Publications

Annual Reports

Annual Report 2020

PDF version

Annual Report 2019

Year in Review 2018

Year in Review 2017

Briefs and publications

Toward a digital one CGIAR: Strategic research on digital transformation in food, land, and water systems in a climate crisis

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Overcoming challenges to digital agribusiness start-ups (2019)

Download PDF

Platform Presentation Leaflet
(2017)

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CGIAR Platform for Big Data in Agriculture – Inspire Challenge Review (2017-2020)

Download PDF

A scalable scheme to implement data-driven agriculture for small-scale farmers (2019)

Download PDF

Accelerating CGIAR’S digital transformation: A high-level assessment of digital strategy across CGIAR (2019)

Download PDF

Plans of Work and Budget

Convention on Big Data in Agriculture Reports

Convention 2018
Full Report

Download PDF

Convention 2017
Full Report

Download PDF

Convention 2017 – Ontology COP Report

Download PDF

Communities of Practice Publications

Download PDF

Findability of Gender Datasets

Download PDF

Blockchain for Food: Making Sense of Technology and the Impact on Biofortified Seeds

Download PDF

Towards a core approach for cross-sectional farm household survey data collection

Download PDF

CoP on Crop Modeling Mini-Grants Report 2018

Download PDF

Plan for Sharing Crop Modeling expertise during 2019

Download PDF

Plan for Sharing Crop Modeling expertise during 2019

Download PDF

Crop Modeling Gaps and Strengths within the CGIAR (2018)

Download PDF

Overcoming challenges to digital agribusiness start-ups in developing countries

Download PDF

Platform-suported Publications

AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data
View publication

Artificial intelligence, systemic risks, and sustainabilityView publication

Towards large-scale decision support for farmers: site and season specific advice for maize agronomy in MalawiDownload PDF

Artificial intelligence for agricultural supply chain risk management

Download PDF

Artificial intelligence for agricultural supply chain risk management: Preliminary prioritization and constraints for the deployment of Al in food chains assessed by CGIAR scientists

Download PDF

Dealing with data privacy and security to support agricultural R&D

Download PDF

Design of a reference architecture for an IoT sensor networkDownload PDF

Design Process of an IoT Architecture for Agriculture at Alliance of Bioversity International and CIAT

Download PDF

E-agriculture in Action: Big data for agriculture

Download PDF

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Discover agricultural data and publications

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

The Agronomy Field Information Management System (AgroFIMS) allows users to create fieldbooks to collect agronomic data that is already tied to a metadata standard (the CG Core Metadata Schema, aligned with the standard Dublin Core), and semantic standards like the Agronomy Ontology (AgrO), generating data that is Findable, Accessible, Interoperable, and Reusable (FAIR) at collection. AgroFIMS therefore standardizes data collection and description for easy aggregation and inter-linking across disparate datasets. The fieldbooks you create can be exported to the Android-based KDSmart data collection application, and collected data imported back to AgroFIMS for statistical analysis and reports. In 2021 AgroFIMS will allow you to set up agronomic survey questionnaires, for data collection via ODK. It will also allow easy upload of your “born FAIR” data to Dataverse repository platforms with Dublin Core-compliant metadata schemas. Funding for AgroFIMS was provided by the Bill and Melinda Gates Foundation’s Open Access, Open Data Initiative, and the CGIAR Platform for Big Data in Agriculture. AgroFIMS is under GPL license. 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

Publications

Annual Reports

Annual Report 2020

Annual Report 2019

Year in Review 2018

Year in Review 2017

Briefs and publications

Toward a digital one CGIAR: Strategic research on digital transformation in food, land, and water systems in a climate crisis

Overcoming challenges to digital agribusiness start-ups (2019)

Platform Presentation Leaflet(2017)

CGIAR Platform for Big Data in Agriculture – Inspire Challenge Review (2017-2020)

A scalable scheme to implement data-driven agriculture for small-scale farmers (2019)

The 2017 CGIAR Inspire Challenge: Innovation strategies for digital agriculture

Accelerating CGIAR’S digital transformation: A high-level assessment of digital strategy across CGIAR (2019)

Building a Data Ecosystem for Food Security and Sustainability in AgTech V3.0.

Plans of Work and Budget

Plan of Work and Budget 2020<img decoding="async" class="aligncenter" src="https://cgspace.cgiar.org/bitstream/handle/10568/107168/13_Big%20Data%20Platform%20POWB%202020.pdf.jpg?sequence=4&amp;isAllowed=y" alt="" width="182" height="235" />

Plan of Work and Budget 2018

Plan of Work and Budget 2017

Convention on Big Data in Agriculture Reports

Convention 2018Full Report

Convention 2017Full Report

Convention 2017 – Ontology COP Report

Communities of Practice Publications

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

Radiation Interception, Conversion and Partitioning Efficiency in Potato Landraces: How Far Are We from the Optimum?

