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.
5,635
datasets available to search
ShareScore release 0.9.0
Dataset results
5,635 results for “3D”
3D model of antenna system embedded into building envelope for improved cellular signal transmission through load-bearing walls
<p>The purpose of this dataset is to supplement the data presented in our journal publication "Electromagnetic–Thermal Analyses of Distributed Antennas Embedded Into a Load-Bearing Wall" (see <a href="https://ieeexplore.ieee.org/document/10151683">https://ieeexplore.ieee.org/document/10151683</a>).</p> <p>This dataset contains the 3-D discretized model, without the internal numerical mesh, of the unit cell of the spiral antenna system embedded in a load bearing wall. The 3D model is in .STP format (see ISO 10303-21:2016), which can be imported into most commercial computer-aided design (CAD) software. The wall's dielectric properties are calculated using the model described in ITU-R P.2040-2 (<a href="https://www.itu.int/rec/R-REC-P.2040/en">https://www.itu.int/rec/R-REC-P.2040/en</a>, material parameter and calculation model are on pages 22-23). Materials used in the antenna system and their electrical and thermal parameters are given in the file materials.txt</p>
Nabro 3D velocity model produced by the FMTOMO code
<p>These files relate to "Seismic tomography of Nabro caldera, Eritrea: insights into the magmatic and hydrothermal systems of a recently erupted volcano" by Gauntlett et al., 2023. </p> <p>Original seismic waveforms are from the Nabro Urgency Array (Hammond et al., 2011; <a href="https://doi.org/10.7914/SN/4H_2011">https://doi.org/10.7914/SN/4H_2011</a>), which is publicly available through IRIS Data Services (<a href="http://service.iris.edu/fdsnws/dataselect/1/">http://service.iris.edu/fdsnws/dataselect/1/</a>). See Hammond et al. (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JB021910#jgrb55017-bib-0025">2011</a>) for further details on waveform data access and availability. Sources were originally located by Lapins et al. (<a href="https://doi.org/10.1029/2021JB021910">2021</a>), and the full catalogue is archived <a href="https://zenodo.org/record/7669717#.ZAXtfuymP0s">here</a> (Lapins, 2022). </p> <p>The FMTOMO package is freely available to download at<a href="http://rses.anu.edu.au/~nick/fmtomo.html"> http://rses.anu.edu.au/~nick/fmtomo.html</a>. </p> <p>This repository contains three 3D velocity models:</p> <ol> <li>vp_model.out</li> <li>vs_model.out</li> <li>vpvs_model.out</li> </ol> <p>These plain text files comprise the output of the FMTOMO tomography algorithm, which inverts for 3D P-wave velocity and S-wave velocity Vp and Vs) structure and Vp/Vs ratio for a grid centred around Nabro volcano. The velocity is given in km/s for Vp and Vs, and the values are dimensionless for Vp/Vs ratio.</p> <p>The grid is specified in the first four lines of the file:</p> <ul> <li>Line 1: for these data, this line will always hold the values `1' and `1'. </li> <li>Line 2: the number of grid nodes in radius (depth), latitude and longitude. </li> <li>Line 3: the radial (depth) node spacing in km, latitude spacing in radians, longitude spacing in radians.</li> <li>Line 4: the grid origin radius (km), latitude (radians) and longitude (radians).</li> </ul> <p>Each node of the grid has a P-wave, S-wave and Vp/Vs ratio associated with it, which is specified in lines 5 onward.</p> <ul> <li>Line 5 - 55229: Value of Vp, Vs or Vp/Vs (given by file name) at each node.</li> </ul> <p>The values loop over the grid, with longitude varying first, and radius last. The first node is at the grid origin. The second node is at the grid origin, plus the grid spacing in longitude. This continues for the `nlon` longitude nodes. The `nlon+1`the point is then at the grid origin, plus the latitude spacing; and so on.</p> <p>If there are `nr` radial nodes, `nlat` latitude nodes and `nlon` longitude nodes, then the following pseudocode shows how to read lines 5 forwards using the imaginary function `readline`, which reads a single real value from a plain text file and moves to the next line:</p> <p>```</p> <p># Comment: have already read the first four lines</p> <p>for ir in 1:nr:</p> <p>for ilat in 1:nlat:</p> <p>for ilon in 1:nlon:</p> <p>grid[ir,ilat,ilon] = readline(file_handle)</p> <p>```</p> <p>The repository also contains the event catalogue, with hypocenter locations after relocation by the FMTOMO code:</p> <p>4. event_catalogue.csv</p> <p>The columns are depth in km (negative values indicate depths below sea level, positive values indicate depths above sea level), latitude in degrees, longitude in degrees, depth error (km), latitude error (km), longitude error (km). </p>
