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3,036 results for “ct”
Harvard Forest site, station Windham County, CT (FIPS 9015), study of human population density in units of numberPerKilometerSquared on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.
Harvard Forest site, station Windham County, CT (FIPS 9015), study of human population (total) in units of number on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains human population (total) measurements in number units and were aggregated to a yearly timescale.
Harvard Forest site, station Windham County, CT (FIPS 9015), study of population (urban) in units of number on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains population (urban) measurements in number units and were aggregated to a yearly timescale.
A single- and two-tile tomographic micro-CT data of the terracotta sculpture "the Torso"
<p><strong>Summary</strong></p> <p>This submission contains two (<em><strong>single-tile</strong></em> and <strong><em>two-tile</em></strong>) tomographic dataset of the terracotta object, the "Torso". This data was collected as part of a larger project including a series of similar terracotta objects (of various body parts), more information can be found in [Scholten 2014].</p> <p>The data is made available as part of Case Study 1 in [Coban 2020].</p> <p> </p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban 2020].</p> <p> </p> <p><strong>Sample Information</strong></p> <p>The sample is a terracotta object from the series of various body parts collection by the sculptor Johan Gregor van der Schardt. The sample in particular is called "Torso", which is a hollow clay statue, originally believed to have been created using two moulds of clay put together.</p> <p>The sample was mounted in a bed of foam, and secured with 2 thin plastic sticks through the legs of the statue. This was done to dampen any vibrations from the rotation while the statue is mounted upright. See Figure 4 in [Coban 2020] for photos of the sample in mount.<br> </p> <p><strong>Experimental Plan</strong></p> <p>The data in this submission was collected via the technique of explorative imaging, in which data is collected upon discussion with the expert and any intermediate reconstruction results. The experiments included in this upload first starts off with a single tile, binned acquisition. The sample is then zoomed in and data is collected in two halves (two detector tiles) to capture details in higher resolution. More information on this process is given in Section 3.1 in [Coban 2020].</p> <p>For both experiments, the sample is rotated 360° in circular and continuous motion, with a dark-field (closed-shutter), and 2 flat-field (open-shutter) images taken before and after the acquisition. For the first experiment, <em><strong>single-tile</strong></em>, we collected 1200 projections at 136 micron resolution (size of each pixel on detector plane). For the second experiment, the object was moved closed to the source, and the data was collected in two tiles (spatial tiles in detector space). Each tiled-data folder contains 2001 projections at 50 spatial resolution.</p> <p>All raw data (i.e. no corrections) is made available in .tif format.</p> <p> </p> <p><strong>List of Contents</strong></p> <p>The contents of the submission is given below.</p> <ul> <li><strong>single-tile</strong>: A "low" resolution scan, taken to answer the initial question.</li> <li><strong>two-tiles:</strong> A higher resolution scan for studying the finer details such as toolmarks on the interior of the statue; contains <strong><em>t1</em></strong> and <strong><em>t2</em></strong> for tile 1 (top) and tile 2 (bottom) respectively.</li> </ul> <p>Each data folder contains</p> <ul> <li>dark-field (or closed-shutter) image, <em>di0000.tif,</em></li> <li>pre flat-field (or open-shutter before acqusition) image, <em>io0000.tif</em>,</li> <li>post flat-field (or open-shutter after acqusition) image, <em>io0001.tif</em>,</li> <li>raw (unprocessed or uncorrected) projections, <em>scan_*.tif</em>,</li> <li><em>data settings XRE.txt</em>, a text file with scanner metadata,</li> <li><em>scan settings.txt</em>, a text file with scanner metadata in more human readeable format, and</li> <li><em>data settings XRE.ini</em>, a snapshot text file of basic geometry information at the start of a scan.</li> <li><em>script.txt</em> and <em>script_executed.txt</em> are the text files containing the list of commands the apparatus has executed.</li> </ul> <p> </p> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a href="https://www.cwi.nl/research/groups/computational-imaging">Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these dataset, please get in touch with </p> <ul> <li>s.b.coban [at] cwi.nl</li> </ul> <p> </p> <p><strong>Acknowledgments</strong></p> <p>We thank Isabelle Garachon of Rijksmuseum for providing this extra-ordinary sample.</p>
