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The latest Big Data in Agriculture news

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The Platform for Big Data and the digital future of CGIAR

December 29, 2021

The CGIAR Platform for Big Data in Agriculture is coming to the end of its cycle. Here are some important proof-points looking back over the Platform's start in 2017 ...

BIG DATA PLATFORM AND ONE CGIAR

December 29, 2021

Read about where BIG DATA's capabilities might appear within the new One CGIAR ...

Webinar – Digital innovations in agriculture

December 28, 2021

This webinar by the Geospatial Data Community of Practice presents four applications of digital innovations for index-based agricultural insurance products ...

New webpage featuring the most used ontologies in the agri-food domain

December 3, 2021

The Ontologies Community of Practice has developed a webpage providing at a glance the most popular ontologies in the agri-food domain along with the ontologies developed by CGIAR and its ...

Big data and agricultural research: Results of online survey for CAS Secretariat commissioned independent evaluation

November 23, 2021

The CGIAR Advisory Services Shared Secretariat (CAS Secretariat), in line with its mandate and 2021 workplan, commissioned an independent evaluation team of subject matter experts to evaluate the work of ...

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Open data: ‘It’s not about giving away data, it’s about giving it back’

October 26, 2017 Innovation, News, Privacy & Ethics

That’s because as a publicly funded institution, we owe it to the public that they have free access to our data.

Big data collaboration for crop loss research – it’s complicated

October 25, 2017 Communities of Practice, Geospatial CoP, News, Privacy & Ethics

We already know we lose a vast amount of crop produce to diseases, pests, and weeds. But how do we find out how much, where, and when?

Presenting our 2017 Inspire Challenge finalists

September 13, 2017 Inspire Challenge, News

Allow us to present the 12 finalists to participate in the final round of the CGIAR Big Data in Agriculture Inspire Challenge 2017.

Press release: Ripe for disruption

May 15, 2017 Press Releases

Ripe for disruption: New global initiative promises to shake-up agriculture – reducing risks, boosting resilience and improving incomes for the world’s most vulnerable farmers.

News from our Communities of Practice

VIDEOS

February 22, 2022

Webinar – Next-gen crop production analytics

January 21, 2022

Webinar – An introductory journey to the Virtual Knowledge Graph approach to data access and data integration

January 12, 2022

Webinar – Potential climate-related impacts on future maize and wheat yields

December 28, 2021

Webinar – Digital innovations in agriculture

December 16, 2021

Webinar – All about GARDIAN

December 16, 2021

Webinar – Old data for new insights

December 9, 2021

Webinar – OpenSAFELY for sensitive agricultural data

December 6, 2021

Webinar – Modeling with 40 years of international wheat data

November 26, 2021

Webinar – The CARE Principles for Indigenous Data Governance

October 28, 2021

Webinar – Cocoa Ontology: A collaborative pathway for FAIR data

October 25, 2021

Webinar – Analytics: How collective data can be turned into actionable data driven agronomy insights

September 9, 2021

Webinar – Labelled property graphs, Web Ontology Language, and WebProtégé

August 9, 2021

Webinar – Governance operational model for ontologies (GOMO)

July 6, 2021

Webinar – Trade-offs and synergies between different extension delivery models

June 29, 2021

Webinar – Ontology tools provided by EBI: OLS, ZOOMA, OxO

June 18, 2021

Webinar – Collaboration with the FAO on AGROVOC and AGRIS – The CGIAR exemplar

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COURSES & WEBINARS

February 22, 2022

Webinar – Next-gen crop production analytics

January 21, 2022

Webinar – An introductory journey to the Virtual Knowledge Graph approach to data access and data integration

January 12, 2022

Webinar – Potential climate-related impacts on future maize and wheat yields

December 28, 2021

Webinar – Digital innovations in agriculture

December 16, 2021

Webinar – All about GARDIAN

December 16, 2021

Webinar – Old data for new insights

December 9, 2021

Webinar – OpenSAFELY for sensitive agricultural data

December 6, 2021

Webinar – Modeling with 40 years of international wheat data

November 26, 2021

Webinar – The CARE Principles for Indigenous Data Governance

October 28, 2021

Webinar – Cocoa Ontology: A collaborative pathway for FAIR data

October 25, 2021

Webinar – Analytics: How collective data can be turned into actionable data driven agronomy insights

September 9, 2021

Webinar – Labelled property graphs, Web Ontology Language, and WebProtégé

August 9, 2021

Webinar – Governance operational model for ontologies (GOMO)

July 6, 2021

Webinar – Trade-offs and synergies between different extension delivery models

June 29, 2021

Webinar – Ontology tools provided by EBI: OLS, ZOOMA, OxO

June 18, 2021

Webinar – Collaboration with the FAO on AGROVOC and AGRIS – The CGIAR exemplar

More webinars

BIG DATA ACROSS CGIAR

Policy seminar: Digital technologies are crucial to transforming Africa’s food systems

December 10, 2019

A recent policy seminar brought together four speakers who addressed how new digital technologies and services are already making an ...

Connecting farmers to better information in Nepal

November 26, 2019

An Interactive Voice Response service is putting remote farmers in touch withwith a ‘world of information’ to improve livelihoods The ...

Scientists develop an early warning system that delivers wheat rust predictions directly to farmers’ phones

November 4, 2019

New research describes a revolutionary early warning system that can predict and mitigate wheat rust diseases in Ethiopia ...

UN-sponsored report acknowledges CIMMYT’s use of data and technologies to promote sustainable farming in Latin America

September 24, 2019

CIMMYT’s work featured on the Counting on the World to Act report, produced by SDSN and TReNDS. What to read ...

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.

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

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Responsible Data Management Guidelines to protect privacy

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ARCHIVING / DISCARDING

PUBLISHING AND DISCOVERY

STORAGE AND ANALYSIS

COLLECTION

PLANNING AND APPROVAL