Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data
Nikki Bialy, Frank Alber, Brenda Andrews, Michael Angelo, Brian Beliveau, Lacramioara Bintu, Alistair Boettiger, Ulrike Boehm, Claire M. Brown, Mahmoud Bukar Maina, James J. Chambers, Beth A. Cimini, Kevin Eliceiri, Rachel Errington, Orestis Faklaris, Nathalie Gaudreault

TL;DR
This paper discusses the development of standards and practices for sharing high-quality bioimage data globally, emphasizing the importance of metadata, quality control, and community collaboration to enhance scientific discovery.
Contribution
It provides a comprehensive set of requirements and recommendations for managing and sharing bioimage data, building on community standards and aiming to democratize access across diverse researchers.
Findings
Progress in community standards for imaging quality control and metadata.
Identification of remaining challenges in bioimage data sharing.
Recommendations for amplifying community efforts and tools.
Abstract
Alongside molecular insights into genes and proteins, biological imaging holds great promise for deepening scientific understanding of complex cellular systems and advancing predictive, personalized therapies for human health. To realize this potential, quality-assured image data must be shared globally across laboratories to enable comparison, pooling, and reanalysis-unlocking value far beyond the original purpose of data collection. Two broad sets of requirements are essential to enable image data sharing in the life sciences. The companion article Enabling Global Image Data Sharing in the Life Sciences outlines the need to develop cyberinfrastructure for sharing bioimage data. In this manuscript, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content,…
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Taxonomy
TopicsScientific Computing and Data Management · Research Data Management Practices · Cell Image Analysis Techniques
MethodsSparse Evolutionary Training
