Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

datasets available to search

ShareScore release 0.9.0

Dataset results

11 results for “2D projection”

Learn how ShareScore rates datasets ↗

zenodo40/100

Large Spots DeepMIB project, synthetic dataset for testing 2D semantic segmentation

A complete DeepMIB project with a synthetic dataset generated for quick tests of semantic segmentation approaches. The dataset includes a trained DeepLabV3-Resnet18 network for detection of large spots on a black background. The network can be opened by loading "2D_LargeSpots_2cl_DeepLabV3.mibCfg" file by<ul><li>MIB->Menu->Tools->Deep learning segmentation->Options tab->Config files->Load </li><li>Drag and drop of the config file into DeepMIB window</li></ul>Microscopy Image Browser: <a href="https://mib.helsinki.fi">https://mib.helsinki.fi</a>

opencc-by-4.0Nov 2023View details →

zenodo40/100

Progeny Project DFT and NAMD data for periodic 2D surfactant

Crystalline unit cell, band structure, and electron-hole recombination dynamics from non-adiabatic molecular dynamics for the periodic 2D surfactant of the Progeny. See Readme.txt for more details.

opencc-by-4.0Nov 2022View details →

dryad36/100

DeepProjection: Specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning

The efficient extraction of image data from curved tissue sheets embedded in volumetric imaging data remains a serious and unsolved problem in quantitative studies of embryogenesis. Here we present DeepProjection (DP), a trainable projection algorithm based on deep learning. This algorithm is trained on user-generated training data to locally classify the 3D stack content and rapidly and robustly predict binary masks containing the target content, e.g., tissue boundaries, while masking highly fluorescent out-of-plane artifacts. A projection of the masked 3D stack then yields background-free 2D images with undistorted fluorescence intensity values. The binary masks can further be applied to other fluorescent channels or to extract the local tissue curvature. DP is designed as a first processing step than can be followed, for example, by segmentation to track cell fate. We apply DP to follow the dynamic movements of 2D-tissue sheets during dorsal closure in Drosophila embryos and of the periderm layer in the elongating Danio embryo. DeepProjection is available as fully documented Python package.

opencc-zeroOct 2022View details →

zenodo36/100

Supplemental material for 'Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection'

