Data Sharing Toolkit

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Module 3

Reusing data from third-party sources

3. two men in a field checking information on a tablet
Module 3

Does this investment build on and contribute to existing data from multiple sources?

More often than not, there are many people and organizations involved in a project, with diverse needs. They may also come with distinct sources of data. There are learnable skills to help ensure these people and sources work together for the best possible outcomes.

eLearning Module

Empower participants to engage with potential stakeholders and their data within the ecosystem.

Tools and Resources

This module has a cheat sheet, a rights and permissions guide, and a persona guide.

What does good look like?

Does it build upon existing systems, and reflect the needs of multiple stakeholders?

Module 3

eLearning Module

Login or sign-up for a free CABI Academy account to get started. In this module you will learn:

  • Describe the challenges of working with data from third party sources
  • Identify the key elements that ensure this data is FAIR and safeguarded
  • Understand the steps involved in working with this data
  • Evaluate the interoperability needs.
3. two men in a field checking information on a tablet
Module 3

Tools and Resources

Cheat Sheet

Working with data from third party sources

All the key points from the eLearning module in one tidy document.

473 KB


Considering rights and permissions

This guide helps identify where data may be created or used within a grant, where other actors may need to access, use or share data, and thinking about potential issues relating to rights and permissions.

729 KB


Understanding personas in agricultural ecosystems

Personas are imaginary characters who help us to understand real people in agriculture — their needs, barriers, motivations, and goals. Each persona is a composite based on the people we interviewed in Ethiopia, India, Seattle, and Tanzania.

677 KB

Module 3

What does good look like?


Does this investment build on and contribute to existing data from multiple sources?

Data Purposeful

The investment looks to build on and contribute to existing data from a mixture of public, private and third sector sources. Interoperability needs are clear. The role of the grantee within the wider data ecosystem is clear and existing datasets have been identified.

The investment is clear how they will build on top of, and contribute to, existing data and best practice.

Engagement with existing data holders has begun and rights and permissions to access, use and share the data are confirmed and inline with funder and in-country policies and regulations.

Data Aware

The investment shows awareness of some existing data from a limited number of sources (e.g. all public sector) and knowledge of the wider data ecosystem is limited. Interoperability needs are unclear. A small number of existing datasets has been identified but it is not clear what rights they have to access and use these datasets or whether they have engaged the data holders. It is unclear how they will contribute, work with or enhance existing data sources.

Data Unintentional

The investment does not consider existing data sources or other collaborators in the data ecosystem.


The investment talks broadly about data but it is not clear where the data comes from, whether they have rights to access and use it, and what they will do after.

To find the definitions of these headings and see what good practice looks like across all seven modules: Download PDF

4. two women in a field using a handheld device

Module 4

Protecting individual's rights when sharing data

If this investment will involve the collection, access, use or sharing of data about people, are their rights considered and protected?

This module looks at key definitions, information about rights over personal data, and some techniques for reducing the risks related to handling personal data.