Enabling reusability of plant phenomic datasets with MIAPPE 1.1

CGIAR modeling approaches for resource-constrained scenarios: II. Models for analyzing socioeconomic factors to improve policy recommendations

BIG DATA Crop Modeling Community of Practice – Work Plan 2020

BIG DATA Geospatial Data Community of Practice – Work Plan 2020

BIG DATA Ontologies Community of Practice – Work Plan 2020

BIG DATA Socio-Economic Data Community of Practice – Work Plan 2020

BIG DATA Data-Driven Agronomy Community of Practice – Work Plan 2020

Findability of Gender Datasets

Blockchain for Food: Making Sense of Technology and the Impact on Biofortified Seeds

Towards a core approach for cross-sectional farm household survey data collection

CoP on Crop Modeling Mini-Grants Report 2018

Plan for Sharing Crop Modeling expertise during 2019

Plan for Sharing Crop Modeling expertise during 2019

Crop Modeling Gaps and Strengths within the CGIAR (2018)

Overcoming challenges to digital agribusiness start-ups in developing countries

BIG DATA Data-Driven Agronomy Community of Practice – Work Plan 2019

BIG DATA Crop Modeling Community of Practice – Work Plan 2019

BIG DATA Ontologies Community of Practice – Work Plan 2019

BIG DATA Socio-economic Data Community of Practice – Work Plan 2019

BIG DATA Geospatial Data Community of Practice – Work Plan 2019

BIG DATA Socio-Economic Data Community of Practice – Work Plan 2018

BIG DATA Crop Modeling Community of Practice – Work Plan 2018

BIG DATA Geospatial Data Community of Practice – Work Plan 2018

BIG DATA Data-Driven Agronomy Community of Practice – Work Plan 2018

BIG DATA Ontologies Community of Practice – Work Plan 2018

BIG DATA Ontologies Community of Practice – Work Plan 2017

BIG DATA Socio-Economic Data Community of Practice – Work Plan 2017

BIG DATA Geospatial Data Community of Practice – Work Plan 2017

Platform-suported Publications

AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data​<img decoding="async" class="aligncenter" src="https://cgspace.cgiar.org/bitstream/handle/10568/115543/fsufs-05-726646.pdf.jpg?sequence=4&amp;isAllowed=y" alt="" width="179" height="253" />View publication

Artificial intelligence, systemic risks, and sustainability<img decoding="async" class="aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2021/09/Artificial-intelligence-systemic-risks-and-sustainability.jpg" alt="" width="179" height="253" />View publication

Towards large-scale decision support for farmers: site and season specific advice for maize agronomy in Malawi<img decoding="async" class="aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2020/11/IVR-PDF.jpg" alt="" width="179" height="253" />Download PDF

Artificial intelligence for agricultural supply chain risk management

<img decoding="async" class="aligncenter" src="https://cgspace.cgiar.org/bitstream/handle/10568/108402/AI_Ag_SupplyChain_RiskManagment.pdf.jpg?sequence=4&amp;isAllowed=y" alt="" width="179" height="253" />Download PDF

Artificial intelligence for agricultural supply chain risk management: Preliminary prioritization and constraints for the deployment of Al in food chains assessed by CGIAR scientists

Dealing with data privacy and security to support agricultural R&D

Design Process of an IoT Architecture for Agriculture at Alliance of Bioversity International and CIAT

E-agriculture in Action: Big data for agriculture

Search the website

Discover agricultural data and publications

Powered by GARDIAN

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

<img class="wp-image-93311 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/REUSE_arrow.png" alt="" width="100" height="100" />

REUSE / TRANSFER

<img class="alignnone size-full wp-image-92805 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/tips-icon-orange-100px.png" alt="" width="100" height="100" />

<img class=" wp-image-93476 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/DONT-DO-ICON.png" alt="" width="100" height="100" />

ARCHIVING / DISCARDING

<img class="alignnone size-full wp-image-92805 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/tips-icon-orange-100px.png" alt="" width="100" height="100" />

<img class=" wp-image-93476 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/DONT-DO-ICON.png" alt="" width="100" height="100" />

<img class="wp-image-93312 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/rss-transparent-300x300px.png" alt="" width="100" height="100" />

PUBLISHING AND DISCOVERY

Platform Presentation Leaflet
(2017)

Plan of Work and Budget 2020

Convention 2018
Full Report

Convention 2017
Full Report

AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data
View publication

Artificial intelligence, systemic risks, and sustainabilityView publication

Towards large-scale decision support for farmers: site and season specific advice for maize agronomy in MalawiDownload PDF

Download PDF