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3,036 results for “ct”
Confocal and micro-CT data of Glyphonotum hsiaoi, holotype, NIGP200729
<p>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Glyphonotum hsiaoi</em> (NIGP200729) from mid-Cretaceous Burmese amber. This dataset is associated with the publication "An enigmatic Cretaceous beetle in Kachin amber with tentative affinities to Pythidae (Coleoptera: Tenebrionoidea)" (DOI:10.1002/ece3.70615).<br> <br>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 561 nm (DPSS 561-10) laser excitation line. The original CZI files are provided, which could be opened by the ZEISS ZEN software.<br> <br>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 2.6143 μm; power, 3 W; acceleration voltage, 40 kV; exposure time, 4 s; projections, 3001. The TIFF stack is provided.</p>
Computed tomography (CT) was used to study the interaction of NMs with the plant cell tissue in vivo using Zeiss Xradia 510 system on Arabidopsis thaliana leaf. T
<p>This work was supported by the National Research Facility for Lab X-ray CT (NXCT) at the µ-VIS X-ray Imaging Centre, University of Southampton, through EPSRC grant EP/T02593X.</p>
CT-FM-SNP cancer bed files
Open the record for dataset details and reuse information.
Generalizable cone beam CT esophagus segmentation using physics-based data augmentation
<p>This upload contains open source AAPM thoracic auto-segmentation data (http://aapmchallenges.cloudapp.net/competitions/3) augmented with physics-based data augmentation technique introduced in this paper:https://iopscience.iop.org/article/10.1088/1361-6560/abe2eb .</p> <p>The original data contained thoracic planning CT along with organs-at-risk segmentation masks for Esophagus, heart, lungs and spinal cord. The physics-based augmentation pipeline was used to convert planning CT images to pseudoCBCT (psCBCT) images which are routinely used in weekly radiotherapy treatment sessions for cancer patients. Further geometric data augmentations are also applied to convert one planning CT/OAR dataset into 23 perfectly paired CT/psCBCT/OAR pairs.</p> <p>This dataset has been used to train deep learning models for organs-at-risk segmentation from psCBCT images and multitask simultaneous CBCT to CT translation and segmentation tasks.</p>
FastText embeddings obtained from SNOMED CT - Spanish and International
<p>FastText model trained in a corpus that was obtained from SNOMED CT by performing random walks. There are three models: one trained using all relations available in the international version of SNOMED CT, another one trained using only is_a relations from the international version of SNOMED and the last one is trained using all relations from the Spanish version of SNOMED CT. This was developed for the end project of the Master Degree in Artificial Intelligence in the Universidad Politécnica de Madrid. More information and the document of the project can be found in <a href="https://github.com/JavierCastellD/SemanticFormalizationSNOMED">https://github.com/JavierCastellD/SemanticFormalizationSNOMED</a>.</p>
MRI versus CT in the detection of brain lesions in patients with infective endocarditis before/after cardiac surgery
<p>Imaging of brain involvement in infective endocarditis can drive the clinical management of this serious condition. MRI is very sensitive, but CT is more readily available. In this retrospective study, we compared the detection rate of CT and MRI.</p> <p>After Ethics Committee approval, we retrospectively reviewed a series of 20 patients (13 males, median age 64 years) who underwent both CT and MRI before or after cardiac surgery for definite infective endocarditis. Unenhanced CT and MRI were evaluated for acute ischemic lesions, both punctuate and large, intraparenchymal hemorrhages, cerebral microbleeds, subarachnoid hemorrhages, abscesses, microabscesses and meningitis. Qualitative and Cohen κ analysis were performed. The value of contrast-enhanced scans (MRI, <em>n</em> = 14; CT, <em>n</em> = 9) and cognitive status were also assessed.</p> <p>While CT rapidly excludes large hemorrhages before cardiac surgery for infective endocarditis, MRI accurately distinguishes the whole spectrum of brain lesions.</p>
A fifteen-tile tomographic micro-CT dataset of a panel painting "Cadmus sowing dragon's teeth" 2/3
