KEY TAKEAWAY
- Generative AI tools can simplify data sharing through automating metadata creation and flagging missed requirements, ultimately enhancing open science.
Artificial intelligence (AI) has proved transformative in scientific research, from experimental design to assisting publishers and streamlining peer review processes. But can it unlock access to research data, code, and protocols frequently lost behind digital and institutional walls? In a recent London School of Economics Impact Blog article, Niki Scaplehorn and Henning Schoenenberger, both at Springer Nature, describe how generative AI could play a pivotal role in reshaping how data are shared, potentially revolutionising open science.
Hurdles to data sharing
The COVID-19 pandemic marked a turning point for open science, with global collaboration and rapid data sharing accelerating breakthroughs. Yet, Scaplehorn and Schoenenberger highlight that there are still considerable challenges to data sharing:
- a lack of consistent guidance and struggles to align with FAIR standards
- confusing and overlapping data sharing policies
- cultural barriers
- a lack of recognition for data sharing, code publication, and protocol documentation in academia.
Springer Nature saw compliance with data sharing requirements jump from 51% to 87% simply by asking authors to justify why they hadn’t deposited data prior to article acceptance. Scaling this approach, however, demands time and manpower. According to Scaplehorn and Schoenenberger, here, generative AI shows potential.
How can AI benefit data sharing?
The authors call for a “product” mindset that treats AI open science tools as services designed around researchers’ needs, rather than top-down mandates or administrative burdens. Scaplehorn and Schoenenberger highlight that AI can benefit data sharing through:
- automation of metadata creation
- flagging missing documentation and overlooked requirements
- suggesting best practices to improve workflows.
“Generative AI could play a pivotal role in reshaping how data are shared, potentially revolutionising open science.”
The path forward
Scaplehorn and Schoenenberger believe that adopting AI tools designed around authors’ needs will streamline the burdensome aspects of data sharing. Ultimately, this will benefit researchers, policymakers, and everyone who relies on access to scientific information through lowering the barriers to open science.
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