Battery imaging library: multi-length scale and multi-modal synchrotron and laboratory battery imaging data for all

Battery research increasingly relies on advanced imaging, yet open access to such data remains rare, scattered across various sources, and difficult to find. The Battery Imaging Library (BIL) is the first open, curated collection of multi-modal and multi-length scale battery imaging datasets, accompanied by a searchable, FAIR-compliant website. Distinctive features include the release of raw experimental data (radiographs, sinograms, X-ray and electron diffraction patterns) together with rare operando and multi-resolution datasets. Each dataset is linked to Zenodo DOIs with metadata, ensuring persistence and citability; open-source Python scripts for preprocessing and reconstruction are also provided for each modality. BIL enables algorithm benchmarking, machine learning, and teaching using realistic, industrially relevant data. By combining coverage across modalities, length scales, and chemistries with raw data accessibility and a FAIR-aligned web platform, the Battery Imaging Library provides a foundation for openness and reproducibility in battery imaging. The library is available here: https://www.batteryimaginglibrary.com