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Data from: Colorado aquatic macroinvertebrate samples and duck counts
<p>Food availability varies considerably over space and time in wetland systems, and consumers must be able to track those changes during nutrient demanding points in the life cycle like breeding. Resource tracking has been studied frequently among herbivores, but receives less attention among consumers of macroinvertebrates. We evaluated the change in resource availability across habitat types and time, and the simultaneous density of waterfowl consumers throughout their breeding season in a high-elevation, flood-irrigated system. We also assessed whether the macroinvertebrate resource density better predicted waterfowl density across habitats, compared to consistency (i.e., temporal evenness) of the invertebrate resource or taxonomic richness. Resource density varied marginally across wetland types but was highest in basin wetlands (i.e., ponds) and peaked early in the breeding season, whereas it remained relatively low and stable in other wetland habitats. Breeding duck density was positively related to resource density, more so than temporal resource stability, for all species. Resource density was negatively related to duckling density, however. These results have the potential to not only elucidate mechanisms of habitat selection among breeding ducks in flood-irrigated landscapes, but also suggest there is not a consequential trade-off to selecting wetland sites based on energy density versus temporal resource stability and that good-quality wetland sites provide both.</p>
Sample data for "Live Cell Fluorescence Microscopy – An End-to-End Workflow for High-Throughput Image and Data Analysis"
<p>This repository contains:</p> <ul> <li> <p>Sample data for the "Live Cell Fluorescence Microscopy – From Sample Preparation to Numbers and Plots" methodology paper by Zahumensky & Malinsky. The paper describes the preparation of live yeast cell samples for microscopy, the subsequent semi-automatic analysis of the microscopy images using our custom-written Fiji macros, and automatic processing of the output (Results table) from the image analys using custom-written R scripts. The data provided here are real experimental data from two publications of our group: Zahumensky et al., 2022 and Vesela et al., 2023</p> </li> <li> <p>"Results tables" from the Fiji based analysis</p> </li> <li> <p>Outputs of the processing of these Results tables using our R scripts, in the form of summary tables, graphs, and statistical analyses</p> </li> </ul>
FIG. 1 in Sampling the depth: New data on the Caecidae (Mollusca, Gastropoda) from northeastern Papua New Guinea
FIG. 1. — Map of northeastern Papua New Guinea showing the sampled stations listed in Table 1.
Natrolite - Sample 2 NanED Round Robin, Data: ESR10
<p><strong><em>Natrolite</em></strong></p> <p>The following submission contains the data collection and processing of the dataset for the sample natrolite under the NanEd round-robin project. A single crystal was identified, and the rotation data acquisition technique was used to collect the dataset on the same crystal. The dataset was processed with XDS software. The table below summarizes the data collection parameters for the dataset. The following data is also in the data folder as a Word file.</p> <p> </p> <p><strong>Continuous Rotation:</strong></p> <table> <tbody> <tr> <td> <p><strong>General information:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Project</p> </td> <td> <p>NanED (www.naned.eu)</p> </td> </tr> <tr> <td> <p>ESR Project</p> </td> <td> <p>ESR10 - Round Robin</p> </td> </tr> <tr> <td> <p>Project Label</p> </td> <td> <p>RR-2</p> </td> </tr> <tr> <td> <p>Sample Label</p> </td> <td> <p>RR-2_SU</p> </td> </tr> <tr> <td> <p>Data set Label</p> </td> <td> <p>RR-2</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Instrumental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Instrument</p> </td> <td> <p>Transmission electron microscope JEOL 2100 LaB6</p> </td> </tr> <tr> <td> <p>Radiation source</p> </td> <td> <p>LaB6</p> </td> </tr> <tr> <td> <p>Accelerating voltage</p> </td> <td> <p>200 kV</p> </td> </tr> <tr> <td> <p>Wavelength</p> </td> <td> <p>0.0251 Å</p> </td> </tr> <tr> <td> <p>Probe Type</p> </td> <td> <p>Parallel beam</p> </td> </tr> <tr> <td> <p>Beam Diameter</p> </td> <td> <p>6 μm</p> </td> </tr> <tr> <td> <p>Beam Convergence</p> </td> <td> <p>Parallel beam, convergence <0.1mrad</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p>Hybrid pixel detector ASI Timepix (bottom mounted)</p> </td> </tr> <tr> <td> <p>Number of pixels in the image</p> </td> <td> <p>512 x 512</p> </td> </tr> <tr> <td> <p>Pixel size</p> </td> <td> <p>55 µm x 55 µm</p> </td> </tr> <tr> <td> <p>Effective camera length</p> </td> <td> <p>250 mm</p> </td> </tr> <tr> <td> <p>Calibration constant</p> </td> <td> <p>0.004990 Å<sup>-1</sup>/pixel</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Sample description:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Name</p> </td> <td> <p>Natrolite</p> </td> </tr> <tr> <td> <p>Chemical composition</p> </td> <td> <p>Na<sub>2</sub>Si<sub>3</sub>Al<sub>2</sub>O<sub>10</sub>.