This is the supplemental data for the manuscript titled ‘Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection’ submitted to Ultramicroscopy. Motivation: Detection of nanoparticles and classification of the material type in scanning transmission electron microscopy (STEM) images can be a tedious task, if it has to be done manually. Therefore, a convolutional neural network (CNN) is trained to do this task for STEM-images of TiO2-WO3 nanoparticle hetero-aggregates. In conventional STEM, only 2D-projection images of the samples can be measured. STEM tomography allows for a 3D-reconstruction but it is a time-consuming and hence expensive task. In the present work, evaluations of 2D-projections are compared quantitatively to evaluations of 3D-reconstructions. For both evaluations a CNN is trained to predict particle positions and classify the material. The present dataset contains training and evaluation data and some code scripts that can be used after installation of the MMDetection toolbox (<a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a>) to train the CNN for 2D-projection data. For 3D-reconstruction a StarDist-3D network is trained (<a href="https://github.com/stardist/stardist">https://github.com/stardist/stardist</a>). Details are provided in the manuscript submitted to Ultramicroscopy and in the comments of the code scripts. For evaluation, we provide Python and MATLAB scripts. Authors and funding: The present dataset was created by the authors. The work was funded by the Deutsche Forschungsgemeinschaft within the priority program SPP2289 under contract numbers RO2057/17-1 and MA3333/25-1 and under contract number INST 144/462-1 FUGG. <pre> </pre> Dataset description: We provide several zip-archives. All of them contain two subfolders, one of them for 2D-projection data, the other one for 3D-reconstruction data. training_data.zip contains the training data. In the 3D case, the subfolder mask contains the ground truth segmentation masks; the subfolder reconstruction contains the corresponding simulated 3D reconstructions. In the 2D case, the subfolder HAADF contains the 2D-projection images. In both cases, the subfolder json contains the annotation. Each file within the json folder provides for each image or reconstruction the following information: ·        aggregat_no: image id, the number of the corresponding image file ·        particle_position_x: list of particle position x-coordinates in nm ·        particle_position_y: list of particle position y-coordinates in nm ·        particle_position_z: list of particle position z-coordinates in nm ·        particle_radius: list of volume equivalent particle radii in nm ·        particle_type: list of particle types, 1: TiO2, 2: WO3 ·        particle_shape: list of particle shapes: 0: sphere, 1: box, 2: icosahedron ·        rotation: list of particle rotations in rad. Each particle is rotated twice by the listed angle (before and after deformation) ·        deformation: list of particle deformations. After the first rotation the particle x-coordinates of the particle’s surface mesh are scaled by the factor listed in deformation, y- and z-coordinates are scaled according to 1/sqrt(deformation). ·        cluster_index: list of cluster indices for each particle ·        initial_cluster_index: list of initial cluster indices for each particle, before primary clusters of the same material were merged ·        fractal_dimension: the intended fractal dimension of the aggregate ·        fractal_dimension_true: the realized geometric fractal dimension of the aggregate (neglecting particle densities) ·        fractal_dimension_weight_true: the realized fractal dimension of the aggregate (including particle densities) ·        fractal_prefactor: fractal prefactor ·        mixing_ratio_intended: the intended mixing ratio (fraction of WO3 particles) ·        mixing_ratio_true: the realised mixing ratio (fraction of WO3 particles) ·        mixing_ratio_volume: the realised mixing ratio (fraction of WO3 volume) ·        mixing_ratio_weight: the realised mixing ratio (fraction of WO3 weight) ·        particle_1_rho: density of TiO2 used for the calculations ·        particle_1_size_mean: mean TiO2 radius ·        particle_1_size_min: smallest TiO2 radius ·        particle_1_size_max: largest TiO2 radius ·        particle_1_size_std: standard deviation of TiO2 radii ·        particle_1_clustersize: average TiO2 cluster size ·        particle_1_clustersize_init: average TiO2 cluster size of primary clusters (before merging into larger clusters) ·        particle_1_clustersize_init_intended: intended TiO2 cluster size of primary clusters ·        particle_2_rho: density of WO3 used for the calculations ·        particle_2_size_mean: mean WO3 radius ·        particle_2_size_min: smallest WO3 radius ·        particle_2_size_max: largest WO3 radius ·        particle_2_size_std: standard deviation of WO3 radii ·        particle_2_clustersize: average WO3 cluster size ·        particle_2_clustersize_init: average WO3 cluster size of primary clusters (before merging into larger clusters) ·        particle_2_clustersize_init_intended: intended WO3 cluster size of primary clusters ·        number_of_primary_particles: number of particles within the aggregate ·        gyration_radius_geometric: gyration radius of the aggregate (neglecting particle densities) ·        gyration_radius_weighted: gyration radius of the aggregate (including particle densities) ·        mean_coordination: mean total coordination number (particle contacts) ·        mean_coordination_heterogen: mean heterogeneous coordination number (contacts with particles of the different material) ·        mean_coordination_homogen: mean homogeneous coordination number (contacts with particles of the same material) ·        material_1: the name of the first material (TiO2) ·        material_2: the name of the second material (WO3) ·        radius_equiv: list of area equivalent particle radii (in projection) in nm ·        k_proj: projection direction of the aggregate: 0: z-direction (axis = 2), 1: x-direction (axis = 1), 2: y-direction (axis = 0) ·        polygons: list of polygons that surround the particle (COCO annotation) ·        bboxes: list of particle bounding boxes ·        aggregate_size: projected area of the aggregate translated into the radius of a circle in nm ·        n_pix: number of pixel per image in horizontal and vertical direction (squared images) ·        pixel_size: pixel size in nm ·        image_size: image size in nm ·        add_poisson_noise: 1 if poisson noise was added, 0 otherwise ·        frame_time: simulated frame time (required for poisson noise) ·        dwell_time: dwell time per pixel (required for poisson noise) ·        beam_current: beam current (required for poisson noise) ·        electrons_per_pixel: number of electrons per pixel ·        dose: electron dose in electrons per Å2 ·        add_scan_noise: 1 if scan noise was added, 0 otherwise ·        beam_misposition: parameter that describes how far the beam can be misplaced in pm (required for scan noise) ·        scan_noise: parameter that describes how far the beam can be misplaced in pix (required for scan noise) ·        add_focus_dependence: 1 if a focus effect is included, 0 otherwise ·        add_partial_coating: 1 if some TiO2 particles were coated by a thin WO3 film, 0 otherwise. ·        data_format: data format of the images, e.g. uint8 For 3D reconstructions, the following information is added: ·        add_image_shifts, 1 if all images of the tilt series were shifted randomly by some pixel to account for misaligned images, 0 otherwise. ·        add_projection_noise: 1 if noise was added to projection angles, 0 otherwise. ·        N_SIRT: number of SIRT iterations for the 3D-reconstruction. ·        proj_angles: list of angles used for the projection directions of the tilt series in rad. For the 2D case, there are 24000 training images, 5500 validation images, 5500 test images, and their corresponding annotations. Aggregates and STEM images were obtained with the algorithm explained in the main work. The important data for CNN training is extracted from the files of individual aggregates and concluded in the subfolder COCO. For training, validation and test data there is a file annotation_COCO.json that includes all information required for the CNN training. For the 3D case, there are 100 simulated reconstructions that are divided into 80 training and 20 validation images within the training script. The zip archive models.zip includes the two networks that were trained, evaluated and used for the investigation in the manuscript. In the 2D case, network weights are stored in the file 2D_projection/logs/fit/20240209-095952/iter_60000.pth. These weights can be loaded with the jupyter-notebook 2D_projection_prediction.ipynb. Furthermore, a configuration file, which is required by the notebooks, is stored as 2D_projection/logs/fit 20240209-095952/config_file.py. In the 3D case, network weights are stored in 3D_reconstruction/model/weights_best.h5. Also for this case, a configuration file is provided. The network can be loaded with the jupyter-notebook 3D_reconstruction_prediction.ipynb. The zip archive experiment_measurement.zip includes the experimental 2D-projection images and the experimental 3D-reconstructions investigated in the manuscript. In the 3D case, we provide measured reconstructions as obtained by MATLAB and as transformed for the prediction with the CNN. The zip archive experiment_prediction.zip includes predictions and visualizations obtained by the 2D and 3D CNNs. The zip archive simulation_measurement.zip includes simulated 2D-projection images and simulated 3D-reconstructions that were not used for the network training. These simulations were used for evaluation of trained networks. The zip archive simulation_prediction.zip includes predictions and visualizations obtained by the 2D and 3D CNNs for the simulated data that was not used during the training process. In the zip archive code.zip, we provide several files with Python and MATLAB code that can be used for training, prediction and evaluation of the CNNs. A lot of information is provided within the comments and markdowns. For the application, it is required that the MMDetection toolbox and the StarDist3D framework are installed. ·        2D_projection_training.py: This Python script can be used for network training of the 2D Mask R-CNN after installation of the MMDetection toolbox. ·        3D_reconstruction_training.py: This Python script can be used for network training of the StarDist-3D network after installation of the StarDist package. ·        2D_projection_prediction.ipynb: This jupyter-notebook can be used for the application of a trained Mask R-CNN to experimental and simulated 2D-projection data. ·        3D_reconstruction_prediction.ipynb: This jupyter-notebook can be used for the application of a trained StarDist-3D network to experimental and simulated 3D-reconstruction data. ·        Evaluation_experiment.m: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D experimental evaluations. ·        Evaluation_simulation.m: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D evaluations of simulations. ·        particle_detection_functions.py: This Python script contains functions required by the jupyter-notebooks. Details can be found within the comments. ·        ASTRA_CM_plot_results.m: This MATLAB script provides functions for the visualization of experimental and simulated 3D-reconstructions. ·        ASTRA_CM_particle_detection.m: This MATLAB script is used for the quantitative evaluation of segmentations of 3D-reconstructions.   There is no confidential data in this dataset. It is neither offensive, nor insulting or threatening. The dataset was generated to discriminate between TiO2 and WO3 nanoparticles in STEM-images and STEM tomography reconstructions. It might be possible that it can discriminate between different materials if the STEM contrast is similar to the contrast of TiO2 and WO3 but there is no guarantee.

