FAIR Process Framework: Resources to implement the Findable, Accessible, Interoperable & Reusable data principles

Comprehensive tools to guide initiatives and organizations in implementing FAIR principles across data-rich agricultural development investments!

The FAIR Process Framework provides governments and development institutions with a structured approach to implementing Findable, Accessible, Interoperable, and Reusable (FAIR) principles in agricultural data management. Designed in collaboration with over 32 global partners, this six-step framework enhances data-driven decision-making, fosters cross-sector collaboration, and maximizes the impact and efficiency of agricultural investments. By ensuring that data is well-organized, accessible, and reusable, it strengthens policy formulation, improves resource allocation, and supports long-term food security strategies.

This technology is validated.

8•7

Scaling readiness: idea maturity 8/9; level of use 7/9

Adults 18 and over: Positive medium

The FAIR Process Framework will improve the accuracy and accessibility of agricultural data to support evidence-based decision-making across multiple stakeholder groups.

The poor: Positive medium

The FAIR Process Framework will improve the accuracy and accessibility of agricultural data to support evidence-based decision-making across multiple stakeholder groups.

Women: Positive medium

The FAIR Process Framework will improve the accuracy and accessibility of agricultural data to support evidence-based decision-making across multiple stakeholder groups.

Farmer climate change readiness: Significant improvement

Applying the FAIR Process Framework to your agricultural development initiatives will improve the accuracy and accessibility of the data to inform climate-friendly farming practices.

Biodiversity: Positive impact on biodiversity

Applying the FAIR Process Framework to your agricultural development initiatives will improve the accuracy and accessibility of the data to inform all relevant stakeholders in the conservation of plants, animals and nature.

Environmental health: Greatly improves environmental health

Applying the FAIR Process Framework to your agricultural development initiatives will improve the accuracy and accessibility of the data to inform users of the data to make evidence-based decisions to improve environmental health.

Soil quality: Improves soil health and fertility

Applying the FAIR Process Framework to your agricultural development initiatives will improve the accuracy and accessibility of the soil data to support the diverse needs of different users to help them make decisions to improve soil health and fertility.

Problem

Data fragmentation and duplication: Public institutions collect similar agricultural data separately, increasing costs and reducing coordination.
Limited data accessibility: Agricultural data stored in isolated systems makes it difficult for policymakers to access information needed for public decision-making.
Poor data integration: Lack of standardization prevents datasets from different institutions from being combined effectively.
Loss of institutional knowledge: Poor data management risks losing important information when projects close or institutional teams change.
Limited value from public data investments: Data collection and storage require resources, but poorly managed data reduces its value for policy and programme planning.
Barriers to scaling agricultural innovations: Limited data sharing makes it difficult to identify, replicate, and scale successful agricultural interventions.

Solution

Practical implementation guidance: The FAIR Process Framework provides governments with structured resources and clear steps for improving agricultural data management.
Improved data accessibility: The framework organizes data so that policymakers and institutions can more easily find and access relevant information.
Standardized data sharing: FAIR principles support consistent data management and responsible sharing across institutions.
Better data integration: Interoperability enables government datasets from different sources to be combined for stronger analysis.
Evidence-based policymaking: Better-quality and accessible data supports policy formulation, resource allocation, and programme planning.
Support for scaling agricultural programmes: Reusable data helps governments identify and expand effective agricultural interventions.

Key points to design your project

To maximize the impact of agricultural data in projects, the FAIR Process Framework provides a structured yet flexible approach to data management and sharing. This ensures that data is Findable, Accessible, Interoperable, and Reusable (FAIR), leading to better decision-making, improved efficiency, and long-term sustainability.

For New Projects: Integrate FAIR principles from the concept note or proposal stage to establish a strong data governance foundation.
For Ongoing Projects: Adopt key FAIR elements at any phase to enhance data quality, accessibility, and usability.
Flexible and Scalable: The framework can be applied across different stages and project sizes, ensuring alignment with best practices.
Support and Resources: Structured guidance, templates, and expert assistance from CABI make implementation straightforward.

