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Supplementary material 1 from: Tran TT, Pham AV, Le MD, Nguyen NH, Ziegler T, Pham CT (2023) A new species of Gracixalus (Anura, Rhacophoridae) from northwestern Vietnam. ZooKeys 1153: 15-35. https://doi.org/10.3897/zookeys.1153.93566
Statistical parsimony network as reconstructed by TCS v1.21
Dataset for: Kidney abnormality segmentation in thorax-abdomen CT scans
<p>215 thoraxabdomen CT scans with segmentations of the kidney and abnormalities in the kidney.</p>
Lung CT COVID-19 batch 6
<p>This data set is part of the public development data for the <a href="http://auc23.grand-challenge.org/">2023 Automated Universal Classification Challenge</a> (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by <a href="https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384">Revel, M. et al (2021)</a>. Data was restructured in compliance with the <a href="https://auc23.grand-challenge.org/">AUC23</a> challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the <a href="https://stoic2021.grand-challenge.org/">STOIC2021</a> challenge.</p> <p>Images are 3D tensors:</p> <ul> <li>0: 3D CT scan</li> </ul> <p>Classification labels:</p> <ul> <li>COVID-19: <ul> <li>0: Negative RT-PCR</li> <li>1: Positive RT-PCR<br> </li> </ul> </li> <li>Severe COVID-19: <ul> <li>0: Alive and no intubation after one month</li> <li>1: Death or intubation after one month</li> </ul> </li> </ul> <p>imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ...</p> <p>Please cite the following article if you are using the <a href="https://registry.opendata.aws/stoic2021-training/">STOIC2021 training dataset</a>:</p> <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> <p>Due to upload size limits, the data set was split into six batches.</p> <p>Batch 1:<a href="https://zenodo.org/record/7969800"> https://zenodo.org/record/7969800</a></p> <p>Batch 2: <a href="https://zenodo.org/record/8042589">https://zenodo.org/record/8042589</a></p> <p>Batch 3: <a href="https://zenodo.org/record/8042817">https://zenodo.org/record/8042817</a></p> <p>Batch 4:<a href="http:// https://zenodo.org/record/8043089"> https://zenodo.org/record/8043089</a></p> <p>Batch 5: <a href="https://zenodo.org/record/8043216">https://zenodo.org/record/8043216</a></p>
Lung CT COVID-19 batch 4
<p>This data set is part of the public development data for the <a href="http://auc23.grand-challenge.org/">2023 Automated Universal Classification Challenge</a> (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by <a href="https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384">Revel, M. et al (2021)</a>. Data was restructured in compliance with the <a href="https://auc23.grand-challenge.org/">AUC23</a> challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the <a href="https://stoic2021.grand-challenge.org/">STOIC2021</a> challenge.</p> <p>Images are 3D tensors:</p> <ul> <li>0: 3D CT scan</li> </ul> <p>Classification labels:</p> <ul> <li>COVID-19: <ul> <li>0: Negative RT-PCR</li> <li>1: Positive RT-PCR<br> </li> </ul> </li> <li>Severe COVID-19: <ul> <li>0: Alive and no intubation after one month</li> <li>1: Death or intubation after one month</li> </ul> </li> </ul> <p>imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ...</p> <p>Please cite the following article if you are using the <a href="https://registry.opendata.aws/stoic2021-training/">STOIC2021 training dataset</a>:</p> <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> <p>Due to upload size limits, the data set was split into six batches.</p> <p>Batch 1:<a href="https://zenodo.org/record/7969800"> https://zenodo.org/record/7969800</a></p> <p>Batch 2: <a href="https://zenodo.org/record/8042589">https://zenodo.org/record/8042589</a></p> <p>Batch 3: <a href="https://zenodo.org/record/8042817">https://zenodo.org/record/8042817</a></p> <p>Batch 5: <a href="https://zenodo.org/record/8043216">https://zenodo.org/record/8043216</a></p> <p>Batch 6: <a href="https://zenodo.org/record/8043218">https://zenodo.org/record/8043218</a></p>
Lung CT COVID-19 batch 2
<p>This data set is part of the public development data for the <a href="http://auc23.grand-challenge.org/">2023 Automated Universal Classification Challenge</a> (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by <a href="https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384">Revel, M. et al (2021)</a>. Data was restructured in compliance with the <a href="https://auc23.grand-challenge.org/">AUC23</a> challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the <a href="https://stoic2021.grand-challenge.org/">STOIC2021</a> challenge.</p> <p>Images are 3D tensors:</p> <ul> <li>0: 3D CT scan</li> </ul> <p>Classification labels:</p> <ul> <li>COVID-19: <ul> <li>0: Negative RT-PCR</li> <li>1: Positive RT-PCR<br> </li> </ul> </li> <li>Severe COVID-19: <ul> <li>0: Alive and no intubation after one month</li> <li>1: Death or intubation after one month</li> </ul> </li> </ul> <p>imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ...