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.
1,273
datasets available to search
ShareScore release 0.9.0
Dataset results
1,273 results for “Deep Learning”
Training data for submitted paper "Wildfire Danger Prediction and Understanding with Deep Learning"
<p>Training data for submitted paper "Wildfire Danger Prediction and Understanding with Deep Learning". To run the code in https://github.com/Orion-AI-Lab/wildfire_forecasting</p>
Deep Learning for Reaction-Diffusion Glioma Growth Modeling: Towards a Fully Personalized Model? — Supporting Data
<p>Supporting data for Martens et al. Deep Learning for Reaction-Diffusion Glioma Growth Modelling: Towards a Fully Personalised Model? arXiv:2111.13404.</p>
JetClass: A Large-Scale Dataset for Deep Learning in Jet Physics
<p>JetClass is a new large-scale dataset to facilitate deep learning research in jet physics. It consists of 100M jets for training, 5M for validation and 20M for testing. The dataset contains 10 classes of jets, simulated with MadGraph + Pythia + Delphes. <br> <br> A detailed description of the JetClass dataset is presented in the paper <a href="https://arxiv.org/abs/2202.03772">Particle Transformer for Jet Tagging</a>. An interface to use the dataset is provided in <a href="https://github.com/jet-universe/particle_transformer">https://github.com/jet-universe/particle_transformer</a>.</p>
TreeSatAI Benchmark Archive for Deep Learning in Forest Applications
<p><strong>Context and Aim</strong></p> <p>Deep learning in Earth Observation requires large image archives with highly reliable labels for model training and testing. However, a preferable quality standard for forest applications in Europe has not yet been determined. The <em>TreeSatAI </em>consortium investigated numerous sources for annotated datasets as an alternative to manually labeled training datasets.</p> <p>We found the federal forest inventory of Lower Saxony, Germany represents an unseen treasure of annotated samples for training data generation. The respective 20-cm Color-infrared (CIR) imagery, which is used for forestry management through visual interpretation, constitutes an excellent baseline for deep learning tasks such as image segmentation and classification.</p> <p> </p> <p><strong>Description</strong></p> <p>The data archive is highly suitable for benchmarking as it represents the <em>real-world</em> data situation of many German forest management services. One the one hand, it has a high number of samples which are supported by the high-resolution aerial imagery. On the other hand, this data archive presents challenges, including class label imbalances between the different forest stand types.</p> <p>The <em>TreeSatAI Benchmark Archive </em>contains:</p> <ul> <li> <p>50,381 image triplets (aerial, Sentinel-1, Sentinel-2)</p> </li> <li> <p>synchronized time steps and locations</p> </li> <li> <p>all original spectral bands/polarizations from the sensors</p> </li> <li> <p>20 species classes (single labels)</p> </li> <li> <p>12 age classes (single labels)</p> </li> <li> <p>15 genus classes (multi labels)</p> </li> <li> <p>60 m and 200 m patches</p> </li> <li> <p>fixed split for train (90%) and test (10%) data</p> </li> <li> <p>additional single labels such as English species name, genus, forest stand type, foliage type, land cover</p> </li> </ul> <p>The geoTIFF and GeoJSON files are readable in any GIS software, such as QGIS. For further information, we refer to the PDF document in the archive and publications in the reference section.</p> <p> </p> <p><strong>Version history</strong></p> <p>v1.0.2 - Minor bug fix multi label JSON file</p> <p>v1.0.1 - Minor bug fixes in multi label JSON file and description file</p> <p>v1.0.0 - First release</p> <p> </p> <p><strong>Citation</strong></p> <p>Ahlswede, S., Schulz, C., Gava, C., Helber, P., Bischke, B., Förster, M., Arias, F., Hees, J., Demir, B., and Kleinschmit, B.: <em>TreeSatAI Benchmark Archive</em>: a multi-sensor, multi-label dataset for tree species classification in remote sensing, Earth Syst. Sci. Data, 15, 681–695, <a href="https://doi.org/10.5194/essd-15-681-2023">https://doi.org/10.5194/essd-15-681-2023</a>, 2023.