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
{"title":"促进生命科学领域的全球图像数据共享","authors":"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","doi":"arxiv-2401.13023","DOIUrl":null,"url":null,"abstract":"Coordinated collaboration is essential to realize the added value of and\ninfrastructure requirements for global image data sharing in the life sciences.\nIn this White Paper, we take a first step at presenting some of the most common\nuse cases as well as critical/emerging use cases of (including the use of\nartificial intelligence for) biological and medical image data, which would\nbenefit tremendously from better frameworks for sharing (including technical,\nresourcing, legal, and ethical aspects). In the second half of this paper, we\npaint an ideal world scenario for how global image data sharing could work and\nbenefit all life sciences and beyond. As this is still a long way off, we\nconclude by suggesting several concrete measures directed toward our\ninstitutions, existing imaging communities and data initiatives, and national\nfunders, as well as publishers. Our vision is that within the next ten years,\nmost researchers in the world will be able to make their datasets openly\navailable and use quality image data of interest to them for their research and\nbenefit. This paper is published in parallel with a companion White Paper\nentitled Harmonizing the Generation and Pre-publication Stewardship of FAIR\nImage Data, which addresses challenges and opportunities related to producing\nwell-documented and high-quality image data that is ready to be shared. The\ndriving goal is to address remaining challenges and democratize access to\neveryday practices and tools for a spectrum of biomedical researchers,\nregardless of their expertise, access to resources, and geographical location.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"32 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enabling Global Image Data Sharing in the Life Sciences\",\"authors\":\"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. 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Enabling Global Image Data Sharing in the Life Sciences
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.