, with the most recent in March, with 14 participants including agronomists, crop modelers and developers from CGIAR centers (CIP, IITA, CIMMYT, Bioversity International, IFPRI), the University of Florida, and Rothamsted Research. A beta version will be released in summer for field testing and digital data collection using the KDSmart application developed in collaboration with Diversity Arrays Technology

Go to AGROFIMS →

Responsible Data Management Guidelines to protect privacy

CGIAR Platform for Big Data in Agriculture advocates open data for agricultural research for development. It considers that opening up research data for scrutiny and reuse confers significant benefits to society.

However, the Platform appreciates that not all research data can be open and that a broad range of legitimate circumstances may require data to be restricted.

As an integral component of its advocacy for open data, the Platform promotes responsible data management through the entire research data lifecycle from planning, collecting, storing, disclosing or publishing, transferring, discovery and archiving.

These guidelines were created from information collected from: review on best and emerging practices across various sectors in the fast changing landscape of privacy and ethics (130 external resources); privacy and ethic materials sourced from seven CGIAR centers; first draft was circulated for input and feedback across CGIAR and incorporated into this edition. It’s important to note that this is an evolving document, the next stage is to consult externally for further input.

These Guidelines are intended to assist agricultural researchers handle privacy and personally identifiable information (PII) in the research project data lifecycle.

Check the guidelines →

REUSE / TRANSFER

Ensure consistency with the DMP-PII and the purpose for which prior informed consent has been obtained
Revaluate likelihood of (re-)identification and risk of harm, particularly if it involves a public data-set containing PII (as above)
Ensure PII is stored securely to protect privacy (as above)
Minimize use of PII and risk of disclosure through pro-privacy access controls and analytical tools (as above)

Don’t transfer data containing PII unless have explicit consent
Don’t transfer data containing PII in the absence of a data sharing agreement identifying aspects such as purpose and scope of use, privacy protections measures, confidentiality and any limitations)
Don’t reuse or transfer PII until any inconsistencies with the DMP-PII and/or purpose compatibility have been resolved (e.g. through updated ethics review or consent from participant)

ARCHIVING / DISCARDING

Plan for archiving or data destruction early in the process. Destroying data can be more secure, however, archiving can be beneficial if the data has ongoing evidentiary, scientific or cultural value. If archiving, identify where and how, the budget require
Ensure DMP-PII and purpose compatibility (as above)
Ensure adequate security measures to protect privacy (as above)

Don’t wait until the end of the project to assess archiving needs when time and resources may be limited
Don’t assume the longevity of a particular format, future-proof your archives data
Don’t forget to budget for archiving data, this should be done as part of your Data Management Plan

PUBLISHING AND DISCOVERY

Ensure DMP-PII and purpose compatibility (as above)
Revaluate likelihood of (re-)identification and risk of harm, particularly if it involves a public data-set containing PII
Indicate in metadata the availability of raw data or minimized data containing PII, if available bilaterally
Minimize use of PII and risk of disclosure through pro-privacy access controls and analytical tools

Don’t include PII in public datasets unless absolutely necessary to preserve the data’s analytic potential, scientific utility or benefit to the participant (and subject to participants informed consent and a rigorous risk assessment)

STORAGE AND ANALYSIS

Ensure compatibility with the DMP-PII (as above) and also the purpose for which prior informed consent has been obtained

Ensure PII is stored securely to protect privacy, through organizational or project specific safeguards to prevent unauthorized access, accidental disclosure or breach of data (physical & technical)

encryption for the storage and transmission of PII
access control measures to limited access to PII
two-factor or multifactor authentication
cloud services & back-end security

Don’t store data in unsecured locations or on unsecured devices or servers

Don’t store encrypted data and encryption keys in locations where they can be easily accessed simultaneously

Don’t underestimate the importance and value of administrative safeguards to standardize practices (i.e. organizational policies, procedures and maintenance of security measures that are designed to protect private information, data and access)