</p> <p> </p> <p><strong>GitHub</strong></p> <p>Full code examples and pre-trained models from the dataset article (Ahlswede et al. 2022) using the <em>TreeSatAI Benchmark Archive</em> are published on the GitLab and GitHub repositories of the Remote Sensing Image Analysis (RSiM) Group (<a href="https://git.tu-berlin.de/rsim/treesat_benchmark">https://git.tu-berlin.de/rsim/treesat_benchmark</a>) and the Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) (<a href="https://github.com/DFKI/treesatai_benchmark">https://github.com/DFKI/treesatai_benchmark</a>). Code examples for the sampling strategy can be made available by Christian Schulz via email request.</p> <p> </p> <p><strong>Folder structure</strong></p> <p>We refer to the proposed folder structure in the PDF file.</p> <ul> <li> <p>Folder “aerial” contains the aerial imagery patches derived from summertime orthophotos of the years 2011 to 2020. Patches are available in 60 x 60 m (304 x 304 pixels). Band order is near-infrared, red, green, and blue. Spatial resolution is 20 cm.</p> </li> <li> <p>Folder “s1” contains the Sentinel-1 imagery patches derived from summertime mosaics of the years 2015 to 2020. Patches are available in 60 x 60 m (6 x 6 pixels) and 200 x 200 m (20 x 20 pixels). Band order is VV, VH, and VV/VH ratio. Spatial resolution is 10 m.</p> </li> <li> <p>Folder “s2” contains the Sentinel-2 imagery patches derived from summertime mosaics of the years 2015 to 2020. Patches are available in 60 x 60 m (6 x 6 pixels) and 200 x 200 m (20 x 20 pixels). Band order is B02, B03, B04, B08, B05, B06, B07, B8A, B11, B12, B01, and B09. Spatial resolution is 10 m.</p> </li> <li> <p>The folder “labels” contains a JSON string which was used for multi-labeling of the training patches. Code example of an image sample with respective proportions of 94% for Abies and 6% for Larix is: "Abies_alba_3_834_WEFL_NLF.tif": [["Abies", 0.93771], ["Larix", 0.06229]]</p> </li> <li> <p>The two files “test_filesnames.lst” and “train_filenames.lst” define the filenames used for train (90%) and test (10%) split. We refer to this fixed split for better reproducibility and comparability.</p> </li> <li> <p>The folder “geojson” contains geoJSON files with all the samples chosen for the derivation of training patch generation (point, 60 m bounding box, 200 m bounding box).</p> </li> </ul> <p>CAUTION: As we could not upload the aerial patches as a single zip file on Zenodo, you need to download the 20 single species files (aerial_60m_…zip) separately. Then, unzip them into a folder named “aerial” with a subfolder named “60m”. This structure is recommended for better reproducibility and comparability to the experimental results of Ahlswede et al. (2022), </p> <p> </p> <p><strong>Join the archive</strong></p> <p>Model training, benchmarking, algorithm development… many applications are possible! Feel free to add samples from other regions in Europe or even worldwide. Additional remote sensing data from Lidar, UAVs or aerial imagery from different time steps are very welcome. This helps the research community in development of better deep learning and machine learning models for forest applications. You might have questions or want to share code/results/publications using that archive? Feel free to contact the authors.</p> <p> </p> <p><strong>Project description</strong></p> <p>This work was part of the project <em>TreeSatAI </em>(Artificial Intelligence with Satellite data and Multi-Source Geodata for Monitoring of Trees at Infrastructures, Nature Conservation Sites and Forests). Its overall aim is the development of AI methods for the monitoring of forests and woody features on a local, regional and global scale. Based on freely available geodata from different sources (e.g., remote sensing, administration maps, and social media), prototypes will be developed for the deep learning-based extraction and classification of tree- and tree stand features. These prototypes deal with real cases from the monitoring of managed forests, nature conservation and infrastructures. The development of the resulting services by three enterprises (liveEO, Vision Impulse and LUP Potsdam) will be supported by three research institutes (German Research Center for Artificial Intelligence, TUB Remote Sensing Image Analysis Group, TUB Geoinformation in Environmental Planning Lab).</p> <p> </p> <p><strong>Project publications</strong></p> <p>Ahlswede, S., Schulz, C., Gava, C., Helber, P., Bischke, B., Förster, M., Arias, F., Hees, J., Demir, B., and Kleinschmit, B.: <em>TreeSatAI Benchmark Archive</em>: a multi-sensor, multi-label dataset for tree species classification in remote sensing, <em>Earth System Science Data</em>, 15, 681–695, <a href="https://doi.org/10.5194/essd-15-681-2023">https://doi.org/10.5194/essd-15-681-2023</a>, 2023.