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3,038 results for “CT”
Lake Sediment Pollen from Mohawk Pond in Litchfield CT from 12860 BP to Present
Aim We analyzed a dataset composed of multiple palaeoclimate and lake-sediment pollen records from New England to explore how postglacial changes in the composition and spatial patterns of vegetation were controlled by regional-scale climate change, a subregional environmental gradient, and landscape-scale variations in soil characteristics. Location The 120,000-km2 study area includes parts of Vermont and New Hampshire in the north, where sites are 150-200 km from the Atlantic Ocean, and spans the coastline from southeastern New York to Cape Cod and the adjacent islands, including Block Island, the Elizabeth Islands, Nantucket, and Martha’s Vineyard. Results Boreal forest featuring Picea and Pinus banksiana was present across the region when conditions were cool and dry 14,000-12,000 calibrated 14C yrs before present (ybp). Pinus strobus became regionally dominant as temperatures increased between 12,000 and 10,000 ybp. The composition of forests in inland and coastal areas diverged in response to further warming after 10,000 ybp, when Quercus and Pinus rigida expanded across southern New England, while conditions remained cool enough in inland areas to maintain Pinus strobus. Increasing precipitation allowed Tsuga canadensis, Fagus grandifolia, and Betula to replace Pinus strobus in inland areas during 9000-8000 ybp, and also led to the expansion of Carya across the coastal part of the region beginning at 7000-6000 ybp. Abrupt cooling at 5500-5000 ybp caused sharp declines in Tsuga in inland areas and Quercus at some coastal sites, and the populations of those taxa remained low until they recovered around 3000 ybp in response to rising precipitation. Throughout most of the Holocene, sites underlain by sandy glacial deposits were occupied by Pinus rigida and Quercus. Main conclusions Postglacial changes in the composition and spatial pattern of New England forests were controlled by long-term trends and abrupt shifts in temperature and precipitation, as well as by
Lake Sediment Pollen from Rogers Lake in Old Lyme CT from 13933 BP to Present
Aim We analyzed a dataset composed of multiple palaeoclimate and lake-sediment pollen records from New England to explore how postglacial changes in the composition and spatial patterns of vegetation were controlled by regional-scale climate change, a subregional environmental gradient, and landscape-scale variations in soil characteristics. Location The 120,000-km2 study area includes parts of Vermont and New Hampshire in the north, where sites are 150-200 km from the Atlantic Ocean, and spans the coastline from southeastern New York to Cape Cod and the adjacent islands, including Block Island, the Elizabeth Islands, Nantucket, and Martha’s Vineyard. Results Boreal forest featuring Picea and Pinus banksiana was present across the region when conditions were cool and dry 14,000-12,000 calibrated 14C yrs before present (ybp). Pinus strobus became regionally dominant as temperatures increased between 12,000 and 10,000 ybp. The composition of forests in inland and coastal areas diverged in response to further warming after 10,000 ybp, when Quercus and Pinus rigida expanded across southern New England, while conditions remained cool enough in inland areas to maintain Pinus strobus. Increasing precipitation allowed Tsuga canadensis, Fagus grandifolia, and Betula to replace Pinus strobus in inland areas during 9000-8000 ybp, and also led to the expansion of Carya across the coastal part of the region beginning at 7000-6000 ybp. Abrupt cooling at 5500-5000 ybp caused sharp declines in Tsuga in inland areas and Quercus at some coastal sites, and the populations of those taxa remained low until they recovered around 3000 ybp in response to rising precipitation. Throughout most of the Holocene, sites underlain by sandy glacial deposits were occupied by Pinus rigida and Quercus. Main conclusions Postglacial changes in the composition and spatial pattern of New England forests were controlled by long-term trends and abrupt shifts in temperature and precipitation, as well as by
Lake Sediment Pollen and Charcoal from Umpawaug Pond in Redding CT from 9377 BP to Present
Aim We analyzed a dataset composed of multiple palaeoclimate and lake-sediment pollen and charcoal records from New England to explore how postglacial changes in forest composition and spatial patterns of vegetation and fire were controlled by regional-scale climate change, a subregional environmental gradient, and landscape-scale variations in soil characteristics. Location The 120,000-km2 study area includes parts of Vermont and New Hampshire in the north, where sites are 150-200 km from the Atlantic Ocean, and spans the coastline from southeastern New York to Cape Cod and the adjacent islands, including Block Island, the Elizabeth Islands, Nantucket, and Martha’s Vineyard. Results Boreal forest featuring Picea and Pinus banksiana was present across the region when conditions were cool and dry 14,000-12,000 calibrated 14C yrs before present (ybp). Pinus strobus became regionally dominant as temperatures increased between 12,000 and 10,000 ybp. The composition of forests in inland and coastal areas diverged in response to further warming after 10,000 ybp, when Quercus and Pinus rigida expanded across southern New England, while conditions remained cool enough in inland areas to maintain Pinus strobus. Fire severity was high during 10,000-8000 ybp. Increasing precipitation allowed Tsuga canadensis, Fagus grandifolia, and Betula to replace Pinus strobus in inland areas during 9000-8000 ybp, and also led to the expansion of Carya across the coastal part of the region beginning at 7000-6000 ybp. Abrupt cooling at 5500-5000 ybp caused sharp declines in Tsuga in inland areas and Quercus at some coastal sites, and the populations of those taxa remained low until they recovered around 3000 ybp in response to rising precipitation. Throughout most of the Holocene, sites underlain by sandy glacial deposits were occupied by Pinus rigida and Quercus. Main conclusions Postglacial changes in the composition and spatial pattern of New England forests were controlled by long-term t
