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2,394 results for “Containers”
GMATA derived ssr files containing Microsatellite data for 128 Phytophthora strains
<p>These 128 files are GMATA software-derived .ssr files, containing Microsatellite data for each Phytophthora strain.</p>
ArchiHeart - Dataset : Software containers, SQlite database and DGRP data
<p><strong>Title : </strong>Genetic architecture of natural variation of cardiac performance in flies</p> <p><strong>Abstract : </strong>Deciphering the genetic architecture of human cardiac disorders is of fundamental importance but their underlying complexity is a major hurdle. We investigated the natural variation of cardiac performance in the sequenced inbred lines of the Drosophila Genetic Reference Panel (DGRP)1. Genome Wide Associations Studies (GWAS) identified genetic networks associated with natural variation of cardiac traits which were extensively validated with <em>in vivo</em> cardiac-specific gene manipulation<em>.</em> Specifically, non-coding variants that we identified were used to map potential regulatory non-coding regions, which in turn were employed to predict Transcription Factors (TFs) binding sites. Cognate TFs, many of which themselves bear polymorphisms associated with variations of cardiac performance, were also validated by heart specific knockdown. Although rarely studied, the genetic control of phenotypic variability is of primary importance, with both medical and fundamental implications. We showed that the natural variations associated with variability in cardiac performance affect a set of genes overlapping with those associated with average traits but through different variants in the same genes. Furthermore, we showed that phenotypic variability is also associated with gene regulatory network deviations. More importantly, we documented correlations between genes associated with cardiac phenotypes in both flies and humans, which supports a conserved genetic architecture regulating adult cardiac function from arthropods to mammals. Specifically, roles for PAX9 and EGR2 in the regulation of the cardiac rhythm were established in both models, illustrating that the characteristics of natural variations in cardiac function identified in Drosophila can accelerate discovery in humans.</p> <p><strong>Data</strong> :</p> <ul> <li>phenosnip_singleageanalysis.img : singularity v2.6 image with R 3.4.4 and python 2.7, used for preliminary statistical analysis.</li> <li>phenosnip_singleagegwas.img : singualrity v2.6 image with FastLMM and plink, used for GWAS analysis</li> <li>phenosnip_singleageepistasis.img : singualrity c2.6 image with fastEpistasis and plink, used for epistasis analysis</li> <li>Phenosnip.sqlite.tar.gz : SQlite database containing the genotype information from DGRP consortium and the phenotype data of the study (1 week aged drosophila)</li> <li>dgrp2.tar.gz : BED, BIM and FAM files with genetic information of the DGRP lines (data issued from the DGRP consortium)</li> <li>datasets_saha_et_al.zip : raw data (individual phenotypes of DGRP lines / variants identified by GWAS / individual phenotypes from validation experiments) and large scale datasets used for analyses (PPI and genetic interactions / regulatory variants) </li> </ul>
A collection of X-ray projections of 131 pieces of modeling clay containing stones for machine learning-driven object detection
<p><strong>Summary</strong></p> <p>This submission contains a collection of 235800 X-ray projections of 131 pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as an extensive and easy-to-use training dataset for supervised machine learning driven object detection. The ground truth locations of the stones are included. The data is supplementary material to the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022].</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></p> <p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p> <p><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections have been corrected with flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images). Both the X-ray projections and the ground truth images are resized to 128x128 pixels. The raw data is made available in another (larger) submission for complete reproduction (<a href="https://zenodo.org/record/5866228">https://zenodo.org/record/5866228</a>). All images are stored in .tif format. The data for samples with 5-8 stones are put in a separate folder from the data with 0-3 stones. The size of the completely unpacked dataset is 19.6 GB.</p> <p><strong>NOTE</strong>: Because the dataset consists of 471600 files, fully extracting the dataset may take a while. Therefore, an additional and significantly smaller zip-file is included for previewing the data, with one X-ray projection for each sample.</p> <p> </p> <p><strong>Additional Links</strong><br> These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow enabling deep learning for X-ray based foreign object detection", 2022 (in preparation)<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p>If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.</p>
Protective immune trajectories in early viral containment of non-pneumonic SARS-CoV-2 infection