</p> <p>Schulz, C., Förster, M., Vulova, S. V., Rocha, A. D., and Kleinschmit, B.: Spectral-temporal traits in Sentinel-1 C-band SAR and Sentinel-2 multispectral remote sensing time series for 61 tree species in Central Europe. <em>Remote Sensing of Environment</em>, <em>307</em>, 114162, <a href="https://doi.org/10.1016/j.rse.2024.114162">https://doi.org/10.1016/j.rse.2024.114162</a>, 2024.</p> <p> </p> <p><strong>Conference contributions</strong></p> <p>Ahlswede, S. Madam, N.T., Schulz, C., Kleinschmit, B., and Demіr, B.: <em>Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods</em>, IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, <a href="https://doi.org/10.48550/arXiv.2201.07495">https://doi.org/10.48550/arXiv.2201.07495</a>, 2022.</p> <p>Schulz, C., Förster, M., Vulova, S., Gränzig, T., and Kleinschmit, B.: <em>Exploring the temporal fingerprints of mid-European forest types from Sentinel-1 RVI and Sentinel-2 NDVI time series</em>, IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, <a href="https://doi.org/10.1109/IGARSS46834.2022.9884173">https://doi.org/10.1109/IGARSS46834.2022.9884173</a>, 2022.</p> <p>Schulz, C., Förster, M., Vulova, S., and Kleinschmit, B.: <em>The temporal fingerprints of common European forest types from SAR and optical remote sensing data</em>, AGU Fall Meeting, New Orleans, USA, 2021.</p> <p>Kleinschmit, B., Förster, M., Schulz, C., Arias, F., Demir, B., Ahlswede, S., Aksoy, A.K., Ha Minh, T., Hees, J., Gava, C., Helber, P., Bischke, B., Habelitz, P., Frick, A., Klinke, R., Gey, S., Seidel, D., Przywarra, S., Zondag, R., and Odermatt B.: <em>Artificial Intelligence with Satellite data and Multi-Source Geodata for Monitoring of Trees and Forests</em>, Living Planet Symposium, Bonn, Germany, 2022.</p> <p>Schulz, C., Förster, M., Vulova, S., Gränzig, T., and Kleinschmit, B.: <em>Exploring the temporal fingerprints of sixteen mid-European forest types from Sentinel-1 and Sentinel-2 time series</em>, ForestSAT, Berlin, Germany, 2022.</p>
Dataset of very-high-resolution satellite RGB images to train deep learning models to detect and segment high-mountain juniper shrubs in Sierra Nevada (Spain)
<p>This dataset provides annotated very-high-resolution satellite RGB images extracted from Google Earth to train deep learning models to perform instance segmentation of Juniperus communis L. and Juniperus sabina L. shrubs. All images are from the high mountain of Sierra Nevada in Spain. The dataset contains 810 images (.jpg) of size 224x224 pixels. We also provide partitioning of the data into Train (567 images), Test (162 images), and Validation (81 images) subsets. Their annotations are provided in three different .json files following the COCO annotation format.</p>
Dataset of very-high-resolution satellite RGB images to train deep learning models to recognize high-mountain juniper shrubs from Sierra Nevada (Spain)
<p>This dataset provides annotated very-high-resolution satellite RGB images extracted from Google Earth to train deep learning models to recognize Juniperus communis L. and Juniperus sabina L. shrubs. All images are from the high mountain of Sierra Nevada in Spain. The dataset contains 2000 images (.jpg) of size 512x512 pixels partitioned into two classes: Shrubs and NoShrubs. We also provide partitioning of the data into Train (1800 images), Test (100 images), and Validation (100 images) subsets.</p>
A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer
<p>Dataset access for the paper: A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer</p>
Dataset for Cloud Identification in Mars Daily Global Maps with Deep Learning
