BioimageAIpub: a toolbox for AI-ready bioimaging data publishing
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915683857596416 |
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| author | Dvoretskii, Stefan Archit, Anwai Pape, Constantin Moore, Josh Nolden, Marco |
| author_facet | Dvoretskii, Stefan Archit, Anwai Pape, Constantin Moore, Josh Nolden, Marco |
| contents | Modern bioimage analysis approaches are data hungry, making it necessary for researchers to scavenge data beyond those collected within their (bio)imaging facilities. In addition to scale, bioimaging datasets must be accompanied with suitable, high-quality annotations and metadata. Although established data repositories such as the Image Data Resource (IDR) and BioImage Archive offer rich metadata, their contents typically cannot be directly consumed by image analysis tools without substantial data wrangling. Such a tedious assembly and conversion of (meta)data can account for a dedicated amount of time investment for researchers, hindering the development of more powerful analysis tools. Here, we introduce BioimageAIpub, a workflow that streamlines bioimaging data conversion, enabling a seamless upload to HuggingFace, a widely used platform for sharing machine learning datasets and models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15820 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BioimageAIpub: a toolbox for AI-ready bioimaging data publishing Dvoretskii, Stefan Archit, Anwai Pape, Constantin Moore, Josh Nolden, Marco Image and Video Processing Computer Vision and Pattern Recognition Modern bioimage analysis approaches are data hungry, making it necessary for researchers to scavenge data beyond those collected within their (bio)imaging facilities. In addition to scale, bioimaging datasets must be accompanied with suitable, high-quality annotations and metadata. Although established data repositories such as the Image Data Resource (IDR) and BioImage Archive offer rich metadata, their contents typically cannot be directly consumed by image analysis tools without substantial data wrangling. Such a tedious assembly and conversion of (meta)data can account for a dedicated amount of time investment for researchers, hindering the development of more powerful analysis tools. Here, we introduce BioimageAIpub, a workflow that streamlines bioimaging data conversion, enabling a seamless upload to HuggingFace, a widely used platform for sharing machine learning datasets and models. |
| title | BioimageAIpub: a toolbox for AI-ready bioimaging data publishing |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.15820 |