Simulation dataset to benchmark 3D force inference methods
<p>Dataset of 47 artificial images (.tif) and corresponding segmentation masks (.tif), generated from simulations of foam-like cell structures (early embryos) of various cell numbers (2 to 11), cell sizes and interfacial tensions.<br> The ground truth simulation tensions and pressures to be inferred are provided as Numpy arrays (.npy).</p> <p>This dataset was used to benchmark a method to infer cellular forces in 3D from microscopy images of multicellular contours, that is available on <a href="https://github.com/VirtualEmbryo/foambryo">https://github.com/VirtualEmbryo/foambryo</a>.<br> Non-manifold multimaterial meshes corresponding to artificial microscopy images are also provided as binary files (.rec) and may be opened with our delaunay-watershed Python code, available on <a href="https://github.com/VirtualEmbryo/delaunay-watershed">https://github.com/VirtualEmbryo/delaunay-watershed</a>.</p> <p><strong>Credits, contact, citations</strong><br> If you use this dataset, please cite the published version of the following preprint: <br> <em>Ichbiah, S., Delbary, F., McDougall, A., Dumollard, R., & Turlier, H. (2023). Embryo mechanics cartography: inference of 3D force atlases from fluorescence microscopy. bioRxiv, 2023-04. </em><a href="https://doi.org/10.1101/2023.04.12.536641">https://doi.org/10.1101/2023.04.12.536641</a><br> <br> We hope that this dataset may be useful to benchmark future 3D force inference methods.<br> If you have any question on this dataset, please contact <a href="mailto:herve.turlier@college-de-france.fr?subject=%5BZenodo%5D%203D%20tension%20inference%20benchmark%20dataset">Hervé Turlier</a>.</p> <p><strong>License</strong><br> Copyright (c) 2023 Turlier Lab - <a href="https://www.turlierlab.com/">https://www.turlierlab.com/</a><br> This dataset is licensed under the <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
Mechanism for the Uplift of Gongga Shan in the Southeastern Tibetan Plateau Constrained by 3D Magnetotelluric Data
<p>The *.data, *.rho, and *.zip files are associated with a paper titled 'Mechanism for the Uplift of Gongga Shan in the Southeastern Tibetan Plateau Constrained by 3D Magnetotelluric Data' in Geophysical Research Letters published in 2022. On the basis of this data and inversion model, we addressed that the rapid uplift of the Gongga Shan massif likely occurred by the underthrusting of the Yangtze Craton. More details about the electrical resistivity model and its interpretations can be found in our journal paper. </p> <p>All the resulting files from ModEM are included in the 'ModEM_Inversion_Results.zip'. All the figures in the paper and supplementary are included in the 'GRL_All_Figures.zip' and 'Figure_S5_All_Responses.zip'.</p> <p>The resulting model and data output in ModEM format can be found in .rho and .data files. The ModEM is an open-source code package for MT 3D inversion, which is provided by Gary Egbert, Anna Kelbert, and Naser Meqbel and can be found on this website: <a href="https://sites.google.com/site/modularem/download">https://sites.google.com/site/modularem/download</a>. </p> <p>Please note that the 3D resistivity model files in general format includes four columns -- longitude, latitude, depth, and resistivity, the one who wants to plot the model via GMT, MATLAB, Surface, etc., can find these files in 'Gongga_3D_Resistivity_Model_Files.zip'. In this zip, you will find the resistivity model of each horizontal slice of different depths and a file including all the slices. A MATLAB script called 'see_slice.m' is included in the folder which can help to quickly view these resistivity slices.</p>
Images of 3D digitisation of articles of traditional attire