Dynamic 3D X-ray micro-CT data of a tablet dissolution in a water-based gel with dynamic changes in the scanning geometry
<p><strong>Summary</strong></p> <p>This submission contains a dynamic tomographic X-ray data of a tablet dissolving in a water-based gel. The data is collected over a 5-minute period during which the sample is rotated rapidly as effervescent bubbles are formed and travelling to the surface of the gel.</p> <p>This is the second experiment detailed in Case Study 3 in [Coban 2020], and this submission can be treated as a follow up to [Coban&Lucka 2019]. </p> <p> </p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban 2020].</p> <p> </p> <p><strong>Sample Information</strong></p> <p>The setup consists of a store-bought denture cleaning tablet, placed at the bottom of a clear cylindrical plastic container. These tablets are typically designed to be fast-dissolving, and produce small and compact channels of bubbles. We use a denture cleaning tablet in particular as the dissolution time in water varies from 3 to 5 minutes, meaning the bubbles are produced at a slower rate. In addition, we use a store-bought water-based gel instead of water to slow down the bubble displacement during the experiment.</p> <p> </p> <p><strong>Experimental Plan</strong></p> <p>This experiment is performed such that 150 projections are collected over 360 degrees, for a total of 166 rotations, with exposure time 12 ms for each projection. This means that in total the submission contains 25000 projections. This experiment took 5 minutes of acquisition time, during which we (at user's command) zoom in onto the bottom of the sample holder (i.e. where the tablet rests). We later (again, at user's command) shift the view (i.e. the tube and the detector) upwards to the top of the sample to observe foaming on the surface. Finally, before the end of the 5-minute acquisition period, we zoom out to the original magnification. Every time the geometry undergoes a major change such as zooming in (which would affect the reconstruction), the system creates a new data settings file with the new geometrical information, appended by the projection number, therefore marking the change. However, since there is no major change created by the vertical shift of the tube and detector (as in no change in geometry that would affect the reconstructed images), there is no new data settings file for this event. </p> <p>The spatial resolution for this data is 193μm at the beginning (or end) of the experiment, which at an arbitrary point changes to 76μm. For a smooth data transfer, each projection image is binned down to the size of 486px-by-384px. No centrifugal force effect was observed on the bubbles travelling during the scan or in our test runs at the given rotational speed.</p> <p>All raw data (i.e. with no corrections) is made available in .tif format.</p> <p> </p> <p><strong>List of Contents</strong></p> <p>The contents of the submission is given below.</p> <ul> <li><strong>scan_1</strong>: A 5-minute dynamic CT data folder containing <ul> <li>dark-field (or closed-shutter) image, <em>di000000.tif,</em></li> <li>pre flat-field (or open-shutter before acquisition) image, <em>io000000.tif</em>,</li> <li>post flat-field (or open-shutter after acquisition) image, <em>io000001.tif</em>,</li> <li>raw (unprocessed or uncorrected) projections, <em>scan_*.tif</em> (25000 projections in total),</li> <li><em>data settings XRE.txt</em>, a text file with scanner metadata (this is the final geometry info file),</li> <li><em>data settings XRE_5220.txt</em> (geometry info recorded after the zoom-in)</li> <li><em>data settings XRE__22610.txt</em> (geometry info recorded after the zoom-out, same as <em>data settings XRE.txt</em>)</li> </ul> </li> </ul> <p> </p> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a href="https://www.cwi.nl/research/groups/computational-imaging">Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these dataset, please get in touch with </p> <ul> <li>s.b.coban [at] cwi.nl</li> </ul> <p> </p> <p><strong>Acknowledgments</strong></p> <p>We thank Dr. Samuel McDonald and Prof. Philip Withers for the useful discussion, and Dr. Manuel Dierick for his advice in making this experiment possible.</p>