<p><strong>Summary</strong><br> This submission contains a fifteen-tile tomographic dataset of a panel painting and 15 postprocessed images used for dendrochronological measurements (see related datasets).The data is made available as part of [Domínguez-Delmás et al., 2021].</p> <p><strong>Apparatus</strong><br> The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban 2020].<br> <br> <strong>Sample Information</strong><br> The sample is panel painting Cadmus Sowing dragon's teeth. H 26.2cm x w 42.3cm [Rijksmuseum inventory number SK-A-4051, https://www.rijksmuseum.nl/en/collection/SK-A-4051]. It was mounted on a foam base on the rotation stage, see [Domínguez-Delmás et al., 2021] for images.</p> <p><br> <strong>Experimental Plan</strong><br> The data in this submission was collected to facilitate measurements of the tree rings (dendrochronological research) in a cross-section. The panel was mounted in such a way that the desired cross section would be obtained in the vertical direction. A section of a few cm wide along the entire height was imaged slightly off centre of the painting, to avoid metal nails that had showed up in several projections. In total 15 tiled CT scans were collected, moving source and detector vertically by the same distance. Each tile consisted of 1600 projections, each an average of 2 projections with 300ms acquisition time. We used tube settings 70 kV, 70 mA and a 0.1 cm thickness copper filter. Dark-field (closed-shutter), and 2 flat-field (open-shutter) images were taken for each tile after the acquisition with 100 averaged images of 300 ms acquisition time. Reconstructed image resolution was 37 µm.<br> All raw data (i.e. no corrections) is made available in .tif format. Postprocessing steps are described in the supplementary material of [Domínguez-Delmás et al., 2021].<br> <br> <strong>List of Contents</strong><br> The content of the submission is divided in three datasets, with in total 16 subfolders: Images, containing the 15 postprocessed images used for dendrochronological measurements and Tile1-Tile15, each containing the data from one of the tiled scans.<br> Each data folder contains:</p> <ul> <li>dark-field (or closed-shutter) image, di000000.tif,</li> <li>flat-field (or open-shutter) image after acquisition, io000000.tif,</li> <li>raw (unprocessed or uncorrected) projections, scan_*.tif,</li> <li>data settings XRE.txt, a text file with scanner metadata,</li> <li>metadata.toml, a text file with a summary of the scanner metadata</li> </ul> <p><strong>Additional Links</strong><br> 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><br> For more information or guidance in using these datasets, please get in touch with</p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p> <br> <strong>Acknowledgments</strong><br> We thank Petria Noble for providing us with the opportunity to scan this interesting object. We thank Erma Hermens , Moorea Hall-Aquitania and Marta Domínguez Delmás for bringing the object and research questions to our attention and inviting our collaboration on this research project.<br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project numbers 341-60-001, 639.073.506 and 628.007.033 and Netherlands Institute for Conservation, Art and Science (NICAS).</p>
A fifteen-tile tomographic micro-CT dataset of a panel painting "Cadmus sowing dragon's teeth" 1/3
<p><strong>Summary</strong><br> This submission contains a fifteen-tile tomographic dataset of a panel painting and 15 postprocessed images used for dendrochronological measurements (see related datasets).The data is made available as part of [Domínguez-Delmás et al., 2021].</p> <p><strong>Apparatus</strong><br> The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban 2020].<br> <br> <strong>Sample Information</strong><br> The sample is panel painting Cadmus Sowing dragon's teeth. H 26.2cm x w 42.3cm [Rijksmuseum inventory number SK-A-4051, https://www.rijksmuseum.nl/en/collection/SK-A-4051]. It was mounted on a foam base on the rotation stage, see [Domínguez-Delmás et al., 2021] for images.</p> <p><br> <strong>Experimental Plan</strong><br> The data in this submission was collected to facilitate measurements of the tree rings (dendrochronological research) in a cross-section. The panel was mounted in such a way that the desired cross section would be obtained in the vertical direction. A section of a few cm wide along the entire height was imaged slightly off centre of the painting, to avoid metal nails that had showed up in several projections. In total 15 tiled CT scans were collected, moving source and detector vertically by the same distance. Each tile consisted of 1600 projections, each an average of 2 projections with 300ms acquisition time. We used tube settings 70 kV, 70 mA and a 0.1 cm thickness copper filter. Dark-field (closed-shutter), and 2 flat-field (open-shutter) images were taken for each tile after the acquisition with 100 averaged images of 300 ms acquisition time. Reconstructed image resolution was 37 µm.