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167 results for “x-ray imaging”
X-ray imaging of 30 year old wine grape wood reveals cumulative impacts of rootstocks on scion secondary growth and harvest index
Open the record for dataset details and reuse information.
X-ray diffraction images of the beta4 tetramer of the C-terminal peptide of the split chain transketolase
<p>X-ray images for PDB entry 6YAJ</p> <p>DOI for the pdb is https://doi.org/10.2210/pdb6YAJ/pdb</p> <p>Title: A 'Split-Gene' Transketolase From the Hyper-Thermophilic Bacterium Carboxydothermus hydrogenoformans : Structure and Biochemical Characterization.<br> Journal: Front Microbiol<br> Volume: 11<br> Pages: 592353 - 592353<br> Year: 2020<br> PubMed ID: 33193259<br> DOI: 10.33 89/fmicb .2020.592353</p> <p> </p> <p> </p>
X-ray diffraction images of the alpah2beta2 heterotetramer of the split chain transketolase
<p>Data were collected on Diamond I04-1 14 Dec 2013.</p> <p> James, P.,Isupov, M.N.,De Rose, S.A.,Sayer, C.,Cole, I.S.,Littlechild, J.A.<br> <br> A 'Split-Gene' Transketolase From the Hyper-Thermophilic Bacterium Carboxydothermus hydrogenoformans : Structure and Biochemical Characterization.<br> <br> Journal: Front Microbiol<br> Volume: 11<br> Pages: 592353 - 592353<br> Year: 2020<br> PubMed ID : 3319 3259<br> DOI: 10.3389/fmicb.2020.592353<br> <br> PDB DOI: https://doi.org/10.2210/pdb6YAK/pdb</p>
Supplementary data for article 'Estimating and abstracting the 3D structure of feline bones using neural networks on X-ray (2D) images'
<p>3D DICOM volumes (CT scans) of feline femora, PNGs generated from them as DRRs using MeVisLab, and STLs generated from the DICOM volumes with MIMICS or MeshLab. Software to work with these files can be found at http://doi.org/10.5281/zenodo.3829423</p>
Simultaneous three-dimensional vascular and tubular imaging of whole mouse kidneys with X-ray µCT
<p>µCT dataset of a mouse kidney injected with contrast agent XlinCA and scanned with 3.3 µm voxel size. Detailed sample preparation and image acquisition protocols are published as <a href="https://doi.org/10.1017/S1431927620001725">"Simultaneous three-dimensional vascular and tubular imaging of whole mouse kidneys with X-ray µCT"</a> in <em>Microscopy and Microanalysis</em>.</p> <p>Segmentations of the vascular and tubular lumina along with the renal tissue are provided as masks. The three different segmented features were combined into a single dataset and encoded as different gray values:</p> <p>0: Background<br> 51: Tubules<br> 204: Tissue<br> 255: Blood vessels</p> <p>The Supplemental Video features a computer graphics visualization of the segmented masks. Blood vessel lumina are rendered in red, tissue in transparent blue and tubular lumina in yellow.</p>
Image Dataset for 'Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning'
<p>Dataset used in the manuscript 'Digitally Deconstructing Leaves in 3D Using X-ray microcomputed Tomography and Machine Learning'. Please cite the paper presenting this dataset:</p> <p><strong>Citation:</strong> Théroux-Rancourt, G., M. R. Jenkins, C. R. Brodersen, A. McElrone, E. J. Forrestel, and J. M. Earles. 2020. Digitally deconstructing leaves in 3D using X-ray microcomputed tomography<strong> </strong>and machine learning. <em>Applications in Plant Sciences</em> 8(7): .