By embedding the FAIR Process Framework in national projects, governments and institutions can optimize data use, reduce duplication, and strengthen evidence-based policymaking for sustainable agricultural development.

IP

Open source / open access

Scaling Readiness describes how complete a technology's development is and its ability to be scaled. It produces a score that measures a technology's readiness along two axes: the level of maturity of the idea itself, and the level to which the technology has been used so far.

Each axis goes from 0 to 9 where 9 is the “ready-to-scale” status. For each technology profile in the e-catalogs we have documented the scaling readiness status from evidence given by the technology providers. The e-catalogs only showcase technologies for which the scaling readiness score is at least 8 for maturity of the idea and 7 for the level of use.

The graph below represents visually the scaling readiness status for this technology, you can see the label of each level by hovering your mouse cursor on the number.

Scaling readiness score of this technology

Maturity of the idea 8 out of 9

Uncontrolled environment: tested

Level of use 7 out of 9

Common use by projects NOT connected to technology provider

Maturity of the idea										Level of use
9
8
7
6
5
4
3
2
1
	1	2	3	4	5	6	7	8	9

Countries with a green colour: Tested & adopted
Countries with a bright green colour: Adopted
Countries with a yellow colour: Tested
Countries with a blue colour: Testing ongoing

Countries where the technology is being tested or has been tested and adopted
Country	Testing ongoing	Tested	Adopted
Egypt	Testing ongoing	–Not tested	–Not adopted
Ethiopia	–No ongoing testing	Tested	–Not adopted
Ghana	–No ongoing testing	Tested	–Not adopted
Kenya	Testing ongoing	–Not tested	–Not adopted
Malawi	–No ongoing testing	Tested	–Not adopted
Rwanda	–No ongoing testing	Tested	–Not adopted
Tanzania	–No ongoing testing	Tested	–Not adopted
Zambia	–No ongoing testing	Tested	–Not adopted

This technology can be used in the colored agro-ecological zones. Any zones shown in white are not suitable for this technology.

Agro-ecological zones where this technology can be used
AEZ	Subtropic - warm	Subtropic - cool	Tropic - warm	Tropic - cool
Arid
Semiarid
Subhumid
Humid

Source: HarvestChoice/IFPRI 2009

The United Nations Sustainable Development Goals that are applicable to this technology.

Goal 2: zero hunger

Managing, governing and sharing data responsibly can fast track innovations in the agricultural sector to solve food insecurity.

Goal 8: decent work and economic growth

Managing, governing and sharing data responsibly can fast track innovations in the agricultural sector to improve productivity and contribute to economic growth.

Goal 13: climate action

Managing, governing and sharing data responsibly can improve access to agricultural data to inform evidence-based climate action.

Goal 15: life on land

Managing, governing and sharing data responsibly can provide greater access to evidence and fast track solutions to conserve life on land.

Goal 17: partnerships for the goals

Developing partnerships is a key element of implementing the FAIR Process Framework, to improve data sharing and data access across agricultural data projects.

1. For New Projects (Concept Note or Proposal Stage)

- Start at Step 1 of the FAIR Process Framework to integrate FAIR principles from the beginning.
- Follow the structured, step-by-step approach to build a strong foundation for data management.

2. For Ongoing Projects

- Even if your project is already underway, you can still adopt FAIR elements at any stage.
- Identify relevant steps to enhance data quality, accessibility, and interoperability.

3. For Any Stage of the Project Life Cycle

- The framework is flexible and adaptable, allowing teams to integrate FAIR practices at different phases.
- Implementing even a few key elements improves data stewardship and long-term usability.

4. For Additional Support

- The FAIR Process Framework includes guidance, templates, and resources to assist teams unfamiliar with FAIR principles.
- CABI offers expert support as a sub-awardee, providing training and implementation assistance to ensure successful adoption.