A reasonable request for true data sharing

Abstract

Modern biomedical science and clinical trials are critical for health advancement but carry prohibitive costs, especially for laboratories in lower-income countries.These fields generate comprehensive and complex datasets containing a huge amount of information.However, the true potential of these datasets is rarely tapped, with research papers often focusing only on a fraction of the data, leaving most of the information unused and a lot of questions unanswered.Recognizing this underutilization, there was a global shift towards mandatory data sharing to maximize the value of these expensive datasets.Interpretations of what constitutes data sharing vary, and while the International Committee of Medical Journal Editors (ICMJE) encourages data-sharing statements, the actual sharing of datasets is not mandatory. 1Both the United States and Europe have implemented open data policies to ensure that the wealth of information generated by substantial investments is widely accessible.As noted in the UNESCO Open Science Toolkit, these policies are pivotal for developing science, technology, and innovation systems that are efficient, trusted, impactful, inclusive, and responsive to societal needs. 2 The notion of providing data upon "reasonable request" has, paradoxically, turned into an inside joke and a meme in the scientific community, which highlights the gap between policy and practice.While the pretext often cited for this guarded approach is the protection of sensitive patient information-a concern universally respected-the reality frequently belies this justification.In many cases, accessing anonymized datasets becomes an insurmountable challenge even with no perceivable risk to patient privacy.In 2022, researchers in Croatia contacted 1792 authors to gauge whether their data were truly available upon request; 93% of authors ignored the email or denied sharing. 3Individual scientists often face mixed incentives for data sharing, influenced by research domain, methods, and resources, highlighting the need for more supportive infrastructures and clear benefits to encourage open science practices. 4Restricting datasets are also important to ensure data owners are not scooped.

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