</p> <p> </p> <p><strong>Description of the dataset</strong></p> <p>A 'Cabernet Sauvignon' grapevine (<em>Vitis vinifera</em> L.) leaf from a plant of the BOKU experimental vineyard in Tulln, Austria, was scanned using microCT at the Swiss Light Source. The original reconstructions of the scans are using the gridrec (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Gridrec_reconstruction_downsized.zip?versionId=28d98982-f69d-4eac-9dfa-efcc89c6823c">Gridrec_reconstruction_downsized.zip</a>) and the paganin, or phase-contrast, algortithm (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Phase_contrast_reconstruction_downsized.zip?versionId=bef3260d-2865-4c9b-b5e1-e692edefb691">Phase_contrast_reconstruction_downsized.zip</a>). To facilitate automated segmentation, the size of the image in the <em>x </em>and <em>y</em> dimensions have been halved, so that the size of the pixels is 0.325 µm in those dimensions, but 0.1625 µm in the <em>z</em> (slices) dimension.</p> <p>A binary image segmenting the leaf cells and the airspace for each gridrec and phase-contrast stacks are created, and both are combined together (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Binary_stack_for_local_thickness.zip?versionId=165e3938-b490-4e56-9c8e-a2084cb39d49">Binary_stack_for_local_thickness.zip</a>), a map of the local thickness is created (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Local_thickness_map.zip?versionId=ce0a7dc7-5e3f-44a4-8881-cf84b6efd87c">Local_thickness_map.zip</a>). This map gives information on the largest diameter of the pixels labeled as cells in the binary stack.</p> <p>Hand-labeled slices or ground truths were drawn on the following slices: 80, 140, 200, 260, 340, 400, 440, 540, 620, 740, 800, 860, 940, 1060, 1140, 1240, 1300, 1400, 1480, 1540, 1600, 1690, 1740, 1840 (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Hand_labelled_slices.tif?versionId=a21a13ac-fa47-4ef8-a903-ecc433787184">Hand_labelled_slices.tif</a>).</p> <p>Using the hand-labeled slices and the different images, a random-forest model was trained, which allowed to automatically segment the remaining slices of the stack (<a href="https://zenodo.org/api/files/bbca544a-15d0-40f3-8cc9-a3ee3c08fd7e/Fullstack_Prediction_Example-6_training_slices-6_testing_slices.zip?versionId=02b69e65-da85-492e-9b72-9b2b3ccd085f">Fullstack_Prediction_Example-6_training_slices-6...</a>).</p> <p>The source code for the segmentation program is available <a href="https://github.com/plant-microct-tools/leaf-traits-microct/tree/master">here</a>, and the source code for the testing used in the paper is available <a href="https://github.com/plant-microct-tools/leaf-traits-microct/tree/nb-slices-eval">here</a>.</p>
X-ray diffraction images for DPF3 tandem PHD fingers co-crystallized with an acetylated histone-derived peptide
<p>This submission includes a tar archive of bzipped diffraction images recorded with the ADSC Q315r detector at the Advanced Photon Source of Argonne National Laboratory, Structural Biology Center beam line 19-ID. Relevant meta data can be found in the headers of those diffraction images.</p> <p>Please find below the content of an input file XDS.INP for the program XDS (Kabsch, 2010), which may be used for data reduction. The "NAME_TEMPLATE_OF_DATA_FRAMES=" item inside XDS.INP may need to be edited to point to the location of the downloaded and untarred images.