Enabling Global Image Data Sharing in the Life Sciences
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arXiv
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| Format: | Preprint |
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2024
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| author | Bajcsy, Peter Bhattiprolu, Sreenivas Boerner, Katy Cimini, Beth A Collinson, Lucy Ellenberg, Jan Fiolka, Reto Giger, Maryellen Goscinski, Wojtek Hartley, Matthew Hotaling, Nathan Horwitz, Rick Jug, Florian Kreshuk, Anna Lundberg, Emma Mathur, Aastha Narayan, Kedar Onami, Shuichi Plant, Anne L. Prior, Fred Swedlow, Jason Taylor, Adam Keppler, Antje |
| author_facet | Bajcsy, Peter Bhattiprolu, Sreenivas Boerner, Katy Cimini, Beth A Collinson, Lucy Ellenberg, Jan Fiolka, Reto Giger, Maryellen Goscinski, Wojtek Hartley, Matthew Hotaling, Nathan Horwitz, Rick Jug, Florian Kreshuk, Anna Lundberg, Emma Mathur, Aastha Narayan, Kedar Onami, Shuichi Plant, Anne L. Prior, Fred Swedlow, Jason Taylor, Adam Keppler, Antje |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_13023 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enabling Global Image Data Sharing in the Life Sciences Bajcsy, Peter Bhattiprolu, Sreenivas Boerner, Katy Cimini, Beth A Collinson, Lucy Ellenberg, Jan Fiolka, Reto Giger, Maryellen Goscinski, Wojtek Hartley, Matthew Hotaling, Nathan Horwitz, Rick Jug, Florian Kreshuk, Anna Lundberg, Emma Mathur, Aastha Narayan, Kedar Onami, Shuichi Plant, Anne L. Prior, Fred Swedlow, Jason Taylor, Adam Keppler, Antje Other Quantitative Biology 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. |
| title | Enabling Global Image Data Sharing in the Life Sciences |
| topic | Other Quantitative Biology |
| url | https://arxiv.org/abs/2401.13023 |