<p><strong>scRNA-seq data</strong></p> <p>Data were processed using cellranger v 4.0.0 with the refdata-gex-GRCh38-2020-A reference.</p> <p><em>h5files.zip</em>: contains all h5-Files of raw feature-barcode counts (e.g. 20094_0001_A_B_raw_feature_bc_matrix.new.h5 )</p> <p><em>raw_feature_bc_matrices.zip</em>: contains the <em>same data</em> as h5files.zip, but also in mtx-format.</p> <p>covid_object_ncomms<em>.RDS</em>: contains the Seurat file with which all analyses were conducted.</p> <p><em>samples2condition.df</em>: text file containing sample to condition information</p> <p><strong>Bulk RNA-seq</strong></p> <p><em>covid_bulk.zip</em> contains the count matrices extracted from the zUMIs runs for the bulk cohort.</p> <p><em>nasal_swabs.zip</em> contains the count matrices extracted from the zUMIs run for the nasal swab cohort.</p> <p>The extracted count matrices were then used with the bulk analysis scripts provided with the source code.</p> <p><strong>Source Code</strong></p> <p>All <strong>source code</strong> for the publication is available from: <a href="https://github.com/mjoppich/covidSC">https://github.com/mjoppich/covidSC</a> or from tagged releases: <a href="https://github.com/mjoppich/covidSC/releases/tag/ncomms">https://github.com/mjoppich/covidSC/releases/tag/ncomms</a></p> <p>When using any of these data, please cite:<br> <br> Pekayvaz et al., Protective immune trajectories in early viral containment of non-pneumonic SARS-CoV-2 infection, Nature Communications 2022</p>
A collection of 131 CT datasets of pieces of modeling clay containing stones - Part 5 of 5
<p><strong>Summary</strong></p> <p>This submission contains a collection of 131 CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022]. This submission consists of three parts in total.</p> <p> </p> <p><strong>Parts</strong></p> <p>The 131 CT scans are divided into 5 separate submissions:<br> Part 1 of 5<em>:</em> 001-028: <a href="https://doi.org/10.5281/zenodo.5866228">10.5281/zenodo.5866228</a><br> Part 2 of 5: 029-056: <a href="https://doi.org/10.5281/zenodo.5866322">10.5281/zenodo.5866322</a><br> Part 3 of 5: 057-084: <a href="https://doi.org/10.5281/zenodo.5866363">10.5281/zenodo.5866363</a><br> Part 4 of 5: 085-111: <a href="https://doi.org/10.5281/zenodo.5866365">10.5281/zenodo.5866365</a><br> Part 5 of 5: 112-131: <a href="https://doi.org/10.5281/zenodo.5866367">10.5281/zenodo.5866367</a> <strong>(this upload)</strong></p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></p> <p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p> <p><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections are unprocessed files, except that a binning been applied FleX-ray lab software. The resulting image sizes are 956x760. Flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images) are included with each object. All images are stored in .tif format. The data for samples with 0-3 stones are contained in parts 1 to 4, while the samples with 5-8 stones constitute part 5. The size of the completely unpacked dataset (all 5 parts) is ca. 343.5 GB.</p> <p>The processed data (with generated ground truth) is made available in another (smaller) submission for object detection purposes: <a href="https://zenodo.org/record/5681008">https://zenodo.org/record/5681008</a></p> <p> </p> <p><strong>Additional Links</strong><br> These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow to enable deep learning for X-ray based foreign object detection", 2022 (submitted)<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p>If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.</p>
A collection of 131 CT datasets of pieces of modeling clay containing stones - Part 4 of 5
<p><strong>Summary</strong></p> <p>This submission contains a collection of 131 CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022]. This submission consists of three parts in total.</p> <p> </p> <p><strong>Parts</strong></p> <p>The 131 CT scans are divided into 5 separate submissions:<br> Part 1 of 5<em>:</em> 001-028: <a href="https://doi.org/10.5281/zenodo.5866228">10.5281/zenodo.5866228</a><br> Part 2 of 5: 029-056: <a href="https://doi.org/10.5281/zenodo.5866322">10.5281/zenodo.5866322</a><br> Part 3 of 5: 057-084: <a href="https://doi.org/10.5281/zenodo.5866363">10.5281/zenodo.5866363</a><br> Part 4 of 5: 085-111: <a href="https://doi.org/10.5281/zenodo.5866365">10.5281/zenodo.5866365</a> <strong>(this upload)</strong><br> Part 5 of 5: 112-131: <a href="https://doi.org/10.5281/zenodo.5866367">10.5281/zenodo.5866367</a></p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></p> <p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p> <p><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections are unprocessed files, except that a binning been applied FleX-ray lab software. The resulting image sizes are 956x760. Flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images) are included with each object. All images are stored in .tif format. The data for samples with 0-3 stones are contained in parts 1 to 4, while the samples with 5-8 stones constitute part 5. The size of the completely unpacked dataset (all 5 parts) is ca. 343.5 GB.</p> <p>The processed data (with generated ground truth) is made available in another (smaller) submission for object detection purposes: <a href="https://zenodo.org/record/5681008">https://zenodo.org/record/5681008</a></p> <p> </p> <p><strong>Additional Links</strong><br> These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow to enable deep learning for X-ray based foreign object detection", 2022 (submitted)<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p>If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.</p>
