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303 results for “hyperspectral”
Gloss estimation of chocolate sprinkles with Hyperspectral Imaging
<p>Gloss is an important characteristic in the quality evaluation in chocolate production. However, standard glossing<br> measuring devices face several challenges when measuring food products, in particular those with curved surfaces and<br> small, such as chocolate sprinkles. Therefore, gloss evaluation of chocolate sprinkles is typically done by human visual<br> assessment. In this respect, hyperspectral imaging (HSI), combining spectroscopy and imaging, has gained attention as a<br> non-destructive and non-contact real-time detection tool for food quality analysis and control. This technique adds an<br> extra dimension to traditional machine vision techniques by providing images at a larger number of more narrow<br> wavebands. This can potentially increase the discrimination power.<br> <br> The main task of this dataset is to classify between 5 classes of chocolate production stages. The labels are indicated on the file names. The labels are: EXTRUDER, GLUCOSE, STAGE1, STAGE2, STAGE3.</p> <p> The .zip file contains two folders named:</p> <p>- envi_sprinkles_dataset_11_04_22 (Train Set)</p> <p>- envi_sprinkles_test_dataset_11_04_22 (Test Set)</p> <p>In envi_sprinkles_dataset_11_04_22 the file name convention is BATCH_CLASSNAME_INDEX.hdr</p> <p>In envi_sprinkles_test_dataset_11_04_22 the file name convention is CLASSNAME_INDEX.hdr</p> <p>Note that in the train set the batch information is present in the file name, if two samples belongs to the same batch means that they were produced at the same time.</p> <p>The samples format is ENVI. Consist of a header file and ENVI binary data file with file extensions <code>.hdr</code> and <code>.raw</code>, respectively. The function writes the wavelength and metadata information to the ENVI header file and the data cube containing the hyperspectral images to the ENVI binary data file.</p> <p>The image shape is [272x512x16] , [HEIGHT, WIDTH, BANDS], the pixel format is uint16.</p> <p> </p> <p> </p>
Hyperspectral image using Specim IQ camera from Chatzivariti Winery (survey date 2021-08-13) #33
<p>The image contains the hyperspectral data from Chatzivariti Winery captured with Specim IQ hyperspectral camera. The grape variety of the field is Traminer (Gewürztraminer). The image captured at 2021-08-13 (#33) and it was processed using Specim IQ Studio.</p>
Hyperspectral image using Specim IQ camera from Chatzivariti Winery (survey date 2021-07-23) #20
<p>The image contains the hyperspectral data from Chatzivariti Winery captured with Specim IQ hyperspectral camera. The grape variety of the field is Traminer (Gewürztraminer). The image captured at 2021-07-23 (#20) and it was processed using Specim IQ Studio.</p>
Hyperspectral image using Specim IQ camera from Chatzivariti Winery (survey date 2021-08-13) #17
<p>The image contains the hyperspectral data from Chatzivariti Winery captured with Specim IQ hyperspectral camera. The grape variety of the field is Xinomavro. The image captured at 2021-08-13 (#17) and it was processed using Specim IQ Studio.</p>
HeiPorSPECTRAL - the Heidelberg Porcine HyperSPECTRAL Imaging Dataset of 20 Physiological Organs
<p>Hyperspectral Imaging (HSI) is a relatively new medical imaging modality that exploits an area of diagnostic potential formerly untouched. Although exploratory translational and clinical studies exist, no surgical HSI datasets are openly accessible to the general scientific community. To address this bottleneck, this publication releases HeiPorSPECTRAL (<a href="https://www.heiporspectral.org">https://www.heiporspectral.org</a>), the first annotated high-quality standardized HSI dataset. It comprises 5,758 spectral images acquired with the TIVITA Tissue and annotated with 20 physiological porcine organs in a total number of 11 pigs. Each HSI image features a resolution of 480 x 640 pixels acquired over the 500-1000 nm wavelength range. The acquisition protocol has been designed such that the variability of organ spectra as a function of several parameters including the camera pose and the individual can be assessed. A comprehensive technical validation confirmed both the quality of the raw data and the annotations. We envision potential reuse within this dataset, but also its reuse as baseline data for future research questions outside this dataset, such as the detection of pathologies.</p>
Hyperspectral Mixture Models in the CHIME Mission Implementation for Topsoil Texture Retrieval
<p>This dataset provides the steps of the image analysis techniques used to soil texture classes retrieval related to the paper 'Hyperspectral Mixture Models in the CHIME Mission Implementation for Topsoil Texture Retrieval' in wich the principles of the spectral mixture analyses are used.</p>
The Importance of Hyperspectral Soil Albedo Information for Improving Earth System Model Projections
