GFAR blog

Data sharing might not be easy, but it’s the right thing to do

COVID sparked a scientific response: sharing DNA sequencing, rethinking old ways, and creating a vaccine. But what else can we achieve through concerted data sharing?

When COVID struck, the response amongst the scientific community was to share DNA sequencing and to rethink traditional ways of working to solve the urgent challenge of creating a vaccine, writes Chipo Msengezi, Project Manager, Digital Development, CABI.

This leads to some big questions about what else can be achieved through more concerted approaches to data sharing. What impact could this have on farming across the global south and the drive towards more sustainable food production for example?

Researchers in agri-food systems often go to great lengths to collect data. They also know, better than anyone, the value of accessing data from third parties, and yet very little of the data collected becomes accessible to others. How can that be?

Value of data sharing

There can be good reasons why organisations, or individuals, feel uncomfortable about sharing data. But the more these issues are confronted in stakeholder engagement processes, the more likely it is that workable solutions can be found, and appropriate safeguards can be put in place.

CABI have been working with an increasing number of donors and partners on the issue at hand. They are viewed as experts in data collection, storage, and sharing. In 2018, the Bill & Melinda Gates Foundation initiated collaboration with CABI to investigate the reasons behind the underutilization of data, despite the commitment to data sharing among all involved parties.

As a result of discussions with the foundation-funded soil projects in India and Ethiopia, several issues were identified that hindered data sharing. These challenges were primarily non-technical in nature. They included the lack of necessary resources, processes, and procedures, including a specific mandate for data sharing.

Furthermore, practical issues were observed. Research platforms are typically associated with project funding and may not be accessible once the projects conclude. Consequently, data often gets lost, becomes difficult to locate, or is available in formats that are not user-friendly, despite the original intention being the opposite.

Challenges in the data space

In the wider data environment, these challenges have resulted in the emergence of various initiatives, including the Open Data Movement. CABI has conducted extensive work on open data and discovered that it presents its own set of challenges. For instance, not all collected data can or should be shared. . There are now global and national data protections to safeguard people’s personal information. This means most data needs some curation and redaction.

CABI’s work interested the foundation and recognized the opportunity to support grantees in implementing their recently adopted data access policies. CABI proposed the utilization of the FAIR data principles as a framework for enhancing data accessibility.

The FAIR data principles are defined as the characteristics of data being Findable, Accessible, Interoperable, and Reusable. This framework has gained widespread acceptance, particularly within the EU Horizon program and among other research funders.

In collaboration with the foundation, efforts were made within funded projects to address the challenges faced by these projects. However, significantly, CABI also commenced working with foundation staff to provide them with the necessary knowledge and tools to evaluate and support promising data projects, ensuring the incorporation of good practices from the outset. It was crucial to make the foundation aware of any potential pitfalls in a proposal as early as possible. Equally important was the provision of assistance and support from experts proficient in delivering the FAIR data principles to program officers and grantees.

FAIR access

The FAIR data principles serve as an excellent foundation. However, they were initially formulated for an audience well-versed in technical terminology, which may create stress and alienation for others who also play a crucial role in designing robust projects.

CABI adopts a collaborative approach to data sharing by working closely with partners to customize and establish project-specific FAIR principles. Their methodology involves gaining a comprehensive understanding of the individuals within a data ecosystem, their interrelationships, as well as the challenges, needs, and opportunities they face.

CABI encourages those they collaborate with to provide input on what FAIR and data sharing truly mean to them. They believe that engagement with the community responsible for supplying and utilizing the data is more effective in defining these concepts.

From a series of conversations with the foundation-funded soil projects in India and Ethiopia, some issues emerged that worked against data sharing (Credit: CABI).

Furthermore, CABI supports the practical implementation of the FAIR approach by developing improved processes and providing training to teams. Their approach begins with a thorough understanding of the problem at hand, followed by mapping the landscape to identify areas where they can have the greatest impact in promoting better data sharing.

Currently, CABI is experimenting with a process designed to guide organizations in formulating their FAIR implementation strategies. Often, this is done in conjunction with assisting donors and governments in enhancing data governance, strategy, and policy design.

Building awareness

Achieving a state where an organization can readily share its data is a lengthy and resource-intensive process that demands a significant amount of time. Additionally, the majority of the advantages gained from this endeavor will be realized by external entities. However, the potential benefits resulting from these transformations can be truly transformative. For instance, data can contribute to enhancing resilience among smallholder farmers, their communities, and the natural environment. Nevertheless, for an organization to effectively share their data, it requires strong leadership starting from the topmost level of the organization.

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