Dataset for "Low-cost CMOS-based luminescence lifetime imaging of oxygen, temperature or pH"

Dataset Description This dataset contains the experimental data supporting the conclusions presented in "Low-cost CMOS-based luminescence lifetime imaging of oxygen, temperature or pH" by Gauthier Rousseau, Marianne Pons, Hessel Adelerhof, Nathan Pellerin, Mart Giesbergen, Bastien Carde, Martin Wolff, Koen Blanckaert, Sergey M. Borisov, and Benoit Fond. The data were generated using the CLICC (Cumulative Lifetime Imaging on CMOS-Camera) platform for luminescence lifetime imaging across three applications: oxygen sensing, temperature mapping, and pH imaging. CLICC platform control code available at: https://gitlab.com/groussea/clicc Dataset Contents Oxygen Sensing Data Calibration data: Lifetime measurements vs. oxygen partial pressure (pO₂) for PtTPTBPF-based planar optodes Time-domain reference measurements for validation RLD imaging data with different window durations (Δt = 15 μs and 25 μs) Dynamic response data from flow-through cell experiments Spatial gradient imaging data from sediment simulation experiments Nitrogen jet response measurements Temperature Imaging Data Calibration data: TADF lifetime vs. temperature for Zr(MesIPDP^t-Bu Ph)₂ nanoparticles Time-domain reference decay measurements RLD temperature imaging data from fluid injection experiments pH Imaging Data DLR calibration data: Ratio response vs. pH using aza-BODIPY indicator and Egyptian blue reference pH gradient imaging data from buffer experiments Sigmoidal curve fitting parameters File Formats and Structure Raw Image Data: Stored in HDF5 format with application-specific structures: High-resolution images (2056 × 2464 pixels, float64): background correction images and multiple reference images (REF1, REF2) Experimental parameters vary by application: Oxygen sensing: oxygen concentration, LED timing, exposure parameters Temperature imaging: temperature values, Laser timing, exposure parameters pH imaging: pH values, LED timing, exposure parameters Total file sizes: ~30 MB per HDF5 file Additional Data: Calibration curves (CSV) and analysis scripts (Python) Reading HDF5 Data The HDF5 files can be accessed using standard libraries: Python: import h5py with h5py.File('filename.h5', 'r') as f: background = f['data/background'][:] image_ref1 = f['data/image_REF1'][:] image_ref2 = f['data/image_REF2'][:] # Access experimental parameters oxygen = f['data/Oxygen'][0] # for oxygen sensing data MATLAB: background = h5read('filename.h5', '/data/background'); image_ref1 = h5read('filename.h5', '/data/image_REF1'); experimental_param = h5read('filename.h5', '/data/Oxygen'); The provided algorithms enable direct calculation of ratios and subsequent lifetime determination from the raw HDF5 image data across all three applications (oxygen, temperature, and pH). Please note that the way the HDF5 files are structured can vary from one file to another, you can use the python files to identify which treatment you need to apply to a specific file. Acquisition Parameters All data acquired using Ximea MC050MG-SY-UB camera with SONY IMX250 sensor (2056 × 2464 resolution), synchronized with LED/laser excitation sources via Arduino R3 controller. Experimental conditions and parameters specific to each application (oxygen, temperature, or pH) are embedded within the respective HDF5 files.