<p>These files are associated with the article "The Importance of Hyperspectral Soil Albedo Information for Improving Earth System Model Projections". </p> <p>1. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/soil_hyper_albedo_RF_int.nc">soil_hyper_albedo_RF_int.nc</a> - hyperspectral soil albedo </p> <p>2. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/lai_hyper_albedo_RF_int.nc">lai_hyper_albedo_RF_int.nc</a> - hyperspectral surface albedo</p> <p>3. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/atmos_F2000climo_clm5sp.21_50-F2000climo_clm5sp_blue_diff_red_dir.21_50.tgz">atmos_F2000climo_clm5sp.21_50-F2000climo_clm5sp_bl ...</a> - diagnostic results of the atmospheric model CAM between broadband and hyperspectral simulations. </p> <p>4. <a href="https://zenodo.org/api/files/1f7bc8d5-f9f5-4305-9c52-f6ebbf94c072/F2000climo_clm5sp.21_50-F2000climo_clm5sp_blue_diff_red_dir.21_50.tgz">F2000climo_clm5sp.21_50-F2000climo_clm5sp_blue_dif ...</a> - diagnostic results of the land model CLM between broadband and hyperspectral simulations. </p>
Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the agricultural land at Demmin, Germany
<p>The HYPERNETS project (www.hypernets.eu) aims to ensure that high-quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument-pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site in Demmin, Germany [53°52'5.80"N,13°16'6.80"E] (DEGE). It is a subset of the complete data record, consisting of the measurements withEthaturements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as HDRF = π L / E where L is the directional upwelling radiance (with the field o, view of 5 degrees), and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically, no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-XR sensor was installed on 22 July 2021 at the top of a 10m mast on an extended 5 m horizontal boom to minimise interruption of the field of view. The boom faces South at the right angle towards bare soil. The mast is located at 53.868278°N, 13.268556°E. Data are collected every 30 minutes between 9:00 and 17:00 (UTC) from different zenith and azimuth angles.</p> <p>The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with an FWHM of 3 nm, and the SWIR sensor has 220 channels between 1000 and 1700 nm with an FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) propagated using the CoMet toolkit (www.comet-toolkit.org). </p> <p>To obtain this dataset, we start from the full DEGE data record and omit all the data that do not pass all quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was also developed to remove outliers and supply the best quality data suitable for satellite validation. To remove the outliers, a sigma-clipping method is used. First, reflectances are extracted in separate 2-hour windows throughout the day (to account for BRDF differences due to different solar positions) for four different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum of 30 data points), calculating the standard deviation from this trend, and masking any data that is more than three standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the four different wavelengths are then combined (keeping only measurements for which none of the four wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely. </p>
An Adaptive Channel Selection Method for Assimilating the Hyperspectral Infrared Radiances
<p>1. The channel sets selected by the ER-based selection method with different thresholds.</p> <p>2. The analysis fields assimilated by the ER-based selection method and the adaptive method.</p> <p>3. Note: The compressed file 'clearsky/allsky' contains four files with different thresholds of the ER-based selection method (ER0.8, ER0.85, ER0.9, ER0.95) and six files with different thresholds of the adaptive method (PaPb0.9, PaPb0.95, PaPb0.96, PaPb0.97, PaPb0.98, PaPb0.99), along with one file for all channels.</p>
Stereo hyperspectral dataset
<p>Dataset contains 73 hyperspectral stereo pairs. Image resolution is 512 * 512, each image has 204 channels (from 397 to 1003 nanometers), camera - Specim IQ. Dataset was assembled to study the effect of color vignetting (color lens shading). Tar file contains two folders, hyper_0 and hyper_1, which contain images with same names (stereo pares) in .raw format, and .hdr with metadata for each image.</p>
Munsell soil color chart: A hyperspectral dataset
<p>This dataset contains hyperspectral images obtained using SPECIM IQ for the Munsell soil color chart (MSC). </p> <p>The hyperspectral images are stored in ENVI format. For those who are only interested in the endmember spectra for the MSC, we also provided the spectral library .sli and .hdr inside the <strong>endmembers </strong>folder. </p> <p>The acquisition details for each image can be found in the .hdr file and metadata folder inside the <strong>whole</strong> folder. For the whole image, the acquisition details are:</p> <p>Table 1. Acquisition details</p> <table> <tbody> <tr> <td>samples</td> <td>512</td> </tr> <tr> <td>lines</td> <td>512</td> </tr> <tr> <td>bands</td> <td>204</td> </tr> <tr> <td>default bands</td> <td>70, 53,19</td> </tr> <tr> <td>binning</td> <td>1,1</td> </tr> <tr> <td>tint (integration time)</td> <td>10 (ms)</td> </tr> <tr> <td>fps</td> <td>100</td> </tr> <tr> <td>wavelength range</td> <td>397.32 - 1003.58 nm</td> </tr> </tbody> </table> <p>The dataset is organized into several folders, each containing different types of datasets. </p> <ul> <li><strong>whole</strong> folder contains the entire scene hyperspectral image. This folder contains <strong>capture</strong>, <strong>metadata, </strong>and<strong> results </strong>subfolder. <ul> <li>.png inside the folder is natural color plotting (RGB from default bands in Table. 