Dynamic 3D X-ray micro-CT data of a tablet dissolution in a water-based gel
<p><strong>Summary</strong></p> <p>This submission contains a dynamic tomographic X-ray data of a tablet dissolving in a water-based gel. The data is collected over a 5-minute period during which the sample is rotated rapidly as effervescent bubbles are formed and travelling to the surface of the gel.</p> <p>The data is made available as part of Case Study 3 in [Coban 2020].</p> <p> </p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban 2020].</p> <p> </p> <p><strong>Sample Information</strong></p> <p>The setup consists of a store-bought denture cleaning tablet, placed at the bottom of a clear cylindrical plastic container. These tablets are typically designed to be fast-dissolving, and produce small and compact channels of bubbles. We use a denture cleaning tablet in particular as the dissolution time in water varies from 3 to 5 minutes, meaning the bubbles are produced at a slower rate. In addition, we use a store-bought water-based gel instead of water to slow down the bubble displacement during the experiment.</p> <p> </p> <p><strong>Experimental Plan</strong></p> <p> </p> <p>This experiment is performed such that 120 projections are collected over 360 degrees, for a total of 83 rotations, with exposure time 30 ms for each projection. This means that in total the submission contains 10000 projections. This took a total of 5 minutes of acquisition time, during which the tablet moved due to saturation but did not completely dissolve. The spatial resolution is 95μm, and the field of view was cropped to the boundaries of the sample holder (each projection image is of size 647px×768px). Our experimental setup allowed the collection of open and closed shutter (flat- and dark-field) images before decanting the gel onto the tablet. No centrifugal force effect was observed on the bubbles travelling during the scan or in our test runs at the given rotational speed.</p> <p>All raw data (i.e. no corrections) is made available in .tif format.</p> <p> </p> <p><strong>List of Contents</strong></p> <p>The contents of the submission is given below.</p> <ul> <li><strong>scan</strong>: A 5-minute dynamic CT data folder containing <ul> <li>dark-field (or closed-shutter) image, <em>di000000.tif,</em></li> <li>pre flat-field (or open-shutter before acquisition) image, <em>io000000.tif</em>,</li> <li>post flat-field (or open-shutter after acquisition) image, <em>io000001.tif</em>,</li> <li>raw (unprocessed or uncorrected) projections, <em>scan_*.tif</em> (10000 projections in total),</li> <li><em>data settings XRE.txt</em>, a text file with scanner metadata,</li> <li><em>script.txt</em> and <em>script_executed.txt</em> are the text files containing the list of commands the apparatus has executed.</li> </ul> </li> </ul> <p> </p> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a href="https://www.cwi.nl/research/groups/computational-imaging">Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these dataset, please get in touch with </p> <ul> <li>s.b.coban [at] cwi.nl</li> </ul> <p> </p> <p><strong>Acknowledgments</strong></p> <p>We thank Dr. Samuel McDonald and Prof. Philip Withers for the useful discussion, and Dr. Manuel Dierick for his advice in making this experiment possible.</p> <p> </p>
Three tomographic CT datasets of a woven fabric
