Livestock Data

Community of Practice

Welcome!

Livestock Data for Decisions (LD4D) is a Community of Practice launched in January 2017 that aims to drive informed livestock decision making through the better use of existing data and analyses.

LD4D brings together livestock and data modellers with project implementers and decision makers from industry, the public sector and NGOs. Through cataloging and organizing livestock data, making it more discoverable and digestible, LD4D ultimately aims to guide investments that will improve the livelihoods of smallholder farmers in low and middle-income countries.

Get in touch

Karen Smyth, COP Lead | Email
Deputy Director of the Supporting Evidence-Based Decisions (SEBI) project at the Royal (Dick) School of Veterinary Studies in Midlothian, United Kingdom.

Twitter | Email | Sign up for LD4D news updates.

Visit the LD4D website

Engage with the Community

BE INFORMED

Tune in to the latest CoP updates by subscribing to our newsletter.

ENGAGE

Interact with community members through our LinkedIn Group.

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CONTRIBUTE

Share your expertise in our working groups.

Working groups

LD4D hosts a series of short-life working groups and projects where the real work of the community takes place.

Livestock Fact Check

The Livestock Fact Check project digs into the evidence behind popular facts about livestock, to discover whether those facts are accurate and up to date. More…

LiveGAPS

LiveGAPS aims to identify ways to maximise yields in livestock systems for poverty alleviation, global food security and sustainability. More…

Decision makers’ needs study

LD4D has commissioned a pilot study into the data information needs of local stakeholders interested in livestock productivity and causes of mortality in the dairy sector in Ethiopia. More…

Knowledge platform and data portal

A hub for livestock information and resources, including a data portal, which will house an inventory of current and previous livestock projects and descriptions of associated databases. More…

Data visualisations and dashboards

This group will consider how we can move toward the forecasting of livestock disease through the use of data visualisations. This group will also consider the potential to collect and share real-time data using dashboards. More…

Data quality

This group will explore elements of data quality by considering the fitness of purpose of data obtained from secondary sources and practices to generate good quality primary data. More…

Modelling Impact

This group will bring together modellers and Bill and Melinda Gates Foundation grantees to model productivity gains brought about as a result of BMGF grantees work. More…

Livestock Modelling Group

This group, which brings together modellers who work within the livestock sector under the LD4D banner, is looking to facilitate collaboration to answer some of the big questions relating to livestock. More…

Communications

This group will plan and deliver communications and engagement activities that support knowledge sharing within the community and help inform livestock discussions more widely. More…

Community News

Articles in this section were originally posted on the Livestock Data for Decisions website.

New maps expose hidden facts on herders and rangelands

June 4, 2021 / Vanessa Meadu

First efforts to assemble vital evidence about pastoral people and rangelands ...

More meat, milk and eggs: a look at livestock yield gaps in Tanzania and Nigeria

April 15, 2021 / Vanessa Meadu

The first-ever study to estimate the gap between actual and potential livestock productivity ...

Meet the LD4D Steering Committee

April 9, 2021 / Vanessa Meadu

The new steering committee will guide the community to develop solutions ...

First ever global database of Veterinary Medicines Regulators to help countries access better treatments

March 10, 2021 / Vanessa Meadu

Global database aims to help the industry connect more effectively with veterinary medicine regulators ...

What do livestock eat in Tanzania, India and Vietnam? Explore crowd-sourced data

February 9, 2021 / Vanessa Meadu

A tool to help farmers make better decisions about what to feed their livestock has generated a wealth of open-access ...

Boran Cattle in Yabello, Ethiopia. Common standards are needed to present livestock data in a consistent manner. Photo: C. Hanotte (ILRI)

Update from the livestock ontologies working group

December 18, 2020 / Vanessa Meadu

Recap of Livestock Ontologies Working Group meeting, 9 December 2020 ...

Communities

AGRONOMY

CROP MODELING

GEOSPATIAL

INFO & DATA MANAGEMENT

LIVESTOCK

ONTOLOGIES

SOCIO-ECONOMIC

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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

Livestock Data

Welcome!

Get in touch

Engage with the Community

BE INFORMED

ENGAGE

CONTRIBUTE

Working groups

LD4D hosts a series of short-life working groups and projects where the real work of the community takes place.

Livestock Fact Check

LiveGAPS

Decision makers’ needs study

Knowledge platform and data portal

Data visualisations and dashboards

Data quality

Modelling Impact

Livestock Modelling Group

Communications

Community News

Communities

Interested in joining our community of 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

<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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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" />

<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" />

<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" />