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: Without a structured approach, multiple institutions collect similar data separately, leading to inefficiencies and wasted resources.
Limited accessibility: Valuable agricultural data is often stored in isolated systems, making it difficult for policymakers, researchers, and development partners to access and use.
Integration challenges: Data that isn’t standardized cannot be easily combined with other datasets, limiting its usefulness for evidence-based decision-making.
Loss of institutional knowledge: When data is poorly managed, it risks being lost over time, especially during leadership transitions or project closures.
Reduced return on investment: Collecting and storing data is costly; when it isn’t Findable, Accessible, Interoperable, or Reusable, its full potential remains untapped.
Barriers to scaling agricultural innovations: Data that cannot be easily shared or adapted limits opportunities for scaling successful interventions across regions.

Solution

Practical guidance for easy adoption: The framework includes bite-sized resources, clear instructions, and real-world use cases to simplify implementation.
Reduced entry barriers: Funders, grantees, and project partners can easily integrate FAIR principles without requiring extensive technical expertise.
Standardized and responsible data sharing: Ensures that data is Findable, Accessible, Interoperable, and Reusable, making it more useful for decision-making and long-term development.
Improved data discoverability and accessibility: Agricultural data is organized and structured so that stakeholders can easily find and access relevant information.
Seamless data integration: Promotes interoperability, allowing datasets from different sources to be combined for deeper insights and better policy formulation.
Maximized project impact: Ensures that collected data remains relevant, reusable, and valuable, supporting future research, scaling, and evidence-based agricultural innovations.

Key points to design your program

The FAIR Process Framework is a structured six-step methodology that helps governments and development institutions implement Findable, Accessible, Interoperable, and Reusable (FAIR) principles in agricultural data management. By improving the organization, accessibility, interoperability, and reuse of agricultural data, the framework strengthens evidence-based decision-making, enhances cross-sector collaboration, improves resource allocation, and maximizes the impact of agricultural investments. It is well suited for digital agriculture, agricultural research, policy development, and knowledge management programmes, contributing to SDGs 2 (Zero Hunger), 8 (Decent Work and Economic Growth), 13 (Climate Action), 15 (Life on Land), and 17 (Partnerships for the Goals). The framework supports governments, research institutions, and development organizations in building sustainable, interoperable, and reusable agricultural data systems. To successfully integrate this framework, consider the following key actions :

Identify priority programmes, institutions, and agricultural datasets where improved data management can strengthen planning, implementation, and evidence-based decision-making.
Establish partnerships among government agencies, research institutions, universities, development partners, and digital agriculture stakeholders to coordinate the implementation of FAIR principles and promote collaborative data governance.
Assess existing data governance systems, institutional capacities, digital infrastructure, and data management practices to identify gaps in data quality, accessibility, interoperability, and reuse.
Strengthen institutional and technical capacity by training data managers, researchers, policymakers, and programme staff on the FAIR Process Framework, data stewardship, metadata standards, and interoperable data management practices.
Promote the adoption of standardized data management procedures, interoperable information systems, and FAIR-compliant workflows that improve data discoverability, accessibility, sharing, and long-term reuse.
Support cross-sector collaboration by facilitating data sharing across research, government, extension services, and development programmes to strengthen policy formulation, investment planning, innovation, and agricultural service delivery.
Monitor programme performance through indicators such as the number of FAIR-compliant datasets, institutional adoption of FAIR practices, data-sharing partnerships, data reuse, and evidence-based policy and programme decisions.

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 8 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.