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All data to support our findings are available through the links listed in Supplementary Table 2. Further details can be found in the Reporting Summary.
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We would like to thank everybody who helped developing and testing CLIJ. In particular thanks goes to A. Herbert (University of Sussex), B. C. Vellutini (MPI-CBG), C. Rueden (UW-Madison LOCI), D. Krunic (DKFZ), D. J. White (GE), G. G. Martins (IGC), S. Culley (LMCB MRC), G. Cardone (MPI Biochem), J. Brocher (Biovoxxel), J. Girstmair (MPI-CBG), J. Gluch (Fraunhofer IKTS), K. Miura, L. Thomas (Acquifer), N. Stuurman (UCSF), P. Haub, P. Rajasekhar (Monash University), T. Pietzsch (MPI-CBG), W. Adams (VU Biophotonics). R.H. was supported by the German Federal Ministry of Research and Education (BMBF) under the code 031L0044 (Sysbio II) and D.S. received support from the German Research Foundation (DFG) under the code JU3110/1-1. P.T. was supported by the European Regional Development Fund in the IT4Innovations national supercomputing center-path to exascale project, project number CZ.02.1.01/0.0/0.0/16_013/0001791 within the Operational Programme Research, Development and Education.
The authors declare no competing interests.
Supplementary Figures 1–7, Supplementary Tables 1 and 2, and Supplementary Notes 1 and 2.
Results from processed time-lapse for benchmarking workflow. A video showing a developing Drosophila embryo in maximum projection and cylinder maximum projection with marked spot detection and spot count plotted over time.
CLIJ usage examples: Scripts demonstrating CLIJ in various ImageJ/Fiji compatible programming languages.
Reviewed version of CLIJ software.
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Haase, R., Royer, L.A., Steinbach, P. et al. CLIJ: GPU-accelerated image processing for everyone. Nat Methods (2019) doi:10.1038/s41592-019-0650-1