Lake Sediment Pollen and Charcoal from West Side Pond in Goshen CT from 13397 BP to Present
Aim We analyzed a dataset composed of multiple palaeoclimate and lake-sediment pollen and charcoal records from New England to explore how postglacial changes in forest composition and spatial patterns of vegetation and fire were controlled by regional-scale climate change, a subregional environmental gradient, and landscape-scale variations in soil characteristics. Location The 120,000-km2 study area includes parts of Vermont and New Hampshire in the north, where sites are 150-200 km from the Atlantic Ocean, and spans the coastline from southeastern New York to Cape Cod and the adjacent islands, including Block Island, the Elizabeth Islands, Nantucket, and Martha’s Vineyard. Results Boreal forest featuring Picea and Pinus banksiana was present across the region when conditions were cool and dry 14,000-12,000 calibrated 14C yrs before present (ybp). Pinus strobus became regionally dominant as temperatures increased between 12,000 and 10,000 ybp. The composition of forests in inland and coastal areas diverged in response to further warming after 10,000 ybp, when Quercus and Pinus rigida expanded across southern New England, while conditions remained cool enough in inland areas to maintain Pinus strobus. Fire severity was high during 10,000-8000 ybp. Increasing precipitation allowed Tsuga canadensis, Fagus grandifolia, and Betula to replace Pinus strobus in inland areas during 9000-8000 ybp, and also led to the expansion of Carya across the coastal part of the region beginning at 7000-6000 ybp. Abrupt cooling at 5500-5000 ybp caused sharp declines in Tsuga in inland areas and Quercus at some coastal sites, and the populations of those taxa remained low until they recovered around 3000 ybp in response to rising precipitation. Throughout most of the Holocene, sites underlain by sandy glacial deposits were occupied by Pinus rigida and Quercus. Main conclusions Postglacial changes in the composition and spatial pattern of New England forests were controlled by long-term t
Multi-organ Abdominal CT Reference Standard Segmentations
<p>DenseVNet Multi-organ Segmentation on Abdominal CT</p> <p>This dataset includes the multi-organ abdominal CT reference segmentations publicly released in conjunction with the IEEE Transactions on Medical Imaging paper "Automatic Multi-organ Segmentation on Abdominal CT with Dense V-networks" <a href="#1">[1]</a>.</p> <p>The data comprises reference segmentations for 90 abdominal CT images delineating multiple organs: the spleen, left kidney, gallbladder, esophagus, liver, stomach, pancreas and duodenum.</p> <p>The abdominal CT images and some of the reference segmentations were drawn from two data sets: <a href="http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU">The Cancer Image Archive (TCIA) Pancreas-CT data set</a> [<a href="#2">2</a>-<a href="#4">4</a>] and the <a href="https://doi.org/10.7303/syn3193805">Beyond the Cranial Vault (BTCV) Abdomen data set</a> [<a href="#5">5</a>-<a href="#6">6</a>]. The Pancreas-CT data set comprises abdominal CT acquired at the National Institutes of Health Clinical Center from pre-nephrectomy healthy kidney donors or patients with neither major abdominal pathologies nor pancreatic cancer lesions. Segmentations of the pancreas are included with this data set; images were manually labeled slice-by-slice by a medical student, and verified/modified by an experienced radiologist. The BTCV data set comprises abdominal CT acquired at the Vanderbilt University Medical Center from metastatic liver cancer patients or post-operative ventral hernia patients. Segmentations of the spleen, right and left kidney, gallbladder, esophagus, liver, stomach, aorta, inferior vena cava, portal vein and splenic vein, pancreas, right adrenal gland, left adrenal gland are included in this data set; images were manually labeled by two experienced undergraduate students, and verified by a radiologist on a volumetric basis using the MIPAV software.</p> <p>Segmentations that were not present in the original data sets were performed interactively using Matlab 2015b and ITK-SNAP 3.2 by an image research fellow under the supervision of a board-certified radiologist with 8 years of experience in gastrointestinal CT and MRI image interpretation. Segmentations that were present in the original data sets were edited to ensure a consistent segmentation protocol across the data set.