</p> <p>Please cite the following article if you are using the <a href="https://registry.opendata.aws/stoic2021-training/">STOIC2021 training dataset</a>:</p> <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> <p>Due to upload size limits, the data set was split into six batches.</p> <p>Batch 1:<a href="https://zenodo.org/record/7969800"> https://zenodo.org/record/7969800</a></p> <p>Batch 3: <a href="https://zenodo.org/record/8042817">https://zenodo.org/record/8042817</a></p> <p>Batch 4: <a href="https://zenodo.org/record/8043089">https://zenodo.org/record/8043089</a></p> <p>Batch 5: <a href="https://zenodo.org/record/8043216">https://zenodo.org/record/8043216</a></p> <p>Batch 6: <a href="https://zenodo.org/record/8043218">https://zenodo.org/record/8043218</a></p>
Lung CT COVID-19 batch 3
<p>This data set is part of the public development data for the <a href="http://auc23.grand-challenge.org/">2023 Automated Universal Classification Challenge</a> (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by <a href="https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384">Revel, M. et al (2021)</a>. Data was restructured in compliance with the <a href="https://auc23.grand-challenge.org/">AUC23</a> challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the <a href="https://stoic2021.grand-challenge.org/">STOIC2021</a> challenge.</p> <p>Images are 3D tensors:</p> <ul> <li>0: 3D CT scan</li> </ul> <p>Classification labels:</p> <ul> <li>COVID-19: <ul> <li>0: Negative RT-PCR</li> <li>1: Positive RT-PCR<br> </li> </ul> </li> <li>Severe COVID-19: <ul> <li>0: Alive and no intubation after one month</li> <li>1: Death or intubation after one month</li> </ul> </li> </ul> <p>imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ...</p> <p>Please cite the following article if you are using the <a href="https://registry.opendata.aws/stoic2021-training/">STOIC2021 training dataset</a>:</p> <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> <p>Due to upload size limits, the data set was split into six batches.</p> <p>Batch 1:<a href="https://zenodo.org/record/7969800"> https://zenodo.org/record/7969800</a></p> <p>Batch 2: <a href="https://zenodo.org/record/8042589">https://zenodo.org/record/8042589</a></p> <p>Batch 4: <a href="https://zenodo.org/record/8043089">https://zenodo.org/record/8043089</a></p> <p>Batch 5: <a href="https://zenodo.org/record/8043216">https://zenodo.org/record/8043216</a></p> <p>Batch 6: <a href="https://zenodo.org/record/8043218">https://zenodo.org/record/8043218</a></p>
Kidney CT Abnormality
<p>This data set is part of the public development data for the <a href="http://auc23.grand-challenge.org/">2023 Automated Universal Classification Challenge</a> (AUC23). The data set concerns the detection of kidney abnormalities in computed tomography (CT) scans and was originally introduced by <a href="https://zenodo.org/record/8014290">Humpire-Mamani et al. (2023)</a>. Data was restructured in compliance with the <a href="https://auc23.grand-challenge.org/">AUC23</a> challenge format.</p> <p>Images are 3D tensors:</p> <ul> <li>0: 3D CT scan</li> </ul> <p>Classification labels:</p> <ul> <li>0: Normal</li> <li>1: Abnormal</li> </ul> <p>imagesTr (root folder with all patients and studies)<br> ├── kidneyabnormalityKiTS-0000_0000.mha (3D CT for study 0)<br> ├── kidneyabnormalityKiTS-0001_0000.mha (3D CT for study 1)<br> ├── ...</p> <p>Please cite the following data set if you are using the kidney CT abnormality data :</p> <p>Gabriel E. Humpire-Mamani, Luc Builtjes, Colin Jacobs, Bram van Ginneken, Mathias Prokop, & Ernst Th. Scholten. (2023). Dataset for: Kidney abnormality segmentation in thorax-abdomen CT scans [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8014290</p>
Confocal and micro-CT data of Granulobium whitei, holotype, NIGP201648
<p>This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Granulobium whitei</em> (NIGP201648) from mid-Cretaceous Burmese amber. This dataset is associated with the publication "Earliest fossil record of Eucradinae in mid-Cretaceous amber from northern Myanmar (Coleoptera: Ptinidae)" (DOI:10.3140/bull.geosci.1876).</p> <p>Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 488 nm (Argon) laser excitation line. The original CZI files are provided, which could be opened by the ZEISS ZEN software.</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, 2.6255 μm; power, 4 W; acceleration voltage, 50 kV; exposure time, 1.5 s; projections, 3001. The TIFF stack is provided.</p>
Dataset with results of "Joint 2D to 3D image registration workflow for comparing multiple slice photographs and CT scans of apple fruit with internal disorders"
