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ZeroCostDL4Mic - DeepSTORM training and example dataset

<p><strong>Name</strong>: ZeroCostDL4Mic - DeepSTORM training and example dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p>&nbsp;</p> <p><strong>Data type</strong>: Time-series of simulated, randomly distributed single-molecule localization (SMLM) data (Training dataset).&nbsp;Experimental time-series dSTORM acquisition of Glial cells stained with phalloidin for actin (Example dataset).&nbsp;</p> <p><strong>Microscopy data type</strong>: Simulated SMLM and experimental dSTORM super-resolution microscopy data. The experimental dataset was temporally binned in groups of 4 (BIN4) or 10 (BIN10) consecutive time frames in order to simulate higher density of emitters.</p> <p><strong>Microscope</strong>: Nikon N-STORM</p> <p><strong>Cell type</strong>: Glial cell in a culture of rat hippocampal neurons, fixed and stained with phalloidin-Alexa Fluor 647.</p> <p><strong>File format</strong>: .tif (16-bit or 32-bit)</p> <p><strong>Image size</strong>: 64x64 px (160 nm / pixel), 20 frames (Training dataset),</p> <p>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 256 x 256 px (160 nm / pixel), 14,975 frames (Example dataset)</p> <p><strong>Author(s)</strong>: Christophe Leterrier<sup>1,2</sup>, Romain F. Laine<sup>3,4,5</sup></p> <p><strong>Contact email</strong>: christophe.leterrier@univ-amu.fr, r.laine@ucl.ac.uk</p> <p>&nbsp;</p> <p><strong>Affiliation(s)</strong>:&nbsp;</p> <p>1) Aix Marseille Universit&eacute;, CNRS, INP UMR7051, NeuroCyto, Marseille, France</p> <p>2) ORCID: 0000-0002-2957-2032</p> <p>3) MRC-Laboratory for Molecular Cell Biology. University College London, London, UK</p> <p>4) The Francis Crick Institute, London, UK</p> <p>5) ORCID: 0000-0002-2151-4487</p> <p>&nbsp;</p> <p><strong>Associated publications</strong>: Unpublished</p> <p><strong>Funding bodies</strong>: C.L. acknowledges CNRS ATIP-AO2016. R.F.L. acknowledges a MRC Skills development fellowship (MR/T027924/1).</p>

ShareScore

32/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
4
Access
16
Reuse readiness
0
Engagement
4

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