Partnerships

Towards Responsible Collective Action

Partnerships for responsible collective action

CGIAR Platform for Big Data in Agriculture

The CGIAR Platform for Big Data in Agriculture (Platform) harnesses the capabilities of ‘Big Data’ to accelerate and enhance the impact of international agricultural research for smallholder farmers.

Operating for an initial period of 2017 – 2021, the Platform provides global leadership for CGIAR on Big Data, focusing on three key pillars:

Organize

Organizing open data

Convene

Convening partners to develop innovative ideas

Inspire

Demonstrating the power of big data analytics through inspiring project

As part of CONVENE, the Platform supports CGIAR Centers in pursuing ambitious partnerships focused on digital transformation and extending the reach of CGIAR researchers to collaborate with a wide range of partners across the AgriFood ecosystem. The Platform also brokers pan-CGIAR partnerships and access to products and services which leverage CGIAR’s economies of scale.

Motivation for Collective Action

The 2030 Sustainable Development Goals (SDGs) approved by UN members set ambitious objectives for creating a more sustainable world. Organizations such as CGIAR which undertake impact-driven Agricultural Research for Development (AgR4D) are well placed to contribute to the SDGs by providing an innovation pipeline focusing on sustainable agriculture, food security and improved nutrition. Such organizations, however, often struggle to sufficiently scale their AgR4D innovations to reach smallholder farmers, cooperatives and SMEs in emerging markets.

Barriers to scaling are a common constraint in addressing global development challenges. This is reflected in SDG 17 ‘Partnerships for the Goals’ which acknowledges the importance of multi-stakeholder partnerships that mobilize and share knowledge, expertise, technology and financial resources, in achieving development outcomes at scale.

Principles for Collective Action in Sustainable Agriculture and Food Security

To foster a more enabling environment for multi-stakeholder partnerships in agriculture, the Platform in collaboration with One Planet Solutions have developed high-level Principles for Collective Action in Sustainable Agriculture and Food Security (Principles for Collective Action) which are summarised below.

These have been designed, in particular, to facilitate engagement with the private sector and to leverage market channels for scaling.

Principles for collective action

Maximize Impact

Maximize sustainability and impact through a shared vision, clear objectives, a roadmap for scaling and measurable indicators for success

Align Interests

Align interests and expectations by balancing pre-competitive & pro-commercial priorities

Use Data Responsibly

Empower or safeguard data providers through responsible data practices

Bio-innovate and share

Use biodiversity and traditional knowledge responsibly in innovation

Be Transparent and Inclusive

Communicate early and often to build trust, and listen to stakeholders

Call for Collective Action and AgData Alliance

These Principles for Collective Action are intended to facilitate responsible collaboration between organizations undertaking AgR4D and organizations involved in the co-development and scaling or commercialization of AgR4D innovations.

They will underpin a “call for collective action” in which the Platform calls on industry to harness synergies and strengthen collective action with CGIAR to help transform sustainable agriculture, food security and improved nutrition in the 10 years remaining of the SDGs.

This call will be issued as part of the CGIAR Digital Strategy, to be announced early in 2020. The strategy will include an ‘AgData Alliance’ initiative, coordinated by the Platform as a trusted broker, which will facilitate responsible access, transfer and use of agricultural research data and smallholder farmer data to a broad range of stakeholders, including the private sector.

The Principles for Collective Action will be used in the design and implementation of the alliance and the digital strategy overall. Pursuant to the “call for collective action” industry partners will be invited to express their interest in the Alliance and their commitment to the Principles, as per the template letter.

Tell us about your interest in supporting the Principles for Collective Action for Sustainable Agriculture and Food Security and joining the AgData Alliance!

Expression of support

Partnership opportunities

The Big Data in Agriculture Convention

Get in touch about opportunities for our 2020 event.

Get in touch

The Inspire Challenge

Get in touch about opportunities for our 2020 Inspire Challenge.

Get in touch

Current Partners

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.

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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

Partnerships

Partnerships for responsible collective action

CGIAR Platform for Big Data in Agriculture

Organize

Convene

Inspire

Motivation for Collective Action

Principles for Collective Action in Sustainable Agriculture and Food Security

Principles for collective action

Maximize Impact

Align Interests

Use Data Responsibly

Bio-innovate and share

Be Transparent and Inclusive

Call for Collective Action and AgData Alliance

Tell us about your interest in supporting the Principles for Collective Action for Sustainable Agriculture and Food Security and joining the AgData Alliance!

Partnership opportunities

The Big Data in Agriculture Convention

The Inspire Challenge

Current 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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