Harmonizing the Generation and Pre-publication Stewardship of FAIR Image data. Bialy, N., Alber, F., Andrews, B., Angelo, M., Beliveau, B., Bintu, L., Boettiger, A., Boehm, U., Brown, C. M, Maina, M. B., Chambers, J. J, Cimini, B. A, Eliceiri, K., Errington, R., Faklaris, O., Gaudreault, N., Germain, R. N, Goscinski, W., Grunwald, D., Halter, M., Hanein, D., Hickey, J. W, Lacoste, J., Laude, A., Lundberg, E., Ma, J., Malacrida, L., Moore, J., Nelson, G., Neumann, E. K., Nitschke, R., Onami, S., Pimentel, J. A, Plant, A. L, Radtke, A. J, Sabata, B., Schapiro, D., Schöneberg, J., Spraggins, J. M, Sudar, D., Adrien Maria Vierdag, W., Volkmann, N., Wählby, C., Wang, S. S., Yaniv, Z., & Strambio-De-Castillia, C. ArXiv, February, 2024.
Harmonizing the Generation and Pre-publication Stewardship of FAIR Image data [link]Paper  abstract   bibtex   1 download  
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 (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). 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 common practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.
@article{bialy_harmonizing_2024,
	title = {Harmonizing the {Generation} and {Pre}-publication {Stewardship} of {FAIR} {Image} data},
	copyright = {cc by-nc-sa},
	issn = {2331-8422},
	url = {https://europepmc.org/articles/PMC10862930},
	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 (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). 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 common practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.},
	language = {eng},
	urldate = {2024-02-21},
	journal = {ArXiv},
	author = {Bialy, Nikki and Alber, Frank and Andrews, Brenda and Angelo, Michael and Beliveau, Brian and Bintu, Lacramioara and Boettiger, Alistair and Boehm, Ulrike and Brown, Claire M and Maina, Mahmoud Bukar and Chambers, James J and Cimini, Beth A and Eliceiri, Kevin and Errington, Rachel and Faklaris, Orestis and Gaudreault, Nathalie and Germain, Ronald N and Goscinski, Wojtek and Grunwald, David and Halter, Michael and Hanein, Dorit and Hickey, John W and Lacoste, Judith and Laude, Alex and Lundberg, Emma and Ma, Jian and Malacrida, Leonel and Moore, Josh and Nelson, Glyn and Neumann, Elizabeth Kathleen and Nitschke, Roland and Onami, Shuichi and Pimentel, Jaime A and Plant, Anne L and Radtke, Andrea J and Sabata, Bikash and Schapiro, Denis and Schöneberg, Johannes and Spraggins, Jeffrey M and Sudar, Damir and Adrien Maria Vierdag, Wouter-Michiel and Volkmann, Niels and Wählby, Carolina and Wang, Siyuan Steven and Yaniv, Ziv and Strambio-De-Castillia, Caterina},
	month = feb,
	year = {2024},
	pmid = {38351940},
	pmcid = {PMC10862930},
	pages = {arXiv:2401.13022v4},
}
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