From open data to meaningful impact: Data sharing impact stories

Author: Hannah Tippett Simpson

Research Data Specialist

The 2025 State of Open Data report looks back at a decade of survey responses from researchers to see how attitudes, behaviours, and policies around open data have evolved. In this blog, we explore the impact of data sharing more personally, through individual experiences of shared data. What happens when the barriers to data sharing are overcome, and how can shared data make an impact beyond one’s own work?  

By opening and sharing their data, researchers gain more visibility and credibility for their work. When data is open, individual studies can more effectively support long-term scientific progress. Results can be verified and contribute to creating new knowledge across disciplines. 

In the 2025 State of Open Data report, we learned that researchers recognise the benefits of sharing open data, and that familiarity with FAIR principles (findability, accessibility, interoperability, and reusability) has risen sharply. The report also highlights that researchers still face challenges around reuse, recognition, and infrastructure.  

But what does data sharing and its benefits and challenges look like in practice? While writing the 2025 report, we spoke with researchers, librarians, and data experts about open data. For this blog, we collected their real-world examples of how data sharing has shaped research and collaboration. 

From data sharing to enabling reuse at scale 

When data is openly shared, it can become a reusable resource far beyond its original purpose. Brian Nosek, Co-Founder and Executive Director of the Center for Open Science and Psychology Professor at the University of Virginia in the United States, saw this happen with his data on bias.  

Nosek developed Project Implicit in 1998 with Mazarin Bonaji and Tony Greenwald. Project Implicit was a popular website that allowed people to measure their own implicit biases, and for twenty years, more than a million people a year contributed to the project.  

“It generated enormous amounts of data that could be used for all kinds of social and behavioural research,” Nosek explains. In 2013, when he opened the Center for Open Science, the Project Implicit team suggested to add its data: “No one else has such a data source, it's totally unique.”  

Project Implicit data is shared systematically every year, and it has generated enormous numbers of cross-disciplinary research applications in epidemiology, political science, sociology, and behavioural medicine. “These things are way beyond the work that we did with implicit bias and it just wouldn't have happened without making the data available. It’s been very exciting and gratifying to see it get reused that way,” Nosek says. 

This example shows how unexpected and rich the contribution of open data can be, and why sustained availability and documentation can mean long-lasting impact.   

Connecting data to unlock new insights 

Combining datasets can create knowledge that no single dataset could deliver. Melissa Haendel PhD, FACMI, is Sarah Graham Kenan Distinguished Professor, Director of Precision Health and Translational Informatics, Deputy Director of Computational Sciences – NC Tracs, The University of North Carolina (UNC) School of Medicine in the United States. She shares from her experience of linking and interpreting information to improve outcomes.   

Together with Chris Mungall at the Lawrence Berkeley National Laboratory and with support of international collaborators, Haendel leads the Monarch Initiative. This initiative brings a variety of different open data sources together from humans and model organisms. “Once we've integrated the data, we can use it to improve the diagnosis of patients with rare diseases, really bringing those open data resources to the point of care, and using the data to help improve the lives of patients with rare diseases,” Haendel explains.  

In another project, the All of Us Research Program aims to recruit a million participants from across the United States, from diverse races, demographics, social structures, and religion. The study collects survey data, wearable data, genomic data, and electronic health record data. At the Center for Linkage and Acquisition of Data, Haendel and colleagues link that data to a variety of other data sources.  

“Participants have agreed to share, but not all of them are able to get their data into the program,” she explains. “We've partnered with a national health information network, eHealth Exchange, to go retrieve their electronic health record, and bring it back into the system. This is the first time anybody's ever done this, and it is so exciting because we finally actually have representation of their actual data from the place where they're receiving care.” 

Haendel’s work shows that interoperability and linkage can lead to meaningful impact. When data is available to be combined, value emerges, from research to healthcare and beyond.  

From open data to real-world and societal outcomes 

An example like Haendel’s Monarch Initiative also exhibits how data sharing can directly support cultural, health, or societal outcomes. Joy Owango, Founding Director of Training Centre in Communication (TCC Africa) in Kenya, illustrates how data sharing can have societal impact by supporting communities in preserving, managing, and sharing knowledge. 

The National Museum of Nigeria contacted TCC Africa because they are digitising and launching their digital library. This is a first step in data management; collect, curate, store, and share. But many questions arise: “How do you store it? Where do you store it? Now they are thinking about what kind of technology they can use to share our indigenous knowledge and protect our sovereignty. Something that acknowledges our ownership and contribution to research,” Owango explains. 

Impact is not only about citations and publications. Considerations of ethics, ownership, and trust are essential, as this experience shows.  

Catalysing collaboration and paradigm shifts 

Data sharing can lead to new collaborations and broader networks. Dawei Zhang is Deputy Director of the National Materials Corrosion and Protection Data Centre, and Professor at the School of Advanced Materials Innovation, University of Science and Technology Beijing in China. His shared data led to new and exciting collaborations that opened new research directions in the field. 

Zhang’s group was the first in the world to use AI models to process data obtained by a corrosion sensor in a typical atmospheric environment in China. The results showed that the combination of high-throughput corrosion data and AI models could help understand and even predict how materials can degrade.  

“A leading Croatian scientist in Belgium asked if we could share the data,” Zhang shares. “At that time only a minority of people wanted to believe that this data-driven machine learning model, which we often call black boxes, could lead to something that is really transformative. By using the data we shared, they not only published several very important works, but also helped us broaden our impact and extend the collaboration to many other countries,” Zhang explains.  

These collaborators from different countries are now driving the transformation of the entire corrosion research paradigm. Zhang’s experience exemplifies how sharing data can lead to collaboration, which can accelerate new insights, paradigm shifts, and scientific development.  

From data shared to data reused: What your data needs to create impact 

The 2025 State of Open Data report shows that the foundations for open data are in place. They should be strengthened and supported to make data sharing the standard across the research community. The impact stories above are just a few examples from across disciplines around the globe, that illustrate how meaningful intentional sharing can be providing data are curated, documented, and well-governed. As we’ve seen in the 2025 State of Open Data report, and as amplified in these stories, openness alone is not enough.  

Making shared data count is not only about making it available, but ensuring it can be reused in meaningful ways. Infrastructures, standards, and services are required to enable data to travel across geographical locations and scientific disciplines to deliver more impact.  

These impact stories illustrate what is possible when openness is combined with intention and purpose. If your work generates high-quality data, share it to create your own impact story. Turn your work into a resource that can drive reuse, collaboration, and long-term impact.  

Get resources and support to share your data openly in Springer Nature’s Open Science hub, and explore the 2025 State of Open Data report to learn more about global attitudes towards open data.  

Related content

• Supporting open science practices: Why share your research data? 
• Open data sharing: What a decade of researcher insights tells us Starting your career?
• Starting your career? Here’s how Springer can help you publish open access 

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