A collection of 131 CT datasets of pieces of modeling clay containing stones - Part 2 of 5
<p><strong>Summary</strong></p> <p>This submission contains a collection of 131 CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022]. This submission consists of three parts in total.</p> <p> </p> <p><strong>Parts</strong></p> <p>The 131 CT scans are divided into 5 separate submissions:<br> Part 1 of 5<em>:</em> 001-028: <a href="https://doi.org/10.5281/zenodo.5866228">10.5281/zenodo.5866228</a><br> Part 2 of 5: 029-056: <a href="https://doi.org/10.5281/zenodo.5866322">10.5281/zenodo.5866322</a> <strong>(this upload)</strong><br> Part 3 of 5: 057-084: <a href="https://doi.org/10.5281/zenodo.5866363">10.5281/zenodo.5866363</a><br> Part 4 of 5: 085-111: <a href="https://doi.org/10.5281/zenodo.5866365">10.5281/zenodo.5866365</a><br> Part 5 of 5: 112-131: <a href="https://doi.org/10.5281/zenodo.5866367">10.5281/zenodo.5866367</a> </p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></p> <p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p> <p><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections are unprocessed files, except that a binning been applied FleX-ray lab software. The resulting image sizes are 956x760. Flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images) are included with each object. All images are stored in .tif format. The data for samples with 0-3 stones are contained in parts 1 to 4, while the samples with 5-8 stones constitute part 5. The size of the completely unpacked dataset (all 5 parts) is ca. 343.5 GB.</p> <p>The processed data (with generated ground truth) is made available in another (smaller) submission for object detection purposes: <a href="https://zenodo.org/record/5681008">https://zenodo.org/record/5681008</a></p> <p> </p> <p><strong>Additional Links</strong><br> These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow to enable deep learning for X-ray based foreign object detection", 2022 (submitted)<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p>If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.</p>
A collection of 131 CT datasets of pieces of modeling clay containing stones - Part 1 of 5
<p><strong>Summary</strong></p> <p>This submission contains a collection of 131 CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022]. This submission consists of three parts in total.</p> <p> </p> <p><strong>Parts</strong></p> <p>The 131 CT scans are divided into 5 separate submissions:<br> Part 1 of 5<em>:</em> 001-028: <a href="https://doi.org/10.5281/zenodo.5866228">10.5281/zenodo.5866228</a><strong> (this upload)</strong><br> Part 2 of 5: 029-056: <a href="https://doi.org/10.5281/zenodo.5866322">10.5281/zenodo.5866322</a><br> Part 3 of 5: 057-084: <a href="https://doi.org/10.5281/zenodo.5866363">10.5281/zenodo.5866363</a><br> Part 4 of 5: 085-111: <a href="https://doi.org/10.5281/zenodo.5866365">10.5281/zenodo.5866365</a><br> Part 5 of 5: 112-131: <a href="https://doi.org/10.5281/zenodo.5866367">10.5281/zenodo.5866367</a></p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></p> <p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p> <p><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections are unprocessed files, except that a binning been applied FleX-ray lab software. The resulting image sizes are 956x760. Flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images) are included with each object. All images are stored in .tif format. The data for samples with 0-3 stones are contained in parts 1 to 4, while the samples with 5-8 stones constitute part 5. The size of the completely unpacked dataset (all 5 parts) is ca. 343.5 GB.</p> <p>The processed data (with generated ground truth) is made available in another (smaller) submission for object detection purposes: <a href="https://zenodo.org/record/5681008">https://zenodo.org/record/5681008</a></p> <p> </p> <p><strong>Additional Links</strong><br> These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow to enable deep learning for X-ray based foreign object detection", 2022 (submitted)<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p>If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.</p>
A collection of 131 CT datasets of pieces of modeling clay containing stones - Part 3 of 5