<p><strong>Overview:</strong></p> <p>This repository stores cloud masks and MDGMS for Martian Years (MYs) 28-33. MDGMs were obtained from Harvard Dataverse (<a href="https://doi.org/10.7910/DVN/U3766S">https://doi.org/10.7910/DVN/U3766S</a>), and cloud masks were created using the cloudmask model (<a href="https://github.com/03kalven/cloudmask">https://github.com/03kalven/cloudmask</a>). The cloud masks contained in the binary folders have already been binarized using the threshold of 0.912. This dataset is considerably smaller in size than the floating-point cloud mask dataset and is suited for researchers that prefer to use the default threshold of 0.912.</p> <p> </p> <p><strong>Quick breakdown of the files and folders:</strong></p> <ul> <li>phase folders contain the complete set of MDGMs and cloud masks for Mars Reconnaisance Orbiter mission phases P, B, G, D, F, and J (MYs 28-33)</li> <li>phase_binary folders contain the complete set of binary MDGMs and cloud masks for Mars Reconnaisance Orbiter mission phases P, B, G, D, F, and J (MYs 28-33)</li> <li>view_masks.ipynb has a few handy methods to plot cloud masks and MDGMs</li> </ul> <p> </p> <p><strong>Phase folders:</strong></p> <p>Each folder is organized based on phase (P, B, G, D, F, J) and subphase (_01 to _23). In each subphase, there are cloudmasks and mdgms folders, as well as a .txt file with MY and solar longitude (Ls) data for each day.</p> <p> </p> <p><strong>MDGM and cloud mask formats:</strong></p> <ul> <li>mdgm: JPEG, 3600x1801</li> <li>cloudmask: (NETCDF4_CLASSIC data model, file format HDF5): <ul> <li>dimensions(sizes): x(3600), y(1801)</li> <li>variables(dimensions): float32 longitude(x), float32 latitude(y), float32/int16 cloudmask(y, x)</li> </ul> </li> </ul> <p>The cloud masks' values for any pixel are -999 for NaN and a float from 0 to 1 reporting the model's confidence in that pixel being a cloud. The cloud masks can be binarized using get_cloudmask() included in view_masks.ipynb. A binarized mask would report -999 for NaN, 0 for no cloud, and 1 for cloud. The default threshold is 0.912, but this value can be adjusted if desired. The cloud masks' (0,0) coordinate is the lower left corner of the map, so it may be needed to flip the cloudmask vertically before plotting on a Martian map. The cloud mask NetCDF files are constructed the same way as Wang and González Abad's (<a href="https://doi.org/10.7910/DVN/WU6VZ8">https://doi.org/10.7910/DVN/WU6VZ8</a>).</p>
Dataset used in "Deep learning with multisite data reveals the lasting effects of soil type, tillage and vegetation history on biopore genesis"
<p>Please see the paper "Deep learning with multisite data reveals the lasting effects of soil type, tillage and vegetation history on biopore genesis" how the images were captured, manual counting was performed, training datasets were prepared and models were trained.</p>
EDLO2ID: An Efficient-deep-learning-and-object-oriented Image Dataset for Large-scene Mapping
<p>EDLO2ID: An Efficient-deep-learning-and-object-oriented Image Dataset for Large-scene Mapping </p> <p>The dataset can be unzipped and includes an image dataset and a vector dataset, which includes nine land use/land cover categories (i.e., cropland, orchard, forestland, grassland, construction land, transportation land, water body, bare land, terrace) for object-oriented remote sensing image mapping using deep learning.</p>
Data from: imageseg: An R package for deep learning-based image segmentation
<p>1. Convolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications, and are particularly suited for image data. Image segmentation (the classification of all pixels in images) is one such application and can for example be used to assess forest structural metrics. While CNN-based image segmentation methods for such applications have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists.</p> <p>2. Here, we present R package imageseg which implements a CNN-based workflow for general-purpose image segmentation using the U-Net and U-Net++ architectures in R. The workflow covers data (pre)processing, model training, and predictions. We illustrate the utility of the package with image recognition models for two forest structural metrics: tree canopy density and understory vegetation density. We trained the models using large and diverse training data sets from a variety of forest types and biomes, consisting of 2877 canopy images (both canopy cover and hemispherical canopy closure photographs) and 1285 understory vegetation images.</p> <p>3. Overall segmentation accuracy of the models was high with a Dice score of 0.91 for the canopy model and 0.89 for the understory vegetation model (assessed with 821 and 367 images, respectively). The image segmentation models performed significantly better than commonly used thresholding methods, and generalized well to data from study areas not included in training. This indicates robustness to variation in input images and good generalization strength across forest types and biomes.</p> <p>4. The package and its workflow allow simple yet powerful assessments of forest structural metrics using pre-trained models. Furthermore, the package facilitates custom image segmentation with single or multiple classes and based on color or grayscale images, e.g. for applications in cell biology or for medical images. Our package is free, open source, and available from CRAN. It will enable easier and faster implementation of deep learning-based image segmentation within R for ecological applications and beyond.</p>