</p> <p>!!! Paste lines below in to a file named XDS.INP</p> <p>DETECTOR=ADSC MINIMUM_VALID_PIXEL_VALUE=1 OVERLOAD= 65000<br /> DIRECTION_OF_DETECTOR_X-AXIS= 1.0 0.0 0.0<br /> DIRECTION_OF_DETECTOR_Y-AXIS= 0.0 1.0 0.0<br /> TRUSTED_REGION=0.0 1.05<br /> MAXIMUM_NUMBER_OF_JOBS=10<br /> ORGX= 1582.82 ORGY= 1485.54<br /> DETECTOR_DISTANCE= 150<br /> ROTATION_AXIS= -1.0 0.0 0.0<br /> OSCILLATION_RANGE=1<br /> X-RAY_WAVELENGTH= 1.2821511<br /> INCIDENT_BEAM_DIRECTION=0.0 0.0 1.0<br /> FRACTION_OF_POLARIZATION=0.90<br /> POLARIZATION_PLANE_NORMAL= 0.0 1.0 0.0<br /> SPACE_GROUP_NUMBER=20<br /> UNIT_CELL_CONSTANTS= 100.030 121.697 56.554 90.000 90.000 90.000<br /> DATA_RANGE=1 180<br /> BACKGROUND_RANGE=1 6<br /> SPOT_RANGE=1 3<br /> SPOT_RANGE=31 33<br /> MAX_CELL_AXIS_ERROR=0.03<br /> MAX_CELL_ANGLE_ERROR=2.0<br /> TEST_RESOLUTION_RANGE=8.0 3.8<br /> MIN_RFL_Rmeas= 50<br /> MAX_FAC_Rmeas=2.0<br /> VALUE_RANGE_FOR_TRUSTED_DETECTOR_PIXELS= 6000 30000<br /> INCLUDE_RESOLUTION_RANGE=50.0 1.7<br /> FRIEDEL'S_LAW= FALSE<br /> STARTING_ANGLE= -100 STARTING_FRAME=1<br /> NAME_TEMPLATE_OF_DATA_FRAMES= ../x247398/t1.0???.img</p> <p>!!! End of XDS.INP</p> <p> </p> <p> </p>
High resolution X-ray diffraction images for yeast 5-aminolevulinic acid dehydratase complexed with levulinic acid.
<p>X-ray diffraction images collected at DESY Hamburg in June 1998 using beamline BW7B. </p>
X-ray diffraction images for endothiapepsin co-crystallised with inhibitor H189 to 0.94 Angstrom resolution.
<p>X-ray diffraction images collected on 23rd May 2000 at the BW7B beamline of DESY (Hamburg). </p>
dataset- Tuberculosis detection using Squid Game Optimization with Deep Learning Model on Chest X-Ray Images
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PENGWIN Task 2: Pelvic Fragment Segmentation on Synthetic X-ray Images
<p>The <a href="https://pengwin.grand-challenge.org/">PENGWIN segmentation challenge</a> is designed to advance the development of automated pelvic fracture segmentation techniques in both 3D CT scans (Task 1) and 2D X-ray images (Task 2), aiming to enhance their accuarcy and robustness. The full 3D dataset comprises CT scans from 150 patients scheduled for pelvic reduction surgery, collected from multiple institutions using a variety of scanning devices. This dataset represents a diverse range of patient cohorts and fracture types. Ground-truth segmentations for sacrum and hipbone fragments have been semi-automatically annotated and subsequently validated by medical experts, and are available <a href="https://doi.org/10.5281/zenodo.10927452">here</a>. From this 3D data, we have generated high-quality, realistic X-ray images and corresponding 2D labels from the CT data using <a href="https://github.com/arcadelab/deepdrr">DeepDRR</a>, incorporating a range of virtual C-arm camera positions and surgical tools. This dataset contains the training set for fragment segmentation in synthetic X-ray (task 2).</p> <p>The training set is derived from 100 CTs, with 500 images each, for a total of <strong>50,000 training images and segmentations</strong>. The C-arm geometry is randomly sampled for each CT within reasonable parameters for a full-size C-arm. The virtual patient is assumed to be in a head-first supine position. Imaging centers are randomly sampled within 50 mm of a fragment, ensuring good visibility. Viewing directions are sampled uniformly on the sphere within 45 degrees of vertical. Half of the images (IDs XXX_0250 - XXX_0500) contain up to 10 simulated K-wires and/or orthopaedic screws oriented randomly in the field of view.</p> <p>The input images are raw intensity images without any windowing or normalization applied. It is standard practice to first apply the negative log transformation and then window each image appropriately for feeding into a model. See the included augmentation pipeline in `pengwin_utils.py` for one approach. For viewing raw images, the <a href="https://imagej.net/software/fiji/">FIJI</a> image viewer is a viable option, but it is recommended to use the included visualization functions in `pengwin_utilities.py` to first apply CLAHE normalization and save to a universally readable PNG (see example usage below).