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250 results for “Synthetic dataset”
Synthetic automotive LiDAR with non-systematic error and automotive LiDAR based on active stereo dataset
<p>The synthetic dataset was generated by transforming the original dataset using several methods. Each of these transformations occurs from a use case:</p><ul><li>UC1 is the original dataset obtained from [1] and represents a point cloud dataset captured by an ideal LiDAR</li><li>UC2 is a realistic point cloud dataset obtained by simulating the non-systematic error of a Velodyne HDL-64E and applying to UC1</li><li>UC3 is our approach to replace the LiDAR with an active stereo setup. Where the point cloud are captured using two cameras, operating stereoscopically, and a dot projector. The cameras are perfectly calibrated and the triangulation is always correct.</li><li>UC4 also obtains the point clouds through triangulation. However we introduced a calibration error on the right camera. The error has the value of 1 pixel and is added to every dimension of the rotation matrix of the right camera.</li><li>UC5 performs an ideal triangulation, same as UC3. However, in this use case, we introduce camera noise to the point clouds.</li><li>UC6 is a combination of the triangulation from UC4 and the camera noise from UC5.</li></ul><p>Additionally, the labels, images and calibration file are also in [1]. For further details, please check the dataset generation source code [2]. </p><p>[1] https://zenodo.org/records/7184990</p><p>[2] https://github.com/RobertoGraca/Active_Stereo_Based_LiDAR</p>
6DAPose - Synthetic Assembly Pose Dataset
<p>6DAPose dataset contains an object assembly in an ordered manner following its assembly steps. Each dataset contains 431 simulated view samples of the assembly for each assembly step. For each view sample, RGB and Depth Image, Segmentation map, ground truth values for 6D object pose and camera pose and model information are recorded following the <a href="https://bop.felk.cvut.cz/home/">BOP format </a>. There are two datasets representing a 3-step fidget gear assembly and a 4-step Nema17 gear reducer assembly.</p>
Rulers2023: An Annotated Dataset of Synthetic and Real Images for Ruler Detection Using Deep Learning
<p>Annotated datasets of synthetic and real ruler images:<br>1. Synthetic-train<br>2. Real-train<br>3. Real-test</p><p><strong>Citation:</strong> Matuzevičius D. Rulers2023: An Annotated Dataset of Synthetic and Real Images for Ruler Detection Using Deep Learning. <i>Electronics</i>. 2023; 12(24):4924. https://doi.org/10.3390/electronics12244924</p><p> </p>
Synthetic Out-Of-Vocabulary IAM dataset with Latent Diffusion Models
<p>SyntheticHTR: Handwritten Text Image Synthesis based on Latent Diffusion Models</p>
Datasets of synthetic task flow graphs for evaluating a latency/energy optimization task allocation framework
<p>These datasets of synthetic task flow graphs were generated to evaluate the performance and scalability of an optimal task allocation approach for applications of various structures and sizes in an environment following the edge/hub/cloud paradigm. The system under study comprised an edge device (e.g., a single-board computer attached to an unmanned aerial vehicle (UAV)) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. The objective was the minimization of either overall latency or overall energy consumption, under memory, storage, energy, and task precedence constraints. We considered that a percentage of the tasks required fixed allocation on the edge or hub device.<br> <br>We generated 18 task flow graphs of parallel, serial, and mixed (a combination of parallel and serial) structure with 10, 100, and 1000 nodes, and various in/out degrees, utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size) were included post-generation, using representative random values. More details are provided in README.txt and in [3].<br> <br>Note: These datasets are released under a Creative Commons Attribution license. If you utilize these datasets in your work, please cite us using the corresponding Zenodo DOI https://doi.org/10.5281/zenodo.10654551.<br> <br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.<br>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.<br>[3] A. Kouloumpris, G. L. Stavrinides, M. K. Michael, and T. Theocharides, "An optimization framework for task allocation in the edge/hub/cloud paradigm," Future Generation Computer Systems, vol. 155, pp. 354-366, Jun. 2024, doi: 10.1016/j.future.2024.02.005.</p>
dataset figures - Assessing the Performance of Fuel Cell Electric Vehicles Using Synthetic Hydrogen Fuel - Article Energies
<p>Dataset for Table 1 - 2 and for Figure 4</p>
SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part I)