1) from captured hyperspectral image. </li> <li><strong>capture </strong>folder contains dark reference, white reference and radiance data </li> <li><strong>metadata </strong>folder<strong> </strong>contains the metadata of the acquisition and device settings. </li> <li><strong>results </strong>contains the reflectance calculated by the device (in .dat, .hdr and rendered natural plotting in .png ) from the hyperspectral camera and .png images of the scene, background, and viewfinder from the device's RGB camera. </li> </ul> </li> <li> <p><strong>chips</strong> folder contains only the cropped 20*20 voxels for each color chip reflectances. Each page has its own folder and each folder contains .hdr and .img for each color chip. </p> </li> <li> <p><strong>endmembers</strong> folder contains the spectral library (.sli and .hdr). Each page in MSC have their own .sli and .hdr.</p> </li> </ul> <p>Some of the code snippets that might help to read the dataset</p> <p>using python spectral library to load the dataset</p> <pre><code class="language-python">from spectral import * import matplotlib.pyplot as plt # load the hyperspectral image .hdr and store it to a variable hsi = open_image(PATH) # get the natural RGB plotting of the hyperspectral image using the SPECIM main band hsi_rgb = hsi[:,:,[70,53,19]] # read the spectral library .sli and store it to a variable sli = open_image(PATH) # plot the first endmember plt.plot(sli.spectra[0]) # get the endmembers name sli.names</code></pre> <p>if you have any question kindly reach me on riestiyf@stud.ntnu.no</p>
Simulation of hyperspectral imaging recordings of the human exposed cortex
<p>This dataset contains the results of the simulation of a hyperspectral imaging recording of the exposed cortex of a human brain. Monte-Carlo (MC) simulations of the propagation of visible and near-infrared light in the exposed cortex of human were carried out, and the results of the MC simulation were processed to produce an hypercube, i.e., a set of images at various wavelength. Here, the light propagation of 41 wavelengths from 500 to 900nm were simulated and the images reconstructed.</p> <p>The simulation was based on the MCX software (<a href="https://github.com/fangq/mcx).%20">https://github.com/fangq/mcx). </a>The parameters of the simulations (at every wavelength) can be found in the ConfigurationFiles.zip file (1 per wavelength, named: cfg_WMC_wavelengthNumber). For the full details of the files structure, see: https://github.com/fangq/mcx.</p> <p>The domain used for the simulations was generated from an RGB image of the exposed cortex acquired during a surgical procedure. The image was segmented with a semi-automated procedure into three classes: gray matter, large blood vessel, and capillaries. Pixels were clustered into ten clusters using the K-means algorithm from the python library OpenCV (v4.8.0). The components of each cluster were manually sorted and attributed to the three classes. Three functional regions were defined as 1cm disk based on electrical brain stimulation findings, which lead to three other classes: activated grey matter, activated large blood vessel and activated capillaries.</p> <p>Once the image segmented into six classes, the brain volume was modelled. The binary segmentation masks were replicated along the z axis on 2cm to avoid any photon loss. Then the blood vasculature was modelled using morphological erosion. The structuring element used for the erosion was set to 0 (in pixels) for z=0 (in pixels) and was increased of 1 pixel while increasing z axis. The binary volumes of the six classes were finally merged together with a final isotropic resolution of 75um.</p> <p>The Hypercubes.mat file contains the results of the reconstructed ideal images at the surface of the brain, as calculated in reference [1], with a resolution of 116 x 116 pixels (0.5285 x 05285 mm). The absorption properties considered for this reconstruction are available in the ConfigurationFiles.zip file (variable: mua_WMC).</p> <p>The variables of the Hypercube.mat file are :</p> <ul> <li>Reflectance: the reflectance matrix (116 pixel x 116 pixels x 41 wavelengths).</li> <li>Wavelength: the wavelength vector (41 wavelengths)</li> <li>Mean_path_length: the image of the mean photon pathlength for each pixel and each wavelength (116 pixel x 116 pixels x 41 wavelengths)</li> </ul> <p>Reference:</p> <p>[1] Yao, R., Intes, X. and Fang, Q., “Direct approach to compute Jacobians for diffuse optical tomography using perturbation Monte Carlo-based photon ‘replay,’” Biomed. Opt. Express 9(10), 4588 (2018).</p> <p> </p>
Rapid species discrimination of similar insects using hyperspectral imaging and lightweight edge artificial intelligence
Open the record for dataset details and reuse information.
