Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
478
datasets available to search
ShareScore release 0.9.0
Dataset results
478 results for “3D data”
Survival data and code: Camouflage using 3D surface disruption
Open the record for dataset details and reuse information.
Data from: Digitizing extant bat diversity: an open-access repository of 3D μCT-scanned skulls for research and education
Open the record for dataset details and reuse information.
AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold Networks
Open the record for dataset details and reuse information.
Data from: Integrating 3D models with morphometric measurements to improve volumetric estimates in marine mammals
Open the record for dataset details and reuse information.
Data from: Energetic fitness: field metabolic rates assessed via 3D accelerometry complement conventional fitness metrics
Open the record for dataset details and reuse information.
Data from: Development of a 3D simulator for training the mouse in utero electroporation
Open the record for dataset details and reuse information.
Data from: Diurnal moths have larger hearing organs: Evidence from comparative 3D morphometric study on geometrid moths
Open the record for dataset details and reuse information.
Bathymetry of the Antarctic continental shelf and ice shelf cavities from a 3D inversion of circumpolar gravity anomalies constrained by other data
Open the record for dataset details and reuse information.
Data from: Transcriptional regulation of human <em>NMNAT2</em>: Insights from 3D genome sequencing and bioinformatics
Open the record for dataset details and reuse information.
Data from: Anatomy of an agricultural antagonist: Feeding complex structure and function of three xylem sap-feeding insects illuminated with synchrotron-based 3D imaging
Open the record for dataset details and reuse information.
3D data obtained with a MicroScribe digitising arm and photogrammetry to address bioarchaeological research questions
Open the record for dataset details and reuse information.
Data from: A 3D geometric morphometric analysis of the bovid distal humerus, with special reference to Rusingoryx atopocranion (Pleistocene, Eastern Africa)
Open the record for dataset details and reuse information.
Data and 3D-files for: 3D Printing for Low-Cost and Versatile Attenuated Total Reflection Infrared Spectroscopy
<p>Data and 3D files for :<a href="https://doi.org/10.1021/acs.analchem.9b04043">https://doi.org/10.1021/acs.analchem.9b04043</a></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>
Gemini 3D test data
<p>DEPRECATED: data now at: <a href="https://zenodo.org/record/3962801">https://zenodo.org/record/3962801</a></p> <p>---</p> <p>Gemini 3D simulation reference data</p> <p>https://www.github.com/gemini3d/gemini</p> <p>v3.2.0 new api / meta</p>
Data for paper "Effects of inflow conditions on plunge points and vertical profiles of turbidity currents on sloping flume based on 3D numerical simulation"
<p>This set of xlsx files, metadata of model results and Matlab code accompanies the manuscript "Effects of inflow conditions on plunge points and vertical profiles of turbidity currents on sloping flume based on 3D numerical simulation" by Ruoyin Zhang, Baosheng Wu, and Bangwen Zhang. This is the first release of the data.</p>
Landmarks and 3D scan data of Macaca fascicularis
<p><b>Objectives</b>: Magnitudes of morphological integration may constrain or facilitate craniofacial shape variation. The aim of this study was to analyze how the magnitude of integration in the skull of <i>Macaca fascicularis</i> changes throughout ontogeny in relation to developmental and/or functional modules.</p> <p><b>Materials and Methods</b>: Geometric morphometric methods were used to analyze the magnitude of integration in the macaque cranium and mandible in 80 juvenile and 40 adult <i>M. fascicularis</i> specimens. Integration scores in skull modules were calculated using ICV (Integration Coefficient of Variation of eigenvalues) based on a resampling procedure. Resultant ICV scores between the skull as a whole, and developmental and/or functional modules were compared using Mann-Whitney U tests.</p> <p><b>Results</b>: Results showed that most skull modules were more tightly integrated than the skull as a whole, with the exception of the chondrocranium in juveniles without canines, the chondrocranium/face complex and the mandibular corpus in adults, and the mandibular ramus in all juveniles. The chondrocranium/face and face/mandibular corpus complexes were more tightly integrated in juveniles than adults, possibly reflecting the influences of early brain growth/development, and the changing functional demands of infant suckling and later masticatory loading. This is also supported by the much higher integration of the mandibular ramus in adults compared with juveniles.</p> <p><b>Discussion</b>: Magnitudes of integration in skull modules reflect developmental/functional mechanisms in <i>M. fascicularis</i>. However, the relationship between 'evolutionary flexibility' and developmental/functional mechanisms was not direct or simple, likely because of the complex morphology, multifunctionality, and various ossification origins of the skull.</p>
Fibronectin-Based Nanomechanical Biosensors to Map 3D Surface Strains in Live Cells and Tissue (Raw Data)
<p>This is the raw microscope imaging data for the manuscript titled "Fibronectin-Based Nanomechanical Biosensors to Map 3D Surface Strains in Live Cells and Tissue."</p>
Data from: Estimating body mass of free-living whales using aerial photogrammetry and 3D volumetrics
1. Body mass is a key life history trait in animals. Despite being the largest animals on the planet, no method currently exists to estimate body mass of free-living whales. 2. We combined aerial photographs and historical catch records to estimate the body mass of free-living right whales (Eubalaena sp.). First, aerial photogrammetry from unmanned aerial vehicles was used to measure the body length, width (lateral distance) and height (dorso-ventral distance) of free-living southern right whales (E. australis; 48 calves, 7 juveniles and 31 lactating females). From these data, body volume was estimated by modelling the whales as a series of infinitely small ellipses. The body girth of the whales was next calculated at three measurement sites (across the pectoral fin, the umbilicus and the anus) and a linear model was developed to predict body volume from the body girth and length data. To obtain a volume-to-mass conversion factor, this model was then used to estimate the body volume of eight lethally caught North Pacific right whales (E. japonica), for which body mass was measured. This conversion factor was consequently used to predict the body mass of the free-living whales. 3. The cross-sectional body shape (height-width ratio) of the whales was slightly flattened dorso-ventrally at the anterior end of the body, almost circular in the mid region, and significantly flattened in the lateral plane across the posterior half of the body. Compared to a circular cross-sectional model, our body mass model incorporating body length, width and height improved mass estimates by up to 23.6% (mean=6.1%, SD=5.27). Our model had a mean error of only 1.6% (SD=0.012), compared to 9.5% (SD=7.68) for a simpler body length-to-mass model. The volume-to-mass conversion factor was estimated at 754.63kg m-3 (SD=50.03). Predicted body mass estimates were within a close range of existing body mass measurements. 4. We provide a non-invasive method to accurately estimate body mass of free-living whales while accounting for both their structural size (body length) and relative body condition (body width). Our approach can be directly applied to other marine mammals by adjusting the model parameters (body mass model script provided).
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.