<p><strong>Summary</strong><br> This submission contains three tomographic datasets of a fragment of fabric woven in tapestry weave. The data is collected at three different zoom levels to achieve different reconstructed image resolution.<br> The data is made available as part of [Bossema 2020]. </p> <p><strong>Apparatus</strong><br> The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban 2020].</p> <p><strong>Sample Information</strong><br> The sample is a fragment of woven fabric approximately 7cm x 15cm in size. The fabric was hung vertically on a piece of foam, held on the top by a wooden stick through one of the holes and on the bottom by a piece of plastic tape. See Figure 5 in [Bossema 2020] for a picture of the object and examples of the reconstruction. <br> </p> <p><strong>Experimental Plan</strong><br> The data in this submission was collected to illustrate the use of zooming for the investigation of cultural heritage objects. Three region-of-interest scans of the lower part, containing a hole, were collected at different zoom levels. For each scan, the sample was rotated 360° in circular and continuous motion, with a dark-field (closed-shutter), and flat-field (open-shutter) image taken before the acquisition. Each dataset consists of 1200 projections. The source-detector distance was kept at 1098mm. At the first level of zooming, the object was placed at 963mm from the source yielding a magnification of 1.14 and 131 micron resolution. For the second level the object was moved closer to the source so that the source-object distance was 603mm, yielding a magnification of 1.82 and 82 micron resolution. At the third level, the source-object distance was reduced to 243, yielding a magnification of 4.5 and 33 micron resolution.<br> All raw data (i.e. no corrections) is made available in .tif format.</p> <p> </p> <p><strong>List of Contents</strong></p> <p>The content of the submission is given below.</p> <ul> <li><strong>level1: </strong>lowest resolution scan.</li> <li><strong>level2:</strong> higher resolution scan.</li> <li><strong>level3:</strong> highest resolution scan.</li> </ul> <p>Each data folder contains:</p> <ul> <li>dark-field (or closed-shutter) image, <em>di000000.tif</em>,</li> <li>flat-field (or open-shutter before acquisition) image, <em>io000000.tif</em>,</li> <li>raw (unprocessed or uncorrected) projections,<em> scan_*.tif</em>,</li> <li><em>data settings XRE.txt</em>, a text file with scanner metadata,</li> <li><em>scan settings.txt</em>, a text file with scanner metadata in a human readable format, and</li> <li><em>data settings XRE.ini</em>, a snapshot text file of basic geometry information at the start of a scan.</li> <li><em>script_executed.txt</em>, the text file containing the list of commands the apparatus has executed.</li> </ul> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a href="https://www.cwi.nl/research/groups/computational-imaging">Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a href="http://github.com/cicwi">GitHub page</a>.</p> <p><strong>Contact Details</strong><br> For more information or guidance in using these datasets, please get in touch with </p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p><strong>Acknowledgments</strong><br> We thank Suzan Meijer of the Rijksmuseum for providing this sample.</p>
COVID-19 CT Lung and Infection Segmentation Dataset
<p>This dataset contains 20 labeled COVID-19 CT scans. Left lung, right lung, and infections are labeled by two radiologists and verified by an experienced radiologist. <br> To promote the studies of annotation-efficient deep learning methods, we set up three segmentation benchmark tasks based on this dataset <a href="https://gitee.com/junma11/COVID-19-CT-Seg-Benchmark">https://gitee.com/junma11/COVID-19-CT-Seg-Benchmark</a>.</p> <p>In particular, we focus on learning to segment left lung, right lung, and infections using</p> <ul> <li>pure but limited COVID-19 CT scans;</li> <li>existing labeled lung CT dataset from other non-COVID-19 lung diseases;</li> <li>heterogeneous datasets include both COVID-19 and non-COVID-19 CT scans.</li> </ul>
Supplementary material 2 from: Bernardes M, Le MD, Nguyen TQ, Pham CT, Pham AV, Nguyen TT, Rödder D, Bonkowski M, Ziegler T (2020) Integrative taxonomy reveals three new taxa within the Tylototriton asperrimus complex (Caudata, Salamandridae) from Vietnam. ZooKeys 935: 121-164. https://doi.org/10.3897/zookeys.935.37138
Time-calibrated tree of Tylototriton based on ND2 sequences. The values indicate the split time (in million years ago) calculated by BEAST 1.8.0.