<p>These files are images digitisations of traditional, handcrafted dresses, shoes, handbags, and fabrics. These items are manufactured during the 21st century following traditional manufacturing methods and utilising designs and motifs from Greek antiquities. The 3D models were photogrammetrically captured. These images correspond to the 3D models in <a href="http://doi.org/10.5281/zenodo.8098709">https://doi.org/10.5281/zenodo.8098709</a></p>
Thumbnails of 3D digitisation of articles of traditional attire
<p>These files are thumbnails of 3D digitisations of traditional, handcrafted dresses, shoes, handbags, and fabrics. These items are manufactured during the 21st century following traditional manufacturing methods and utilising designs and motifs from Greek antiquities. The 3D models were photogrammetrically captured.</p>
3D-rhi-synth-2000- Synthetic Rhinophyma Visual Dataset
<p>In the real world, only a handful of data is available for the Rhinophyma skin condition, typically numbering in the hundreds. This repository contains a Synthetic Dataset of Rhinophyma, generated through 3D head models of one male and one female. The purpose of this data generation is to address the data scarcity of the Rhinophyma skin condition within the medical visual data and computer vision community. By generating such data, we aim to bridge the gap in data scarcity for this disease condition, as well as introduce a proof-of-concept methodology for generating synthetic data for specialized disease conditions.</p> <p>The <code>highlight</code> folder 'highlight_female_male_rendered' provides a glimpse of the entire dataset. The file <code>'2000_deformations.npy</code>' contains the 2000 values of deformations applied during rendering.</p> <p>The dataset is divided into two main folders: 'female_rendered' and 'male_rendered'. Within each of these folders, there are three subfolders: 'configu', 'images', and 'points.</p> <p>1. 'configu': This subfolder contains `.json` files with configuration details for each model. The files include various parameters, such as:<br> - "total_num_cameras": the total number of cameras.<br> - "active_camera_name": the name of the active camera.<br> - "camera_focal_len": the camera's focal length.<br> - "camera_loc": the camera's location.<br> - "camera_rot": the camera's rotation.<br> - "nose_deformation_severity": a measure of the severity of nose deformation.<br> - "label": the label for the model (e.g., "Severe").<br> - "nose_variants": additional details about nose variants.</p> <p>2. 'images': This subfolder contains the rendered images in resolution 960x540. They are named according to the following convention e.g.'Nose_Deformation_Severity_0_2.716669764843742_Camera_00001', with specific details related to the deformation severity and camera number. There are images for 10 different cameras.</p> <p>3. 'points': This subfolder contains polygon files corresponding to each model. These files represent the deformations applied to the models during rendering.</p> <p>|-- Dataset Root<br> |-- female_rendered<br> | |-- configu<br> | | |-- Nose_Deformation_Severity_0_2.212930927821943_Camera_00001.json<br> | | |-- Nose_Deformation_Severity_0_2.212930927821943_Camera_00002.json<br> | | |-- ...<br> | |<br> | |-- images<br> | | |-- Nose_Deformation_Severity_0_2.212930927821943_Camera_00001.png<br> | | |-- Nose_Deformation_Severity_0_2.212930927821943_Camera_00002.png<br> | | |-- ...<br> | |<br> | |-- points<br> | | |-- Nose_Deformation_Severity_0_2.212930927821943.ply<br> | | |-- ...<br> | <br> |-- male_rendered<br> |-- configu<br> | |-- Nose_Deformation_Severity_0_2.716669764843742_Camera_00001.json<br> | |-- Nose_Deformation_Severity_0_2.716669764843742_Camera_00002.json<br> | |-- ...<br> |<br> |-- images<br> | |-- Nose_Deformation_Severity_0_2.716669764843742_Camera_00001.jpg<br> | |-- Nose_Deformation_Severity_0_2.716669764843742_Camera_00002.jpg<br> | |-- ...<br> |<br> |-- points<br> |-- Nose_Deformation_Severity_0_2.716669764843742.ply<br> |-- ...</p> <p>Together, these folders and files comprise a dataset designed to represent and analyze the Rhinophyma condition in both male and female 3D head models that we have created. These 3D models will be made available upon a genuine request to the authors of this dataset.</p>
Whole-brain background-suppressed pCASL MRI with 1D-accelerated 3D RARE Stack-Of-Spirals Readout- Dataset 2
Open the record for dataset details and reuse information.