</p> <p>Terms of use</p> <p>The terms of use of this data set include the terms of use of both the <a href="http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU">TCIA Pancreas-CT data set</a> (see tabs for data links and terms of use) and the <a href="https://doi.org/10.7303/syn3193805">Beyond the Cranial Vault (BTCV) Abdomen data set</a> (<a href="https://doi.org/10.7303/syn3193805">terms of use</a>; after <a href="https://www.synapse.org/#!Synapse:syn3193805/wiki/217753">registration</a>, you can <a href="https://www.synapse.org/#!Synapse:syn3376386">access the data</a>). If you use these reference segmentations, please cite the above manuscript and the references below. Because these data include manual segmentations of images from the Beyond the Cranial Vault challenge test data, they may not be used to develop submissions for the challenge.</p> <p>References</p> <p>[1] Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, Davidson B, Pereira SP, Clarkson MJ, Barratt DC. Automatic multi-organ segmentation on abdominal CT with dense v-networks. IEEE Transactions on Medical Imaging, 2018.</p> <p>[2] Roth HR, Farag A, Turkbey EB, Lu L, Liu J, and Summers RM. (2016). Data From Pancreas-CT. The Cancer Imaging Archive. <a href="http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU">http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU</a></p> <p>[3] Roth HR, Lu L, Farag A, Shin H-C, Liu J, Turkbey EB, Summers RM. DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation. N. Navab et al. (Eds.): MICCAI 2015, Part I, LNCS 9349, pp. 556–564, 2015. <a href="http://arxiv.org/pdf/1506.06448.pdf">http://arxiv.org/pdf/1506.06448.pdf</a></p> <p>[4] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. <a href="http://doi.org/10.1007/s10278-013-9622-7">http://doi.org/10.1007/s10278-013-9622-7</a></p> <p>[5] Xu Z, Lee CP, Heinrich MP, Modat M, Rueckert D, Ourselin S, Abramson RG, and Landman BA, "Evaluation of six registration methods for the human abdomen on clinically acquired CT," IEEE Trans. Biomed. Eng., vol. 63, no. 8, pp. 1563–1572, 2016.<a href="http://doi.org/10.1109/TBME.2016.2574816">http://doi.org/10.1109/TBME.2016.2574816</a></p> <p>[6] Landman BA, Xu Z, Igelsias JE, Styner M, Langerak TR, and Klein A, "MICCAI multi-atlas labeling beyond the cranial vault - workshop and challenge," 2015, <a href="https://doi.org/10.7303/syn3193805">https://doi.org/10.7303/syn3193805</a></p> <p>File format Labels are in NIfTI format with the following label definitions. Labels marked with * are only available in the BTCV data set.</p> <ol> <li>spleen</li> <li>right kidney*</li> <li>left kidney</li> <li>gallbladder</li> <li>esophagus</li> <li>liver</li> <li>stomach</li> <li>aorta*</li> <li>inferior vena cava*</li> <li>portal vein and splenic vein*</li> <li>pancreas</li> <li>right adrenal gland*</li> <li>left adrenal gland*</li> <li>duodenum</li> </ol> <p>Subjects included in the dataset</p> <p>The data comprises segmentation volumes for 90 cases, and the cropping coordinates (cropping.csv) used in the manuscript. The abdominal CT can be obtained from the links above. The reference standard segmentations may be incomplete outside of the specified cropping region. The cases are listed by their subject identifiers in their original data set:</p> <p> </p> <p><span class="math-tex">\(\begin{bmatrix} 1 & TCIA & Pancreas-CT & 0002\\ 2 & TCIA & Pancreas-CT & 0003\\ 3 & TCIA & Pancreas-CT & 0004\\ 4 & TCIA & Pancreas-CT & 0005\\ 5 & TCIA & Pancreas-CT & 0006\\ 6 & TCIA & Pancreas-CT & 0007\\ 7 & TCIA & Pancreas-CT & 0008\\ 8 & TCIA & Pancreas-CT & 0009\\ 9 & TCIA & Pancreas-CT & 0010\\ 10 & TCIA & Pancreas-CT & 0011\\ 11 & TCIA & Pancreas-CT & 0012\\ 12 & TCIA & Pancreas-CT & 0013\\ 13 & TCIA & Pancreas-CT & 0014\\ 14 & TCIA & Pancreas-CT & 0016\\ 15 & TCIA & Pancreas-CT & 0017\\ 16 & TCIA & Pancreas-CT & 0018\\ 17 & TCIA & Pancreas-CT & 0019\\ 18 & TCIA & Pancreas-CT & 0020\\ 19 & TCIA & Pancreas-CT & 0021\\ 20 & TCIA & Pancreas-CT & 0022\\ 21 & TCIA & Pancreas-CT & 0024\\ 22 & TCIA & Pancreas-CT & 0025\\ 23 & TCIA & Pancreas-CT & 0026\\ 24 & TCIA & Pancreas-CT & 0027\\ 25 & TCIA & Pancreas-CT & 0028\\ 26 & TCIA & Pancreas-CT & 0029\\ 27 & TCIA & Pancreas-CT & 0030\\ 28 & TCIA & Pancreas-CT & 0031\\ 29 & TCIA & Pancreas-CT & 0032\\ 30 & TCIA & Pancreas-CT & 0033\\ 31 & TCIA & Pancreas-CT & 0034\\ 32 & TCIA & Pancreas-CT & 0035\\ 33 & TCIA & Pancreas-CT & 0038\\ 34 & TCIA & Pancreas-CT & 0039\\ 35 & TCIA & Pancreas-CT & 0040\\ 36 & TCIA & Pancreas-CT & 0041\\ 37 & TCIA & Pancreas-CT & 0042\\ 38 & TCIA & Pancreas-CT & 0043\\ 39 & TCIA & Pancreas-CT & 0044\\ 40 & TCIA & Pancreas-CT & 0045\\ 41 & TCIA & Pancreas-CT & 0046\\ 42 & TCIA & Pancreas-CT & 0047\\ 43 & TCIA & Pancreas-CT & 0048\\ 44 & Synapse & BeyondTheCranialVault & 0001\\ 45 & Synapse & BeyondTheCranialVault & 0002\\ 46 & Synapse & BeyondTheCranialVault & 0003\\ 47 & Synapse & BeyondTheCranialVault & 0004\\ 48 & Synapse & BeyondTheCranialVault & 0005\\ 49 & Synapse & BeyondTheCranialVault & 0006\\ 50 & Synapse & BeyondTheCranialVault & 0007\\ 51 & Synapse & BeyondTheCranialVault & 0008\\ 52 & Synapse & BeyondTheCranialVault & 0009\\ 53 & Synapse & BeyondTheCranialVault & 0010\\ 54 & Synapse & BeyondTheCranialVault & 0021\\ 55 & Synapse & BeyondTheCranialVault & 0022\\ 56 & Synapse & BeyondTheCranialVault & 0023\\ 57 & Synapse & BeyondTheCranialVault & 0024\\ 58 & Synapse & BeyondTheCranialVault & 0025\\ 59 & Synapse & BeyondTheCranialVault & 0026\\ 60 & Synapse & BeyondTheCranialVault & 0027\\ 61 & Synapse & BeyondTheCranialVault & 0028\\ 62 & Synapse & BeyondTheCranialVault & 0029\\ 63 & Synapse & BeyondTheCranialVault & 0030\\ 64 & Synapse & BeyondTheCranialVault & 0031\\ 65 & Synapse & BeyondTheCranialVault & 0032\\ 66 & Synapse & BeyondTheCranialVault & 0033\\ 67 & Synapse & BeyondTheCranialVault & 0034\\ 68 & Synapse & BeyondTheCranialVault & 0035\\ 69 & Synapse & BeyondTheCranialVault & 0036\\ 70 & Synapse & BeyondTheCranialVault & 0037\\ 71 & Synapse & BeyondTheCranialVault & 0038\\ 72 & Synapse & BeyondTheCranialVault & 0039\\ 73 & Synapse & BeyondTheCranialVault & 0040\\ 74 & Synapse & BeyondTheCranialVault & 0061\\ 75 & Synapse & BeyondTheCranialVault & 0062\\ 76 & Synapse & BeyondTheCranialVault & 0063\\ 77 & Synapse & BeyondTheCranialVault & 0064\\ 78 & Synapse & BeyondTheCranialVault & 0065\\ 79 & Synapse & BeyondTheCranialVault & 0066\\ 80 & Synapse & BeyondTheCranialVault & 0067\\ 81 & Synapse & BeyondTheCranialVault & 0068\\ 82 & Synapse & BeyondTheCranialVault & 0069\\ 83 & Synapse & BeyondTheCranialVault & 0070\\ 84 & Synapse & BeyondTheCranialVault & 0074\\ 85 & Synapse & BeyondTheCranialVault & 0075\\ 86 & Synapse & BeyondTheCranialVault & 0076\\ 87 & Synapse & BeyondTheCranialVault & 0077\\ 88 & Synapse & BeyondTheCranialVault & 0078\\ 89 & Synapse & BeyondTheCranialVault & 0079\\ 90 & Synapse & BeyondTheCranialVault & 0080\\ \end{bmatrix}\)</span></p>
Dataset for the comparison of two Computational Thinking (CT) test for upper primary school (grades 3-4) : the Beginners' CT test (BCTt) and the competent CT test (cCTt)
<p>This dataset contains quantitative student data acquired during the administration of two validated Computational Thinking (CT) assessments for upper primary school (grades 3 and 4): the Beginners' CT test (BCTt) [1] and the comptent CT test (cCTt) [2]</p> <p>To compare the psychometric properties of both instruments a comparative analysis was conducted with data acquired in schools in Portugal from the same school districts. More specifically, we analyse the results of: </p> <p>- the BCTt test administered in March 2020 to 374 students in grades 3-4,</p> <p>- the cCTt test administered in April 2021 to 201 different students in grades 3-4.</p> <p>These students had no prior experience in Computational Thinking, as this was not part of the national curriculum at the times of administration. </p> <p> </p> <p>The detailed psychometric comparison is published in Frontiers in Psychology - Educational Psychology [3] and provides indications regarding the use of both instruments for grades 3-4. </p> <p> </p> <p>A README is included and provides additional information regarding :</p> <p>- the requirements for re-use. </p> <p>- the specific content of the 2 csv files</p> <p> </p> <p>The BCTt is available upon request to maria.zapata@urjc.es and the cCTt items are available in [2] with an editable version being available upon request to laila.elhamamsy@epfl.ch. </p> <p>In case of other inquiries, please contact: laila.elhamamsy@epfl.ch, maria.zapata@urjc.es or pedro.marcelino@treetree2.org</p> <p> </p> <p><strong>References</strong></p> <p>[1] M. Zapata-Cáceres, E. Martín-Barroso and M. Román-González, "Computational Thinking Test for Beginners: Design and Content Validation," <em>2020 IEEE Global Engineering Education Conference (EDUCON)</em>, 2020, pp. 1905-1914, doi: 10.1109/EDUCON45650.2020.9125368.</p> <p>[2] El-Hamamsy, L., Zapata-Cáceres, M., Barroso, E. M., Mondada, F., Zufferey, J. D., & Bruno, B. (2022). The Competent Computational Thinking Test: Development and Validation of an Unplugged Computational Thinking Test for Upper Primary School. <em>Journal of Educational Computing Research</em>, <em>60</em>(7), 1818–1866. <a href="https://doi.org/10.1177/07356331221081753">https://doi.org/10.1177/07356331221081753</a></p> <p>[3] <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=LailaEl-Hamamsy&UID=781667">Laila El-Hamamsy</a>* , <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=Mar%C3%ADaZapata-C%C3%A1ceres&UID=2073859">María Zapata-Cáceres</a>, Pedro Marcelino, Jessica Dehler Zufferey, <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=BarbaraBruno&UID=893934">Barbara Bruno</a>, <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=EstefaniaMart%C3%ADn&UID=2086979">Estefanía Martín-Barroso</a> and <a href="http://www.frontiersin.org/Community/WhosWhoActivity.aspx?sname=MarcosRom%C3%A1n-Gonz%C3%A1lez&UID=760761">Marcos Román-González</a> (2022). <a href="http://www.frontiersin.org/Journal/Abstract.aspx?d=0&name=Educational_Psychology&ART_DOI=10.3389/fpsyg.2022.1082659">Comparing the psychometric properties of two primary school Computational Thinking (CT) assessments for grades 3 and 4: the Beginners' CT test (BCTt) and the competent CT test (cCTt)</a>. <em>Front. Psychol.</em> doi:10.3389/fpsyg.2022.1082659</p>
Dynamic X-ray CT of Synthetic magma for Digital Volume Correlation analysis
<p>Dataset of synthetic magma subjected to compression, useful for Digital Volume Correlation analysis, ref [1,2]. The data has been acquired at the Diamond Light Source synchrotron, with a bespoke thermo-mechanical rig (“P2R”) on the I12 beamline, ref [3,4,5]. Dataset 0 has no applied compression, while dataset 1 has applied compression.</p> <p>The data was saved with numpy 1.21 with <a href="https://numpy.org/doc/1.21/reference/generated/numpy.lib.format.html#format-version-1-0">NumPy format version 1.0</a> as dataset_0.npy and dataset_1.npy, and NumPy can be used to read it back in. Both data files have a header specifying how the data is stored, and following the header comes the array data.</p> <p>In particular the header length is 128 bytes, and the data consists of a 3 dimensional matrix of size (1520, 1257, 1260) stored in unsigned integer 8 bit, Fortran order. The screenshot named import_imagej.png shows how to import the data in with <a href="https://imagej.nih.gov/ij/">ImageJ</a>.</p> <p> </p> <p>A <a href="https://github.com/Kitware/MetaIO">METAImage</a> header describing the data in text form for each dataset is also provided, i.e. dataset_0.mhd and dataset_1.mhd,</p>
