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.
193
datasets available to search
ShareScore release 0.9.0
Dataset results
193 results for “labeled data”
Data from: Safety of single low-dose primaquine in glucose-6-phosphate dehydrogenase deficient falciparum-infected African males: two open-label, randomized, safety trials
Open the record for dataset details and reuse information.
RNA-seq and label-free quantitative proteomics data from: KDM4A serves as an α-tubulin demethylase regulating microtubule polymerization and cell mitosis
Open the record for dataset details and reuse information.
Echolocation clicks and anthropogenic detections with neural network labels in Hawaiian Island HARP data from Kona, Kaua`i, and Pearl and Hermes Reef
Open the record for dataset details and reuse information.
Labeled data for citation field extraction
Open the record for dataset details and reuse information.
Data from: Dynamics of the CD9 interactome during bacterial infection of epithelial cells by proximity labelling proteomics
Open the record for dataset details and reuse information.
Data from: Label-free imaging of intracellular structures in living mammalian cells via external apodization phase-contrast microscopy
Open the record for dataset details and reuse information.
Machine learning feature data from EHR, labels, and estimates for next generation sequencing-based assay
Open the record for dataset details and reuse information.
Codes and source data files for: Proximity labeling identifies LOTUS domain proteins that promote the formation of perinuclear germ granules in C. elegans
Open the record for dataset details and reuse information.
Data from: Long-lived metabolic enzymes in the crystalline lens identified by pulse-labeling of mice and mass spectrometry
Open the record for dataset details and reuse information.
Supplementary methods and data for: Dogs with a vocabulary of object label remember labels for at least two years
Open the record for dataset details and reuse information.
Data from: Proximity-labeling proteomics reveals remodeled interactomes and altered localization of pathogenic SHP2 variants
Open the record for dataset details and reuse information.
Multispectral and augmented Landsat data with land cover labels
<p>Benchmark set at 77.1% O.A at: https://doi.org/10.1117/1.JRS.14.048503</p> <p>The dataset consists of 60,000 images, corresponding to Landsat patches of 33x33 pixels with 102 bands. Randomly selected from Mexico (country). Each patch is labeled with one of 12 Land Use and Vegetation classes according to the classification described at https://doi.org/10.3390/rs6053923.</p> <p>The zip file contains 12 folders numbered 1-12 and each contains 5,000 .npy python files (can be loaded with the NumPy library).</p> <p>The labeled classes correspond to the following identifier.</p> <p>1, Temperate Coniferous forest<br> 2, Temperate Decidius Forest<br> 3, Temperate Mixed Forest<br> 4, Tropical Evergreen Forest<br> 5, Tropical Deciduous Forest<br> 6, Scrubland<br> 7, Wetland Vegetation<br> 8, Agriculture<br> 9, Grassland<br> 10, Water body<br> 11, Barren Land<br> 12, Urban Area</p> <p>To build that dataset, we take the information of the National Continuum of Land Use and Vegetation series number 5 generated by the National Institute of Statistics and Geography from Mexico (INEGI) from The National Commission for the Knowledge and Use of Biodiversity (CONABIO) web page (http://geoportal.conabio.gob.mx/metadatos/doc/html/usv250s5ugw.html).</p> <p>The file used for this dataset construction is the shape format file with geographic coordinates located in http://www.conabio.gob.mx/informacion/gis/maps/geo/usv250s5ugw.zip.<br> Later, a transformation to Albers equal-area conic projection was done with the followings parameters:</p> <p>Fake east: 2500000.0<br> Fake North: 0.0<br> Origin longitude: -102.0º<br> Origin latitude: 12.0º<br> First standard parallel: 17.5º<br> Second standard parallel: 29.5º<br> Linear unit: Meter (1.0)<br> Reference ellipsoid: GRS80</p> <p><br> Once the data was projected, using the classes identified in the National Continuum of Land Use and Vegetation, correspondence was applied to the classes identified in https://doi.org/10.3390/rs6053923, these classes being: Agriculture, Barren land, Grassland, Scrubland, Temperate coniferous forest, Temperate deciduous forest, Temperate mixed forest, Tropical deciduous forest, Tropical evergreen forest, Urban area, Waterbody and Wetland vegetation.