<br> All raw data (i.e. no corrections) is made available in .tif format. Postprocessing steps are described in the supplementary material of [Domínguez-Delmás et al., 2021].<br> <br> <strong>List of Contents</strong><br> The content of the submission is divided in three datasets, with in total 16 subfolders: Images, containing the 15 postprocessed images used for dendrochronological measurements and Tile1-Tile15, each containing the data from one of the tiled scans.<br> Each data folder contains:</p> <ul> <li>dark-field (or closed-shutter) image, di000000.tif,</li> <li>flat-field (or open-shutter) image after acquisition, io000000.tif,</li> <li>raw (unprocessed or uncorrected) projections, scan_*.tif,</li> <li>data settings XRE.txt, a text file with scanner metadata,</li> <li>metadata.toml, a text file with a summary of the scanner metadata</li> </ul> <p><strong>Additional Links</strong><br> 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><br> For more information or guidance in using these datasets, please get in touch with</p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p> <br> <strong>Acknowledgments</strong><br> We thank Petria Noble for providing us with the opportunity to scan this interesting object. We thank Erma Hermens , Moorea Hall-Aquitania and Marta Domínguez Delmás for bringing the object and research questions to our attention and inviting our collaboration on this research project.<br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project numbers 341-60-001, 639.073.506 and 628.007.033 and Netherlands Institute for Conservation, Art and Science (NICAS).</p>
A fifteen-tile tomographic micro-CT dataset of a panel painting "Cadmus sowing dragon's teeth" 3/3
<p><strong>Summary</strong><br> This submission contains a fifteen-tile tomographic dataset of a panel painting and 15 postprocessed images used for dendrochronological measurements (see related datasets).The data is made available as part of [Domínguez-Delmás et al., 2021].</p> <p><strong>Apparatus</strong><br> The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban 2020].<br> <br> <strong>Sample Information</strong><br> The sample is panel painting Cadmus Sowing dragon's teeth. H 26.2cm x w 42.3cm [Rijksmuseum inventory number SK-A-4051, https://www.rijksmuseum.nl/en/collection/SK-A-4051]. It was mounted on a foam base on the rotation stage, see [Domínguez-Delmás et al., 2021] for images.</p> <p><br> <strong>Experimental Plan</strong><br> The data in this submission was collected to facilitate measurements of the tree rings (dendrochronological research) in a cross-section. The panel was mounted in such a way that the desired cross section would be obtained in the vertical direction. A section of a few cm wide along the entire height was imaged slightly off centre of the painting, to avoid metal nails that had showed up in several projections. In total 15 tiled CT scans were collected, moving source and detector vertically by the same distance. Each tile consisted of 1600 projections, each an average of 2 projections with 300ms acquisition time. We used tube settings 70 kV, 70 mA and a 0.1 cm thickness copper filter. Dark-field (closed-shutter), and 2 flat-field (open-shutter) images were taken for each tile after the acquisition with 100 averaged images of 300 ms acquisition time. Reconstructed image resolution was 37 µm.<br> All raw data (i.e. no corrections) is made available in .tif format. Postprocessing steps are described in the supplementary material of [Domínguez-Delmás et al., 2021].<br> <br> <strong>List of Contents</strong><br> The content of the submission is divided in three datasets, with in total 16 subfolders: Images, containing the 15 postprocessed images used for dendrochronological measurements and Tile1-Tile15, each containing the data from one of the tiled scans.<br> Each data folder contains:</p> <ul> <li>dark-field (or closed-shutter) image, di000000.tif,</li> <li>flat-field (or open-shutter) image after acquisition, io000000.tif,</li> <li>raw (unprocessed or uncorrected) projections, scan_*.tif,</li> <li>data settings XRE.txt, a text file with scanner metadata,</li> <li>metadata.toml, a text file with a summary of the scanner metadata</li> </ul> <p><strong>Additional Links</strong><br> 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><br> For more information or guidance in using these datasets, please get in touch with</p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p> <br> <strong>Acknowledgments</strong><br> We thank Petria Noble for providing us with the opportunity to scan this interesting object. We thank Erma Hermens , Moorea Hall-Aquitania and Marta Domínguez Delmás for bringing the object and research questions to our attention and inviting our collaboration on this research project.<br> The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project numbers 341-60-001, 639.073.506 and 628.007.033 and Netherlands Institute for Conservation, Art and Science (NICAS).</p>