Trabecular bone – screw interaction. Micro-CT models and experimental push-in results.
<p>The dataset disclosed herein was employed to build the screw-bone interaction models, specifically for tasks related to screw push-in simulation.</p>
VoroCrack3d: An annotated data set of 3d CT concrete images with synthetic crack structures
<p>VoroCrack3d is an annotated data set of 3d CT images of concrete with synthetic crack structures. Its main purpose is the training and testing of machine learning models for 3d crack segmentation. The data set comprises 1344 images together with their corresponding ground truths. The concrete backgrounds are cropped out sections of size 400x400x400 voxels of CT images of concrete. To this end, several different concrete samples were scanned (normal concrete (NC), high-performance concrete (HPC), ultra-high-performance concrete (UHPC), air pore concrete; without and with reinforcements (straight steel fibers, crimped steel fibers, hooked-end steel fibers, polypropylene fibers, fibers made of glass fiber-reinforced polymer). The original concrete images have a resolution between 2.8 and 106 micrometers.</p> <p>The crack structures are modeled via minimum-weight surfaces in Voronoi diagrams according to the paper</p> <p>[1] C. Jung, C. Redenbach, Crack Modeling via Minimum-Weight Surfaces in 3d Voronoi Diagrams, Journal of Mathematics in Industry, 13, 10 (2023). https://doi.org/10.1186/s13362-023-00138-1.</p> <p>The surfaces are discretized, dilated and superimposed on the concrete backgrounds.</p> <p>The data set offers a high variety regarding concrete types, noise levels and crack widths, shapes, regularity and branching. This makes it suitable for studying the generalizability and robustness of 3d crack segmentation methods.</p> <p>______________________________________________________________________________________________</p> <p>The folder 'data' contains seven subfolders, each containing the data generated from a specific concrete type (NC, HPC, air pore concrete, polypropylene fiber-reinforced concrete, steel fiber-reinforced concrete (straight, crimped and hooked-end steel fibers)).</p> <p>Each subfolder again contains four subfolders according to the point process model that was used for generating the 3d Voronoi diagrams. The point processes and Voronoi diagrams are restricted to windows of size 400x150x400. </p> <p>- 'hc': Hard core point process with 60% volume density and intensity 0.000025 obtained from force-biased sphere packing.<br>- 'matclust': Matérn cluster process with parent intensity 0.0002/50, offspring intensity 50 and cluster radius 20.<br>- 'ppp': Poisson point process with intensity 0.0002.<br>- 'ppp-scaled': Poisson point process with intensity 0.0002 (but inside 200x150x200 window). The resulting Voronoi diagram is stretched in x- and z- direction by a factor of 2.</p> <p>Each of these contains five subfolders: one for the 3d input images, two for the corresponding labels (ground truths; one with and one without pores/fibers), one for the input and label previews (slice z=200 for each of the images) and a misc folder containing the concrete background without crack and, if applicable, the pore/fiber segmentation image.</p> <p>The data itself then contains 48 images:<br>1a-1d: crack with up to seven branches; fixed crack width (~1 voxel).<br>2a-2d: crack with up to four branches; fixed crack width (~1 voxel).<br>3a-3d: crack with up to one branch; fixed crack width (~1 voxel).<br>4a-4d: crack with no branches; fixed crack width (~1 voxel).<br>5a-5d: crack with no branches; fixed crack width (~3 voxels).<br>6a-6d: crack with no branches; fixed crack width (~5 voxels).<br>7a-7d: crack with no branches; fixed crack width (~7 voxels).<br>8a-8d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.01);<br>9a-9d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.02);<br>10a-10d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.05);<br>11a-11d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.1);<br>12a-12d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.2);</p> <p>The names 'a'-'d' indicate level of added noise added to the image:<br>a: None.<br>b: Uniformly on [-sigma,sigma] <br>c: Uniformly on [-2*sigma,2*sigma] <br>d: Uniformly on [-4*sigma,4*sigma] <br>Negative values are mapped to 0. <br>For inputs of type int, noise values are rounded to the nearest integer.<br>(sigma = standard deviation of voxel greyvalues in image)</p> <p>Note that the grey values in the ground truths correspond to the local crack width. They can be thresholded to obtain binary masks.</p> <p>For more details, we refer to [1].</p>
Micro-CT images of deep brain stimulation leads
<p>The dataset contain micro-CT images of leads used in deep brain stimulation. A lead comprises multiple electrodes and enables the delivery of electrical pulses to the brain to treat medical conditions such as Parkinson's disease, essential tremor or epilepsy. Images were acquired with a Skyscan 1276 micro-CT system from Bruker. Each image is provided in Nifti format (.nii) along with its corresponding log file (.log) generated by the scanner. The file names indicate the manufacturer and sample model. 'BS' denotes Boston Scientific.<br><br>Images can be visualized at:<br>https://activgroup.github.io/DBS-lead-microCT/<br><br>To contribute, please contact thomas.billoud@uniklinik-freiburg.de</p>