(H<sub>2</sub>O)<sub>2</sub></p> </td> </tr> <tr> <td> <p>Sample source</p> </td> <td> </td> </tr> <tr> <td> <p>Sample preparation</p> </td> <td> <p>Powder crushed in an agate mortar and deposited on a Cu grid with lacey C film</p> </td> </tr> <tr> <td> <p><strong>Experimental:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data Type</p> </td> <td> <p>Electron diffraction data - 3D ED</p> </td> </tr> <tr> <td> <p>Data collection method</p> </td> <td> <p>Continuous Rotation</p> </td> </tr> <tr> <td> <p>Temperature (K) used during data collection</p> </td> <td> <p>293 K</p> </td> </tr> <tr> <td> <p>Number of crystals contributing to the data set</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>Number of experimental frames</p> </td> <td> <p>513</p> </td> </tr> <tr> <td> <p>tilt range, tilt step, tilt per frame</p> </td> <td> <p> -48.35°-70.58°, 0.453°/s, 0.2318°/frame</p> </td> </tr> <tr> <td> <p>Exposure time per frame</p> </td> <td> <p>0.5</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Software:</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Software used for the data collection</p> </td> <td> <p>Instamatic</p> </td> </tr> <tr> <td> <p>Software used for processing</p> </td> <td> <p>XDS</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Authorship and bibliography</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Author(s) of the data</p> </td> <td> <p>Lei Wang (ESR 10)</p> </td> </tr> <tr> <td> <p>Related data</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Publication(s)</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><strong>Files and data formats</strong></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Image folder</p> </td> <td> <p>REDp: Folder containing images of the diffraction pattern from each frame</p> <p>XDS: Folder containing images of the diffraction pattern from each frame</p> </td> </tr> <tr> <td> <p>Image format</p> </td> <td> <p>mrc and img</p> </td> </tr> <tr> <td> <p>Additional folders/files</p> </td> <td> <p>Crystal image : image of the crystal</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>cRED_log: log file of data collection</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>1.ed3d: input file for REDp</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p>XDS.INP: input file for the program XDS</p> </td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td> <p><strong>Notes:</strong></p> <p>*Project Label "RR-2" stands for Round Robin 2</p> <p>*RR-2 contains 1 dataset.</p> </td> </tr> </tbody> </table>
Data from: Primer sets evaluation and sampling method assessment for the monitoring of fish communities in the North-western part of the Mediterranean Sea through eDNA metabarcoding
<p>Environmental DNA (eDNA) metabarcoding appears to be a promising tool for surveying fish communities. However, the effectiveness of this method relies on primer set performance and on a robust sampling strategy. While some studies have evaluated the efficiency of several primers for fish detection, it has not yet been assessed <em>in situ </em>for the Mediterranean Sea. In addition, mainly surface waters were sampled and no filter porosity testing was performed. In this pilot study, our aim was to evaluate the ability of six primer sets, targeting 12S rRNA (AcMDB07; MiFish; Tele04) or 16S rRNA (Fish16S; Fish16SFD; Vert16S) loci, to detect fish species in the Mediterranean Sea using a metabarcoding approach. We also assessed the influence of sampling depth and filter pore size (0.45 µm <em>versus</em> 5 µm filters). To achieve this, we developed a novel sampling strategy allowing simultaneous surface and bottom filtration of large water volumes along on-site the same transect. We found that 16S rRNA primer sets enabled more fish taxa to be detected across each taxonomic level. The best combination was Fish16S/Vert16S/AcMDB07, which recovered 95% of the 97 fish species detected in our study. There were highly significant differences in species composition between surface and bottom samples. Filters of 0.45 µm led to the detection of significantly more fish species. Therefore, to maximize fish detection in the studied area, we recommend to filter both surface and bottom waters through 0.45 µm filters and to use a combination of these three primer sets.</p>
Imaging Mass Cytometry (IMC) data for TNBC Samples
<p>Imaging mass cytometry (IMC) data was collected on multiple regions of interest (ROIs) from a racially balanced and clinically matched cohort of 57 surgically resected tissues, primarily TNBC, as identified by H&E images. This cohort consisted of 26 self-reported Black American (BA) women and 31 self-reported White American (WA) women. ROIs were selected from both the tumor center and tumor periphery, and were categorized as either immune-rich or immune-poor.</p>
PREPCLIM sample data set for publication in "Geoscientific Model Development" 2024
<p>Data set illustrating the functionality of software developed in the PREPCLIM project and used in the proposed paper:</p> <p>A Modeling System for Identification of Maize Ideotypes, optimal sowing dates and nitrogen<br>fertilization under climate change – PREPCLIM-v1</p> <p>https://doi.org/10.5194/gmd-2024-105<br>Preprint. Discussion started: 11 July 2024<br>c Author(s) 2024. CC BY 4.0 License.</p>
