How do we make agriculture research data more discoverable, reusable and reproducible?

By Fiona Smith, Jamie Fawcett and Ruthie Musker


In September 2016, representatives from DFID, Bill and Melinda Gates Foundation (BMGF), and USAID met at the GODAN Summit to discuss how to help their implementing researchers get greater value out of open agriculture research data. Their key challenge was summarised well by one USAID programme manager:

“How can we find the commonalities and align our policies so that we can better facilitate data sharing more quickly, more widely, and then identify what our common data standards and data practices are?”

With so much investment in agriculture research to advance productivity frontiers, transform production systems, and enhance nutrition and food safety- research data is increasingly recognised, and treated as a public good. According to donortracker, the US alone provided US$1.7B in official development assistance (ODA) to the agriculture sector in 2015.

As a public good, funders of agriculture research programmes have a duty to make research and related programmatic data openly available to researchers, policymakers and citizens to unleash new discoveries and innovation (subject to appropriate privacy safeguards and ethical considerations). Yet there has been little attempt so far to understand if donor open data or open access policies are aligned, and to identify what best practice looks like in the agriculture sector.

Earlier this year the Open Data Institute (ODI) and GODAN were invited to help these donors assess their current open data policies, and review the quality of implementation across 5 jointly-funded agriculture research programmes. The aim of the research was to identify how donors can work towards a more aligned approach towards open data in policy and practice across the agriculture sector.

You can find a summary of the research findings, recommendations and implementation resources below.

We applaud the vision of the donors who participated in this research, and organisations like them, who are taking the first steps towards harmonising their approach towards open data. Their ambition for greater impact is represented in this joint statement.

Summary of report findings

From our policy review and data quality assessment, we found the following emerging patterns:

We found a significant volume of raw and secondary research data is being made open- including everything from spatial, genomic, socioeconomic, opinion, and baseline data. Generally there is a strong approach towards open licensing and machine readability, and good examples of making data easily discoverable (for example publishing to open research data repository Harvard Dataverse).

From an operational point of view, we found coordination between implementing researchers and funders is being negotiated and navigated on a case by case basis. However, there is a strong desire for streamlining this process and making it easier for researchers to know what is required of them.

We also found many tangible benefits researchers are receiving from consuming open data. The NextGen Cassava team, for instance, were able to draft a joint paper using data from three different cassava breeding programs, and produce a publication on best practices for regulation of transgenic crops in Africa. In the words of one researcher:

“The main benefit of open data sharing is reproducibility- the ability to accumulate, analyse, and synthesise many independently-generated datasets...and open data provides opportunities for smaller, less well-funded labs to mine answers from previously generated data for specific areas of interest.”

Recommendations for harmonising and improving open data in agriculture

At the ODI, we believe a policy is a good starting point for strong open data practice. It helps researchers to make decisions about publishing open data, and signals to external stakeholders how an organisation will be releasing its data and ways they can be involved. A more harmonised open data policy landscape can also help to foster more coherent and interoperable research data infrastructure.

However, having a strong policy is only one side of the equation. Policy implementation must be supported by capacity building, leadership, clear communication and a mixture of incentives to make the process of culture transformation possible.

The report provides many recommendations for practical steps donor organisations can take to improve open data policy and practice. They include:

  • Incentivise researchers by evaluating their performance differently- by making data publication a core deliverable, rewarding data reuse and innovation, and crediting good quality data publication by assigning a DOI to datasets;
  • Share and adopt common templates reduce the cost of compliance-  by adopting shared templates and approaches towards budgeting, planning, and setting milestones (see for example CGIAR’s toolkit and data management plan);
  • Regular dialogue with researchers and wider research community- by monitoring policy implementation, capturing use cases to demonstrate impact, and revising implementation guidance as needed;
  • Provide capacity support for researchers- by peer learning networks for data managers, data ‘sprints’ to accelerate open data publication, simple tools to help with quality validation (e.g open data certificates), and building training for local research teams within research grants.

Call to action

If you are a donor or research institution, we encourage you to reflect on your open data or open access policy to see how you may improve their own policy and implementation practices.

Below are some helpful tools we used and adapted in the course of this research. They are all open source and ‘working guides’- updated based on feedback we receive from users.

  • Policy checklist- a self-assessment tool for checking whether your organisation’s policy contains best practice open data principles. Suitable for organisations of any type including public sector, private sector, NGO or research institution.
  • Guide on writing a good open data policy- an accompanying guide explaining the principles underlying a good open data policy.
  • Open data certificates- a free online tool to assess and recognise the sustainable publication of quality open data. It assess the legal, practical, technical and social aspects of publishing open data using best practice guidance.

Get in touch!

If you have any questions on how to use these templates, or you would like to share your results, please email

If you are a donor who would like to join the donor dialogue, please email

If you would like to give feedback on the Donor Open Data Policy and Practice report, please fill out the Google Form.