Hyperspectral X-ray CT datasets of an aluminium phantom containing three metal-based powders
<p><strong>General Data description:</strong></p> <p>This is a set of two hyperspectral (energy-resolved) X-ray CT projection datasets of a multi-phase phantom. It was acquired in a custom-built, laboratory micro-CT scanner with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The following data contains all the files necessary for reconstruction, following two hyperspectral scans of a metal, multi-phase phantom. The phantom consists of an external aluminium cylinder, with three holes, each filled with a different metal-based powder (CeO<sub>2</sub>, ZnO, Fe). Each powder provides a unique attenuation signal, with CeO<sub>2</sub> in particular producing a distinct spectral marker which can be measured by an energy-sensitive detector. Two identical scans were acquired, with only the exposure time per projection changed.</p> <p>Note: Zenodo Version 2 of this dataset contains the incorrect version of the 180s, 180 projection phantom dataset, if wishing to analyse the dataset used in the associated hyperspectral paper. This version (Version 3) contains the correct dataset from the paper.</p> <p><strong>File descriptions:</strong></p> <p>Contained is an image (.jpg) of the sample, along with five MATLAB (.mat) data files, as well as a single text (.txt) file. Where necessary, the files have been named to match the dataset they belong to, based on the different exposure times used for each dataset.</p> <p>Phantom_design_measurements.jpg contains a photograph of the physical phantom, combined with a diagram showing full sample measurements.</p> <p>Powder_phantom_scan_geometry.txt gives a breakdown of the full sample and detector geometry used when acquiring the raw projections for both scans.</p> <p>Powder_phantom_30s_30Proj_sinogram.mat contains the 4D sinogram constructed following flatfield normalisation of the raw projection data, where an exposure time of 30 s was used for each projection. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired during scanning. The total number of channels in the file is 200.</p> <p>Powder_phantom_180s_180Proj_sinogram.mat is the 4D sinogram for the dataset, when exposure times of 180 s were used for each projection, following flatfield normalisation. A discontinuity occurs at projection 137 due to an interruption in the scan procedure. The total number of channels in the file is 200.</p> <p>Energy_axis.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning. This is the same for both datasets.</p> <p>FF_30s.mat contains the 4D flatfield data acquired when no sample was present, in the case of 30 s exposure times. This data was used to normalise the projection datasets, as the sinogram was constructed. The first 200 channels are included.</p> <p>FF_180s.mat contains the 4D flatfield data for the dataset where 180 s exposure times were used. The first 200 channels are included.</p>
CT scans of COVID-19 patients
<p>Datasets contain CT scans of COVID-19 patients from Faculty hospital of Královké Vinohrady in DICOM (and TIFF) used in paper <em>Estimation of Covid-19 lungs damage based on computer tomography images analysis</em> presenting the tool is available on F1000reserach DOI: <a href="http://dx.doi.org/10.12688/f1000research.109020.1">10.12688/f1000research.109020.1</a>. The tool sued for the analysis of the dataset is published in Zenodo (<a href="https://doi.org/10.5281/zenodo.5805990">10.5281/zenodo.5805990</a>). Data were anonymized before exporting. Each patient has a folder with a unique ID, subfolder contains TIFF image for reach CT slice, and whenever possible DICOM files are added. All files contain ID and data format in the name. The CT data overview is in CSV for the whole dataset.</p> <p>Contributions:<br> Martin SCHÄTZ: Dataset preparation and couration<br> Olga RUBEŠOVÁ: Data selection and cleaning<br> David GIRSA: Data measuring and selection<br> Katarína NAĎOVA: Data measuring and selection</p> <p>The work was funded by the Ministry of Education, Youth and Sports by grant ‘Development of Advanced Computational Algorithms for evaluating post-surgery rehabilitation’ number LTAIN19007. The work was also supported from the grant of Specific university research – grant No FCHI 2022-001.</p>
Laboratory-measured and X-ray CT-derived volumetric composition of a permafrost core
<p>This dataset contains data on the volumetric composition of a permafrost core which has been drilled in a Yedoma upland in northeast Siberia (72.36613 N, 126.27272 E) in September 2017. This dataset supplements a research article to be submitted to the scientific journal <em>The Cryosphere</em>. It contains the following files:</p> <p><strong><em>volumetric_contents_sampleRes_lab+CT.csv</em> </strong><br> Contains the volumetric contents of total ice, organic, and mineral measured in the laboratory at AWI Potsdam at a coarse resolution. It further contains the volumetric contents of gas, excess ice, and two sediment phases (A,B) derived from a CT scan at UFZ Halle, downsampled to the resolution of the laboratory samples.