Media Kit

Big Data in Agriculture Convention 2019

#BDPHYDERABAD2019

@CGIAR_DATA

We hope you’ll join us in promoting the Big Data Convention and its key messages via your social media channels and related networks! To help you do so, we created this online social media toolkit with suggested social media posts and accompanying images.

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Convention event page

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Meet the 2019 Inspire Challenge finalists

THE PLATFORM

The CGIAR Platform for Big Data in Agriculture is where information becomes power: power to predict, prescribe, and produce more food, more sustainably. It aims to democratize decades of agricultural data empowering analysts, statisticians, programmers and more to mine information for trends and quirks, and develop rapid, accurate and compelling recommendations for farmers, researchers and policymakers.

THE EVENT

At the third annual Big Data in Agriculture Convention, we’ll dive into ways to build trust in institutions, in firms, in dynamic and expanding human communities, and in the technologies themselves that can help us build a food secure future. This year’s theme is “TRUST: Humans, Machines & Ecosystems.”

The Convention will highlight new, high-potential models for managing ethics, multi-stakeholder coordination, and digital technologies that demonstrate the agility, precision and insights needed to effectively manage a complex net of interactions between the biosphere, economy, technology, and society.

You can expect to hear from more than 60 speakers and industry experts during more than 50 sessions over the course of three days. We’ve structured the program to be dynamic and interactive. In addition to hosting structured panel sessions, we are providing deep-dive breakout sessions and intimate Q&A’s where you will have direct access to put your burning questions to leading experts in the data science sector. Like last year, will be closing the event with a bang by announcing the winners of the 2019 Inspire Challenge in a special awards ceremony.

We are expecting up to 500 members of the scientific community and digital agriculture practitioners who make the Platform for Big Data in Agriculture successful.

BRANDING

COLLABORATION & PHOTOS

Are you writing articles about the Convention? We’ve prepared these quotes ready for use. We will be updated throughout the event.

Please share your stories with Marianne McDade, Head of Communications, at m.mcdade@cgiar.org so we can help promote your piece!

Photos from the Convention will be uploaded to the BIG DATA Platform Flickr account at the end of each day. Please use the image credit “Photo: ThoughtFolks Media”

KEY MESSAGES

Building resilient global food security requires us to navigate a complex net of interactions between the biosphere, economy, technology, and society.
Machines and machine-to-machine systems shape and accelerate our social and economic lives, even as climates and ecosystems are threatened. Our ethical frameworks, human communities, and institutions struggle to stay abreast the rate of change. Therefore, we can no longer consider distinct facets of food security in isolation; we need holistic solutions.
There are growing insights about how humanity can live within the natural, biological, and climatic boundaries of the planet.
To claim these potential solutions for the future of food security we need trust. Trust in institutions, in firms, in dynamic and expanding human communities, and in the technologies themselves that can help us build the future.

KEY THEMES UP FOR DISCUSSION

Why trust?
Trust: Humans, machines, and ecosystems
Digital trust in India
Trust and the human-machine ecology
2019 Inspire Challenge finalists
Blockchain-enabled transformation of food systems
Cross-platform design session: Big data enabling women’s economic empowerment
Digital technologies enabling large-scale collective action
Reinforcement Learning for crop-management: Sequential decision-making under uncertainty
Human-centered design for AI
Agriculture, Aadhaar, and the India stack
Anchor solutions for trust
Leadership in data for agriculture
Ethical dilemmas of the digital age in agriculture
Fintech, meet agtech
Introduction to innovation pipeline
Financing innovation pipeline for GFS
Can we really eat within planetary boundaries?
Building enabling environments
AI in agriculture: What stands in the way?
Commons debate: Genetic resources, dematerialization, and benefits-sharing
IoT for crop improvement and smart-farming
How can we build ethical, trustworthy digital advisory services?
Technologies and good practices throughout the research data life cycle
Technologies and standards for responsible data
Unified information architecture for CGIAR
Trust for the future of big data in agriculture

