News

The latest Big Data in Agriculture news

Featured

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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Latest news

Grow more food and mitigate climate change? Agricultural soil data could help

April 16, 2018 Access & Discoverability, Blog Competition, Privacy & Ethics

A more comprehensive understanding of agricultural soils is needed to support sustainable development decision making - but collecting the necessary data is challenging.

Consuming information: Agriculture at the crossroads of sustainability

April 16, 2018 Application & Use, Blog Competition

Extension officers, radio, and mobile phones remain crucial for the farmers to access and discover agricultural information.

Transdisciplinary thinking essential to enable sustainable data-intensive agroecosystems

April 16, 2018 Application & Use, Blog Competition

Transdisciplinary thinking is a fundamental requirement if we are to take meaningful steps towards using big data to solve complex socio-environmental problems.

Meet Bob. The farmer needing your help to revolutionize agriculture

April 16, 2018 Access & Discoverability, Blog Competition, Privacy & Ethics

Farmers are contributing to the huge amounts of information that can be harnessed for global food security solutions. But they need good allies to ensure its full, and ethical, potential is reached.

For the rural farmer, does big data raise more questions than answers?

April 12, 2018 Application & Use, Blog Competition

Does big data raise more questions than answers?

Big data needed for an agricultural boom

April 12, 2018 Access & Discoverability, Blog Competition

Big data in agriculture is instrumental not just for farmers who produce our food but for various stakeholders and along the agriculture value chain.

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 ...

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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

News

The latest Big Data in Agriculture news

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Latest news

News from our Communities of Practice

VIDEOS

Youth in Data

COURSES & WEBINARS

BIG DATA ACROSS CGIAR

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

REUSE / TRANSFER

ARCHIVING / DISCARDING

PUBLISHING AND DISCOVERY

STORAGE AND ANALYSIS

COLLECTION

PLANNING AND APPROVAL