opencc-by-4.0May 2024View details →

zenodo36/100

UTC STRIDE Project G2: Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation

This repository contains the extracted traffic data used in the case study for UTC STRIDE project G2 "Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation". The traffic data was extracted using manual or AI-based methods (presented in the project final report) from two 30-minute videos with high and low traffic density.  Below are the description of each data file: <ul> <li>high_density_both_lane_raw_AI_speed.csv <ul> <li>Description: extracted individual vehicle speed data of the high traffic density video using the AI-based method.</li> <li>Data Fields: <ul> <li>object_id: unique id for each detected and tracked vehicle</li> <li>frame_index: frame index of the video when other tracked vehicle exit the virtual speed loop.</li> <li>lane_id: lane id (1=outer lane, 2=inner lane)</li> <li>speed: average speed traveling through the virtual speed loop (kph)</li> </ul> </li> </ul> </li> <li>high_density_inner_lane_traffic_AI_count_1min_bin.csv <ul> <li>Description: AI extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li>high_density_inner_lane_traffic_manual_count.csv <ul> <li>Description: manually extracted individual vehicle speed, and headway data on the inner lane of the high traffic density data </li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li>high_density_inner_lane_traffic_manual_count_1min_bin.csv <ul> <li>Description: manually extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li>high_density_outer_lane_traffic_AI_count_1min_bin.csv <ul> <li>Description: AI extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li>high_density_outer_lane_traffic_manual_count.csv <ul> <li>Description: manually extracted individual vehicle speed, and headway data on the outer lane of the high traffic density data </li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li>high_density_outer_lane_traffic_manual_count_1min_bin.csv <ul> <li>Description: manually extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li>low_density_inner_lane_traffic_manual_count.csv <ul> <li>Description: manually extracted individual vehicle speed, and headway data on the inner lane of the low traffic density data </li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li>low_density_inner_lane_traffic_manual_count_1min_bin.csv <ul> <li>Description: manually extracted count, and speed data on the inner lane of the low traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li>low_density_outer_lane_traffic_manual_count.csv <ul> <li>Description: manually extracted individual vehicle speed, and headway data on the outer lane of the low traffic density data </li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li>low_density_outer_lane_traffic_manual_count_1min_bin.csv <ul> <li>Description: manually extracted count, and speed data on the outer lane of the low traffic density data (aggregated into 1-minute bins)</li> <li>Data Fields: <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0Nov 2022View details →