<p><strong>Summary</strong></p> <p>This dataset contains all results from the paper "Joint 2D to 3D image registration workflow for comparing multiple slice photographs and CT scans of apple fruit with internal disorders". Most notably, this dataset contains the corresponding CT slices for slice photographs of 1347 'Kanzi' apples. This dataset also contains data of the results section, metadata required to make the registration code run, and segmentation masks of the apple slice photographs. The "raw" data that was used to produce these results can be found in another Zenodo dataset: <a href="../record/8167285">https://zenodo.org/record/8167285</a>.</p> <p><br><strong>Description</strong></p> <ul> <li><strong>registered ct photo side-by-side view.zip </strong>is the easiest way to explore the registered CT photo image pairs. For every apple slice it contains a .png image consisting of the slice photo, registered CT slice and a combined view (photo=green, CT=purple) side-by-side. The resolution was reduced to reduce the file size.</li> <li><strong>registered ct slices.zip </strong>contains the full resolution CT slices as .tiff files. The matching slice photos can be found in <strong>slice_photos_crop.zip</strong> in <a href="../record/8167285">https://zenodo.org/record/8167285</a>.</li> <li><strong>photo metadata.zip </strong>contains all metadata files required to run the code on <a href="https://github.com/D1rk123/apple_photo_ct_workflow">https://github.com/D1rk123/apple_photo_ct_workflow</a>.</li> <li><strong>results.zip</strong> contains the IPCED annotations and per apple metrics that were used to calculate all the average metrics and tables in the results section of the paper.</li> <li><strong>subset experiment registered annotation slice.zip </strong>contains the full resolution CT slices of the annotation slice in the subset experiment as .tiff files.</li> <li><strong>segmentation masks.zip </strong>contains slice photo segmentation masks as .png images. There are subfolders for the training set, the test set and the masks used for the workflow in the paper.</li> </ul> <p><br><strong>Research group</strong><br>This dataset was 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><strong>Contact details</strong><br>dirk [dot] schut [at] cwi [dot] nl</p> <p><strong>Acknowledgments</strong><br>This work was funded by the Dutch Research Council (NWO) through the UTOPIA project (ENWSS.2018.003).</p>
VSDFullBodyBoneReconstruction: Segmentations and surface models of bones of the entire lower body created from cadaver CT scans from the VSDFullBody collection
<p>The segmentations and models of the bones of the lower extremities were created from anonymized postmortem CT scans of the whole body originally published by Michael Kistler in the Swiss Institute for Computer Assisted Surgery Medical Image Repository (<a href="https://www.smir.ch/">smir.ch</a>) as open access Virtual Skeleton Database (VSD). A mirror of the VSD is available at Zenodo: <a href="https://doi.org/10.5281/zenodo.8270364">10.5281/zenodo.8270364</a>. </p><p><strong>However</strong>, this is a stand-alone upload and the full VSD is not required to use this upload. Further information can be found in the following publication:</p><p>Fischer, M. C. M. Database of segmentations and surface models of bones of the entire lower body created from cadaver CT scans. <i>Sci. Data</i> <strong>10</strong>, 763; <a href="https://doi.org/10.1038/s41597-023-02669-z">10.1038/s41597-023-02669-z</a> (2023).</p><p>Post-processed 3D surface models stored as MATLAB MAT files were released as Git repository at <a href="https://github.com/MCM-Fischer/VSDFullBodyBoneModels">https://github.com/MCM-Fischer/VSDFullBodyBoneModels</a> and versioned via Zenodo: <a href="https://doi.org/10.5281/zenodo.8316730">10.5281/zenodo.8316730</a>. The use of the MAT files is explained by examples for MATLAB and Python in the Git repository.</p><p><strong>Usage</strong><br>The CT volume data, segmentations, reconstructions and raw PLY mesh files of each subject are linked by a project file (MRML scene file) that can be opened with the open-source medical imaging software 3D Slicer (<a href="https://www.slicer.org/">slicer.org</a>).</p>
Supplementary material 1 from: Stenger L, Große Hovest A, Nguyen TQ, Pham CT, Rauhaus A, Le MD, Rödder D, Ziegler T (2023) Assessment of the threat status of reptile species from Vietnam - Implementation of the One Plan Approach to Conservation. Nature Conservation 53: 183-221. https://doi.org/10.3897/natureconservation.53.106923
Species list, distribution, endemism, status, threats
Automated detection, segmentation and classification of pericardial effusions on chest CT using a deep convolutional neural network
<p>Test data set</p>
Micro-CT tomographic data set of 38 mummy labels from the BNU in Strasbourg (1/2)