Laboratory-based hyperspectral visible near-infrared reflectance spectral dataset of soil samples across a range of surface orientations
Open the record for dataset details and reuse information.
Alaska Peatland Experiment (APEX): Near-earth hyperspectral measurements from June of 2016.
This dataset contains hyperspectral data from the Bonanza Creek APEX site taken over several days in June of 2016. The data were collected over in each of the three water table manipulation plots that comprise the APEX site. Spectral reflectance measurements were taken at APEX using an Analytical Spectral Devices Fieldspec Pro that measured reflectance in 1-nm bandwidths between 300 and 2500 nm. The purpose of this data collection was to link vegetation change associated with the experimental manipulation with spectral reflectance characteristics.
Hubbard Brook Experimental Forest: Hyperspectral Reflectance Imagery, August 2012
Airborne remote sensing data were acquired specifically for the EPSCoR NH Ecosystems and Society project to provide vegetation biometric and land surface optical properties at the landscape-scale. Data were acquired for targeted field sites that include the Lamprey River Watershed, the Hubbard Brook Experimental Forest and the Bartlett Experimental Forest, where soil and aquatic sensors are deployed and intensive field sample plots have been established to measure a range of vegetation and land surface properties. Two image data collection campaigns were deployed—one in summer (August 2012) to capture peak growing season conditions in the state, and one in winter (Feb/March 2013). This data package contains the flightlines for Hubbard Brook. Data are georegistered and atmospherically corrected to surface reflectance for August 7, 2012.
Hubbard Brook Experimental Forest: Hyperspectral Reflectance Imagery, March 2013
Airborne remote sensing data were acquired specifically for the EPSCoR NH Ecosystems and Society project to provide vegetation biometric and land surface optical properties at the landscape-scale. Data were acquired for targeted field sites that include the Lamprey River Watershed, the Hubbard Brook Experimental Forest and the Bartlett Experimental Forest, where soil and aquatic sensors are deployed and intensive field sample plots have been established to measure a range of vegetation and land surface properties. Two image data collection campaigns were deployed—one in summer (August 2012) to capture peak growing season conditions in the state, and one in winter (Feb/March 2013). This data package contains the flightlines for Hubbard Brook. Data are georegistered and atmospherically corrected to surface reflectance for March 9, 2013.
Hubbard Brook Experimental Forest: Hyperspectral Reflectance Imagery, February 2013
Airborne remote sensing data were acquired specifically for the EPSCoR NH Ecosystems and Society project to provide vegetation biometric and land surface optical properties at the landscape-scale. Data were acquired for targeted field sites that include the Lamprey River Watershed, the Hubbard Brook Experimental Forest and the Bartlett Experimental Forest, where soil and aquatic sensors are deployed and intensive field sample plots have been established to measure a range of vegetation and land surface properties. Two image data collection campaigns were deployed—one in summer (August 2012) to capture peak growing season conditions in the state, and one in winter (Feb/March 2013). This data package contains the flightlines for Hubbard Brook. Data are georegistered and atmospherically corrected to surface reflectance for February 22, 2013.
Hyperspectral imagery for Hog Island, VA, 2013
High resolution hyperspectral imagery Hog Island, Northampton County, VA, collected on May 26, 2013 on behalf of the USACE Engineer Research and Development Center using the Coastal Zone Mapping and Imaging Lidar (CZMIL) system. CZMIL integrates a lidar sensor with topographic and bathymetric capabilities, a digital camera and a hyperspectral imager on a single remote sensing platform for use in coastal mapping and charting activities. LiDAR data is provided as a separate VCRLTER dataset. Four data entities are included here: RADIANCE data across (a) 48 and (b) 96 spectral bands at 1 and 2 meters spatial resolution, respectively; and atmospherically-corrected FLAASH reflectance data (c & d) across the same bands and resolutions. The 48-band data cover the spectral range of 369.1 to 1040.0 nm (+/- 7.1 nm) and the 96-band data cover a similar range of 360.4 to 1038.4 nm (+/- 3.6 nm). Individual bands are described in the metadata for each data entity. Both the FLAASH reflectance and the radiance data have had a water mask applied to them; therefore no hyperspectral data is currently available for nearshore and offshore environments nor for deeper marsh creeks and ponds. FLAASH zip files also include the corresponding ENVI template files containing the FLAASH Atmospheric Model Input Parameters.