Key speakers

Kalvakuntla Taraka Rama Rao
Minister for IT, Industries, MA & UD
Twitter

Yvonne Pinto
Managing Director, ALINE Impact Limited
LinkedIn

Monique Morrow
President and Co-Founder, The Humanized Internet, Presiden, The VETRI Foundation
Twitter | LinkedIn

Peter Carberry
Director General, ICRISAT
Twitter | LinkedIn

Gareth Johnstone
Director General, WorldFish
Twitter

Marieke de Ruyter de Wildt
Founder, The Fork
Twitter | LinkedIn

Andy Jarvis
Decision and Policy Analysis Research Area Director, International Center for Tropical Agriculture (CIAT)
Twitter | LinkedIn

Brian King
Coordinator, CGIAR Platform for Big Data in Agriculture
Twitter | LinkedIn

KEY TEAM MEMBERS

Brian King, Coordinator, CGIAR Platform for Big Data in Agriculture

b.king@cgiar.org

Marianne McDade, Head of Communications, CGIAR Platform for Big Data in Agriculture.

m.mcdade@cgiar.org

Medha Devare, Module Lead, CGIAR Platform for Big Data in Agriculture.

m.devare@cgiar.org

Jawoo Koo, Geospatial Data Community of Practice Lead, CGIAR Platform for Big Data in Agriculture.

j.koo@cgiar.org

Andy Jarvis, Co-pioneer of the CGIAR Platform for Big Data in Agriculture.

a.jarvis@cgiar.org

PARTNERS

The CGIAR Platform for Big Data in Agriculture is co-led by the International Center for Tropical Agriculture (CIAT) and the International Food Policy Research Institute (IFPRI). The Convention is hosted by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT).

LOOKING BACK AT THE 2018 AND 2017 CONVENTIONS

2018: Decoding the Data Ecosystem

The second Big Data in Agriculture Convention: Decoding the Data Ecosystem was held in October 2018 at the ICRAF campus in Nairobi, Kenya. We welcomed over 400 attendees—60 percent of which were non-CGIAR and represented over 150 organizations, institutes, and governments. Additionally, over 2500 people participated remotely via the live-stream.

We learned that in order to transform agriculture, we must invest in the methods and frameworks that enable connecting the tangible world of food and farming with the digital world of data and analytics. Doing so requires us to grapple with the incredible complexity of food systems and wrangle with the explosion of global data generated by research and the course of virtually everyone’s social and economic lives.

We awarded five research proposals, the 2018 Inspire Challenge pilot project grant winners, with US$100K each. In addition, we awarded US$500,000 in total to three of the five winners from the 2017 Challenge to help them expand the scale of their innovation.

2017: An Alliance for a Data Revolution

We held our first Big Data in Agriculture Convention: An Alliance for a Data Revolution in 2017, at the CIAT campus in Cali, Colombia. Over 300 participants from the international scientific community gathered to discuss ways in which big data can transform smallholder agriculture, enabling farmers to boost food production and respond to pressing challenges such as climate change. Additionally, we awarded five research proposals, the 2017 Inspire Challenge grant winners, with US$100K each.

Press release: Big Data in Agriculture Convention of 2017: An Alliance for a Data Revolution

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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

Media Kit

Big Data in Agriculture Convention 2019

Convention event page

Meet the 2019 Inspire Challenge finalists

THE PLATFORM

THE EVENT

BRANDING

COLLABORATION & PHOTOS

KEY MESSAGES

KEY THEMES UP FOR DISCUSSION

Key speakers

KEY TEAM MEMBERS

PARTNERS

LOOKING BACK AT THE 2018 AND 2017 CONVENTIONS

2018: Decoding the Data Ecosystem

2017: An Alliance for a Data Revolution

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

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ARCHIVING / DISCARDING

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PUBLISHING AND DISCOVERY

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<img class="wp-image-93295 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/data-analysis-icon.png" alt="" width="100" height="100" />

STORAGE AND ANALYSIS

<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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<img class=" wp-image-93249 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/data-collection-icon.png" alt="" width="100" height="75" />

COLLECTION

<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-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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