Confocal and micro-CT data of Microtrogossita qizhihaoi, holotype, NIGP173910
<p>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Microtrogossita qizhihaoi</em> (NIGP173910) from mid-Cretaceous Burmese amber. Additional confocal data for <em>Burmacateres longicoxa</em> (NIGP173911, NIGP173912) are also provided. This dataset is associated with the publication "An exquisitely preserved tiny bark-gnawing beetle (Coleoptera: Trogossitidae) from mid-Cretaceous Burmese amber and the phylogeny of Trogossitidae" (DOI:10.1111/jzs.12515).</p> <p>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using 488 nm Argon laser excitation line. Both original CZI files and exported TIFF stacks are provided.</p> <p>Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 2.4165 μm; power, 4 W; acceleration voltage, 50 kV; exposure time, 1.5 s; projections, 3001. The TIFF stack is provided.</p>
Fig. 17 in Morphology of the Braincase in the Cretaceous Hybodont Shark Tribodus limae (Chondrichthyes: Elasmobranchii), Based on CT Scanning
Fig. 17. Anterior (A) and posterior (B) views of the Tribodus endocast with otic labyrinth removed,
Fig. 27 in Morphology of the Braincase in the Cretaceous Hybodont Shark Tribodus limae (Chondrichthyes: Elasmobranchii), Based on CT Scanning
Fig. 27. SEM of Tribodus limae cephalic spine, AMNH 13959. Scale bar is 1 mm.
Fig. 11 in Morphology of the Braincase in the Cretaceous Hybodont Shark Tribodus limae (Chondrichthyes: Elasmobranchii), Based on CT Scanning
Fig. 11. Ventral view of the Tribodus braincase, anterior at top (surface rendering). No scale.
Fig. 5 in Morphology of the Braincase in the Cretaceous Hybodont Shark Tribodus limae (Chondrichthyes: Elasmobranchii), Based on CT Scanning
Fig. 5. Tribodus limae, UERJ-PMB-114. A, dorsal; B, ventral views. Scale bar is 2 cm.
Fig. 4 in Morphology of the Braincase in the Cretaceous Hybodont Shark Tribodus limae (Chondrichthyes: Elasmobranchii), Based on CT Scanning
Fig. 4. Tribodus limae, UERJ-PMB-40. A, dorsal; B, ventral views. Scale bar is 2 cm.
Extended data table 1: Taqman array card results showing all individual target hits with Ct values and whether validated by conventional microbiology and/or microbial sequencing.
<p>Data from study, protocol published at 10.5281/zenodo.5081880</p> <p><strong>Extended Data Table 1: TAC results showing all individual target hits with Ct values and whether validated by conventional microbiology and/or microbial sequencing. </strong>(BAL: bronchoalveolar lavage.) *Not included in validation numbers as duplicate at sub-species or genus level detection, **MecA was not included in validation numbers. TAC hits which did not pass the internal quality control standards required for reporting are indicated by (not reported). Samples which did not undergo sequencing indicated by <em>ND</em>. (✓) indicates low confidence hits from sequencing.</p>
Artificial Intelligence and COVID-19 using chest CT scan and chest X-ray images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment
<p>We uploaded the Table of included articles in the systematic review "Artificial Intelligence and COVID-19 using chest CT scan and chest X-ray images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment"</p>
Manual 4D annotations of Micro X-ray CT time-series (4D dataset)
<p>The 4D (3D+time) manual annotations of https://doi.org/10.5281/zenodo.4293394. For the annotation the SuRVoS workbench was used (https://doi.org/10.5281/10.5281/zenodo.247547) and our proposed hidden Markov model (HMM-T, https://doi.org/10.5281/zenodo.4416013 ) designed to refine 4D semantic segmentations made by a 3D semantic segmentation CNN after its applied on 4D data. Only slices 740-742 and 747-749 (refining to the first axis) are partially annotated. We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</p>
CT data for: Insight into the evolutionary assemblage of cranial kinesis from a Cretaceous bird
