Enabling Global Image Data Sharing in the Life Sciences

Peter Bajcsy, Sreenivas Bhattiprolu, Katy Borner, Beth Cimini, Lucy Collinson, Jan Ellenberg, Reto Fiolka, Maryellen Giger, Wojtek Goscinski, Matthew Hartley, Nathan Hotaling, Rick Horwitz, Florian Jug, Anna Kreshuk, Emma Lundberg, Aastha Mathur, Kedar Narayan, Shuichi Onami, Anne L. Plant, Fred Prior, Jason Swedlow, Adam Taylor, Antje Keppler
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Abstract

Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects). In the second half of this paper, we paint an ideal world scenario for how global image data sharing could work and benefit all life sciences and beyond. As this is still a long way off, we conclude by suggesting several concrete measures directed toward our institutions, existing imaging communities and data initiatives, and national funders, as well as publishers. Our vision is that within the next ten years, most researchers in the world will be able to make their datasets openly available and use quality image data of interest to them for their research and benefit. This paper is published in parallel with a companion White Paper entitled Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data, which addresses challenges and opportunities related to producing well-documented and high-quality image data that is ready to be shared. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.
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促进生命科学领域的全球图像数据共享
在本白皮书中,我们首先介绍了一些最常见的使用案例以及生物和医学影像数据的关键/新兴使用案例(包括人工智能在生物和医学影像数据中的应用),这些案例将从更好的共享框架(包括技术、资源、法律和伦理方面)中获益匪浅。在本文的后半部分,我们将描绘一个理想的世界场景,即全球图像数据共享如何发挥作用并惠及所有生命科学及其他领域。由于距离实现这一目标还有很长的路要走,最后我们将针对我们的机构、现有的图像社区和数据计划、国家资助者以及出版商提出几项具体措施。我们的愿景是,在未来十年内,世界上大多数研究人员都能公开他们的数据集,并使用他们感兴趣的高质量图像数据进行研究,从中获益。本文与题为 "协调 FAIR 图像数据的生成和出版前管理 "的配套白皮书同时发布,该白皮书探讨了与生成记录完备、可随时共享的高质量图像数据相关的挑战和机遇。其驱动目标是解决剩余的挑战,并使生物医学研究人员(无论其专业知识、资源获取能力和地理位置如何)都能民主地获取日常实践和工具。
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