COLLECTION

Ensure compatibility with the DMP-PII
De-identify data to anonymize by default unless it will impair the data’s analytic potential, scientific utility or benefit to the participant,
If you cannot anonymize, minimize the PII and pseudonymize to reduce the disclosure risk
Provide research participants sufficient information to use reasoned judgment to decide whether or not they wish to participate in the project
Ensure informed consent is designed to address the following elements:
- competence, comprehension, full disclosure, voluntariness
- legitimate scientific purpose for which the PII is collected and scope of use (e.g. stored, transferred, published and whether as anonymized, minimized or raw data)
- foreseeable risk of privacy loss and consequences
- meaningful alternatives including opt-in protection/anonymization
- safeguards to protect privacy, conditions on which PII may be shared and any limitations on reuse or third- party access and use of PII
- permission to follow-up or contact the participant and for what purpose (including by third- parties)
- participant’s right to withdraw and rights regarding their data (e.g. to be informed; to access; to rectify; to object; to erase)
- inclusion of physical, phone and/or electronic contact (at least two forms of contact) that participant can reach to exert her/rights
- explicit consent and participant’s acknowledgement of understanding
- if written, provide the participant a copy of processed informed consent
Use plain language and adapt informed consent to meet the needs of vulnerable populations (e.g. obtain orally or in local language)

Don’t collect PII unless you have a Data Management Plan and any necessary approvals in place, including the recorded approval of the potential participant
Don’t collect PII unless you absolutely need it
Don’t assume that removal of direct identifiers is sufficient to anonymize data or that all de-identification techniques will result in anonymized data. Consider the risk of re-identification of a research participant, particularly if datasets are combined. If there is a reasonable risk of re-identification the information should be handled as PII (i.e. undertake risk analysis, evaluate stronger anonymization techniques, seek informed consent for the disclosure of data and explain its possible consequences)
Don’t include vulnerable participants or communities if their ability or capacity to provide voluntary informed consent is genuinely in question
Don’t underestimate the potential of quasi or indirect identifiers to identify an individual, particularly the inherent ability of location-based data to identify participants and their communities, and the increased risk of harm this may pose to potentially vulnerable individuals/communities
Avoid seeking overly broad consent that may call into question transparency or a research participant’s understanding regarding the use of their PII, be specific regarding the activities, purpose and limitations associated with PII so that the participant can make a genuinely informed decision and downstream users can evaluate purpose compatibility and seek fresh consent if needed

PLANNING AND APPROVAL

Develop a Data Management Plan which governs the handling of PII in the research project and beyond (DMP-PII). It should address:
- the type and nature of PII
- compliance requirements (including necessary forms for obtaining consent, and ethics clearance, if applicable)
- legitimate research objectives that will be advanced by the PII
- foreseeable risks and consequences if participants are identified from the data
- privacy protection measures (or lack thereof) for collection, storage, transfer and publishing
- process for obtaining informed consent
- timeframe or trigger for archiving or deletion of PII
Employ stricter standards for research involving vulnerable populations such as children or illiterate participants or sensitive data such as ethnicity or religious beliefs
Undertake due-diligence of datasets previously collected by you or third parties to ensure you are entitled/permitted to use for your research project
Consult the legal, IRB or ethics clearance committee or any other relevant institutional group for specific institutional, local, regional or national policies and regulatory frameworks that may apply to PII in the context of your work

Don’t leave the handling of PII and privacy protection as an after-thought, plan ahead!
Don’t forget to check local laws and donor or third-party requirements in addition to institutional policies governing research ethics and privacy protection (seek expert support if unsure!)
Don’t ignore ethical practices/standards, if your institution does not have an ethics framework or clearance process in place self-assess!
In assessing whether information is capable of identifying someone (i.e. PII) don’t limit your focus to direct identifiers, also consider indirect/quasi identifiers. Appreciate this will depend on the context of the research project, the data in question and external data which is or may become otherwise available (i.e. there is no exhaustive list).
In assessing risk of harm don’t forget to consider potential harm to the participant’s community or groups of individuals that can otherwise be identified or associated with the participant

Ontologies

Welcome!

Read more

Get in touch

CoP Lead

CoP Communication

Engage with the Community

BE INFORMED

ENGAGE

CONTRIBUTE

Working groups

Plant Phenotypes Ontologies

See Ontologies Development Leaders

See Ontologies Development Partners

See CGIAR Crop Ontology Curators

Fish Ontology

Agronomy Ontology

SociO!

Interested in contributing to the development of these ontologies?

Community News

Resources

Tools

Ontology registries

Ontologies developed/curated by the CoP

View tools

Documents

Workshops

Webinars

Events

Communities

Interested in joining our community of practice?

Become a youth in data partner

Submit an initiative!

AgroFIMS: Your new companion for easy standardization of data collection and description

Responsible Data Management Guidelines to protect privacy

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REUSE / TRANSFER

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ARCHIVING / DISCARDING

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PUBLISHING AND DISCOVERY

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STORAGE AND ANALYSIS

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COLLECTION

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<img class=" wp-image-93217 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/planning-icon.png" alt="" width="100" height="114" />

PLANNING AND APPROVAL

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