</p> <p>Once the information layer was generated with the 12 classes indicated above, the reference layer was rasterized.<br> Thus, a national grid of 1,975,940 regions of 1 x 1 kilometers was generated and the percentage of pixels of the dominant class in each corresponding 1 km region was associated.</p> <p>A total of cells with 70% or more pixels from one dominant class corresponds to 1,640,827 which represents a total of 83% of the Mexican territory. That means, only 17% of cells have less than 70% of their pixels from one dominant class.<br> Then, 5000 regions were randomly selected from each land cover class at the national level. For this random selection only were selected the regions in which cells have 70% or more of their pixels from one dominant class. The above, for looking to have consistent and reliable data for the automatic classification task. This random selection generates a total of 60,000 regions selected.</p> <p>Image patches were extracted from the selected regions in the sample.</p> <p>The image used is the result of the application of multiple time series analysis algorithms on a cube of image data with mainly Tier 1 (T1) quality and a few Tier 2 (T2) as described in https: // www. usgs.gov/land-resources/nli/landsat/landsat-collection-1. An Open Data Cube (ODC, https://www.opendatacube.org/) was constructed from 3,515 Landsat 5 and 7 images corresponding to the year 2011, which is the same reference year of the National Continuum of Land Use and Vegetation Series 5.</p> <p>From the analysis of the ODC images, the Geomedian (https://doi.org/10.1109/TGRS.2017.2723896) was calculated, which generated a national cloud-free mosaic from 2011, pixels at 30 meters resolution and 6 spectral bands (blue, green, red, nir, swir 1, swir 2). Finally, 15 spectral indices were calculated for each pixel in the image. This resulted in 15 national mosaics from the analysis of the time series of each pixel available for the year 2011 using all the combinations of normalized difference indices, which were possible with the 6 bands that were incorporated into the data cube, with which resulted in 102 information channels. Since Landsat images have a resolution of 30 meters, we have images of 33 pixels x 33 pixels for each region of 1 km x 1 km.</p> <p>The 102 channels in the patches correspond to:</p> <p>Geomedian Bands (6): blue, green, red, nir, swir 1, swir 2<br> Geomedian Based Indexes (15): evi, bu, sr, arvi, ui, ndbi, ibi, ndvi, ndwi, mndwi, nbi, brba, nbai, baei, bi<br> Geomedian Based Tasseled cap transformation (6): brightness, greenness, wetness, fourth, fifth, sixth</p> <p>2011 Landsat Time Analysis Series by Pixel</p> <p>(red-swir 1)/(red+swir 1); (5): min, mean, max, std, median<br> (red-nir)/( red+nir); (5): min, mean, max, std, median<br> (swir 1-swir 2)/( swir 1+swir 2); (5): min, mean, max, std, median<br> (nir-swir 2)/(nir+swir 2); (5): min, mean, max, std, median<br> (nir-swir 1)/( nir+swir 1); (5): min, mean, max, std, median<br> (red-swir 2)/( red+swir 2); (5): min, mean, max, std, median<br> (green-swir 2)/(green+swir 2); (5): min, mean, max, std, median<br> (green-swir 1)/(green+swir 1); (5): min, mean, max, std, median<br> (green-red)/(green+red); (5): min, mean, max, std, median<br> (green-nir)/(green+nir); (5): min, mean, max, std, median<br> (blue-swir 2)/(blue+swir 2); (5): min, mean, max, std, median<br> (blue-swir 1)/(blue+swir 1); (5): min, mean, max, std, median<br> (blue-red)/(blue+red); (5): min, mean, max, std, median<br> (blue-nir)/(blue+nir); (5): min, mean, max, std, median<br> (blue-green)/( blue+green); (5): min, mean, max, std, median</p>
Mars orbital image (HiRISE) labeled data set version 3.2
<p> </p> <p><strong>Please note that the file hirise-map-proj-v3_2.zip below contains the latest images and labels associated with this data set. </strong></p> <p> </p> <p>This dataset contains a total of 64,947 landmark images that were detected and extracted from HiRISE browse images, spanning 232 separate source images.</p> <p>This set was formed from 10,815 original landmarks. Each original landmark was cropped to a square bounding box that included the full extent of the landmark plus a 30-pixel margin to the left, right, top, and bottom. Each landmark was then resized to 227x227 pixels. 9,022 of these images were then augmented to generate 6 additional landmarks using the following methods:</p> <p>1. 90 degrees clockwise rotation<br> 2. 180 degrees clockwise rotation<br> 3. 