Whole-brain background-suppressed pCASL MRI with 1D-accelerated 3D RARE Stack-Of-Spirals Readout- Dataset 3
Open the record for dataset details and reuse information.
Sila National Park - 3D Point cloud data
<p>This dataset contains 3 types of data.</p> <ul> <li>GPS data (the ones starting with <em>"GPS"</em>) of sampling plot centers collected with a Trimble GPS and post processed to ensure positioning errors lower than 2 meters.</li> <li>TLS data, (the ones starting with <em>"ID_"</em>): such data were collected in the end of August 2019 with a mobile terrestrial laser scanner (mobile ZEB TLS) in a squared area of approximatively 30x30m. Data have been normalized using TreeLS package in R.</li> <li>ALS data collected in the end of July 2019. For the entire study area, we upload 2 different ALS data: "<em>merged.las</em>" is the original point cloud; "<em>myLas_norm_lt22.las</em>" is the normalised point cloud, cut at 22 meters from the ground in order to perform specific analysis (i.e. paper under submission).</li> </ul> <p>Data collection was founded by the <em>AGRIDIGIT Selvicoltura</em> project.</p>
VR-Together Pilot 1: 3D Animated Character Meshes
<p>This dataset contains the 3D Animated Characters as used for the Pilot 1 experience of the VR-Together project. The animated characters come as a set of FBX files with associated diffuse and normal maps. </p>
3D scans of two types of railway ballast including shape analysis information
<p>This data set contains 3D scanner data of two types of railway ballast “Calcite” (stems from Croatia) and “Kieselkalk”, also known as Helvetic Siliceous Limestone, (stems from Switzerland).<br> From each type of ballast 25 stones are scanned. The files are provided in .ply format.<br> For the scanned meshes several shape descriptors are provided: elongation, flatness, sphericity, convexity index.<br> Additional to the 3D scans, both simplified and rounded versions of the meshes are included.<br> For these meshes information on three different angularity indices are available.<br> The scanned ballast types are the same, as previously investigated in uniaxial compression tests and direct shear tests:<br> Suhr, Bettina, & Six, Klaus. (2018).<br> "Compression tests and direct shear test of two types of railway ballast [Data set]"<br> Zenodo. http://doi.org/10.5281/zenodo.1423742</p> <p> </p> <p>A detailed shape analysis of the results is conducted in:<br> Bettina Suhr, William A. Skipper, Roger Lewis, and Klaus Six<br> "Shape analysis of railway ballast stones: curvature-based calculation of particle angularity"<br> <em>Scientific Reports, </em><strong>2020</strong><em>, 10</em>, 6045<br> DOI: https://doi.org/10.1038/s41598-020-62827-w</p> <p>A summary of several shape descriptors can be found in:<br> B. Suhr and K. Six:<br> "Simple particle shapes for DEM simulations of railway ballast -- influence of shape descriptors on packing behaviour"<br> Granular Matter, <strong>2020</strong><em>, 22</em><br> DOI: https://doi.org/10.1007/s10035-020-1009-0</p> <p><br> This data set is organised as follows:<br> 1_ScanMeshesCleaned<br> scanned meshes:<br> K_1.ply - K_25.ply Calcite (German: Kalzit)<br> KK_1.ply - KK_25.ply Kieselkalk<br> 2_CSE1 <br> simplifications of the scanned meshes, little simplifications, used in the detailed shape analysis<br> CSE1_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 3_CSE2 <br> simplifications of the scanned meshes, more simplified, used in the detailed shape analysis<br> CSE2_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 4_CSE3 <br> simplifications of the scanned meshes, even more simplified, used in the detailed shape analysis<br> CSE3_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 5_CSE4 <br> simplifications of the scanned meshes, most simplified, used in the detailed shape analysis<br> CSE4_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 