</p> <p><em><strong>volumetric_contents_highRes_CT.csv</strong></em><br> Contains the volumetric contents of gas, excess ice, and two sediment phases (A,B) derived from a CT scan at UFZ Halle at the original resolution of 50µm.</p> <p><em><strong>regression analysis_paper.py</strong></em><br> This pyhton script uses the above listed input files to perform and evaluate a regression analysis<strong><em> </em></strong>of the CT data against the laboratory data. The regression result is the composition of the CT-derived sediment phases (A,B) in terms of pore ice, organic, and mineral. The script furthermore computes evaluation metrics of the lab-CT comparison, and computes volumetric contents of pore ice, total ice, organic, and mineral at the high resolution of the original CT data.</p> <p><em><strong>volumetric_contents_sampleRes_all.csv</strong></em><br> This file can be reproduced by the files listed above and contains, in addition to the data contained in <em>volumetric_contents_sampleRes_lab+CT.csv</em>, the volumetric contents of pore ice, total ice, mineral, and organic as predicted by the regression model at the same (coarse) resolution as the laboratory samples.</p> <p><em><strong>volumetric_contents_highRes_all.csv</strong></em><br> This file can be reproduced by the files listed above and contains, in addition to the data contained in <em>volumetric_contents_highRes_CT.csv</em>, the volumetric contents of pore ice, total ice, mineral, and organic as predicted by the regression model at the same (high) resolution as the original CT data.</p> <p>More details can be found in the article describing the study.</p>
Hyperspectral X-ray CT datasets of three chemical phantoms
<p><strong>General Data description:</strong></p> <p>The following are hyperspectral (energy-resolved) X-ray CT datasets for a set of chemical phantom samples, each containing multiple phases of an aqueous contrast agent at different concentrations. All scans were acquired with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The following data contains all the files necessary for reconstruction of each dataset. The phantom samples were produced as they each offer a distinct spectral marker which, when measured by an energy-sensitive detector, may be used as a form of calibration for spectral analysis. The phantoms were for the common contrast agents of I<sub>2</sub>KI, BaSO<sub>4</sub> and PTA.</p> <p><strong>File descriptions:</strong></p> <p>Contained are four MATLAB (.mat) data files, as well as three text (.txt) metadata files.</p> <p>Iodine_Phantom_scan_parameters.txt provides the full sample and detector geometry of the scan acquisition for the I<sub>2</sub>KI phantom. The concentrations for the iodine phases were 25, 50, 76 and 101 mg/ml of aqueous I<sub>3</sub><sup>-</sup> ions respectively.</p> <p>Barium_Phantom_scan_parameters.txt provides the full sample and detector geometry of the scan acquisition for the BaSO<sub>4</sub> phantom. The concentrations for the BaSO<sub>4</sub> phases were 100, 200 and 400 mg/ml of BaSO<sub>4</sub> respectively.</p> <p>Tungsten_Phantom_scan_parameters.txt provides the full sample and detector geometry of the scan acquisition for the PTA phantom. The concentrations for the PTA phases were 50, 100 and 200 mg/ml of PTA respectively.</p> <p>Iodine_phantom_sinogram.mat contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data for the I<sub>2</sub>KI phantom. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired. In addition, a ring artefact reduction filter was applied, as well as a centre-of-rotation correction.</p> <p>Barium_phantom_sinogram.mat contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data for the BaSO<sub>4</sub> phantom. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired. In addition, a ring artefact reduction filter was applied, as well as a centre-of-rotation correction.</p> <p>Tungsten_phantom_sinogram.mat contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data for the PTA phantom. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired. In addition, a ring artefact reduction filter was applied, as well as a centre-of-rotation correction.</p> <p>Energy_axis.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning.</p>
Hyperspectral X-ray CT datasets for a set of multiply-stained mouse limb specimens
<p><strong>General Data description:</strong></p> <p>The following are hyperspectral (energy-resolved) X-ray CT datasets for a set of mouse limb specimens, each stained with multiple contrast agents. All scans were acquired with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The following data contains all the files necessary for reconstruction of each dataset. The biological specimens were produced as they each contain multiple contrast agents, with distinct spectral markers. When measured by an energy-sensitive detector, each contrast agent may be identified and segmented individually following spectral analysis. A mouse hindlimb was double-stained with elemental iodine and BaSO<sub>4</sub>. A mouse forelimb was triple-stained with I<sub>2</sub>KI, BaSO<sub>4 </sub>and PTA.</p> <p><strong>File descriptions:</strong></p> <p>Contained are two HDF5 (.h5) data files, as well as two (.txt) metadata files and a MATLAB (.mat) file.</p> <p>Hindlimb_scan_parameters.txt provides the full sample and detector geometry of the scan acquisition for the double-stained hindlimb.</p> <p>Forelimb_scan_parameters.txt provides the full sample and detector geometry of the scan acquisition for the triple-stained forelimb.</p> <p>DS_Mouse_hindlimb_sinogram.h5 contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data for the double-stained hindlimb specimen. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired. In addition, a ring artefact reduction filter was applied.</p> <p>TS_Mouse_forelimb_sinogram.h5 contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data for the triple-stained forelimb specimen. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired. In addition, a ring artefact reduction filter was applied.</p> <p>Energy_axis.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning.</p>
