Recent, rapid progress in electronics unveiled a new horizon in experimental techniques, especially in imaging. For example, the beamline ID15 at ESRF is now able to record 2016x2016 x-ray diffraction images with stunning frequency of 1 kHz . With continuous progress in networking devices and detectors/scintillators, the breakthrough towards GHz region is only matter of time . In 3D tomographic imaging, dozens of acquisitions can be performed for single in situ / in operando experiment, resulting in hundreds of thousands features being tracked over time in order to understand the particular process . However, this means that researchers have to deal with growing amount of information, where single experiment is now represented sometimes by terabytes of data. Moreover, to understand the physical phenomena of interest, the multimodal analysis must be performed. There, the experimental data is collected by means of very different techniques, often utilizing exotic data formats. This leads to big-data problem, requiring modern methods for data processing and meta-analysis, e.g. machine learning. Existing solutions for handling the imaging data are usually specific to few modalities related to single research field. For example, XNAT is open-source archive system for neuroimaging, supporting both MRI and XCT scanners.
Unfortunately in case of material science, the variety of modalities and data types (from text files, through images to proprietary formats) makes it more challenging. The aim of this work is to provide open-source, comprehensive, expandable, i.e. plugin based solution (software) for multimodal data provided by experimental techniques used in material science. This includes XCT, MRI, SEM, PDF, XRD, XRS, SAXS, SANS, DCT, XANES, XAS and more. The software will archive the experimental data and store it at different levels of processing, starting from RAW images, on dedicated RAID system. Search engine, together with API supporting well established resource exchange protocol - REST , will allow researchers easy access to data base. Browsing for relevant information will be possible from any platform (Linux, Windows, Mac) via any interface i.e. - direct REST queries, simple web interface, Matlab/Python scripts, etc. Finally, the meta-analysis will link the multiple modalities to enhance the statistical power of the results.
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Institute of Energy and Climate Research (IEK)
Functional Materials group at Fundamental Electrochemistry Institute (IEK-9)