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629 results for “Clay”
A collection of 131 CT datasets of pieces of modeling clay containing stones - Part 2 of 5
<p><strong>Summary</strong></p> <p>This submission contains a collection of 131 CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022]. This submission consists of three parts in total.</p> <p> </p> <p><strong>Parts</strong></p> <p>The 131 CT scans are divided into 5 separate submissions:<br> Part 1 of 5<em>:</em> 001-028: <a href="https://doi.org/10.5281/zenodo.5866228">10.5281/zenodo.5866228</a><br> Part 2 of 5: 029-056: <a href="https://doi.org/10.5281/zenodo.5866322">10.5281/zenodo.5866322</a> <strong>(this upload)</strong><br> Part 3 of 5: 057-084: <a href="https://doi.org/10.5281/zenodo.5866363">10.5281/zenodo.5866363</a><br> Part 4 of 5: 085-111: <a href="https://doi.org/10.5281/zenodo.5866365">10.5281/zenodo.5866365</a><br> Part 5 of 5: 112-131: <a href="https://doi.org/10.5281/zenodo.5866367">10.5281/zenodo.5866367</a> </p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></p> <p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p> <p><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections are unprocessed files, except that a binning been applied FleX-ray lab software. The resulting image sizes are 956x760. Flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images) are included with each object. All images are stored in .tif format. The data for samples with 0-3 stones are contained in parts 1 to 4, while the samples with 5-8 stones constitute part 5. The size of the completely unpacked dataset (all 5 parts) is ca. 343.5 GB.</p> <p>The processed data (with generated ground truth) is made available in another (smaller) submission for object detection purposes: <a href="https://zenodo.org/record/5681008">https://zenodo.org/record/5681008</a></p> <p> </p> <p><strong>Additional Links</strong><br> These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow to enable deep learning for X-ray based foreign object detection", 2022 (submitted)<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p>If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.</p>
A collection of 131 CT datasets of pieces of modeling clay containing stones - Part 1 of 5
<p><strong>Summary</strong></p> <p>This submission contains a collection of 131 CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022]. This submission consists of three parts in total.</p> <p> </p> <p><strong>Parts</strong></p> <p>The 131 CT scans are divided into 5 separate submissions:<br> Part 1 of 5<em>:</em> 001-028: <a href="https://doi.org/10.5281/zenodo.5866228">10.5281/zenodo.5866228</a><strong> (this upload)</strong><br> Part 2 of 5: 029-056: <a href="https://doi.org/10.5281/zenodo.5866322">10.5281/zenodo.5866322</a><br> Part 3 of 5: 057-084: <a href="https://doi.org/10.5281/zenodo.5866363">10.5281/zenodo.5866363</a><br> Part 4 of 5: 085-111: <a href="https://doi.org/10.5281/zenodo.5866365">10.5281/zenodo.5866365</a><br> Part 5 of 5: 112-131: <a href="https://doi.org/10.5281/zenodo.5866367">10.5281/zenodo.5866367</a></p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></p> <p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p> <p><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections are unprocessed files, except that a binning been applied FleX-ray lab software. The resulting image sizes are 956x760. Flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images) are included with each object. All images are stored in .tif format. The data for samples with 0-3 stones are contained in parts 1 to 4, while the samples with 5-8 stones constitute part 5. The size of the completely unpacked dataset (all 5 parts) is ca. 343.5 GB.</p> <p>The processed data (with generated ground truth) is made available in another (smaller) submission for object detection purposes: <a href="https://zenodo.org/record/5681008">https://zenodo.org/record/5681008</a></p> <p> </p> <p><strong>Additional Links</strong><br> These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow to enable deep learning for X-ray based foreign object detection", 2022 (submitted)<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p>If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.</p>
A collection of 131 CT datasets of pieces of modeling clay containing stones - Part 3 of 5
<p><strong>Summary</strong></p> <p>This submission contains a collection of 131 CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022]. This submission consists of three parts in total.</p> <p> </p> <p><strong>Parts</strong></p> <p>The 131 CT scans are divided into 5 separate submissions:<br> Part 1 of 5<em>:</em> 001-028: <a href="https://doi.org/10.5281/zenodo.5866228">10.5281/zenodo.5866228</a><br> Part 2 of 5: 029-056: <a href="https://doi.org/10.5281/zenodo.5866322">10.5281/zenodo.5866322</a><br> Part 3 of 5: 057-084: <a href="https://doi.org/10.5281/zenodo.5866363">10.5281/zenodo.5866363</a> <strong>(this upload)</strong><br> Part 4 of 5: 085-111: <a href="https://doi.org/10.5281/zenodo.5866365">10.5281/zenodo.5866365</a><br> Part 5 of 5: 112-131: <a href="https://doi.org/10.5281/zenodo.5866367">10.5281/zenodo.5866367</a></p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample information</em></p> <p>The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample.</p> <p><em>Apparatus</em></p> <p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p> <p><em>Scanning setup</em></p> <p>For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection.</p> <p><em>Experimental plan</em></p> <p>This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022].