</p> <p>Because X-ray images feature overlapping segmentation maks, the segmentations have been encoded as multi-label uint32 images, where each pixel should be treated as a binary vector with bits 1 - 10 for SA fragments, 11 - 20 for LI, and 21 - 30 for RI. <strong>Thus, the raw segmentation files are not viewable with standard image viewing software.</strong> `pengwin_utilities.py` includes functions for converting to and from this format and for visualizing masks overlaid onto the original image (see below).</p> <p>To use the utilities, first install dependencies with `pip install -r requirement.txt`. Then, to visualize an image with its segmentation, you can do the following (assuming the training set has been downloaded and unzipped in the same folder):</p> <pre><code>import pengwin_utils from PIL import Image image_path = "train/input/images/x-ray/001_0000.tif" seg_path = "train/output/images/x-ray/001_0000.tif" # load image and masks image = pengwin_utils.load_image(image_path) # raw intensity image masks, category_ids, fragment_ids = pengwin_utils.load_masks(seg_path) # save visualization of image and masks # applies CLAHE normalization to the raw intensity image before overlaying segmentations. vis_image = pengwin_utils.visualize_sample(image, masks, category_ids, fragment_ids) vis_path = "vis_image.png" Image.fromarray(vis_image).save(vis_path) print(f"Wrote visualization to {vis_path}") # Obtain predicted masks, category_ids, and fragment_ids # Category IDs are {"SA": 1, "LI": 2, "RI": 3} # Fragment IDs are the integer labels from label_{category}.nii.gz, with 1 corresponding to the main fragment. pred_masks, pred_category_ids, pred_fragment_ids = masks, category_ids, fragment_ids # replace with your model # save the predicted masks for upload to the challenge # Note: cv2 does not work with uint32 images. It is recommended to use PIL or imageio.v3 pred_seg = pengwin_utils.masks_to_seg(pred_masks, pred_category_ids, pred_fragment_ids) pred_seg_path = "pred/train/output/images/x-ray/001_0000.tif" # ensure dir exists! Image.fromarray(pred_seg).save(pred_seg_path) print(f"Wrote segmentation to {pred_seg_path}")</code></pre> <p>The `pengwin_utils.Dataset` class is provided as an example of a Pytorch dataset, with strong domain randomization included to facilitate sim-to-real performance, but it is recommended to write your own as needed.</p>
Thorax x-ray and CT interventional dataset for nonrigid 2D/3D image registration evaluation
<p>Thorax x-ray and CT interventional dataset for nonrigid 2D/3D image registration evaluation. Medical Physics, 2018 Nov;45(11):5343-5351. doi: 10.1002/mp.13174.</p>
Data for the manuscript named 'Soft X-ray imaging of the magnetosheath and cusps under different solar wind conditions: MHD simulations'
<p> This is the data used by the manuscript named 'Soft X-ray imaging of the magnetosheath and cusps under different solar wind conditions: MHD simulations'.</p> <p> The uploaded data is the X-ray intensity data for all the five cases studied in the manuscritpt. 'Casen' (n=1, 2, 3, 4, and 5) in the name of each data file indicates the case number, and 'sat pointX' (X=A, B, C, D) show the satellite positions analyzed in the manuscript. </p> <p> The data can be read by IDL using the following program statments:</p> <p>openr,lun,datai,/get_lun<br> xgse=0. & ygse=0. & zgse=0.