Supplementary material 1 from: Bernardes M, Le MD, Nguyen TQ, Pham CT, Pham AV, Nguyen TT, Rödder D, Bonkowski M, Ziegler T (2020) Integrative taxonomy reveals three new taxa within the Tylototriton asperrimus complex (Caudata, Salamandridae) from Vietnam. ZooKeys 935: 121-164. https://doi.org/10.3897/zookeys.935.37138
Regression of each morphological character to its respective snout-vent length value for taxon 1 and taxon 2
ds-uct-003: Endodontic Instruments: X-Ray micro-CT of five endodontic files.
<p><strong>Summary</strong>:<br> .X-Ray micro-computed tomography (micro-CT) of five endodontic files used for the primary shaping of root canals, endodontic reintervention and supplementary cleaning methods.<br> .The 3D image was generated with an X-Ray micro-CT Scanner version Xradia Versa 510 from Zeiss performed by A Pereira at the UFF micro-CT Facility.<br> .For use of these data, please remember to cite the DOI of the Zenodo repository and relevant papers.</p> <p><strong>Details</strong>:<br> .flat-us (1024) - Voxel size: 10.0 μm; Sample-source: 30 mm; Sample-detector: 176 mm; Optical magnification: 0.4X; Filter: LE#1; Beam energy: 80 kV; Power: 7 W; Exposure time: 1.0 sec; Projections: 1600.<br> .xp-finish (1024) - Voxel size: 15.2 μm; Sample-source: 31 mm; Sample-detector: 110 mm; Optical magnification: 0.4X; Filter: LE#1; Beam energy: 80 kV; Power: 7 W; Exposure time: 0.4 sec; Projections: 1600.<br> .m2-30 (1024) - Voxel size: 18.2 μm; Sample-source: 39.5 mm; Sample-detector: 110 mm; Optical magnification: 0.4X; Filter: LE#1; Beam energy: 80 kV; Power: 7 W; Exposure time: 1.0 sec; Projections: 1600.<br> .m2r-25 (1024) - Voxel size: 18.2 μm; Sample-source: 39.5 mm; Sample-detector: 110 mm; Optical magnification: 0.4X; Filter: LE#1; Beam energy: 80 kV; Power: 7 W; Exposure time: 1.0 sec; Projections: 1600.<br> .reciproc-r25 (1024) - Voxel size: 18.2 μm; Sample-source: 39.5 mm; Sample-detector: 110 mm; Optical magnification: 0.4X; Filter: LE#1; Beam energy: 80 kV; Power: 7 W; Exposure time: 0.8 sec; Projections: 1600.</p> <p><strong>Contents</strong>:<br> ._info_ds-uct-003.txt<br> .ds-uct-003_endodontic_instruments_10um_8bits_flat-us.zip<br> .ds-uct-003_endodontic_instruments_15um_8bits_xp-finish.zip<br> .ds-uct-003_endodontic_instruments_18um_8bits_m2-30.zip<br> .ds-uct-003_endodontic_instruments_18um_8bits_m2r-25.zip<br> .ds-uct-003_endodontic_instruments_18um_8bits_reciproc-r25.zip</p>
Petermann Fjord Sediment Core Computed Tomography (CT) Scans (Cruise OD1507)
<p>Computed tomography (CT) scans of sediment cores collected from Petermann Fjord during the PETERMANN15 expedition of the Swedish Icebreaker Oden, OD1507. Included cores, 03TC, 03PC, 04GC, 06PC, 08GC, 10PC, 10TC, 40PC, 40TC, and 41GC. Data include 2 mm thick coronal slices in DICOM format and SedCT products, including images and CT numbers.</p>
Figure 15. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 15. Cleveland tyrannosaur skull, CMNH 7541. Volume renderings of digitally extracted right mandible derived from CT data in A, dorsal; B, medial; C, lateral views. A–C are stereopairs. D, close-up of mandible in lateral view; numbers correspond to tooth positions, of which there are 16. Scale bars equal 10 cm.