Source codes and datasets for the paper "DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learning"
<p>This contains the<strong> reproducible source code and dataset </strong>for the paper "DRLComplex : Reconstruction of protein quaternary structures using deep reinforcement learning paper"</p>
Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus
<p>We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories. While graph neural networks can efficiently handle the variable structure of hierarchical documents using the permutation invariant message passing operations, we show that we can gain extra performance improvements using our proposed selective graph pooling operation that arises from the fact that some parts of the hierarchy are invariable across different documents. We applied our model to classify clinical trial (CT) protocols into completed and terminated categories. We use bag-of-words based as well as pre-trained transformer-based embeddings to featurize the graph nodes, achieving f1-scores $\simeq 0.85$ on a publicly available large scale CT registry of around 360K protocols. We further demonstrate how the selective pooling can add insights into the CT termination status prediction.</p>
A cooperative deep learning model for stock market prediction using deep autoencoder and sentiment analysis
<p>This data is used for Stock Market Prediction. </p>
Dataset for 'A deep learning-based quality control loop of the extrusion-based bioprinting process'
<p>Zip file containing the dataset for the paper 'A deep learning-based quality control loop of the extrusion-based bioprinting process' (available at <a href="https://ijb.whioce.com/index.php/int-j-bioprinting/article/view/620">https://ijb.whioce.com/index.php/int-j-bioprinting/article/view/620</a>). The dataset is composed of:</p> <ul> <li>A series of video folders, each having a unique ID, and containing the processed video frames.</li> <li>A master.csv file containing all the metadata for each video.</li> </ul> <p>The authors acknowledge the supported by the European Union’s Horizon 2020 research and innovation program under the project GIOTTO: “Giotto: Active ageing and osteoporosis: The next challenge for smart nanobiomaterials and 3D technologies,” grant agreement no. 814410.</p>
Deep Learning based Urban Morphology for City-scale Environmental Modeling
<p>The WRF simulations were performed using the Weather Research and Forecasting (WRF) model, version 4.2.1. The three nested domains are centered over Chicago, USA, with a spatial resolution of 9, 3, and 1 km for the outermost, middle, and innermost domains. The model was implemented with 42 pressure levels, with the first model level located at 21.2 m and the first 1 km vertical height containing 11 model levels. The initial and boundary conditions are taken from the National Centers for Environmental Prediction (NCEP) Final Reanalysis dataset at 1 degree spatial and 6-hourly temporal resolution.</p><p>The physics components include the WRF single moment 6 class for microphysics, Dudhia for shortwave, the Rapid Radiative Transfer Model for longwave radiation parameterizations, Bougeault for the planetary boundary layer, Noah for the land surface model, Building Environment Parametrization (BEP) for the urban model, and Grell for the cumulus scheme (only for the outermost domain of 9 km spatial resolution). The LCZs of Chicago, USA, are generated using the crowd-sourcing method. The training dataset, created manually, is obtained from the WUDAPT portal, and random forest classification is applied to Landsat 8 imagery to derive the LCZs for the desired region. The simulations are performed from 1/Jul/2018 00:00 to 7/Jul/2018 06:00, where the first 6 hours are discarded as spin-up time.