<p><span>The independent movements and flexibility of various parts of the skull, called cranial kinesis, is an evolutionary innovation that is found in living vertebrates only in some squamates and crown birds and is considered to be a major factor underpinning much of the enormous phenotypic and ecological diversity of living birds, the most diverse group of extant amniotes. Compared to the postcranium, our understanding of the evolutionary assemblage of the characteristic modern bird skull has been hampered by sparse fossil records of early cranial materials, with competing hypotheses regarding the evolutionary development of cranial kinesis among early members of the avialans. Here, a detailed three-dimensional reconstruction of the skull of the Early Cretaceous enantiornithine <em>Yuanchuavis kompsosoura</em> allows for its in-depth description, including elements that are poorly known among early diverging avialans but are central to deciphering the mosaic assembly of features required for modern avian cranial kinesis. Our reconstruction of the skull shows evolutionary and functional conservation of the temporal and palatal regions by retaining the ancestral theropod dinosaurian configuration within the skull of this otherwise derived and volant bird. Geometric morphometric analysis of the palatine suggests that loss of the jugal process represents the first step in the structural modifications of this element leading to the kinetic crown bird condition. The mixture of plesiomorphic temporal and palatal structures together with a derived avialan rostrum and postcranial skeleton encapsulated in <em>Yuanchuavis</em> manifests the key role of evolutionary mosaicism and experimentation in early bird diversification.</span></p>
HaN-Seg: The head and neck organ-at-risk CT & MR segmentation dataset
<p><strong>The HaN-Seg: Head and Neck Organ-at-Risk CT & MR Segmentation Dataset</strong> is a publicly available dataset of anonymized head and neck (HaN) images of 42 patients that underwent both CT and T1-weighted MR imaging for the purpose of image-guided radiotherapy planning. In addition, the dataset also contains reference segmentations of 30 organs-at-risk (OARs) for CT images in the form of binary segmentation masks, which were obtained by curating manual pixel-wise expert image annotations. A full description of the HaN-Seg dataset can be found in:</p> <p>G. Podobnik, P. Strojan, P. Peterlin, B. Ibragimov, T. Vrtovec, "HaN-Seg: The head and neck organ-at-risk CT & MR segmentation dataset", Medical Physics, 2023. <a href="https://doi.org/10.1002/mp.16197" target="_blank" rel="noopener">10.1002/mp.16197</a>,</p> <p>and any research originating from its usage is required to cite this paper.</p> <p>In parallel with the release of the dataset, the <strong><a href="https://han-seg2023.grand-challenge.org">HaN-Seg: The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge</a></strong> is launched to promote the development of new and application of existing state-of-the-art fully automated techniques for OAR segmentation in the HaN region from CT images that exploit the information of multiple imaging modalities, in this case from CT and MR images. The task of the HaN-Seg challenge is to automatically segment up to 30 OARs in the HaN region from CT images in the devised test set, consisting of 14 CT and MR images of the same patients, given the availability of the training set (i.e. the herein publicly available HaN-Seg dataset), consisting of 42 CT and MR images of the same patients with reference 3D OAR binary segmentation masks for CT images.<br><br>Please find below a list of relevant publications that address: (1) the assessment of inter-observer and inter-modality variability in OAR contouring, (2) results of the HaN-Seg challenge, (3) development of our multimodal segmentation model, and (4) development of MR-to-CT image-to-image translation using diffusion models:</p> <ol> <li>G. Podobnik, B. Ibragimov, P. Strojan, P. Peterlin, T. Vrtovec, "vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images", Medical Physics, 2024. <a href="https://doi.org/10.1002/mp.16924" target="_blank" rel="noopener">10.1002/mp.16924</a>,</li> <li>G. Podobnik, B. Ibragimov, E. Tappeiner, et al., "HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge", Radiotherapy and Oncology, 2024. <a href="https://doi.org/10.1016/j.radonc.2024.110410" target="_blank" rel="noopener">10.1016/j.radonc.2024.110410</a>,</li> <li>G. Podobnik, P. Strojan, P. Peterlin, B. Ibragimov, T. Vrtovec, "Multimodal CT and MR Segmentation of Head and Neck Organs-at-Risk", MICCAI 2023, 2023. <a href="https://doi.org/10.1007/978-3-031-43901-8_71" target="_blank" rel="noopener">10.1007/978-3-031-43901-8_71</a>,</li> <li>R. M. Šter, G. Podobnik, T. Vrtovec, "Diffusion-based MR-to-CT translation of head and neck images", SPIE Medical Imaging 2025, 2025. <a href="https://doi.org/10.1117/12.3047458" target="_blank" rel="noopener">10.1117/12.3047458</a>.</li> </ol>
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.