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288 results for “Digital Modeling”
Figure 11.8 in Rex, sit: digital modeling of Tyrannosaurus rex at rest
Figure 11.8. When resting on the pubis, the forelimbs are near ground level. They are brought closer to ground level as a consequence of iniatating a standing movement from this resting pose. A modest tipping of the body, by pivoting about the curved anteroventral surface of the prepubis, would have shifted the overall COM anteriorly and returned the point of balance to between the hindfeet. In the process, the forelimbs would have been available to assist in stabilizing, if not actively contributing toward raising, the body, by pushing against the ground.
Data from: A method for mapping morphological convergence on three-dimensional digital models: the case of the mammalian saber-tooth
<p>Morphological convergence can be assessed through a variety of statistical methods. None of the methods proposed to date enable the visualization of convergence. All are based on the assumption that the phenotypes either converge, or do not. However, between species, morphologically similar regions of a larger structure may behave differently. Previous approaches do not identify these regions within the larger structures or quantify the degree to which they may contribute to overall convergence. Here we introduce a new method to chart patterns of convergence on three-dimensional models, deployed with the R function conv.map. The convergence between pairs of models is mapped onto them to visualize and quantify the morphological convergence. We applied conv.map to a well-known case study, the saber-tooth morphotype which has evolved independently among distinct mammalian clades, from placentals to metatherians. Although previous authors have concluded that saber-tooths kill using a stabbing 'bite' to the neck, others have presented different interpretations for specific taxa, including the iconic Smilodon and Thylacosmilus. Our objective was to identify any shared morphological features among the saber-tooths that may underpin similar killing behaviours. From a sample of 49 placental and metatherian carnivores, we found stronger convergence among saber-tooths than for any other taxa. We found that the morphological convergence is most apparent in the rostral and posterior parts of the cranium. The extent of this convergence implies similarity in function among these phylogenetically distant species. In our view this function is most likely the killing of relatively large prey by a stabbing bite.</p>
Enhancing Credit Risk Assessment in Digital Finance through a Hybrid Deep Learning Model Integrated with Blockchain on the Edge of Things F
<p><span>This work proposes a credit risk assessment model using deep learning models such as self-attention generative adversarial networks (SA-GAN) and deep multi-layer perceptron (DMLP). Blockchain is used to improve the security aspects of the model by employing Brakerski-Gentry-Vaikuntanathan (BKV) encryption technique. Further, the proposed system is implemented in Edge-of-things network and communications are enabled via LoRaWAN server.</span></p>
Bathymetric and surface digital model of the river-floodplain system corresponding to the Duero river reach between Toro and Zamora (Castilla y León).
<p>Digitally edited digital model to represent correctly and with hydraulic criteria the bridges, weirs, dips, roads and walls present in the area.</p>
Bathymetric and terrain digital model (representative of the geomorphological reference condition) of the river-floodplain system corresponding to the Douro reach between Toro and Zamora (Castilla y León).
<p>Digitally edited digital model to represent the previous geomorphological situation in the Toro-Zamora section.</p>
Bathymetric and surface digital model of the urban reach of the Douro River through Zamora (Castilla y León)
<p>The RAR file is composed of all files related to the Digital Bathymetric Model (DBM) and the Digital Surface Model (DSM). Among these files there are: the original rasters files; the files digitally modified to correctly represent all the hydraulic characteristics of the Duero River and those urban characteristics of Zamora; the rasters that represent the error made in both models, calculated by geostatistics; the files related to the geostatistics process. Also, it also includes the integration of both models (DBM plus DSM) to form a High Resolution Urban Digital Model (HRDUM) of the Duero River in it passes through Zamora.</p>
SPOT5 (2007), ASTER (2016) and SPOT7 (2018) Digital Elevation Models of Little Kluane Glacier (Yukon Territory)
<p>===========================</p> <p>Authors</p> <p>Etienne BERTHIER</p> <p>LEGOS, Université de Toulouse, CNES, CNRS, IRD, UPS, 31400 Toulouse, France,</p> <p>===========================<br> 1. Collection</p> <p>This collection contains three digital elevation models (DEMs) of "Little Kluane" Glacier (Yukon Territory, Canada) with an horizontal grid spacing of 30 m<br> * SPOT5, 13 September 2007<br> * ASTER, 30 September 2017<br> * SPOT7, 1 October 2018</p> <p>The original SPOT5 DEM was obtained from the SPIRIT project (Korona et al., 2009)<br> ASTER and SPOT7 DEMs have been derived using the Ames Stereo Pipeline (Beyer et al., 2018) from stereo images using the set of correlation of parameters in Deschamps-Berger et al. (2020)</p> <p>All DEMs have been coregistered and bias-corrected to the Copernicus 30 m global DEM following the methods of Berthier & Brun (2019)</p> <p><br> ===========================</p> <p>2. Dataset Acknowledgement</p> <p>* SPOT5 data were obtained thanks to funding from CNES during the fourth international polar year.<br> * SPOT7 data were obtained thanks to DINAMIS Project, for “Dispositif Institutionnel National d’Approvisionnement Mutualisé en Imagerie Satellitaire”, a French platform that acquires and distributes very high resolution Earth satellite imagery for French and foreign institutional users under specific subscription conditions.<br> * ASTER data were provided by NASA (U.S.) and METI (Japan).