</p><p>The Digital Synthetic City (DSC) of Chicago, USA, uses satellite imagery and global-scale population and elevation data as input to the automatic method for producing a statistically similar and synthetic city-scale 3D urban model as output.</p><p>The Control simulations use National Land Cover Database land use/land cover with NUDAPT parameters, the three default WRF urban classes, and corresponding UCPs; the WUDAPT uses the MODIS classes with additional urban LCZs and UCPs from Brousse et al. (2016), and the DSC uses the WUDAPT classes with UCPs generated from DSC method.</p><p>The dataset contains:</p><p>1. Output from DSC in Shapefile.</p><p>2. WRF model output for the third domain (1 km) spatial resolution domain for (a) NUDAPT or Control (b) WUDAPT or LCZs (c) DSC</p>
A deep learning dataset for savanna tree species in Northern Australia
<p>We present a baseline deep learning dataset of 2547 polygons for 36 tree species in Northern Australia. Polygons were drawn on imagery that was collected using Remotely Piloted Aircraft System (RPAS). The dataset consists of:</p> <ul> <li>7 orthomosaics </li> <li>7 shape files with polygon annotations </li> <li>1 training dataset in COCO format</li> <li>1 validation dataset in COCO format</li> </ul> <p>Training and validation datasets were derived from the orthomosaics by tiling each image at 1024x1024 pixel size with 512 pixel step size (overlap). </p> <p>To perform deep learning model training with this dataset go to https://github.com/ajansenn/SavannaTreeAI for more information. </p>
DeLTA (Deep Learning Techniques for noise Annoyance detection) Dataset
<p>The Deep Learning Techniques for noise Annoyance detection (DeLTA) dataset comprises 2,980 15-second binaural audio recordings collected in urban public spaces across London, Venice, Granada, and Groningen (sourced from <a href="https://doi.org/10.5281/zenodo.5578572">International Soundscape Database</a>). A remote listening experiment was designed and hosted on Gorilla Experiment Builder, a professional online platform used for studying complex behaviours. The survey was then distributed via Prolific to a pool of pre-registered participants (N=1,221), and data collected between July 5th and July 23rd, 2021.</p> <p>During the listening experiment, participants listened to ten 15-second-long binaural recordings of urban environments and were instructed to select all the sound sources they could identify within the recording and then to provide an annoyance rating (from 1 to 10). For the sound source recognition task, participants were provided with a list of 24 labels they could select from. To collapse these into a single set of sound sources per recording, a “consensus” approach was considered, i.e., if two or more participants identified a source as being present in a recording, this source was considered to be effectively present. This resulted in a 2890 by 24 data frame (2890 recordings, each with up to 23 possible labels present and an average annoyance rating). On average, each recording has 3.2 identified sound sources present.</p> <p>Due to the constraints of the online survey software, Mp3 files were used for the listening experiment. Higher quality 24- or 32-bit 48kHz WAV files can be made available from the authors upon request. Each binaural audio recording consists of a 2 channel Mp3 file.</p>
MarsQuakeNet: A More Complete Marsquake Catalogue Obtained by Deep Learning Techniques (Supporting Material, JGR Planets)
<p>Supporting Material (JGR Planets, 2022JE007503) - contains:</p> <ul> <li>Extended marsquake catalogue</li> <li>List of all detections</li> <li>Sol-wise predictions</li> <li>Snapshot of MarsQuakeNet code</li> </ul> <p>MarsQuakeNet code (dynamic version) also available from: https://github.com/niko-d/MarsQuakeNet.git</p>
SCM-CNN: A Robust Deep Learning Matting Model for Cloud Removal in Optical Imagery
<p>This is a dataset that can be used for cloud detection and cloud opacity estimation. The data is saved in python-numpy form, and stored in dictionary :dict_keys(['OriginImage', 'Gimage', 'Alpha', 'Trimap', 'CloudMaxDN']) represents the cloud-free remote sensing image, cloud remote sensing image, cloud opacity, trilateration information and cloud brightness respectively. The command {np.load("Path",allow_pickle=True).item()} is used to read, where "Path" is the corresponding path to the file.</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.