RESOLUTE atlas for brain PET/MR pseudo-CT generation
<p>Template and mask images for performing the <em>Region specific optimization of continuous linear attenuation coefficients based on UTE</em> (RESOLUTE) pseudo-CT generation approach. This dataset can be used in conjunction with an open-source C++ implementation of RESOLUTE (<a href="https://github.com/UCL/petmr-RESOLUTE">https://github.com/UCL/petmr-RESOLUTE</a>) for the Siemens mMR scanner.</p>
Human Bony Labyrinth: Co-Registered CT and micro-CT Images, Surface Models and Anatomical Landmarks
<p>This data set consists of 23 specimens of the human bony labyrinth. For each specimen clinical CT (0.15×0.15×0.2 mm3, voxel size) and co-registered microCT (0.06 mm isotropic voxel size) images are available. Image labels for the bony labyrinth are provided for the same image coordinates. From the image labels, 3D surface models were generated. In addition, each specimen has a descriptor file containing the coordinates of anatomical landmarks as well as a cochlear coordinate system. The data set can be used to study the morphology of the inner ear or to evaluate (semi-)automated segmentation algorithms (e.g., for the preoperative planning of surgical procedures such as cochlear implantation).</p>
3C : Cardiac-CT-Covid19
<p>The 3C (Cardiac-CT-COVID-19) database offers CT scans of patients diagnosed with COVID-19, encompassing both individuals with and without cardiac complications (49 females, 58 males, aged between 8 and 89 years). Image interpretation was carried out by two experienced radiologists. The dataset also provides information on the severity of each cardiac condition, making it a valuable resource for examining the impact of COVID-19 on the cardiovascular system.</p>
Tailored Sticky Solutions: 3D-Printed Miconazole Buccal Films for Pediatric Oral Candidiasis - Underlying CT data
<p>Underlying CT data of "<strong>Tailored Sticky Solutions: 3D-Printed Miconazole Buccal Films for Pediatric Oral Candidiasis</strong>"<br><strong>DOI: <a href="https://doi.org/10.1208/s12249-024-02908-5">https://doi.org/10.1208/s12249-024-02908-5</a></strong></p> <p>by </p> <p>Konstantina Chachlioutaki, Anastasia Iordanopoulou, Orestis L. Katsamenis, Anestis Tsitsos, Savvas Koltsakidis, Pinelopi Anastasiadou, Dimitrios Andreadis, Vangelis Economou, Christos Ritzoulis, Dimitrios Tzetzis, Nikolaos Bouropoulos, Iakovos Xenikakis & Dimitrios Fatouros </p> <p> </p> <div> <h3>Authors and Affiliations</h3> <ol> <li> <p>Department of Pharmacy Division of Pharmaceutical Technology, Aristotle University of Thessaloniki, Thessaloniki, Greece</p> <p>Konstantina Chachlioutaki, Anastasia Iordanopoulou, Iakovos Xenikakis & Dimitrios Fatouros</p> </li> <li> <p>Center for Interdisciplinary Research and Innovation (CIRI-AUTH), Thessaloniki, Greece</p> <p>Konstantina Chachlioutaki & Dimitrios Fatouros</p> </li> <li> <p>μ-VIS X-Ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK</p> <p>Orestis L. Katsamenis</p> </li> <li> <p>Institute for Life Sciences, University of Southampton, Southampton, SO17 1BJ, UK</p> <p>Orestis L. Katsamenis</p> </li> <li> <p>Laboratory of Animal Food Products Hygiene - Veterinary Public Health, School of Veterinary Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece</p> <p>Anestis Tsitsos & Vangelis Economou</p> </li> <li> <p>Digital Manufacturing and Materials Characterization Laboratory, School of Science and Technology, International Hellenic University, 14km Thessaloniki–N. Moudania, 57001, Thermi, Greece</p> <p>Savvas Koltsakidis & Dimitrios Tzetzis</p> </li> <li> <p>Department of Oral Medicine/Pathology, School of Dentistry, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece</p> <p>Pinelopi Anastasiadou & Dimitrios Andreadis</p> </li> <li> <p>Department of Food Science and Technology, International Hellenic University, Sindos Campus, 57400, Thessaloniki, Greece</p> <p>Christos Ritzoulis</p> </li> <li> <p>Department of Materials Science, University of Patras, Rio, 26504, Patras, Greece</p> <p>Nikolaos Bouropoulos</p> </li> <li> <p>Foundation for Research and Technology Hellas, Institute of Chemical Engineering and High Temperature Chemical Processes, 26504, Patras, Greece</p> <p>Nikolaos Bouropoulos</p> </li> </ol> </div>
Downsampling of CT-Lymph-Node Dataset for Body Part Regression Tutorial
<p>Down sampling of the<a href="https://wiki.cancerimagingarchive.net/display/Public/CT+Lymph+Nodes#19726546f04e74ab3631480694fcb72cac2e5477"> CT Lymph Node</a> dataset from the TCIA.<br> The files were down sampled to a pixel spacing of 7 mm/pixel. Through zero padding and cropping, all images are provided in the size of 64px x 64 px. Moreover, the HU values were clipped between -1000 HU and 1500 HU and rescaled to -1 and 1. To avoid aliasing effects, an additional Gaussian smoothing filter was applied before down sampling.</p> <p>This dataset was created for a Body Part Regression tutorial.</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.