Population neuroscience data sharing:Policy ecology,infrastructure,practices and challenges
As a frontier in the field of brain science research,population neuroscience holds immense potential for realizing the translational application of neuroimaging to education,psychology,and medicine.However,its progress hinges on the presence of robust support for interdisciplinary data sharing.Motivated by the inherent need for disciplinary advancement,researchers have undertaken a multitude of bottom-up practices in data sharing to increase research reproducibility,reduce financial costs,and improve data reusability.These practices have ushered in the era of open science and initiated a profound transformation in research paradigms and academic exchange.In the contemporary landscape,global data sharing policies are becoming increasingly stringent,rendering data sharing an essential component of academic exchange.Nevertheless,the sustainable promotion of open data sharing still faces various impediments and challenges stemming from cultural,policy,and practical factors.Overcoming these obstacles requires collaboration among all stakeholders,including data contributors,consumers,communities,policymakers,and the general public.This article critically examines the indispensable elements required for the sustainable advancement of open scientific data sharing and delves into their interconnections,analysing the demands and contributions required from stakeholders in data sharing.It addresses scientific data sharing policies from the perspectives of diverse stakeholders,the norms and principles governing scientific data sharing,the standardized guidelines for data repositories,and the practical construction of data storage platforms specific to the domain of population neuroscience.Additionally,it supplements the discourse with examples from case studies such as theScienceDB-Chinese Color Nest Project Data Community.Progress in population neuroscience has benefited immensely from the paradigm of scientific data sharing,enabling researchers to leverage shared data sets to achieve innovative research and collaboration.However,this advancement has not been devoid of challenges.Privacy and security concerns,ethical considerations,the need for robust incentive mechanisms to promote data sharing,and deficiencies in scientific data management proficiency remain pressing issues in the current landscape of scientific data sharing.Nevertheless,with the advent of new scientific technologies,continuous improvements and safeguards for privacy and security are being realized.To address these challenges,this study proposes pertinent recommendations and prognostications concerning policy development,incentive mechanisms,infrastructure augmentation,and the trajectory of interdisciplinary convergence.These recommendations focus on the following key issues:(1)Strengthening top-level design to harness collective wisdom regarding innovation pertaining to data sharing forms and the exploration of issues of privacy,security,and standard specifications;(2)enhancing recognition of data sharing contributions and advancing incentive mechanisms;(3)leveraging high-quality data sharing services to improve standardization,data analysis,and visualization while providing accompanying personnel training to promote data sharing;(4)utilizing the advantages of platforms and AI models to create opportunities for interdisciplinary integration and cross-disciplinary innovation;and(5)exploring the open data journey in China by drawing insights from pathways in different cultural contexts.
scientific data sharingdata policydata standarddata communityScienceDB
万方数据
维普