</p> <p>===========================</p> <p>3. Dataset Attribution</p> <p>This dataset is licensed under a Creative Commons CC BY-NC 4.0 International License (Attribution-NonCommercial).</p> <p><br> ===========================</p> <p>4. Related publication</p> <p>This dataset has been generated for and used in a publication to be submitted to the Journal of Glaciology :<br> Morin, A., Flowers, G. E., Nolan, A., Brinkerhoff, D. J., and Berthier, E.: Exploiting high-slip ?ow regimes to improve bed inference, submitted.<br> </p> <p>===========================</p> <p>5. Collection Location</p> <p>Yukon Territory of Canada<br> Bounding box: WGS 84 / UTM zone 7N</p> <p><north>6771585.730</north><br> <south>6739845.730</south><br> <east>564391.250</east><br> <west>601261.250</west></p> <p><br> ===========================</p> <p>References</p> <p>Berthier, E. and Brun, F.: Karakoram geodetic glacier mass balances between 2008 and 2016: persistence of the anomaly and influence of a large rock avalanche on Siachen Glacier, J Glaciol, 65, 494–507, https://doi.org/10.1017/jog.2019.32, 2019.<br> Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline: NASA’s Open Source Software for Deriving and Processing Terrain Data, Earth and Space Science, 5, 537–548, https://doi.org/10.1029/2018EA000409, 2018.<br> Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M.: Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data, The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, 2020.<br> Korona, J., Berthier, E., Bernard, M., Remy, F., and Thouvenot, E.: SPIRIT. SPOT 5 stereoscopic survey of Polar Ice: Reference Images and Topographies during the fourth International Polar Year (2007-2009), ISPRS J. Photogramm., 64, 204–212, https://doi.org/10.1016/j.isprsjprs.2008.10.005, 2009.</p> <p> </p>
NOAA Coastwatch Satellite Course (Set up an Application Model of Digital Satellite Data Simulation by Video Graphic Technology of Oceanic data Remotely Sensed of algerian coast)
<p>The goal of the course is to familiarize NOAA/university researchers, Sea Grant professionals and agency/org. partners with different types of ocean satellite data, different tools, and teach participants how to use satellite data in their own research/outreach using their choice of software (NOAA ,2023)</p>
Replication materials for: Are Large Language Models a Threat to Digital Public Goods? Evidence from Activity on Stack Overflow
<p>These are the replication materials for the paper:<br>Are Large Language Models a Threat to Digital Public Goods? Evidence from Activity on Stack Overflow<br>By: Maria del Rio-Chanona, Nadzeya Laurentsyeva, and Johannes Wachs. <br>Preprint: https://arxiv.org/abs/2307.07367<br>Under Revision for PNAS Nexus</p> <p> </p>
A digital twin model of urban utility tunnels and its application:One-dimensional comparison verification
<div> <div> <div> <div> <div> </div> 重点词汇</div> <div> <div>93<em>/</em>5000</div> </div> </div> </div> </div> <div> </div> <div> <div> <div> <div> <div> <div>通用场景</div> <div> </div> </div> </div> </div> </div> <div> <div> <p><span>论文《城市综合管廊数字孪生模型及其应用》中一维综合管廊中天然气浓度分布快速预测模型结果对比</span></p> </div> </div> </div>
Modeling Languages for Digital Twins - A Survey Among the German Automotive Industry
<p>This repository contains the replication package for the paper _Modeling Languages for Digital Twins: A Survey Among the German Automotive Industry_ by Jérôme Pfeiffer, Dominik Fuchß, Thomas Kühn, Robin Liebhart, Dirk Neumann, Christer Neimöck, Christian Seiler, Anne Koziolek, and Andreas Wortmann. <br>The paper has been submitted to the practice track of [MODELS 2024](https://conf.researchr.org/track/models-2024/models-2024-technical-track#Practice-Track).</p> <h3>Data</h3> <p>This replication package contains all information from the survey:<br>- `results.csv`: A csv version of all data exported from LimeSurvey (German). Personal information from the participants has been removed. This file can be imported to reproduce the extraction results described in our paper.<br>- `survey_german.pdf`: The pdf version of the original survey in German. <br>- `survey_german.md`: A markdown version of the original survey in German. <br>- `survey_english.md`: A markdown version of the survey translated into English. </p> <h3>Selection of participants and distribution</h3> <p>With both versions, the survey can be executed again with a different target audience in English or German. In our case we wanted to reach as much participants from diverse work areas as possible, where we invited the participants by email via an internal mailing list of 189 members of the SofDCar project. To improve the response rate, we implemented two deadline extensions from the initial one-month-long time frame with 2 weeks of additional response time. Together with the deadline extension, we sent a mail to inform and remind the members of the consortium of the survey.</p> <h3>Data extraction</h3> <p>In total, we had 96 participants, of which 43 completed the questionnaire. For incomplete survey responses, we took only the available answers and did not include the missing answers in our data analysis. For data analysis we utilized the commercial Tool IBM SPSS and custom python scripts.</p> <h2>Research Questions </h2> <p>- RQ1: How is the DT understood in the automotive industry?<br> - RQ1.1: For which phases of automotive development are DTs<br>important?<br> - RQ1.2: What are desired properties of DTs?<br> - RQ1.3: What are desired purposes of using DTs?<br> - RQ1.4: How do these purposes change in relation to different phases of automotive development?<br>- RQ2: Which modeling languages and modeling tools are currently employed in the automotive industry?<br> - RQ2.1: Which kinds of models are important during automotive development?<br> - RQ2.2: How important are which models in the phases of automotive development?<br> - RQ2.3: Which tools are used to create and maintain these models?</p>
ICAERUS UC5 - RURAL LOGISTICS sample dataset from STRUMICA, NORTH MACEDONIA. Photogrammetry Orthomosaic and Digital Surface Model (DSM).
