Gallacher John, Webster Chris
Department of Psychiatry, University of Oxford, Oxford, UK.
Healthy High Density Cities Lab, Department of Urban Planning and Design, HKU Institute of Data Science, HKU Urban Systems Institute, Hong Kong University, Pok Fu Lam, Hong Kong.
R Soc Open Sci. 2024 Jun 26;11(6):231742. doi: 10.1098/rsos.231742. eCollection 2024 Jun.
A major challenge facing the biomedical community is creating and sustaining high-quality research environments. A literature search identified five common themes underlying biomedical research environments comprising collaboration, data access, user-led innovation, data provenance and a deep commitment to public and scientific benefit. Club theory is used to develop a model describing social structures that underpin these themes. It is argued that collaboration underlies impactful science and that collaboration is hindered by high transaction costs. This, combined with poorly defined property rights surrounding publicly funded data, limits the ability of data markets to operate efficiently. Although the science community is best placed to provide solutions for these issues, incentivization by funding agencies to increase the benefits of collaboration and reduce uncoordinated activity will be an accelerator. Given the complexity of emerging datasets and the collaborations needed to exploit them, trust-by-design solutions are suggested. The underlying motivational 'glue' that holds this activity together is the aesthetic and ethical value base underlying good science. The model has implications for data-driven science more generally. As biomedical science in the Global South develops, there is an opportunity to address foundational structural issues prospectively rather than inherit unwanted constraints of current practice.
生物医学界面临的一个主要挑战是创建和维持高质量的研究环境。一项文献检索确定了生物医学研究环境背后的五个共同主题，包括合作、数据获取、用户主导的创新、数据溯源以及对公共利益和科学利益的坚定承诺。俱乐部理论被用于开发一个模型，描述支撑这些主题的社会结构。有人认为，合作是有影响力的科学的基础，而高交易成本阻碍了合作。这一点，再加上围绕公共资助数据的产权界定不清，限制了数据市场有效运作的能力。尽管科学界最有能力为这些问题提供解决方案，但资助机构通过激励措施来增加合作的好处并减少不协调的活动将是一个加速器。鉴于新兴数据集的复杂性以及利用这些数据集所需的合作，建议采用设计信任的解决方案。将这项活动凝聚在一起的潜在激励“粘合剂”是良好科学背后的美学和伦理价值基础。该模型对更广泛的数据驱动科学具有启示意义。随着全球南方生物医学科学的发展，有机会前瞻性地解决基础结构问题，而不是继承当前实践中不必要的限制。
