About the Platform

Using big data to solve agricultural development problems faster, better and at greater scale.

Using big data approaches to solve agricultural development problems faster, better and at a greater scale than before.

Data has become a valuable global commodity. But it is much more than simply information: in expert hands, it is intelligence.

Already, analysts are finding ways to turn big data — the immense stocks of information collected in computers worldwide — into an invaluable resource for planning and decision-making. It is helping accelerate the development of robust responses to some of the most pressing challenges of our time: climate change/variability, food insecurity and malnutrition, and environmental degradation. It is transforming the world of genomics and crop breeding and revolutionizing disciplines from climate modelling to agronomy. It is helping refine policies and improve lives.

The smart and effective use of data will be one of the most important tools for achieving the United Nations’ Sustainable Development Goals. Big data represents an unprecedented opportunity to find new ways of reducing hunger and poverty, by applying data-driven solutions to ongoing research for development impact.

Read the full proposal

The CGIAR Platform for Big Data in Agriculture is carried out with support from the CGIAR Trust Fund, UKAID and through bilateral funding agreements.

Team Members & Contact

Organize, Convene, and Inspire

The ultimate goal of the CGIAR Platform for Big Data in Agriculture is to harness the capabilities of big data to accelerate and enhance the impact of international agricultural research. This 5-year platform (2017 – 2021) will provide global leadership in organizing open data, convening partners to develop innovative ideas, and demonstrating the power of big data analytics through inspiring projects.

CGIAR is uniquely positioned to be a thought leader on the use of big data and information technology in agriculture to drive equitable rural development.

ORGANIZE

Support and improve data generation, access, and management in CGIAR

The Platform embraces the power of big data analytics, supporting CGIAR as it becomes a leader in generating actionable data-driven insights for stakeholders. It builds capacity throughout CGIAR to generate and manage big data, assisting CGIAR and its partners’ efforts to comply with open access / open data principles to unlock important research and datasets. It empowers researchers to strengthen data analytical capacity, developing practical big data tools and services in a coordinated way. It also addresses critical gaps, both organizational and technical, expanding the horizon of CGIAR research.

CONVENE

Collaborate and convene around big data and agricultural development

The Platform empowers CGIAR and its community to deliver on the potential of big data to bring results for smallholder agriculture. It develops ambitious external partnerships, bringing together big data practitioners with global private sector brands, local start-ups, universities, and others to facilitate collaboration and ideation. It convenes an annual big data in agriculture convention, bringing key actors to CGIAR to generate innovations and democratize big data opportunities. The Platform builds capacity internally and externally on big data approaches in agriculture, developing communities of practice that will encourage interaction and further engage a range of actors to produce new ideas to solve development problems.

INSPIRE

Lead by example and inspire how big data can deliver development outcomes

The Platform demonstrates the power of CGIAR big data analytics through “Inspire” projects that solve development challenges at the core of our mission. It challenges partners, universities and others to use our data to create pilot opportunities that scale. It shows the power of big data analytics and ICTs to provide unprecedented multi-disciplinary data to researchers, delivering actionable information to farmers and inspiring others to use big data to create impact. It also provides venture capital that funds novel approaches, democratizing big data to include smallholder farmers and monitoring the state of agriculture and food security in real time to inform critical national, regional and global policies and decisions.

Impacts by 2021

The Platform aims to increase the impact of agricultural development by embracing big data approaches to solve development problems faster, better and at greater scale than before.

The Platform will focus on opening up and sharing agricultural data, demonstrating that CGIAR is able to hold in trust and deliver data-related global public goods. In this way CGIAR will become a broker of big data information, actively promoting data-driven agricultural development.

The Platform will also work to develop novel methodologies and innovative pilot projects to increase the impact of its community or researchers and analysts. It will establish non-traditional partnerships to bring together institutions with complementary big data expertise, connecting CGIAR scientists to a global network of big data practitioners and developers, expanding the delivery capability and horizons of CGIAR research.

The expected outcomes of the Platform include:

Greater data and knowledge sharing across CGIAR – by reducing barriers to information access and reuse, the Platform will democratize information availability and use, to help farmers and policymakers take reliable, informed decisions.
Foster a culture of open access publishing and data sharing across CGIAR Centers and partner organizations
Recognition of CGIAR as a global thought leader on big data in agriculture and  development.

A Global Alliance

The CGIAR Platform for Big Data in Agriculture is global. It connects experts all over the world to tackle stubborn agricultural challenges across the developing world, from Africa and Asia to Latin America and the Caribbean.

The 15 CGIAR Research Centers and 12 Research Programs are partners in the Platform, alongside 70 external thought partners ranging international institutions, universities to private companies. They cover public to private, developing to developed countries, and analytics to ICT deployment.

Partners

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Discover agricultural data and publications

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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) 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

About the Platform

Using big data approaches to solve agricultural development problems faster, better and at a greater scale than before.

Organize, Convene, and Inspire

CGIAR is uniquely positioned to be a thought leader on the use of big data and information technology in agriculture to drive equitable rural development.

ORGANIZE

Support and improve data generation, access, and management in CGIAR

CONVENE

Collaborate and convene around big data and agricultural development

INSPIRE

Lead by example and inspire how big data can deliver development outcomes

Impacts by 2021

A Global Alliance

Partners

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

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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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PLANNING AND APPROVAL

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