Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data

Nikki BialySteven, Frank AlberSteven, Brenda AndrewsSteven, Michael AngeloSteven, Brian BeliveauSteven, Lacramioara BintuSteven, Alistair BoettigerSteven, Ulrike BoehmSteven, Claire M. BrownSteven, Mahmoud Bukar MainaSteven, James J. ChambersSteven, Beth CiminiSteven, Kevin EliceiriSteven, Rachel ErringtonSteven, Orestis FaklarisSteven, Nathalie GaudreaultSteven, Ronald N. GermainSteven, Wojtek GoscinskiSteven, David GrunwaldSteven, Michael HalterSteven, Dorit HaneinSteven, John W. HickeySteven, Judith LacosteSteven, Alex LaudeSteven, Emma LundbergSteven, Jian MaSteven, Leonel MalacridaSteven, Josh MooreSteven, Glyn NelsonSteven, Elizabeth Kathleen NeumannSteven, Roland NitschkeSteven, Shichi OnamiSteven, Jaime A. PimentelSteven, Anne L. PlantSteven, Andrea J. RadtkeSteven, Bikash SabataSteven, Denis SchapiroSteven, Johannes SchönebergSteven, Jeffrey M. SpragginsSteven, Damir SudarSteven, Wouter-Michiel Adrien Maria VierdagSteven, Niels VolkmannSteven, Carolina WählbySteven, SiyuanSteven, Wang, Ziv Yaniv, Caterina Strambio-De-Castillia
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Abstract

Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled Enabling Global Image Data Sharing in the Life Sciences, which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data. In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. 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.
协调 FAIR 图像数据的生成和出版前管理
生物图像与基因和蛋白质的分子知识一起,有望极大地提高人们对复杂细胞系统的科学认识,并为人类健康提供预测性和个性化的治疗产品。要实现这一潜力,必须在全球范围内共享有质量保证的图像数据,以便进行比较、汇集和重新分析,从而释放出超出数据生成初衷的巨大潜力。要实现生命科学领域的图像数据共享,需要满足两大类要求。其中一组要求在题为 "促进生命科学领域的全球图像数据共享 "的配套白皮书中有所阐述,该白皮书是同时发布的,涉及建立共享数字阵列数据的网络基础设施的需求。在这份白皮书中,我们详细介绍了一系列广泛的要求,其中包括收集、管理、展示和传播背景信息,这些信息对于评估质量、理解内容、解释科学意义以及在实验细节背景下重复使用图像数据至关重要。我们首先概述了迄今为止通过国际社区活动获得的主要经验,这些活动最近在制定成像质量控制(QC)和元数据的社区标准实践方面取得了重大进展。然后,我们提出了一套明确的建议,以进一步扩大这项工作。推动这项工作的目标是应对依然存在的挑战,并使生物医学研究人员能够更方便地获取日常实践和工具,而不论他们的专业知识、资源获取能力和地理位置如何。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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