Youth in Data

Workshops

*** APPLICATIONS FOR THE 2020 WORKSHOP CLOSE THURSDAY 15 OCTOBER **

The Youth in Data Workshop takes place every year alongside the Big Data in Agriculture Convention.

The workshop introduces participants to important themes around digital agriculture and how using big data approaches to agricultural development can accelerate food security goals. It also aims to engage youths working in digital agriculture on how to use digital media tools to communicate effectively to increase the impact of their work, and for those already working in media, to show them how to better report on digital agriculture projects.

The workshop runs over five action-packed days. During the first two days, delegates will learn the basics of digital agriculture, including the latest groundbreaking technologies, sector challenges, and career opportunities. They will also learn about using digital media as a powerful tool to communicate about development, covering social media, interviewing, blog writing, data reporting, and other media skills.

Delegates are then able to apply their newly-acquired skills during the three days of the annual CGIAR convention on Big Data in Agriculture, engaging through social media, interviewing and connecting with leading industry experts, speakers and Inspire Challenge finalists, and by writing blogs, which will be published on BIG DATA’s website.

2020 workshop

Online & Global

130 delegates

2019 workshop

Hyderabad

30 delegates

2018 workshop

Nairobi

10 delegates

Read the articles and watch the videos produced by the Youth in Data delegates

Can livestock development be ethical and sustainable? A student looks for the facts

This is one of a series of blogs written by our Youth in Data delegates who participated in the 2020 CGIAR Convention on Big Data in Agriculture. The global event was held virtually 19-23 October, 2020.

Youth can be helpful allies in the collection of quality data to scale for modeling

The Importance of Livestock in Creating Sustainable and Equitable Food Systems

2020 Youth in Data Video Interview – Adekoya Femi and Ram Dhulipala

2020 Youth in Data Delegate Adekoya Femi interviews Ram Dhulipala, Leader of the Digital Agriculture Theme at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), as part of the 2020 Youth in Data Digital workshop which took place alongside the virtual CGIAR Convention on Big Data in Agriculture.

2020 Youth in Data Video Interview – Adekoya Femi and Daniel Jiménez

2020 Youth in Data Delegate Adekoya Femi interviews Dr. Daniel Jiménez, Scientist and Leader Community of Practice Data-Driven Agronomy at the Alliance of Bioversity International and CIAT, as part of the 2020 Youth in Data Digital workshop which took place alongside the virtual CGIAR Convention on Big Data in Agriculture.

2020 Youth in Data Video Interview – Meghana Rangaraju Sai and Ajay Kumar Tannirkulam

2020 Youth in Data Delegate Meghana Rangaraju Sai interviews Ajay Kumar Tannirkulam, 2020 Inspire Challenge Finalist– FarmPhone for Sustaining Incomes & Market Access, as part of the 2020 Youth in Data Digital workshop which took place alongside the virtual CGIAR Convention on Big Data in Agriculture.

October 2, 2018

The Platform’s 2018 convention has these 3 objectives

October 10, 2018

To achieve gender SDGs gender data must be made visible

Three major obstacles for IoTs in agriculture

October 11, 2018

Youth in Data

Three major obstacles for IoTs in agriculture

October 12, 2018

Kenya’s ICT Cabinet Secretary: Data is ‘key’ to smart farming

October 15, 2018

Convention highlights: Blockchain in food systems

October 17, 2018

3 sources of demand for big data in biodiversity innovation

October 19, 2018

5 key challenges facing the agriculture data ecosystem

October 31, 2018

Q&A with Martin Parr from CABI

October 31, 2018

Q&A with Samir Ibrahim from Sun Culture

October 31, 2018

Q&A with David Hughes from Penn State & FAO

October 31, 2018

Q&A with William Jack from Georgetown University

October 31, 2018

Q&A with Ranveer chandra from Microsoft

October 31, 2018

Q&A with Andy Jarvis from CIAT

October 31, 2018

Q&A with Rachel Sibande from Digital Impact Alliance

October 31, 2018

Q&A with Leka Tingitana from Tanzania Flying Labs

October 31, 2018

Q&A with Qrystna Laetitia from FRN-NGO

October 31, 2018

Q&A with Nemo Semret from Gro Intelligence

October 31, 2018

Q&A with Debisi Araba, CIAT Regional Director for Africa

October 31, 2018

Q&A with Christophe Bocquet from Dalberg Data Insights

October 31, 2018

Q&A with Marieke de Ruyter de Wildt from The Fork

October 31, 2018

Q&A with Thijs van den Broek from Accenture

October 31, 2018

Q&A with Marcelo Tyszler – CGIAR Gender Platform

October 31, 2018

Q&A with Christophe Bocquet from Dalberg Data Insights

October 31, 2018

Q&A with Alice Muchugi from the ICRAF

October 31, 2018

Q&A with Sander Janssen from Wageningen University

October 31, 2018

Q&A with James Legg from NuruAI

October 31, 2018

Q&A with Georgia Barrie from Farm.Ink

November 19, 2018

Video: Demystifying ontologies for agriculture

Join the community and see what participants have to say!

#Youth power, energy and enthusiasm at #BDPHYDERABAD2019 !@ICRISAT @ShruthiBelliap1 @joorie @kanchiyana @CGIAR_Data @CGIAR pic.twitter.com/WheMBaxmOC

— Abhishek Rathore (@reach2arathore) October 16, 2019

Happy to see this happening #YouthInData am very sure the outcome was great. #LetsDoMore 🇮🇳 https://t.co/0cd50R4z5t

— 𝗠𝗿. 𝗖𝗹𝗶𝗺𝗮𝘁𝗲 (@OlumideIDOWU) October 14, 2019

One of our #YouthinData in conversation with Manuel Hernandez @IFPRI with his amazing INSPIRE challenge 2019 entry "Show me what you eat"! Catch them live on Facebook!@kanchiyana @ShruthiBelliap1 #BDPHYDERABAD2019 @CGIAR_Data pic.twitter.com/SseCg0kbAI

— Joorie Bhattacharya (@joorie) October 17, 2019

#ApplyNow to join our 2020 #YouthInData Digital Bootcamp! Join up to 100 delegates from for a week of learning, collaborating & creating around #digital ag, #foodsecurity, #tech & food systems: https://t.co/eItFGbWNHC. Delegates will attend the @CGIAR Convention on #BigDataInAg. pic.twitter.com/qML1uuMs4P

— CGIAR Platform for Big Data in Agriculture (@CGIAR_Data) October 12, 2020

Search the website

Discover agricultural data and publications

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Become a youth in data partner

Submit an initiative!

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

Youth in Data

Workshops

2020 workshop

2019 workshop

2018 workshop

Read the articles and watch the videos produced by the Youth in Data delegates

Join the community and see what participants have to say!

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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<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

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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" />

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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" />

<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" />