<p><strong>Summary</strong></p> <p>This submission contains a tomographic dataset of 38 mummy labels from the BNU in Strasbourg used to perceive the anatomical identification possibilities of the woods used for mummy labels and to carry out ring width measurements. The data will be made available as part of [Blondel et al., 2024].</p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the EasyTom 150/160 X-ray tomograph (RX Solutions). This tomograph is equipped with a sealed X-ray generator with a compact tube and an interchangeable-plane sensor fitted with a CsI scintillator. The CT scanner parameters for the session carried out on the mummy labels were set at 90 Kv with an intensity of 195 mA for an acquisition resolution varying between 11 and 42 µm with 2016 projections (that is about 20 images on average per projection) with a frame rate of 12,5 and a temperature of 28°C. Each image was then reconstructed by filtered retroprojection using the XAct software (RX Solutions).</p> <p><strong>Information on placing mummy labels in the tomograph</strong></p> <p>The installation of the mummy labels was the same for all the different labels, some of which varied in size. They were attached to a plastic clamping vice-type support covered in expanded foam to prevent the labels from being marked during clamping, before being placed on the tomograph's rotating platform.</p> <p><strong>Issues relating to the data collected</strong></p> <p>The data collected for this study were carried out to perceive the possibilities of anatomical identification from tomographic images in the transverse plane. The tangential and radial planes were not of sufficiently high resolution due to the dimensions of the mummy labels, see details in [Blondel et al., 2024]. The other objective was to use tomographic imagery to facilitate the acquisition of ring widths in the transverse plane of mummy labels. The mummy labels were not tomographed in their entirety. Only the central part, a few centimetres high, was tomographed to maximise resolution. The number of projections and the resolution per label are specified in table form in [Blondel et al., 2024], as they vary according to the width and thickness of the mummy labels. All raw tomography image data (i.e. without corrections) are available in .tif format. The post-processing steps are described in the methodology of [Blondel et al., 2024].</p> <p><strong>List of Contents</strong></p> <p>The content of the submission is divided into 38 data sets corresponding to the 38 mummy labels. Each set is labelled with the inventory number of the BNU mummy label and its resolution. Each set contains:<br>- All the images of the transverse plane in .tif format, the number of projections of which varies from one label to another depending on the resolution of the acquisitions, see details in [Blondel et al., 2024].<br>- The .xls file containing a summary of the scanner metadata for each of the mummy labels.<br>- The three images processed in the transverse plane for each label, including those used to measure ring width for the 7 labels for which ring width measurement was possible, as presented in [Blondel et al., 2024].<br>- Colour photographs of the front and back of each tomographed mummy label including those on which ring width measurements were taken on their surface, unless otherwise stated<a title="" href="#_ftn1" name="_ftnref1">[1]</a>. All these photographs are marked: Coll._et_photogr._BNU_Strasbourg_OpenLicence, accompanied by the inventory number.</p> <p><strong>Acknowledgments</strong></p> <p>We would also like to thank engineers Damien Favier and Antoine Egele from the Charles Sadron Institute for their work on the tomographic acquisitions carried out on the 38 mummy labels.</p> <div><br> <div> <p><a title="" href="#_ftnref1" name="_ftn1">[1]</a> The photographs of the front and back of mummy label HO255 are not available, as they are currently being studied.</p> </div> </div>
PRospective Evaluation of Interstitial Lung DIsease Progression With Quantitative CT
ClinicalTrials.gov study NCT05609201. IPD Sharing: YES. Countries: 1. Publications: 0.
Clinical Study of a Single-Use, Point-of-Care Molecular Diagnostic Device for the Detection of Neisseria Gonorrhoeae (NG), Trichomonas Vaginalis (TV), and Chlamydia Trachomatis (CT) Utilizing Vaginal
ClinicalTrials.gov study NCT03596151. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Pilot Study Evaluating the Potential of 18F Fluorodeoxyglucose PET-CT Imaging in Diagnosing Cardiac Rejection.
ClinicalTrials.gov study NCT06519240. IPD Sharing: NO. Countries: 0. Publications: 1.
Myocardial Stress Perfusion Computed Tomography in the Evaluation of Patients With Acute Chest Pain Using a Novel 3rd Generation Dual-source CT System
ClinicalTrials.gov study NCT02308475. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Coronary Arteries Anomalies by CT in Children With CHD
ClinicalTrials.gov study NCT06381128. IPD Sharing: Not stated. Countries: 0. Publications: 17.
Computer Assisted Volumetric Analysis of CT-Identified Metastatic Pulmonary Or Hepatic Lesions
ClinicalTrials.gov study NCT00676455. IPD Sharing: Not stated. Countries: 1. Publications: 0.
CT Analysis of the Patellofemoral Joint in Patients Having Knee Surgery
ClinicalTrials.gov study NCT02257099. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.