270 degrees clockwise rotation<br> 4. Horizontal flip<br> 5. Vertical flip<br> 6. Random brightness adjustment</p> <p>The remaining 1,793 images were not augmented. Combining these with the 7*9,022 images, gives a total of 64,947 separate images.</p> <p><br> <strong>Contents:</strong><br> - map-proj-v3_2/: Directory containing individual cropped landmark images<br> - labels-map-proj-v3_2.txt: Class labels (ids) for each landmark image. File includes two columns separated by a space: filename, class_id</p> <p>- labels-map-proj-v3_2_train_val_test.txt: Includes train/test/val labels and upsampling used for trained model. File includes three columns separated by a space: filename, class_id, set<br> - landmarks_map-proj-v3_2_classmap.csv: Dictionary that maps class ids to semantic names</p> <p><strong>Class Discussion:</strong></p> <p>We give a discussion of the various landmarks that make up our classes.<strong> </strong></p> <p>Bright dune and dark dune are two sand dune classes found on Mars. Dark dunes are completely defrosted, whereas bright dunes are not. Bright dunes are generally bright due to overlying frost and can exhibit black spots where parts of the dune are defrosting.</p> <p>The crater class consists of crater images in which the diameter of the crater is greater than or equal to 1/5 the width of the image and the circular rim is visible for at least half the crater's circumference.</p> <p>The slope streak class consists of images of dark flow-like features on slopes. These features are believed to be formed by a dry process in which overlying (bright) dust slides down a slope and reveals a darker sub-surface.</p> <p>Impact ejecta refers to material that is blasted out from the impact of a meteorite or the eruption of a volcano. We also include cases in which the impact cleared away overlying dust, exposing the underlying surface. In some cases, the associated crater may be too small to see. Impact ejecta can also include lava that spilled out from the impact (blobby ("lobate") instead of blast-like), more like an eruption (triggered by the impact). Impact ejecta can be isolated, or they can form in clusters when the impactor breaks up into multiple fragments.</p> <p>Spiders and Swiss cheese are phenomena that occur in the south polar region of Mars. Spiders have a central pit with radial troughs, and they are believed to form as a result of sublimation of carbon dioxide ice. This process can produce mineral deposits on top, which look like dark or light dust that highlights cracks in the CO2 ice. Spiders can resemble impact ejecta due to their radial troughs, but impact ejecta tends to have straight radial jets that fade as they get farther from the center. The spider class also includes fan-like features that form when a geyser erupts through the CO2 layer and the material is blown by the wind away from the cracks. Fans are typically unidirectional (following the wind direction), whereas impact ejecta often extends in multiple directions. Swiss cheese is a terrain type that consists of pits that are formed when the sun heats the ice making it sublimate (change solid to gas).</p> <p>Other is a catch-all class that contains images that fit none of the defined classes of interest. This class makes up the majority of our data set.</p>
MER Opportunity and Spirit Rovers Pancam Images Labeled Data Set
<p><strong>Introduction</strong></p> <p>The data set is based on 3,004 images collected by the Pancam instruments mounted on the Opportunity and Spirit rovers from NASA's Mars Exploration Rovers (MER) mission. We used rotation, skewing, and shearing augmentation methods to increase the total collection to 70,864 (see Image Augmentation section for more information). Based on the <a href="https://merdatacatalog.com/survey">MER Data Catalog User Survey</a> [1], we identified 25 classes of both scientific (e.g. soil trench, float rocks, etc.) and engineering (e.g. rover deck, Pancam calibration target, etc.) interests (see Classes section for more information). The 3,004 images were labeled on <a href="https://www.zooniverse.org/">Zooniverse platform</a>, and each image is allowed to be assigned with multiple labels. The images are either 512 x 512 or 1024 x 1024 pixels in size (see Image Sampling section for more information).