Multi-sensor dataset for testing merge of Hyperspectral, HD and 3D cloud information for image recognition
<p>This data contain multisensor image dataset constructed for benchmarking purposes. It contains multiple images of the constructed scenes -- on which objects made of different materials are placed to test image recognition scenarios. The scene is recorded from various angles by imagining sensors, i.e. HSI camera, HD camera on mobile chassis and MS Kinect to provide complete information.<br> </p> <p><strong>Equipment</strong><br> The imaging was performed with use of three devices for three different approaches to data. Those three devices' imaging characteristics are widely different when it comes to angle and resolution which required them to be separately positioned to acquire the matching images. Therefore while HD camera was being transferred on the moving platform (chassis), both Kinect and SOC710 were placed on a stationary position which was moved between the frames by hand.</p> <p><em>Hyperspectral data</em></p> <p><br> Hyperspectral data acquisition was performed with Surface Optics SOC710 camera. This camera records spectra at VNIR range $377-1046$ nm; the output image has dimensions $696 \times 520$ with 128 bands and $12$ bit dynamic range.</p> <p>The camera is equipped with sensor line translation unit and can be used from static stand as a conventional camera (i.e. it does not require mechanical translation of the observed sample or rotary stand, as in traditional ‘push broom’ hyperspectral cameras). The lighting was provided with four ambient lamps and adjusted for each scenario separately, so that most of the dynamic range of the camera was used and image saturation is avoided. Captured hyperspectral images were subject to a standard calibration procedure, including: the removal of a dark frame, spectral and radiometric calibration as well as reflectance normalization using the calibration panel. </p> <p><em>3D point clouds</em></p> <p><br> The Kinect sensor incorporates several advanced sensing hardware. The depth sensor consists of the IR projector combined with the IR camera, which is a monochrome complementary metaloxide semiconductor (CMOS) sensor. The IR projector is an IR laser that passes through a diffraction grating and turns into a set of IR dots. The relative geometry between the IR projector and the IR camera as well as the projected IR dot pattern are known. If we can match a dot observed in an image with a dot in the projector pattern, we can reconstruct it in 3D using triangulation. Because the dot pattern is relatively random, the matching between the IR image and the projector pattern can be done in a straightforward way by comparing small neighborhoods using, for example, normalized cross correlation. The depth value is encoded with gray values; the darker a pixel, the closer the point is to the camera in space. The black pixels indicate that no depth values are available for those pixels. This might happen if the points are too far (and the depth values cannot be computed accurately), are too close (there is a blind region due to limited fields of view for the projector and the camera), are in the cast shadow of the projector (there are no IR dots), or reflect poor IR lights </p> <p><em>HD Images</em></p> <p><br> The HD images were acquired using 5 Megapixel HD camera mounted on a mobile chassis made by Dawn Robotics, that allowed the camera to be moved freely on the scene. Both camera and mobile chassis was controlled by a Raspberry PI unit which was also responsible to position the camera in accord to the data being collected by other sources. <br> </p> <p><strong>Data</strong></p> <p>The dataset consists of three scenes consisting of various objects -- minerals, fruit, wood plastic and metal -- placed on a stand. The objects, depending on the view are partially covered and seen from different perspective. Each scene is captured from 8 different angles.</p> <p>Scene 1 (denoted <em>SceneEagle</em>) uses mostly inorganic materials, such as wood, metal, plastic and glass all placed on the vertical stand.<br> Scene 2 (<em>SceneFruit</em>) uses fruits normal and artificial, that are similar on HD photography and 3D cloud of point, but differs in hyperspectral image.<br> Scene 3 (<em>SceneFruit2</em>) uses the fruits but also includes printed full colour images of same fruits that are 2-dimensional.</p> <p> </p> <p>The data are formatted as follows:<br> - The HIS images are available in both \text{*.hdr} and \text{*.cube} formats. The separate files with calibrating panel is provided for each frame.<br> - Kinect clouds are provided in \text{*.obj} format, typical for Kinect output files.<br> - Matched Hyperspectral clouds are also provided as \text{*.obj} files<br> - HD photo files are provided in \text{*.jpg} files.<br> </p> <p><br> <strong>Acknowledgements</strong></p> <p>This work has been supported by the National Science Centre, based on decision no. DEC2012/07/N/ST6/03656.</p> <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.