<table> <tbody> <tr> <td><strong>FOLDER NAME </strong></td> <td><strong>DESCRIPTION</strong></td> </tr> <tr> <td>3D_Point_cloud</td> <td>Photogrammetry 3D point cloud in LAS format</td> </tr> <tr> <td>Digital_Surface_Model</td> <td>2.5D Digital surface elevation model in GeoJPG resampled by x5</td> </tr> <tr> <td>Orthomosaic-GeoJPG</td> <td>Orthomosaic in GeoJPG format resampled by x5</td> </tr> <tr> <td>Orthomosaic-KMZ_tiles</td> <td>Orthomosaic in GeoJPG format in Google KMZ tiles</td> </tr> <tr> <td>Orthomosaic-OpenStreetMaps</td> <td>Orthomosaic in OpenStreetMasps format</td> </tr> <tr> <td>Raw_Images</td> <td>Initial Images captured by DJI Mavic 3E drone</td> </tr> </tbody> </table>
Supplementary material 2 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
SV1: EDOF imaging : Explanation note: This video demonstrates the effect of the registered EDOF-calculation.
Supplementary material 1 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
Technical information : Explanation note: Detailed technical information and additional theoretical background.
Supplementary material 3 from: Ströbel B, Schmelzle S, Blüthgen N, Heethoff M (2018) An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. ZooKeys 759: 1-27. https://doi.org/10.3897/zookeys.759.24584
SV2: Illustrative examples : Explanation note: Illustrative examples of insects and snail shell models generated with DISC3D.
Proposal of Attributes for the Data Model of a European Digital Building Renovation Logbook
<p>This document serves as supplementary material to the paper "Envisaging a European Digital Building Renovation Logbook: Proposal of a Data Model", by Marta Gómez-Gil, Sara Karami, José-Paulo de Almeida, Alberto Cardoso, Almudena Espinosa-Fernández and Belinda López-Mesa. Its purpose is to complement and expand upon the methodology outlined in the paper, with a specific focus on the identification of attributes for the development of a data model for the European Digital Building Logbook, focused on renovation.</p>
Dataset for generation of LOD4 models for buildings towards the automated 3D modeling of BIMs and digital twins
<div> <div>This repository contains the dataset used for the automated image-based generation of LOD4 models for buildings, along with the corresponding results. The methodology utilizing this dataset was presented in the paper "Generation of LOD4 models for buildings towards the automated 3D modeling of BIMs and digital twins" by Pantoja-Rosero et., al. (2024) (https://doi.org/10.1016/j.autcon.2024.105822).</div> </div>
ICAERUS UC5 - RURAL LOGISTICS sample dataset from STRUMICA, NORTH MACEDONIA. Photogrammetry Orthomosaic and Digital Surface Model (DSM).
<table> <tbody> <tr> <td><strong>FOLDER NAME </strong></td> <td><strong>DESCRIPTION</strong></td> </tr> <tr> <td>3D_Point_cloud</td> <td>Photogrammetry 3D point cloud in LAS format</td> </tr> <tr> <td>Digital_Surface_Model</td> <td>2.5D Digital surface elevation model in GeoJPG resampled by x5</td> </tr> <tr> <td>Orthomosaic-GeoJPG</td> <td>Orthomosaic in GeoJPG format resampled by x5</td> </tr> <tr> <td>Orthomosaic-KMZ_tiles</td> <td>Orthomosaic in GeoJPG format in Google KMZ tiles</td> </tr> <tr> <td>Orthomosaic-OpenStreetMaps</td> <td>Orthomosaic in OpenStreetMasps format</td> </tr> <tr> <td>Raw_Images</td> <td>Initial Images captured by DJI Mavic 3E drone</td> </tr> </tbody> </table>
Digital reconstruction model of the Philippine Sea region since the Eocene
<p>Insight into the evolution of Philippine Sea-South China Sea plate motions helps reveal the driving mechanisms of the long-term tectonic complexity in Southeast Asia. Here we present a new plate reconstruction model of this region characterized by back-arc extension and subduction since the Eocene by integrating the latest local geological and seismic data. </p>
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </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.