</p> <p><strong>Classes</strong></p> <p>There is a total of 25 classes for this data set. See the list below for class names, counts, and percentages (the percentages are computed as count divided by 3,004). Note that the total counts don't sum up to 3,004 and the percentages don't sum up to 1.0 because each image may be assigned with more than one class. </p> <ul> <li>Class name, count, percentage of dataset</li> <li>Rover Deck, 222, 7.39%</li> <li>Pancam Calibration Target, 14, 0.47%</li> <li>Arm Hardware, 4, 0.13%</li> <li>Other Hardware, 116, 3.86%</li> <li>Rover Tracks, 301, 10.02%</li> <li>Soil Trench, 34, 1.13%</li> <li>RAT Brushed Target, 17, 0.57%</li> <li>RAT Hole, 30, 1.00%</li> <li>Rock Outcrop, 1915, 63.75%</li> <li>Float Rocks, 860, 28.63%</li> <li>Clasts, 1676, 55.79%</li> <li>Rocks (misc), 249, 8.29%</li> <li>Bright Soil, 122, 4.06%</li> <li>Dunes/Ripples, 1000, 33.29%</li> <li>Rock (Linear Features), 943, 31.39%</li> <li>Rock (Round Features), 219, 7.29%</li> <li>Soil, 2891, 96.24%</li> <li>Astronomy, 12, 0.40%</li> <li>Spherules, 868, 28.89%</li> <li>Distant Vista, 903, 30.23%</li> <li>Sky, 954, 31.76%</li> <li>Close-up Rock, 23, 0.77%</li> <li>Nearby Surface, 2006, 66.78%</li> <li>Rover Parts, 301, 10.02%</li> <li>Artifacts, 28, 0.93%</li> </ul> <p><strong>Image Sampling</strong></p> <p>Images in the MER rover Pancam archive are of sizes ranging from 64x64 to 1024x1024 pixels. The largest size, 1024x1024, was by far the most common size in the archive. For the deep learning dataset, we elected to sample only 1024x1024 and 512x512 images as the higher resolution would be beneficial to feature extraction.</p> <p>In order to ensure that the data set is representative of the total image archive of 4.3 million images, we elected to sample via "site code". Each Pancam image has a corresponding two-digit alphanumeric "site code" which is used to track location throughout its mission. Since each "site code" corresponds to a different general location, sampling a fixed proportion of images taken from each site ensure that the data set contained some images from each location. In this way, we could ensure that a model performing well on this dataset would generalize well to the unlabeled archive data as a whole. We randomly sampled 20% of the images at each site within the subset of Pancam data fitting all other image criteria, applying a floor function to non-whole number sample sizes, resulting in a dataset of 3,004 images.</p> <p><strong>Train/validation/test sets split</strong></p> <p>The 3,004 images were split into train, validation, and test data sets. The split was done so that roughly 60, 15, and 25 percent of the 3,004 images would end up as train, validation, and test data sets respectively, while ensuing that images from a given site are not split between train/validaiton/test data sets. This resulted in 1,806 train images, 456 validation images, and 742 test images. </p> <p><strong>Augmentation</strong></p> <p>To augment the images in train and validation data sets (note that images in the test data set were not augmented), three augmentation methods were chosen that best represent transformations that could be realistically seen in Pancam images. The three augmentations methods are rotation, skew, and shear. The augmentation methods were applied with random magnitude, followed by a random horizontal flipping, to create 30 augmented images for each image. Since each transformation is followed by a square crop in order to keep input shape consistent, we had to constrict the magnitude limits of each augmentation to avoid cropping out important features at the edges of input images. Thus, rotations were limited to 15 degrees in either direction, the 3-dimensional skew was limited to 45 degrees in any direction, and shearing was limited to 10 degrees in either direction. Note that augmentation was done only on training and validation images. </p> <p><strong>Directory Contents</strong></p> <ul> <li>images: contains all 70,864 images</li> <li>train-set-v1.1.0.txt: label file for the training data set</li> <li>val-set-v1.1.0.txt: label file for the validation data set</li> <li>test-set-v1.1.0.txt: label file for the testing data set</li> </ul> <p>Images with relatively short file names (e.g., 1p128287181mrd0000p2303l2m1.img.jpg) are original images, and images with long file names (e.g., 1p128287181mrd0000p2303l2m1.img.jpg_04140167-5781-49bd-a913-6d4d0a61dab1.jpg) are augmented images. The label files are formatted as "Image name, Class1, Class2, ..., ClassN".</p> <p> </p> <p><strong>Reference</strong></p> <p>[1] S.B. Cole, J.C. Aubele, B.A. Cohen, S.M. Milkovich, and S.A. Shields, Identifying Community Needs for a Mars Exploration Rovers (MER), Daata Catalog, 51st Lunar and Planetary Science Conference (LPSC), 2020.</p>
Association of oligopaint FISH labeled super-enhancers and genes with polymerase clusters (raw data, SE1-SE6)