zenodo36/100

Clean Sky 2 - SALUTE H2020 Project : 2D smart liner data

This data concerns all obtained measurment in paper :In flow acoustic characterisation of a 2D active liner with local and non local strategies. K. Billon, E. De Bono, M. Perez , E. Salze, G. Mattenand al, Applied Acoustics 191 (2022) 108655, https://doi.org/10.1016/j.apacoust.2022.108655

opencc-by-4.0Jan 2023View details →

dryad36/100

DeepProjection: Specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning

Open the record for dataset details and reuse information.

publicOct 2022View details →

ClinicalTrials.gov24/100

Project 1: Self-Triage by 2D Full-field Digital Mammography or Synthetic Images

ClinicalTrials.gov study NCT05960188. IPD Sharing: YES. Countries: 1. Publications: 0.

controlledIPD-YESFeb 2026View details →

zenodo20/100

Small spots DeepMIB project, synthetic dataset for testing 2D semantic segmentation

A complete DeepMIB project with a synthetic dataset generated for quick tests of semantic segmentation approaches. The dataset includes a trained U-net network for detection of small random spots of 2 colors on a black background.The network can be opened by loading "2D_SmallSpots_3cl_Unet.mibCfg" file by<ul><li>MIB->Menu->Tools->Deep learning segmentation->Options tab->Config files->Load </li><li>Drag and drop of the config file into DeepMIB window</li></ul>Microscopy Image Browser: <a href="https://mib.helsinki.fi">https://mib.helsinki.fi</a>

restrictedcc-by-4.0Nov 2023View details →

zenodo20/100

Large spots DeepMIB project, synthetic dataset for testing 2D patch-wise segmentation

A complete DeepMIB project with a synthetic dataset generated for quick tests of the patch-wise segmentation (classification) approaches The dataset includes a trained Resnet18 network for detection of patches that belong to large white spots on black background. The network can be opened by loading "2D_LargeSpots_Patchwise_Resnet18.mibCfg" by<ul><li>MIB->Menu->Tools->Deep learning segmentation->Options tab->Config files->Load </li><li>Drag and drop of the config file into DeepMIB window</li></ul>Microscopy Image Browser: <a href="https://mib.helsinki.fi">https://mib.helsinki.fi</a>

restrictedcc-by-4.0Nov 2023View details →

zenodo20/100

Cellesce Z projection 2D dataset - Colorectal organoids

Cellesce Z projection 2D dataset - Colorectal organoids

opencc-by-4.0May 2022View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record

Find research datasets worth reusing

Dataset results

Large Spots DeepMIB project, synthetic dataset for testing 2D semantic segmentation

Progeny Project DFT and NAMD data for periodic 2D surfactant

DeepProjection: Specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning

Supplemental material for 'Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection'

UTC STRIDE Project G2: Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation

Clean Sky 2 - SALUTE H2020 Project : 2D smart liner data

DeepProjection: Specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning

Project 1: Self-Triage by 2D Full-field Digital Mammography or Synthetic Images

Small spots DeepMIB project, synthetic dataset for testing 2D semantic segmentation

Large spots DeepMIB project, synthetic dataset for testing 2D patch-wise segmentation

Cellesce Z projection 2D dataset - Colorectal organoids

Understand access before you commit

Allen Brain Atlas

Annotated Behaviour and Observability Dataset (ABODe)

DANDI Archive for NWB datasets

International Brain Laboratory public data

OpenNeuro

Your academic CV and NIH biosketch