
==== Front
ArXiv
ArXiv
arxiv
ArXiv
2331-8422
Cornell University

arXiv:2401.13022v5
2401.13022
5
preprint
Article
Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data
Bialy Nikki
Alber Frank
Andrews Brenda
Angelo Michael
Beliveau Brian
Bintu Lacramioara
Boettiger Alistair
Boehm Ulrike
Brown Claire M.
Maina Mahmoud Bukar
Chambers James J.
Cimini Beth A.
Eliceiri Kevin
Errington Rachel
Faklaris Orestis
Gaudreault Nathalie
Germain Ronald N.
Goscinski Wojtek
Grunwald David
Halter Michael
Hanein Dorit
Hickey John W.
Lacoste Judith
Laude Alex
Lundberg Emma
Ma Jian
Malacrida Leonel
Moore Josh
Nelson Glyn
Neumann Elizabeth Kathleen
Nitschke Roland
Onami Shuichi
Pimentel Jaime A.
Plant Anne L.
Radtke Andrea J.
Sabata Bikash
Schapiro Denis
Schöneberg Johannes
Spraggins Jeffrey M.
Sudar Damir
Vierdag Wouter-Michiel Adrien Maria
Volkmann Niels
Wählby Carolina
Siyuan
Wang
Yaniv Ziv
Strambio-De-Castillia Caterina
30 8 2024
arXiv:2401.13022v523 1 2024
https://creativecommons.org/licenses/by-nc-sa/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. If you remix, adapt, or build upon the material, you must license the modified material under identical terms.
http://arxiv.org/abs/2401.13022v5
nihpp-2401.13022v5.pdf
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.

This manuscript is published with a closely related companion entitled, Enabling Global Image Data Sharing in the Life Sciences, which can be found at the following link, arXiv:2401.13023 [q-bio.OT]
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