5.2 Data Management and Access Plan (DMAP)

Developing a living document that outlines how data will be collected and handled through your investment's life

Why should I do this?

To guarantee that your data-handling practices are not only efficient and regulatory-compliant, but also safeguard against unauthorized access and potential security breaches.

 

By developing a Data Management and Access Plan (DMAP), you create a clear plan for managing the lifecycle of your data from collection to usage, ensuring that all stakeholders understand their roles and responsibilities.

1) If you are a Program Officer (PO), you may want to share this page directly with your grantee, so they can act on it.

2) Use the DMAP template for Step 5, to populate the relevant sections.

3) Consult collaborating partners within the project to gather expertise.

4) Refer to the investment type examples to help you with this activity.

5) If you have created a data governance policy, ensure the DMAP is aligned to that policy.

6) Refer to your previous completed worksheets from Steps 1 to 4. Details from those worksheets will be useful while completing your DMAP.

7) Complete the various sections in your data management access plan, and share it with all the relevant parties.

This section provides guidance for completing the sections of the DMAP.

If you have not created a separate data governance policy, remember to include essential governance elements in your DMAP. For example, defining clear roles and responsibilities is crucial, and must be documented in one of your project’s core documents.

©Gates Archive/Mansi Midha ©Gates Archive/Mansi Midha

Every investment project is unique

The application of the six steps will vary accordingly. To provide examples that align with your project, common characteristics of AgDev investments were researched and three ‘investment types’ were developed.

©Gates Archive/Alissa Everett

AgriConnect: Creating a data management and access plan (DMAP)

Rashima, the lead of AgriConnect, recognized that a comprehensive DMAP was essential for ensuring data collection, management, sharing, and governance activities align with their FAIR principles and the organization’s mission. Rashima initiated a team meeting to outline the key aspects of their DMAP, using the provided template as a guide.

 

Aligning with the data governance policy
Since AgriConnect had already developed a data governance policy (as per Step 5.1), Rashima began by referencing this policy to fill Section One of the DMAP. She reviewed the foundational principles with the team, reaffirming their commitment to data integrity, privacy, security, and transparency.

 

Chris, the third-party publisher responsible for managing the national repository of agricultural indicators, emphasized the importance of aligning AgriConnect’s internal data handling practices with external regulations and standards. He provided insight into compliance with Dataland’s data privacy laws and the practicalities of integrating derived data with the national repository.

 

Establishing roles and responsibilities
Rashima and Marie, the UX consultant, collaborated to assign clear roles. They appointed John, the data steward, as the primary contact for data-related queries. His responsibilities included overseeing the execution of the DMAP, ensuring compliance with internal and external policies, and liaising with data providers such as Chima, the smallholder farmer, and Faisel, the researcher.

 

Data summary
Using the data inventory already created during Step 3 of the FAIR Process Framework, AgriConnect cataloged the various datasets they planned to collect or reuse. This inventory was essential to answering questions about data types, sources, and third-party data policies, in section two of the DMAP.

 

Chima provided details about the raw agricultural data collected through his village’s cooperative. Faisel categorized the types of data they would generate, including survey results, sensor readings, and observational records. They also identified any third-party data AgriConnect planned to utilize, such as national agricultural indicators shared by Chris’s agency.

 

©Gates Archive/Thomas Omondi

AgroThrive: Creating a data management and access plan (DMAP)

Kaira, the lead of AgroThrive, recognized that to provide comprehensive policy recommendations for Datapur’s government, their team needed a clear and actionable DMAP. The project focused on delivering evidence-based agricultural policy advice on credit, infrastructure, climate resilience, and land tenure issues.

 

Integrating data from multiple sources

Kaira began by consulting with partners like Saanvi, the technical consultant on climate resilience, and Imamu, the third-party publisher managing the national repository. AgroThrive’s data sources included farmer surveys, climate sensor readings, soil health assessments, and policy studies. Kaira decided to use common data formats like NetCDF for climate data and CSV for survey results, allowing seamless integration with the national repository.

 

Defining roles and responsibilities in the DMAP

Kaira appointed Anna, the project partner responsible for developing AgDev training modules, as the data lead, overseeing data collection and quality assurance. Haben, the smallholder farmer, played a key role in providing on-the-ground data and validating findings. Aziz, the NGO head, helped align the DMAP with existing governance policy, avoiding policy conflicts.

©Gates Archive/Esther Mbabazi

NGBT: Creating a data management and access plan (DMAP)

Farah, the lead of the NGBT project, knew that developing a climate-resistant and nutritious barley varietal required collaboration among multiple researchers and specialists. The project’s complexity necessitated a DMAP to ensure consistency and transparency in handling data.

 

Coordinating data from diverse disciplines
Farah brought together key partners, including Nasser, the researcher collecting agricultural data, and Charlotte, the climate scientist. NGBT’s data sources ranged from genetic research data and soil analysis to climate impact models. They standardized the formats, using VCF for genetic sequences and GeoTIFF for spatial climate data, ensuring interoperability across disciplines.

 

Setting clear roles and responsibilities
Farah appointed Cali, the agricultural geneticist, as the data manager responsible for overseeing data integrity and compliance. Davu, the smallholder farmer, contributed ground-level data and played a key role in validating field research findings. Jaya, the gender and children specialist, ensured that data collection protocols were sensitive to gender issues and children’s needs.

Researchers are willing to share data if robust governance and regulation frameworks are in place.

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