6_RoundedMeshes<br> artificially rounded versions of the scanned ballast meshes, used in the detailed shape analysis<br> RoundedMeshes_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> 7_TestBodies <br> meshes of artificial test bodies, constructed for testing different angularity indices in the detailed shape analysis<br> TestBodies_AngInfo.csv: contains values of three different angularity indices used in the detailed shape analysis<br> scanMeshesInfo.csv: summary of several shape descriptors of the scanned meshes<br> README.txt </p> <p><br> Check the README.txt file for more information on the technical aspects of scanning.</p> <p> </p>
Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations
<p>This repository provides supplementary material to our paper: <a href="https://doi.org/10.1088/1361-651X/ab7f8c">https://doi.org/10.1088/1361-651X/ab7f8c</a></p> <p><strong>DAMASKPhenoPowerLaw75x75x75TestCase.zip</strong><br> An exemplary DAMASK simulation and corresponding output, generated from DAMASK v2.0.3. We used this to debug more productively the implementation of the post-processing tools. Furthermore we employed this simulation in the paper to identify why the graph clustering grain reconstruction method in many cases fuses neighboring grains in similar orientation.</p> <p><strong>DAMASKPhenoPowerLaw256x256x256ProductionRun.zip</strong><br> All input to run the DAMASK simulation that we discussed in the paper.</p> <p><strong>DAMASKPDTSettings256x256x256ProductionRun.zip</strong><br> All damaskpdt settings files to execute the individual post-processing studies of the paper.</p> <p><strong>DAMASKPDTSlurmSubmissionScripts256x256x256ProductionRun.zip</strong><br> All SLURM scripts we used to execute the compilation of damaskpdt and post-processing on TALOS.</p> <p><strong>DAMASKPDTSlurmLogs256x256x256ProductionRun.zip</strong><br> All logs from the SLURM job management system from the individual post-processing runs.</p> <p><strong>DAMASKPDTSourceCode_USedForAnalyticalDistanceToVoronoiCellFacets.zip</strong><br> The source code to the tool we developed during the revision process of our paper to verify the methods<br> via computing analytically exact distances to the facets of the Poisson-Voronoi tessellation from the<br> DAMASK microstructure instantiation.<br> <br> <strong>DAMASKPDTSourceCode_Production.zip</strong><br> The source code we used to post-process all results from the DAMASK simulations.</p> <p><strong>GitHub repository:</strong><br> https://github.com/mkuehbach/damaskpdt</p>
3D motion of flexible ferromagnetic filaments under rotating magnetic field
<p>This repository contains experimental data and numerical results related to the publication: A. Zaben, G. Kitenbergs, A. Cēbers (2020), 3D motion of flexible ferromagnetic filaments under rotating magnetic field. Soft Matter, <a href="https://doi.org/10.1039/D0SM00403K">https://doi.org/10.1039/D0SM00403K</a> / <a href="https://arxiv.org/abs/2003.03737">https://arxiv.org/abs/2003.03737</a>.</p> <p>Figs_data.xlsx contains the data presented in the figures. Experimental_Data.rar contains experimental images used to obtain the results for Fig. 3 and 9. The files are named with the operating frequency, field strength and filament length. Numerical.rar contains numerical results used in Fig.6, 8 and 9. The files are named with Cm values. The results are in .dat files named with Cm values followed by wt (wend_cm_wt). The first column is for time(t) followed by x,y,z values of filament tips. </p> <p> </p>
Intuitive 3D Control of a Quadrotor in User Proximity with Pointing Gestures (Dataset)