<p>This data set assesses the placement of different genomic regions relative to clusters formed by RNA polymerase II in zebrafish embryos. RNA polymerase was labeled by immunofluorescence, genomic regions by oligopaint DNA fluorescence in-situ hybridization (FISH). Microscopy images were acquired by instant-SIM microscopy, and analyzed using MatLab scripts and the bioformats importer. This data set contains the raw image data as well as all further analysis scripts.</p> <p>Raw image data for super enhancers SE1 to SE6</p>
Association of oligopaint FISH labeled super-enhancers and genes with polymerase clusters (raw data, genes)
<p>This data set assesses the placement of different genomic regions relative to clusters formed by RNA polymerase II in zebrafish embryos. RNA polymerase was labeled by immunofluorescence, genomic regions by oligopaint DNA fluorescence in-situ hybridization (FISH). Microscopy images were acquired by instant-SIM microscopy, and analyzed using MatLab scripts and the bioformats importer. This data set contains the raw image data as well as all further analysis scripts.</p> <p>Raw image data for four genes: rnf19a, cdc25b, celf1, crsp7</p>
Association of oligopaint FISH labeled super-enhancers and genes with polymerase clusters (raw data, SE7-SE12)
<p>This data set assesses the placement of different genomic regions relative to clusters formed by RNA polymerase II in zebrafish embryos. RNA polymerase was labeled by immunofluorescence, genomic regions by oligopaint DNA fluorescence in-situ hybridization (FISH). Microscopy images were acquired by instant-SIM microscopy, and analyzed using MatLab scripts and the bioformats importer. This data set contains the raw image data as well as all further analysis scripts.</p> <p>Raw image data for super enhancers SE7 to SE12</p>
Data from: Single-dose oral ciprofloxacin prophylaxis as a response to a meningococcal meningitis epidemic in the African meningitis belt: a three-arm, open-label, cluster-randomized trial
Background: Antibiotic prophylaxis for contacts of meningitis cases is not recommended during outbreaks in the African meningitis belt. We assessed the effectiveness of single-dose oral ciprofloxacin administered to household contacts and in village-wide distributions on the overall attack rate (AR) in an outbreak of meningococcal meningitis. Methods and findings: In this 3-arm, open-label, cluster-randomized trial during a meningococcal meningitis outbreak in Madarounfa District, Niger, villages notifying a suspected case were randomly assigned (1:1:1) to standard care (the control arm), single-dose oral ciprofloxacin for household contacts within 24 hours of case notification, or village-wide distribution of ciprofloxacin within 72 hours of first case notification. The primary outcome was the overall AR of suspected meningitis after inclusion. A random sample of 20 participating villages was enrolled to document any changes in fecal carriage prevalence of ciprofloxacin-resistant and extended-spectrum beta-lactamase (ESBL)–producing Enterobacteriaceae before and after the intervention. Between April 22 and May 18, 2017, 49 villages were included: 17 to the control arm, 17 to household prophylaxis, and 15 to village-wide prophylaxis. A total of 248 cases were notified in the study after the index cases. The AR was 451 per 100,000 persons in the control arm, 386 per 100,000 persons in the household prophylaxis arm (t test versus control p = 0.68), and 190 per 100,000 persons in the village-wide prophylaxis arm (t test versus control p = 0.032). The adjusted AR ratio between the household prophylaxis arm and the control arm was 0.94 (95% CI 0.52–1.73, p = 0.85), and the adjusted AR ratio between the village-wide prophylaxis arm and the control arm was 0.40 (95% CI 0.19‒0.87, p = 0.022). No adverse events were notified. Baseline carriage prevalence of ciprofloxacin-resistant Enterobacteriaceae was 95% and of ESBL-producing Enterobacteriaceae was >90%, and did not change post-intervention. One limitation of the study was the small number of cerebrospinal fluid samples sent for confirmatory testing. Conclusions: Village-wide distribution of single-dose oral ciprofloxacin within 72 hours of case notification reduced overall meningitis AR. Distributions of ciprofloxacin could be an effective tool in future meningitis outbreak responses, but further studies investigating length of protection, effectiveness in urban settings, and potential impact on antimicrobial resistance patterns should be carried out.