<br> readf,lun,xgse,ygse,zgse ;;;;(satellite position in the GSE coordinate)<br> xsat=0. & ysat=0. & zsat=0.<br> readf,lun,xsat,ysat,zsat ;;;;(satellite position in the GSM coordinate)<br> xpoint=0. & ypoint=0. & zpoint=0.<br> readf,lun,xpoint,ypoint,zpoint ;;;;(satellite pointing of SXI, aim point)<br> nthtmax=0L & nphimax=0L<br> readf,lun,nthtmax,nphimax ;;;;(number of the tht and phi grids)<br> thti=fltarr(nthtmax) & phii=fltarr(nphimax)<br> readf,lun,thti,format='(e14.6)' ;;;;(the tht grids)<br> readf,lun,phii,format='(e14.6)' ;;;;(the phi grids)<br> Pxraytp=fltarr(nthtmax,nphimax)<br> readf,lun,Pxraytp,format='(e14.6)' ;;;;(X-ray intensity)<br> close,lun<br> free_lun,lun</p>
X-ray absorption data and microscopic images of "Atomically dispersed iron(3+) sites catalyze efficient CO2 electroreduction to CO"
<p>XANES and EXAFS data (Figure 1F-H, Figure 3A-B, Figure S2F-H, Figure S3H-I, Figure S10A-B, Figure S11A-B,E-F, Figure S12A, Figure S14D-E)</p> <p>Microscopic images (Figure 1A-D, Figure S2B,D, Figure S4A-B,D-E, Figure S9A-C,E Figure S13A-D)</p> <p>of the research paper 'Atomically dispersed iron(3+) sites catalyze efficient CO2 electroreduction to CO'.</p>
X-ray Emission of Nearby Low-mass and Sun-like Stars with Directly Imageable Habitable Zones: X-ray Spectra and Light Curves
<p>This repository contains the X-ray light curves extracted from the XMM-Newton and Chandra observations and the best-fit X-ray spectral models (with and without emission lines) from all stars detected at high significance in Binder et al. (2024), ApJS, ..., ...</p> <p>Description of Files:</p> <h3>LightCurves.zip</h3> <p>Summary: Light curve files (327) for all stars detected at high significance; in FITS (BinTableHDU) format with 7-14 extensions. The following types of data are included:</p> <ul> <li>"*_source.lc" : EPIC/PN source light curves of stars detected with more than ~500 net counts in an XMM-Newton observation</li> <li>"*_bkg.lc" : EPIC/PN background light curves of stars detected with more than ~500 net counts in an XMM-Newton observation</li> <li>"*_sub_lc.fits" : Background-subtracted light curves of stars detected with more than ~50 net counts in a Chandra observation</li> </ul> <h3>Spectra.zip</h3> <p>Summary: Spectra (152) for all stars detected at high significance (with >500 net counts in Chandra observations and >2000 net counts in XMM-Newton observations) in ASCII (ECSV format). We provide both the continuum spectra and spectra containing line emission. For stars that exhibit X-ray count rate variability, we provide spectra for specific variability types (see Binder et al. 2024 for details). The following types of data are included:</p> <ul> <li>"*_continuum.dat" : Continuum-only best-fit spectra</li> <li>"*_lines.dat" : Best-fit spectra containing line emission</li> </ul>
Fluid Interfaces in Mixed-Wet Bead Packs: Insights from 3D X-Ray Imaging
<p>Data for beads curvature: The data on mean curvature on a 200×140×170 voxel dry image before and after the removal of any points within 5 voxels of bead contacts. </p> <p> </p> <p>Data for fluid interfacial curvature between oil and brine: The data on mean and Gaussian curvature distribution on a 930×930×860 voxel wet image. The curvature data is provided for both before and after the removal of points within one voxel of the three-phase contact line.</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>
Search strategies for generic justification for a slot scanning, biplanar X-ray imaging system (EOS™ imaging system) for the diagnosis and assessment of orthopaedic conditions.