<p><strong>Summary</strong></p> <p>This submission contains a collection of 131 CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022]. This submission consists of three parts in total.</p> <p> </p> <p><strong>Parts</strong></p> <p>The 131 CT scans are divided into 5 separate submissions:<br> Part 1 of 5<em>:</em> 001-028: <a href="https://doi.org/10.5281/zenodo.5866228">10.5281/zenodo.5866228</a><br> Part 2 of 5: 029-056: <a href="https://doi.org/10.5281/zenodo.5866322">10.5281/zenodo.5866322</a><br> Part 3 of 5: 057-084: <a href="https://doi.org/10.5281/zenodo.5866363">10.5281/zenodo.5866363</a> <strong>(this upload)</strong><br> Part 4 of 5: 085-111: <a href="https://doi.org/10.5281/zenodo.5866365">10.5281/zenodo.5866365</a><br> Part 5 of 5: 112-131: <a href="https://doi.org/10.5281/zenodo.5866367">10.5281/zenodo.5866367</a></p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></p> <p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p> <p><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections are unprocessed files, except that a binning been applied FleX-ray lab software. The resulting image sizes are 956x760. Flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images) are included with each object. All images are stored in .tif format. The data for samples with 0-3 stones are contained in parts 1 to 4, while the samples with 5-8 stones constitute part 5. The size of the completely unpacked dataset (all 5 parts) is ca. 343.5 GB.</p> <p>The processed data (with generated ground truth) is made available in another (smaller) submission for object detection purposes: <a href="https://zenodo.org/record/5681008">https://zenodo.org/record/5681008</a></p> <p> </p> <p><strong>Additional Links</strong><br> These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow to enable deep learning for X-ray based foreign object detection", 2022 (submitted)<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p>If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.</p>
Dataset from paper "Weathering of plastic SODIS containers and the impact of ageing on their lifetime and disinfection efficacy"
<ul> <li>Evolution of the molar mass distribution curves for the samples of both polypropylenes for each time of weathering.</li> <li>Evolution of the Differential Scanning Calorimetry (DSC) curves for the first melting for both polypropylenes.</li> </ul>
Raw data for the article "N-terminal selective C-H azidation of proline-containing peptides: a platform for late-stage diversification"
<p>Raw NMR, IR and MS data for the article "N-terminal selective C-H azidation of proline-containing peptides: a platform for late-stage diversification" published in Chemistry- A European Journal, DOI: </p> <p><a href="http://dx.doi.org/10.1002/chem.202200368">http://dx.doi.org/10.1002/chem.202200368</a>.</p> <p>The number of the folders correspond to compounds numbers in the article. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.</p>
Data from 90-day quantitative inhalation toxicology study evaluating the dose-response and fate in the lung and pleura of chrysotile-containing brake dust
<p>The final results from this multi-dose, 90-day inhalation toxicology study in the rat with life-time post-exposure observation have shown a significant fundamental difference in pathological response and tumorgenicity between brake dust generated from brake pads manufactured with chrysotile or from chrysotile alone in comparison to the amphiboles, crocidolite and amosite asbestos.</p> <p>The groups exposed to brake dust showed no significant pathological or tumorigenic response in the respiratory track compared to the air control group at exposure concentrations and deposited doses well above those at which humans have been exposed. Slight alveolar/interstitial macrophage accumulation of particles was noted. Wagner grades were 1–2 (1 = control group), similar to the TiO2 particle control group. Chrysotile was not biopersistent, exhibiting in the lung a deterioration of its matrix which results in breakage into particles and short fibers which can be cleared by alveolar macrophages and which can continue to dissolve. Particle-laden macrophage accumulation was observed, leading to a very-slight interstitial inflammatory response (Wagner grade 1–3). There was no peribronchiolar inflammation, occasional very-slight interstitial fibrosis (Wagner grade 4), and no exposure-related tumorigenic response.</p> <p>The pathological response of crocidolite and amosite compared to the brake dust and chrysotile was clearly differentiated by the histopathology and the confocal analysis. Crocidolite and amosite induced persistent inflammation, microgranulomas, persistent fibrosis (Wagner grades 4), and a dose-related lung tumor response. Confocal microscopy quantified extensive inflammatory response and collagen development in the lung, visceral and parietal pleura as well as pleural adhesions.</p> <p>These results provide a clear foundation for differentiating the innocuous effects of brake dust exposure from the adverse effects following amphibole asbestos exposure.</p>
TRIPEx-pol dataset containing LV2 cloud radar data and polarimetric radar data