<p>The accompanying dataset and code for the ICRA 2020 publication:</p> <p>B. Gromov, J. Guzzi, L. Gambardella, and A. Giusti, "Intuitive 3D Control of a Quadrotor in User Proximity with Pointing Gestures," in 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020.</p> <p>The dataset contains a log of user actions and states of the system collected during the user study. The subjects had to fly a nano quadcopter (Bitcraze Crazyflie 2.0) between three targets placed at different heights by using a conventional joystick interface (Logitech F710) and pointing. The pointing is reconstructed using an inertial sensor (mbientlab MetaWearR+) placed on the user's wrist.</p>
York Archaeological Trust 1981.7.33205.SY102 3D Archaeological Use-wear Raw Measurements
<p>This dataset contains the 51 raw measurements/scans taken before the mesh creation step for object 1981.7.33205.SY102 in the collections of York Archaeological Trust.</p> <p>1981.7.33205.SY102 is a Mortarium in standard Ebor oxidised fabric (M3) (Monaghan 1997, 1028) from Coppergate.</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>This dataset consists of 105,524,551 points - additional metadata included in associated spreadsheet.</p>
York Archaeological Trust 1989.21.2205 3D Archaeological Use-wear Raw Measurements
<p>This dataset contains the 27 raw measurements/scans taken before the mesh creation step for object 1989.21.2205 in the collections of York Archaeological Trust.</p> <p>1989.21.2205 is a small Samian ware cup fragment. Close analysis was not undertaken at time of data capture.</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>This dataset consists of 14,583,615 points - additional metadata included in associated spreadsheet.</p>
York Archaeological Trust 1989.21.2393.LL42 Archaeological 3D Use-wear Raw Measurements
<p>This dataset contains the 18 raw measurements/scans taken before the mesh creation step for object 1989.21.2393.LL42 in the collections of York Archaeological Trust.</p> <p>1989.21.2393.LL42 is a dish of Ebor Ware 2 (Monaghan 1997, 875) from 34-41 Blossom Street (Lion and Lamb).</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>This dataset consists of 26,065,666 points - additional metadata included in associated spreadsheet.</p>
Hunterian GLAHM:B.1951.3137 3D Archaeological Use-wear Raw Measurements
<p>This dataset contains the 32 raw measurements/scans taken before the mesh creation step for object GLAHM:B.1951.3137 in the Hunterian's Archaeology collection in Glasgow, Scotland.</p> <p>GLAHM:B.1951.3137 is a two-handled bowl (Ionic Cup B2) from Greece: <a href="http://collections.gla.ac.uk/#/details/ecatalogue/118198">http://collections.gla.ac.uk/#/details/ecatalogue/118198</a> </p> <p>Comparanda: Gravisca 4, 164-165, 323, tav. 9, type IV/1.</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres.</p> <p>This dataset consists of 45,140,392 points - additional metadata included in associated spreadsheet.</p> <p>Gravisca 4: S. Boldrini, <em>Le Ceramiche Ioniche</em>, Bari 1994.</p>
Hunterian GLAHM:D.160 3D Archaeological Use-wear Raw Measurements
<p>This dataset contains the 40 raw measurements/scans taken before the mesh creation step for object GLAHM:D.160 in the Hunterian's Archaeology collection in Glasgow, Scotland.</p> <p>GLAHM:D.160 is a Black Gloss plate: <a href="http://collections.gla.ac.uk/#/details/ecatalogue/117080">http://collections.gla.ac.uk/#/details/ecatalogue/117080</a></p> <p>Edited in CVA Glasgow, vol. 18, 49, pl. 60.7. Morel 1981, 140, pl. 31, 2141b 1.</p> <p>Data was captured using a Zeiss Comet L3D 2 5M at 100 FOV to enable the analysis of use-wear on archaeological objects. The model is scaled in millimetres. </p> <p>This dataset consists of 55,761,532 points - additional metadata included in associated spreadsheet.</p> <p>CVA Glasgow: E. Moginard 1997, <em>Corpus Vasorum Antiquorum, Great Britain, Fascicule 18, Glasgow</em>, Oxford 1997.-wear on archaeological objects.</p> <p>Morel 1981: J.P. Morel, <em>Céramique campanienne: les formes</em>, Rome 1981.</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.