</p> <p><em>Technical details</em></p> <p>All projections are unprocessed files, except that a binning been applied FleX-ray lab software. The resulting image sizes are 956x760. Flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images) are included with each object. All images are stored in .tif format. The data for samples with 0-3 stones are contained in parts 1 to 4, while the samples with 5-8 stones constitute part 5. The size of the completely unpacked dataset (all 5 parts) is ca. 343.5 GB.</p> <p>The processed data (with generated ground truth) is made available in another (smaller) submission for object detection purposes: <a href="https://zenodo.org/record/5681008">https://zenodo.org/record/5681008</a></p> <p> </p> <p><strong>Additional Links</strong><br> These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p> </p> <p><strong>Contact details</strong><br> zeegers [at] cwi [dot] nl</p> <p> </p> <p><strong>Acknowledgments</strong><br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p> <p><br> <strong>References</strong><br> [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow to enable deep learning for X-ray based foreign object detection", 2022 (submitted)<br> [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p>If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.</p>
Impact of green clay authigenesis on element sequestration in marine settings
<p>Supplementary Information to article "<strong>Impact of green clay authigenesis on element sequestration in marine settings</strong>", published in Nature Communications (Baldermann et al., 2022).</p>
Dataset for the paper: Computational framework for radionuclide migration assessment in clay rocks
<p>This dataset contains OpenGeoSys-6 simulations results and scripts in Jupyter-notebook format for reproducing and visualizing the results of the paper:</p> <p>Garibay-Rodriguez J, Chen C, Shao H, Bilke L, Kolditz O, Montoya V and Lu R (2022) Computational Framework for Radionuclide Migration Assessment in Clay Rocks. Front. Nucl. Eng. 1:919541. doi: 10.3389/fnuen.2022.919541</p> <p>Details about the usage of the scripts can be found in the OpenGeoSys-6 user guide: https://www.opengeosys.org/</p>
FOD CT Data: air pockets in avocado and stone in modelling clay
<p><strong>Summary</strong></p> <p>This submission contains X-ray CT data of avocado fruits and pieces of modelling clay containing pebble stones.<br> Data for every object include binned pre-processed projections and volume segmentations.<br> These datasets can be used for training and testing deep learning methods for foreign object detection.</p> <p>The data is made available as a part of the paper "CT-based data generation for foreign object detection on a single X-ray projection".</p> <p><strong>Data acquisition</strong></p> <p>A majority of raw data for modeling clay (excluding 10 samples without pebble stones in the Test subset) is taken from the dataset<br> "A collection of 131 CT datasets of pieces of modeling clay containing stones"<br> [](https://doi.org/10.5281/zenodo.5866228)</p> <p>The remaining pieces of modeling clay and all avocado fruits were scanned at the FleX-ray laboratory<br> of the Centrum Wiskunde & Informatica (CWI) in Amsterdam, the Netherlands (details can be found in [Coban 2020]).<br> For every fruit, we made scans with significantly different amounts of air pockets by waiting for a few days between experimental acquisitions.<br> The measurements were performed with the voltage of 90 kV, power of 45 W, exposure time of 300 ms per projection, and magnification factor of 1.3.<br> The original X-ray image size was 1912 px x 1520 px with a pixel size of 75 μm, 1440 images were acquired for every sample.<br> For faster deep learning model training, images and reconstructions were downsampled with a factor of 4, leading to the effective pixel size of 300 μm and voxel size of 230 μm.<br> Additional scans of the pieces of modeling clay were acquired with settings similar to the main collection.</p> <p><strong>Data Description</strong></p> <p>The submission is split into "Avocado" and "Playdoh" (pieces of modeling clay) datasets. Each dataset is further split into Training and Test subsets.</p> <p>The folder for every scanned object contains<br> - ./log/ - subfolder with logarithmed X-ray projections after darkfield and flatfield correction.<br> - ./segm/ - subfolder with slices of the segmented volume.<br> - ./scan settings.txt - a file with scanner metadata containing scan geometry<br> - ./volume_info.csv - a file with a voxel count for every class in the segmentation.</p> <p>For playdoh objects, the segmentation classes are modeling clay (Class 1) and pebble stone (Class 2). In this case, pebble stones are foreign objects.</p> <p>For avocado objects, the segmentation classes are peel (Class 1), avocado meat (Class 2), seed (Class 3) and air pockets (Class 4). Air pockets are considered a foreign object.</p> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the Computational Imaging group 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 GitHub page.</p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please get in touch with<br> - vladyslav.andriiashen [at] cwi.nl</p> <p><strong>References</strong></p> <p>[Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p>
Text-fig. 5. Pyracantha coccinea M.ROEM. (Cottbus, herbarium Striegler). Scale bar 10 mm. in New Leaf Species From The Upper Miocene Flora Of The Leaf-Bearing Wischgrund Clay (Lower Lusatia, Brandenburg, Germany)
Text-fig. 5. Pyracantha coccinea M.ROEM. (Cottbus, herbarium Striegler). Scale bar 10 mm.
