The 2017 Inspire Challenge

The Inspire Challenge is about challenging partners, universities, and others to use CGIAR data to create innovative pilot projects that will scale. We’re looking for novel approaches that democratize data-driven insights to inform local, national, regional, and global policies and applications in agriculture and food security in real time; helping people–especially smallholder farmers and producers–to lead happier and healthier lives.

In 2017, we received over 120 proposals to our Inspire Challenges from applicants all over the world to use our data to create pilot opportunities that scale. We wanted to show the power of big data analytics and ICTs to provide unprecedented, multi-disciplinary insights to researchers, delivering actionable information to farmers and inspiring others to use big data to create impact.

Of the 120 excellent proposals, we awarded five US$100,000 each to develop their innovative pilot projects.

PROPOSALS RECEIVED

WINNERS

US$ GRANT / WINNER

Meet the 2017 winners

The CGIAR Platform for Big Data in Agriculture awarded five research proposals US$100K each at the inaugural annual convention, during 19-22 of September.

The five winning teams have 12 months to implement small-scale proof of concept pilots to demonstrate viability.

The five successful pilots were placed on the trajectory to wider-scale implementation, including the possibility of receiving an additional US$250,000 of scale-up funding. They also have additional help in finding continued funding and widespread adoption within CGIAR, to ensure that these innovations become a part of how we operate system-wide. Read the winning proposals below.

Inspire Winner 2017, Scale Up Winner 2018

Seeing is believing – Using smartphone camera data

Pictured-based advisories have improved the correlation between insurance payouts and the damage that farmers experience. Farmers are therefore willing to pay more for a combined advisory-insurance package.

Inspire Winner 2017, Scale Up Runner 2018

MARPLE: Real time diagnostics for devastating wheat rust

The project has developed an affordable, mobile in-field pathogenomics platform called MARPLE (Mobile And Real-time PLant disEase), which provides extremely rapid diagnostics ( < 48H).

Inspire Winner 2017, Scale Up Runner 2018

Farm.ink: Analysing livestock social media data for farmer chatbot

The project has built a Facebook community called Africa Farmers Club and mobile chatbot service. A survey of chatbot users revealed that 92% of respondents said the Club had influenced the way they farmed.

Inspire Winner 2017, Scale Up Winner 2019

PlantVillage Nuru: Pest and disease monitoring using artificial intelligence

The project expects to radically transform pest and disease monitoring by using artificial intelligence (AI), advanced sensor technology and crowdsourcing capable of connecting the global Ag community to help smallholder farmers.

Inspire Winner 2017

Using IVR to connect farmers to market

The IVR market service will help aggregate and formalize the value chain by increasing farmers marketability and bargaining power, ensuring farmers can also find timely services, and helping buyers locate available product.

Winners 2018

Be a part of the innovation revolution

By supporting some of the other excellent 2017 Inspire Challenge proposals

We’re encouraging you to support other inspiring projects to help spur the agricultural transformation around the world. Here are some innovations you can help put into practice.

Targeting sustainable intensification technologies with a hyper-local LandPKS tool

This tool will guide spatial targeting of sustainable intensification technologies to provide ‘best fit’ options for risk reduction and enhancement of local adaptation of farmers within target geographies ...

Solanify: Crowd Sourcing of Potato Diversity Monitoring

This project will monitor potato diversity and distribution in the Andes by developing an open source application as a tool to help identify traditional potato varieties from photos using a visual recognition software ...

ICT-based citizen science for preventing Banana Xanthomonas Wilt

This innovation will provide farmers and extensionists with mobile phone-based Banana Xanthomonas Wilt (BXW) management advice that's based on their report of BXW incidence and severity ...

Geocoded soil data and AI to provide an estimate and indicator for soil health

This project will generate new geocoded soil data and leverage the transformative potential of artificial intelligence to provide an estimate and an indicator of soil health ...

Local Market Enhancement Tool

This project will build a platform with location-based information on when and where which products are sown and harvested by local farmers to avoid temporal overflows of local markets with certain goods and ensure a more continuous and balanced flow of seeding and harvesting throughout the year ...

Livestock monitoring from the sky

This innovation will explore how data at different scales can be collected, validated and used to provide a near real-time prediction of livestock numbers in a specific location. ...

RiceFocus: Scaling Crop Advisory Services to Smallholders

This innovation will generate locally-relevant farmer advice that incorporates climatic variability and diversity of land management approaches within rice-dominant landscapes ...

Tomorrow’s Rain in Africa

This project will build an artificial intelligence next-day rain forecast to drive better decision-making and results on and off the farm ...

DRuID: An informed-Decision platform to Reduce the risk of RIce Diseases

The tool will allow for managing rice disease in real-time and for defining breeding priorities for specific regions ...

Cost of Inaction Calculator

The Cost of Inaction (COI) Calculator will be an online platform that translates agricultural climate change risk into potential lost production to smallholders ...

COMPAdRE: COMmunication and PArticipative REsilience

This project will combine participatory approach of climate services with an ICT-centered monitoring and evaluation approach to incorporate feedback mechanisms and two-way communication loops ...

Beyond: Smart IoT Solution for Plant Diagnosis

This innovation will help farmers diagnose pests, diseases and nutrient deficiencies of rice and cassava ...

From big data to small-scale decisions: what is the impact?

This innovation will develop big data-based information systems that empower farmer’s management strategies ...

Enabling real-time wheat blast management advisories in Bangladesh and Brazil

This proposal responds to the crop disease threats by leveraging mobile microscopy, citizen science, and machine learning to enable ICT-based real-time management advisories in Bangladesh and Brazil ...

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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

The 2017 Inspire Challenge

Of the 120 excellent proposals, we awarded five US$100,000 each to develop their innovative pilot projects.

PROPOSALS RECEIVED

WINNERS

US$ GRANT / WINNER

Meet the 2017 winners

The five winning teams have 12 months to implement small-scale proof of concept pilots to demonstrate viability.

Be a part of the innovation revolution

We’re encouraging you to support other inspiring projects to help spur the agricultural transformation around the world. Here are some innovations you can help put into practice.

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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<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

<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" />

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