<p>The dataset includes the complete, reproducible search strategies for all literature databases searched during this project. The search strategies address the following research questions:</p> <p>RQ 1 To determine the test accuracy, clinical benefits and safety of slot scanning devices (EOS system) compared to conventional X-ray imaging for diagnosis of scoliosis and evaluation or monitoring of scoliosis patients.</p> <p>RQ 2 To determine the test accuracy, clinical benefits and safety of slot scanning devices (EOS system) compared to current practice for evaluation, monitoring or diagnosis of patients with other known or suspected orthopaedic conditions.</p>
Edge illumination X-ray phase contrast imaging with alternative gratings: dataset
<p>This dataset contains results from Edge illumunation X-ray phase contrast simulations with alternative gratings, as shown in 'Setup.png' The simulations are performed with the monte-carlo software Gate. Postprocessing is done in Matlab. Four different grating geometries were simulated: Conventional, sheared, curved and folded gratings. As phantom, a row of Aluminum cylinders is chosen.</p> <p>The simulation parameters can be found in the excel-file 'Simulation_parameters.xlsx'.</p> <p>The folder 'gate' contains the macros that where used for the monte carlo-simulation.</p> <p>The folder 'matlab' contains the results of post-processing in matlab for each grating geometry. They can be opened with the file 'results_script.m</p> <p>The folder 'results' contains images of the results for each geometry, including, flatfield, projection, threefold contrast and fitting parameters.</p> <table> <tbody> <tr> <td>This research was supported by EU Interreg Flanders - Netherlands Smart*Light (0386), Fonds wetenschappelijk onderzoek (G090020N, G094320N), and Agentschap Innoveren \& Ondernemen (Vlaio) (HBC.2020.2159). Nathanaël Six and Ben Huyge have a PhD fellowship of the FWO (11D8319N, 1S46122N).</td> </tr> </tbody> </table> <p> </p> <p></p>
Ultrafast dark-field X-ray microscopy to image strain wave propagation
<p>This dataset includes DF-XRM data obtained at an X-FEL source.<br> The sample is a diamond single crystal with a strain wave propagating through the center, which is imaged in a stroboscopic fashion.<br> This data originated the LCLS, see <a href="https://arxiv.org/abs/2211.01042">arXiv:2211.01042</a> for a description of the experiment.<br> Run 538 contains a time series with 1 ns between each step, run 540 contains a time series with 72 ns steps (one full period) while run 536 contains a rocking curve.</p> <p>Each run is divided into hdf5 files with 2000 shots each. Each hdf5 file has then been zipped using the BZIP2 algorithm to reduce the overall size.</p> <p>Each run contains header information with the status of the X-FEL beam and the laser (the laser excites the strain wave). The shots where the X-FEL beam is off should be used to subtract the detector background. Run 536 additionally contains motor positions for the goniometer.</p> <p>Please note: for run 538 and 540 there are ~120 shots at each position. However, the timing tool is unreliable, giving a ±1 ns error, and to analyze this data the correct time of each shot must be first identified. This can be done by looking at the position of the strain wave.<br> <br> Using python, the data may be unziped, opened, and visualized in the following way:</p> <pre><code class="language-python">import zipfile import numpy as np import h5py #unzip with zipfile.ZipFile(f'538_0.zip', 'r') as zip_ref: zip_ref.extractall('') with h5py.File(f'538_0.hdf5', 'r') as f: # print contents of file for key in f.keys(): print(key, f[key].shape, f[key].dtype) # approximate the detector background mask = np.array(f[b'lightStatus_xray']) == 0 Images_DF_Xray_OFF = np.array(f['images_dark_field_arm'][mask,:,:]) df_noise = np.median(Images_DF_Xray_OFF, axis = 0) # extract desired frames ims = np.array(f['images_dark_field_arm'][120*1:120*2]) im = np.average(ims, axis = 0) # plot the detector background and the average of the selected frames import matplotlib.pyplot as plt fig, axes = plt.subplots(1,2, figsize = (4, 12), dpi = 300) axes[0].imshow(df_noise, vmin = np.percentile(df_noise,1), vmax = np.percentile(df_noise,99)) axes[1].imshow(im-df_noise, vmin = np.percentile(im-df_noise,1), vmax = np.percentile(im-df_noise,99))</code></pre> <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.