Text-fig. 3. Lower Lusatian Tertiary Forest in Cottbus: Taxodium swamp. in New Leaf Species From The Upper Miocene Flora Of The Leaf-Bearing Wischgrund Clay (Lower Lusatia, Brandenburg, Germany)
Text-fig. 3. Lower Lusatian Tertiary Forest in Cottbus: Taxodium swamp.
Clay pan / bowl
High poly pan/bowl with clay PBR texture. Feel free to use for your renders. Source: Objaverse 1.0 / Sketchfab
Clay Pipe - Thames - 200k faces
A clay pipe found on banks of the river thames while mudlarking. The pipe bears the Hanover coat of arms which through minor research seems to have been popular from 1714-1760. I will update later when i can be more sure as to the maker but currently it appears to have been a Richard Manby Junior of Hermitage Bridge, London. Source: Objaverse 1.0 / Sketchfab
Clay Pipe (2020a8765)
**Clay smoking pipe** Location: Coweeta Creek site (31Ma34), Macon County, North Carolina. Period: South Appalachian Mississippian, Middle Qualla phase (AD 1450-1700). Material: ceramic. Dimensions: length, 35.3 mm; width, 32.1 mm; height, 37.5 mm. Notes: Catalog no. 2020a8765, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Steve Davis. Source: Objaverse 1.0 / Sketchfab
Clay Pipe (2378a617-1)
**Clay smoking pipe** Location: Fredricks site (31Or231), Orange County, North Carolina. Period: Historic (AD 1700). Material: ceramic. Dimensions: length, 49.3 mm; width, 40.1 mm; height, 42.2 mm. Notes: Catalog no. 2378a617, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Steve Davis. Source: Objaverse 1.0 / Sketchfab
Clay Pipe (2378a617-2)
**Terracotta clay smoking pipe** Location: Fredricks site (31Or231), Orange County, North Carolina. Period: Historic (AD 1700). Material: ceramic. Dimensions: length, 104.4 mm; width, 22.2 mm; height, 44.1 mm. Notes: Catalog no. 2378a617, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Steve Davis. Source: Objaverse 1.0 / Sketchfab
Ancient clay pot
Small prop of an ancient clay pot, modeled in Zbrush, retopologized in Maya and textured in Substance Painter. If you would want any kind of small edit on this model, send me a message, thx. Source: Objaverse 1.0 / Sketchfab
Clay Pipe Fragment (with scale bar)
Fragment of clay pipe, found on Avoch shore, with scale bar attached for reference. Source: Objaverse 1.0 / Sketchfab
clay pot (photoscanned and remeshed)
This is a pot that I scanned at my village home, it's not a perfect shape but I don't know why, maybe someone messed up during the drying process. Anyway, this was my first time remeshing a photoscanned asset, so there might be a few issues with the model. Hope you like it. For advices and suggestions, please comment down below. Thank you very much! Source: Objaverse 1.0 / Sketchfab
Savisylinteri, clay cylinder KM6560
Savisylinteri kuningas Nebukadnessar II:sen hallinnon ajalta, 605-562 eaa. Nuolenpääkirjoitus kuvaa Lugad-Maradin temppelin rakennustöitä Maradin kaupungissa, Babyloniassa. Neo-Babylonian clay cylinder from the reign of King Nebuchadnezzar II, 605-562 BCE. Discusses the building works of the temple of Lugal-Marad in the city of Marad, Babylonia. 3D-digitoitu osana Making Home Abroad / Kotona kulttuurissa -tutkimushanketta 2020-2021, käyttäen digitaalista fotogrammetriaa. 3D-digitointia on yksinkertaistettu huomattavasti katselukokemuksen sujuvoittamiseksi. 3D digitized as part of the Making Home Abroad / Kotona kulttuurissa research project 2020-2021, using digital photogrammetry. The 3D digitization is strongly simplified to ensure a smooth online visualization. Source: Objaverse 1.0 / Sketchfab
Female clay figurine
ID no.: MOD-5/7197 Archaeological Museum in Kraków https://muzea.malopolska.pl/en/objects-list/2142 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Clay vessel
ID no.: MAK/832 Archaeological Museum in Kraków https://muzea.malopolska.pl/en/objects-list/1477 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Clay Cup (2101a97)
**Clay cup** Location: Leak Site (31Rh1), Richmond County, North Carolina. Period: Mississippian (AD 1150-1400). Material: ceramic. Dimensions: height, 31.4 mm; diameter, 46.8 mm. Notes: Catalog no. 2101a97, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Abigail Gancz. Source: Objaverse 1.0 / Sketchfab
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
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.