<p>This dataset contains all data used for the publication von Terzi et al. 2022, ACP: "Ice microphysical processes in the dendritic growth layer: A statistical analysis combining multi-frequency and polarimetric Doppler cloud radar observations". This dataset combines the observations from vertically pointing X-, Ka- and W-Band radars and observations from a polarimetric W-Band radar pointing at 30° elevation.The dataset also contains variables derived from Doppler spectra observations: The spectral Edge velocity derived from Ka-Band vertically pointing radar and the maximum of the spectral ZDR (sZDRmax) from the polarimetric W-Band radar at 30° elevation. The data where averaged within periods when the polarimetric W-Band radar was measuring at 30° elevation. This corresponds to approximately 5 minute periods. The non-averaged LV2 data from the vertically pointing X-, Ka- and W-band radars is further available at: 10.5281/zenodo.5025636</p>
Long Live The Image: Container-Native Data Persistence in Production
<p>The (Docker) source files for creating a read-only database container.</p> <p><strong>Note:</strong></p> <p>"tail -F /var/log/mysql/error.log" (after the demo of selecting all data) makes the container keep alive as a MySQL server.</p> <p><strong>To be Updated:</strong></p> <p>The database user and password are still hardcoded.</p>
Travel Time and Speed Statistics for Links Containing Locks 16 through 20
<p>This is an Excel spreadsheet that records the travel times/speeds of each trip through a lock in the range of Lock & Dam 16 through Lock & Dam 20 in the Upper Mississippi River. Each lock is part of a three sublink set: a sublink upriver from the lock, the lock itself, and a sublink downriver . A table of links used by the study, a link map, and heat maps are also included.</p>
Metabolomics data for HS fed flies containing a w1118 background and CG4625 knockdown via RNAi
<p>Full metabolomics of normalized peak height for fat body tissue from w1118 background and <em>CG4625</em> knockdown (via RNAi) flies fed a high-sugar diet for three weeks.</p>
Dataset for the Manuscript: Structural and mechanistic insights into the cleavage of clustered O-glycan patches-containing glycoproteins by mucinases of the human gut (in revision)
<p>This dataset provides the classical and QM/MM MD simulation trajectory data to the manuscript:</p> <p><strong>Structural and mechanistic insights into the cleavage of clustered O-glycan patches-containing glycoproteins by mucinases of the human gut</strong></p> <p>The data set contains classical MD simulations of AM0627 with three substrate peptides P1, P2, P9, and BT4244 with glycopeptides, as well as QM/MM metadynamics simulations for our manuscript. PDB files for Figures 4,5 and Figure S5-8,10 are also included.</p>
Dataset of paper "Solar water disinfection in high-volume containers: From the laboratory to the field. A case study in Tigray, Ethiopia"
<p>Dataset of paper "Solar water disinfection in high-volume containers: From the laboratory to the field. A case study in Tigray, Ethiopia"</p> <ul> <li>Data of the global daily dose and average, maximum, and minimum temperatures during the year in Tigray (Ethiopia) and Almería (Spain).</li> <li><strong>: </strong>Data of the experimental <em>E. coli </em>inactivation profiles under different conditions of UV radiation, water temperature, water composition and container’s volume.</li> </ul>
Supp. Info. for Further analysis of metagenomic datasets containing GD and GX pangolin CoVs indicates widespread contamination, undermining pangolin host attribution
<p>Supplemanty Information for <strong>Further analysis of metagenomic datasets containing GD and GX pangolin CoVs indicates widespread contamination, undermining pangolin host attribution</strong></p> <p>Files:</p> <p>Supp_Info_1_PRJNA641544_DG14_DG18.xlsx</p> <p>Supp_Info_3_PRJNA606875_SRR11093270_reads_blast_nt_seq5_hsps1_PCT80_E0.05_hsps.txt</p> <p>Supp_Info_4_PRJNA573298_Analysis.xlsx</p>
Amino acid sequences of RWP-RK domain containing proteins used for the construction of phylogenetic tree shown in Fig. 1
<p><span>The RWP-RK protein family is a group</span><span> of transcription factors containing </span><span>the RWP-RK DNA-binding domain. The RWP-RK DNA-binding domain is an ancient motif that emerged before the establishment of the Viridiplantae (green plants), which consist of green algae and land plants. This domain is mostly absent in other kingdoms but widely distributed in Viridiplantae. In green algae, a liverwort, and several angiosperms, RWP-RK proteins play essential roles in nitrogen responses and sexual reproduction-associated processes, which</span><span> </span><span>are seemingly unrelated phenomena but possible interdependent processes</span><span> </span><span>in autotrophs. Consistent with</span><span> related but diversified roles of the RWP-RK proteins in these organisms, the RWP-RK protein family appears to have expanded intensively, but independently, in the algal and land plant lineages. Therefore, bryophyte RWP-RK proteins occupy a unique position in the evolutionary process of establishing the RWP-RK protein family. In this review, we summarize current knowledge about the RWP-RK protein family in the Viridiplantae, and discuss the significance of bryophyte RWP-RK proteins in clarifying the relationship between diversification in the RWP-RK protein family and </span><span>procurement</span><span> of sophisticated mechanisms for adaptation to the terrestrial environment.</span></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.