Supporting publication for 'Guidelines for reporting 2017 prevalence sample-based data in accordance with SSD2 data model'
<p>These two Excel documents help you to map terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes and offer you examples on how prevalence data can be reported using SSD2.</p>
Sample data for annofilt
<p>Accurate pangenome analysis can be hindered by missassembled genes; annofilt is designed to use a reference pangeme of well-curated strains to check annotations in an assembly for those failing to meet a length or quality threshold. This dataset can be used for testing or development of the software.</p>
Detrital zircon data for samples from western North Dakota
<p>This data table include all the detrital zircon data for samples from western North Dakota, that are associated with the Tectonics paper titled "Cenozoic sediment provenance in the northern Great Plains corresponds to four episodes of tectonic and magmatic events in the central North American Cordillera“ by Li and Fan. </p>
Supporting publication for 'Guidelines for reporting 2018 prevalence sample-based data in accordance with SSD2 data model'
<p>These two Excel documents help you to map terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes and offer you examples on how prevalence data can be reported using SSD2</p>
Raw data of diversity, abundance and soil samples
<p>Raw data of diversity, abundance and soil samples of the paper: Secondary Succession under invasive species (<em>Pteridium aquilinum</em>) conditions in a seasonal dry tropical forest in southeastern Mexico</p>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 38-42
<p>This upload contains samples 38 - 42 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 33-37
<p>This upload contains samples 33 - 37 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 25-32
<p>This upload contains samples 25 - 32 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 17-24
<p>This upload contains samples 17 - 24 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 9-16
<p>This upload contains samples 9 - 16 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Cone-Beam X-Ray CT Data Collection Designed for Machine Learning: Samples 1-8
<p>This upload contains samples 1 - 8 from the data collection described in</p> <p>Henri Der Sarkissian, Felix Lucka, Maureen van Eijnatten, Giulia Colacicco, Sophia Bethany Coban, Kees Joost Batenburg, "A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning", <em>Sci Data</em> <strong>6, </strong>215 (2019). <a href="https://doi.org/10.1038/s41597-019-0235-y">https://doi.org/10.1038/s41597-019-0235-y</a> or <a href="https://arxiv.org/abs/1905.04787">arXiv:1905.04787</a> (2019)</p> <p>Abstract:<br> "Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction: Forty-two walnuts were scanned with a laboratory X-ray setup to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation."</p> <p>The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://xre.be/">XRE nv</a>and located in the FleX-ray Lab at the <a href="https://www.cwi.nl/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another.</p> <p>Please refer to the paper for all further technical details.</p> <p>The complete data set can be found via the following links: <a href="https://doi.org/10.5281/zenodo.2686725">1-8</a>, <a href="https://doi.org/10.5281/zenodo.2686970">9-16</a>, <a href="https://doi.org/10.5281/zenodo.2687386">17-24</a>, <a href="https://doi.org/10.5281/zenodo.2687634">25-32</a>, <a href="https://doi.org/10.5281/zenodo.2687896">33-37</a>, <a href="https://doi.org/10.5281/zenodo.2688111">38-42</a></p> <p>The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on <a href="https://github.com/cicwi/WalnutReconstructionCodes">github</a></p> <p>For more information or guidance in using these dataset, please get in touch with</p> <ul> <li>henri.dersarkissian [at] gmail.com</li> <li>Felix.Lucka [at] cwi.nl</li> </ul>
Replication Data for: Magnetic measurements on micron-size samples under high pressure using designed NV centers
<pre><em>Data description.pdf </em>describes the uploaded data. <em>Figure Data.xlsx</em> contains the data represented in the figures of the main text and supplementary information. <em>Iron.zip and MgB2.zip </em>include the raw experimental data. <em>PlotIronData.m, PlotMgB2Data.m, SimulatedIronMagneticField.m</em>, <em>SphereField.m</em>, <em>CarttoPol.m</em>, <em>PoltoCart.m </em>are Matlab codes used to process and fit the data. </pre>
Teenage Pregnancy and School Dropout - Data Sample - Casa do Adolescente Sao Paulo - SP
<p>This data set was built from the data set stored at <em>https://doi.org/10.5281/zenodo.2633222</em> as part of an applied study about probable causality relations between teenage pregnancy and school dropout,<br> among other variables.<br> The variables assembled in the data set are: age, gender, ethnic group, occurrence of pregnancy, employment status, scholar enrollment and occurrence of pregnancy of the teenager's mother when they were teenagers.<br> The respondents are 343 in total.</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.