Convention Highlights

Through the COVID-19 pandemic, we’ve seen how thousands of small crises can unfold differently, and in different ways, around the world. Digital dynamism opens up the possibility of moving beyond simplified models of the world, to being able to hyperlocalize, to understand changes in fluid and dynamic contexts, and to act with agility and precision amidst that complexity.

For the 2020 convention theme, Digital Dynamism for Adaptive Food Systems, we examined food system resilience and highlighted how digital tools and technologies can help us sense, respond and (re)build better systems in times of global food security crises.

Key takeaways

Innovation
Digital Dynamism across One CGIAR
Open, data-driven collaboration is powerful
Inclusivity and access
Collective global action

At a glance

registrants

with 1300 participants actively engaging through the Whova platform

speakers

including 50% of women

companies, organizations & institutions

75% of registrants were non-CGIAR

audience reach

awarded

to 7 Inspire projects

Digital dynamism across One CGIAR

The 2020 virtual CGIAR Convention on Big Data in Agriculture was the first One CGIAR convention. The event highlighted how CGIAR’s 13 global research centers are employing dynamic digital methods to combat food security challenges flowing from current crises.

Various CGIAR centers created glimpse videos to highlight their scientific innovations for food, land and water systems can be deployed faster, at a larger scale, and at reduced cost, having greater impact where they are needed the most:

AfricaRice: Helping rice farmers in Africa with big data and digital tools

Alliance of Bioversity International and CIAT: Putting agricultural tools and information in the hands of farmers

International Institute for Tropical Agriculture (IITA): Harnessing integrated digital tools to accelerate agricultural transformation in Sub-Saharan Africa

International Potato Center (CIP): Leveraging big data and technologies to conserve genetic biodiversity, develop new crop varieties, and improve agronomic decision-making

International Rice Research Institute (IRRI): Testing new techniques and improving access to equipment for farmers in order to ensure food security

International Crops Research Institute for the Semi-Arid Tropics (ICRISAT): Using cutting edge technologies in order to create resilient and adaptive food systems

International Maize and Wheat Improvement Center (CIMMYT): Increasing the productivity of maize and wheat cropping systems using data-driven approaches to agricultural research

International Water Management Institute (IWMI): Researching methods of harvesting, using, and deploying diverse data innovations to achieve a water-secure world

International Center for Agricultural Research in the Dry Areas (ICARDA): Responding to the climate crisis through dynamic digitization of its research for development

WorldFish: Driving aquatic foods research with data and digital technologies

Innovation

The 2020 Inspire Challenge

120 applications



7 winners



US$700K awarded

Most ever pilot projects awarded

Seven groundbreaking, big data projects were selected as winners of CGIAR’s 2020 Inspire Challenge at the convention. The seven finalists were chosen from a group of 15 finalists projects–the largest cohorts of Inspire Challenge finalists and pilot awards to date.
The virtual space allowed us to showcase this record-setting number of highly qualified finalists, in which almost all of the 13 CGIAR Centers are represented. Project teams pitched live during the convention to a panel of expert judges that included: Sieglinde Snapp (Michigan State University), Rhiannan Price (DevGlobal Partners), Cheryl Palm (University of Florida), Dina Najjar (ICARDA), and Chong Kean Ng (Asian Development Bank).
In light of this year’s food system shocks due to the recent pandemic, the 2020 Inspire Challenge evaluation included specific COVID-19 response, recovery, and resilience metrics added to the judging rubric. Applications were received under four categories: Sustaining Farm Income, Measuring and Building Resilience, Sensing and Renewing Ecosystems, and Revealing Food Systems, with the latter two being co-designed with funders to specifically address pandemic-related challenges.

During the live awards ceremony, grants of US$100,000 were awarded to the 7 following projects:

“The 2020 Inspire winners aim to create more resilient food value chains in the face of the COVID-19 pandemic and climate shocks. Projects such as these will help assure safer food, collect much needed ‘real time’ diet diversity information for tracking and targeting malnutrition, and their experimentation with AI platforms such as IBM Watson will help provide advisory messages at times that extension agents cannot get to the field. It will be exciting to see how these innovative teams roll out their projects.”

Inspire judge Cheryl Palm, Research Professor in Agricultural and Biological Engineering and a core faculty of the Institute for Sustainable Food Systems at the University of Florida.

Tech demos

Inclusivity and access

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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

Convention Highlights

Key takeaways