![\"image](\"https:\/\/www.finden.co.uk\/wp-content\/uploads\/2025\/10\/BILimage1.jpeg\")
<\/p>\n<\/p>\n
We are pleased to announce the launch of the Battery Imaging Library (BIL)<\/strong> – www.batteryimaginglibrary.com<\/a><\/p>\n BIL is the first open-access collection of multi-modal and multi-length scale battery imaging datasets. A combination of laboratory and synchrotron X-ray CT (micro-CT, nano-CT, XRD-CT), neutron CT, SEM\/EDX, EBSD, and S3DXRD. One unique feature is the release of raw experimental data: radiographs, sinograms, X-ray diffraction patterns, and electron Kikuchi diffraction patterns. The library already hosts more than 3.5 TB of compressed data, openly available for the community.<\/p>\n You can find open-access Python notebooks with example workflows at https:\/\/github.com\/antonyvam\/BatteryImagingLibrary<\/a><\/p>\n BIL has been designed to support: BIL is the result of collaboration between multiple institutions and facilities including Imperial College London<\/a>, Diamond Light Source,<\/a> ESRF – The European Synchrotron<\/a>,<\/a> DESY<\/a>, \u03bc-VIS X-ray Imaging Centre (muvis)<\/a>, UCL Chemistry<\/a>, The University of Manchester<\/a>, ISIS Neutron and Muon Source<\/a>, UKBIC, National Renewable Energy Laboratory and<\/a>\u00a0WMG, University of Warwick.\u00a0<\/a> Funding for Dr Antony Vamvakeros’s work was provided by The Royal Society<\/a>\u00a0through their Industry Fellowship.<\/p>\n
\n\u2022 Educators who want to give students hands-on access to real experimental datasets.
\n\u2022 Researchers developing and benchmarking algorithms for reconstruction, denoising, segmentation and other machine learning approaches using experimental industrially relevant data.
\n\u2022 The wider community through FAIR principles, with all datasets permanently archived with DOIs and rich metadata.<\/p>\n