Data from: Ophthalmologic evaluation of severely obese patients undergoing bariatric surgery: a pilot, monocentric, prospective, open-label study
Purpose: The aim of this study was to investigate the pathogenic role of obesity on blinding eye diseases in a population of severely obese patients with no history of eye diseases, and to verify whether weight loss induced by bariatric surgery may have a protective effect. Methods: This was a pilot, monocentric, prospective, and open label study conducted at the University Hospital of Pisa. Fifty-seven severely obese patients with a mean body mass index value of 44.1 ± 6 kg/m2 were consecutively recruited and received a complete ophthalmological evaluation and optical coherence tomography. Twenty-nine patients who underwent gastric bypass were evaluated also 3 months, and 1 year after surgery. Results: At baseline, blood pressure value were directly and significantly related to intraocular pressure values (p<0.05, R = 0.35). Blood pressure values were also significantly and inversely related to retinal nerve fiber layer thickness, particularly in the temporal sector (RE p<0.05 r-0.30; LE p<0.01, R = -0.43). Moreover, minimum foveal thickness values were significantly and inversely associated with body mass index (RE p<0.02, R = -0.40; LE p<0.02, R = -0.30). A significant reduction of body mass index (p<0.05) and a significant (p<0.05) improvement of blood pressure was observed three months and one year after gastric bypass, which were significantly associated with an increase in retinal nerve fiber layer thickness and minimum foveal thickness values in both eyes (p<0.05). Conclusions: The results of this study suggest that obese patients may have a greater susceptibility to develop glaucomatous optic nerve head damage and age-related macular degeneration. Moreover, weight reduction and improvement of comorbidities obtained by bariatric surgery may be effective in preventing eye disease development by improving retinal nerve fiber layer and foveal thickness.
Data from: Novel reverse radioisotope labelling experiment reveals carbon assimilation of marine calcifiers under ocean acidification conditions
<p><span>1. Ocean acidification by anthropogenic carbon dioxide emissions is projected to depress metabolic and physiological activity in marine calcifiers. To evaluate the sensitivity of marine organisms against ocean acidification, the assimilation of nutrients into carbonate shells and soft tissues must be examined.</span></p> <p><span>2. We designed a novel experimental protocol, reverse radioisotope labelling, to trace partitioning of nutrients within a single bivalve species under ocean acidification conditions. Injecting CO<sub>2</sub> gas, free from radiocarbon, can provide a large contrast between carbon dissolved in the water and the one assimilated from atmosphere. By culturing modern aquifer organisms in acidified seawater, we were able to determine differences in the relative contributions of the end members, dissolved inorganic carbon (DIC) in seawater and metabolic CO<sub>2</sub>, to shell carbonate and soft tissues. </span></p> <p><span>3. Under all pCO<sub>2</sub> conditions (463, 653, 872, 1137, and 1337 μatm), radiocarbon (Δ<sup>14</sup>C) values of the bivalve (<i>Scapharca broughtonii</i>) shell were significantly correlated with seawater DIC values; therefore, shell carbonate was derived principally from seawater DIC. The Δ<sup>14</sup>C results together with stable carbon isotope (δ<sup>13</sup>C) data suggest that in <i>S. broughtonii</i> shell δ<sup>13</sup>C may reflect the kinetics of isotopic equilibration as well as end-member contributions; thus, care must be taken when analyzing end-member contributions by a previous method using δ<sup>13</sup>C. The insensitivity of <i>S. broughtonii</i> to perturbations in pCO<sub>2</sub> up to at least 1337 µatm indicates that this species can withstand ocean acidification.</span></p> <p><span>4. Usage of radioisotope to dope for tracer experiments requires strict rules to conduct any operations. Yet, reverse radioisotope labeling proposing in this study has a large advantage and is a powerful tool to understanding physiology of aquifer organisms that can be applicable to various organisms and culture experiments, such as temperature, salinity, and acidification experiments, to improve understanding of the proportions of nutrients taken in by marine organisms under changing environments.</span></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.