Figure 12. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 12. Cleveland tyrannosaur skull, CMNH 7541. Volume (A) and surface (B) renderings of digitally extracted left palatine derived from CT data in lateral view. Both sets are stereopairs. Surface rendering (B) is partially transparent to reveal the internal pneumatic sinuses; note that the two sinuses do not communicate. Scale bar equals 2 cm. See Appendix for abbreviations.
Figure 11. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 11. Cleveland tyrannosaur skull, CMNH 7541. Volume (A) and surface (B) renderings of digitally extracted left quadratojugal derived from CT data in lateral view. Both sets are stereopairs. Surface rendering (B) is partially transparent to reveal the internal pneumatic sinus. In the actual specimen, the jugal process was displaced relative to the rest of the bone, but has been digitally reattached here. Scale bar equals 5 cm. See Appendix for abbreviations.
Figure 9. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 9. Cleveland tyrannosaur skull, CMNH 7541. Surface renderings of digitally extracted braincase derived from CT data, made partially transparent to reveal brain endocast (light blue) and internal pneumatic sinuses, in A, left lateral; B, left rostroventrolateral; C, caudoventral views. Scale bar equals 10 cm. See Appendix for abbreviations.
Figure 6. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 6. Cleveland tyrannosaur skull, CMNH 7541. Surface renderings of digitally extracted braincase derived from CT data in A, left lateral; B, left rostroventrolateral; C, caudal views. Figure 5A–C shows corresponding stereopairs of volume renderings. Scale bar equals 10 cm. See Appendix for abbreviations.
Figure 4. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 4. Cleveland tyrannosaur skull, CMNH 7541. Volume renderings of the skull derived from the CT data in A, rostral view; B, right lateral view; C, left lateral view; D, dorsal view; E, caudal view; F, ventral view. Densities corresponding to plaster have been excluded, revealing the extent of plaster restoration. Arrows in C indicate the transverse oblique plane of fracture that the specimen experienced prior to restoration. Scale bar equals 10 cm.
Figure 2. Cleveland tyrannosaur skull, CMNH 7541 in The Cleveland tyrannosaur skull (Nanotyrannus or Tyrannosaurus): new findings based on CT scanning, with special reference to the braincase
Figure 2. Cleveland tyrannosaur skull, CMNH 7541. Stereophotographs of A, close-up of left antorbital region; B, skull in rostral view; C, skull in ventral view. Orientations reflect posture with lateral semicircular canal horizontal. Scale bars equal 10 cm.
ds-uct-006: Serjania Corrugata: X-Ray micro-CT of a Serjania corrugata (Sapindaceae) stem sample.
<p><strong>Summary</strong>:<br> .X-Ray micro-computed tomography (micro-CT) of a Serjania corrugata (Sapindaceae) stem sample, including both raw projection data and the final reconstructions, for a sigle resolution (voxel sizes of 40 μm).<br> .The 3D image was generated with an X-Ray micro-CT Scanner version Xradia Versa 510 from Zeiss performed by A Pereira at the UFF micro-CT Facility.<br> .For use of these data, please remember to cite the DOI of the Zenodo repository and relevant papers.</p> <p><strong>Details</strong>:<br> .Tomo - Voxel size: 40 μm; Sample-source: 120 mm; Sample-detector: 86.78 mm; Optical magnification: 0.4X; Filter: LE#1; Beam energy: 40 kV; Power: 3 W; Exposure time: 10.0 sec; Projections: 1600.</p> <p><strong>Contents</strong>:<br> ._info_ds-uct-006.txt<br> .ds-uct-006_serjania_corrugata_40um_8bits.zip<br> .ds-uct-006_serjania_corrugata_40um_1600p.txrm<br> .ds-uct-006_serjania_corrugata_40um_1600p_Drift.txrm<br> .ds-uct-006_serjania_corrugata_40um_1600p_recon.txm</p>
ScienceDex guides
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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.