BioimageAIpub: a toolbox for AI-ready bioimaging data publishing
This addresses a bottleneck for researchers in bioimage analysis by reducing time spent on data preparation, though it is incremental as it builds on existing platforms.
The authors tackled the problem of tedious data wrangling for bioimaging datasets by introducing BioimageAIpub, a workflow that streamlines conversion and enables seamless upload to HuggingFace.
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.