<p><strong>This page only provides the ground-level image dataset. </strong></p> <ul> <li><strong>For the drone-view image dataset, please visit <a href="https://zenodo.org/records/13955516" target="_blank" rel="noopener"><em>SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part II)</em></a>.</strong></li> <li><strong>For the point clouds, please visit <em><a href="https://zenodo.org/records/14228467" target="_blank" rel="noopener">SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part III)</a>.</em></strong></li> </ul> <p>The dataset contains ground-level RGB images, depth maps, semantic segmentation labels, and instance segmentation labels collected from different scenes. Data from each scene is stored in a separate .7z file, along with a <code>color_palette.xlsx</code> file, which contains the RGB_id and corresponding RGB values.</p> <p>All files follow the naming convention: <code>{central_tree_id}_{timestamp}</code>, where <code>{central_tree_id}</code> represents the ID of the tree centered in the image, which is typically in a prominent position, and <code>timestamp</code> indicates the time when the data was collected.</p> <p>Specifically, each 7z file includes the following folders:</p> <ul> <li> <p><strong>rgb</strong>: This folder contains the RGB images (PNG) of the scenes and their metadata (TXT). The metadata describes the weather conditions and the world time when the image was captured. An example metadata entry is: <code>Weather:Snow_Blizzard,Hour:10,Minute:56,Second:36</code>.</p> </li> <li> <p><strong>depth_pfm</strong>: This folder contains absolute depth information of the scenes, which can be used to reconstruct the point cloud of the scene through reprojection.</p> </li> <li> <p><strong>semantic_segmentation</strong>: This folder contains grayscale images representing semantic segmentation labels, where 1 indicates tree trunks and 0 represents other elements.</p> </li> <li> <p><strong>instance_segmentation</strong>: This folder stores instance segmentation labels (PNG) for each tree in the scene, along with metadata (TXT) that maps <code>tree_id</code> to <code>RGB_id</code>. The <code>tree_id</code> can be used to look up detailed information about each tree in <code>obj_info_final.xlsx</code>, while the <code>RGB_id</code> can be matched to the corresponding RGB values in <code>color_palette.xlsx</code>. This mapping allows for identifying which tree corresponds to a specific color in the segmentation image.</p> </li> <li> <p><strong>obj_info_final.xlsx</strong>: This file contains detailed information about each tree in the scene, such as position, scale, species, and various parameters, including trunk diameter (in cm), tree height (in cm), and canopy diameter (in cm).</p> </li> <li> <p><strong>landscape_info.txt</strong>: This file contains the ground location information within the scene, sampled every 0.5 meters.</p> </li> </ul> <p>For forest datasets: birch_forest, broadleaf_forest, burned_forest, rainforest and redwood_forest, there's an additional folder called <strong>coco_annotation</strong> where we generated the COCO-format annotation files (.json) for each image.</p> <p>⚠️: 7z files that begin with "<strong>!</strong>" indicate that the RGB values in the images within the <code>instance_segmentation</code> folder cannot be found in <code>color_palette.xlsx</code>. Consequently, this prevents matching the trees in the segmentation images to their corresponding tree information, which may hinder the application of the dataset to certain tasks. This issue is related to a bug in Colossium/AirSim, which has been reported in <a href="https://github.com/microsoft/AirSim/issues/3423" target="_blank" rel="noopener">link1</a> and <a href="https://github.com/microsoft/AirSim/issues/1852" target="_blank" rel="noopener">link2</a>.</p>
SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part III)
<p><strong>This page only provides point clouds. </strong></p> <ul> <li><strong>For the ground-level image dataset, please visit <a href="https://zenodo.org/records/13570934" target="_blank" rel="noopener"><em>SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part I)</em></a>.</strong></li> <li><strong>For the drone-view image dataset, please visit <a href="https://zenodo.org/records/13955516" target="_blank" rel="noopener"><em>SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part II)</em></a>.</strong></li> </ul> <p>This dataset contains point clouds collected from different virtual forest scenes. Data from each scene is stored in a separate .7z file, along with a <code>point_cloud_color_palette.txt</code>file, which contains the Tree_id and corresponding RGB values.</p> <p>Specifically, each 7z file includes the following folders:</p> <ul> <li> <p><strong>tree</strong>: This folder contains the point cloud data of every single tree within the forest scene. Each tree is stored separately in a <code>.ply</code> file including both location and color infomation. For performance reasons, the maximum number of point clouds for each tree is limited to 10,000.</p> </li> <li> <p><strong>ground</strong>: This folder contains a <code>landscape.ply</code> describing the ground information. The color of the point cloud is set to [0,0,0].</p> </li> </ul> <p><strong>The unit of the point cloud is meters (m).</strong></p>
Dataset on virtual proteins realized with a synthetic hologram
<p>dataset reporting the useof an OAM sorter to study a virtual protein realized with a synthetic hologram</p>
Dataset for "selection rules in symmetry-broken systems by symmetries in synthetic dimensions"
<p>Data for the article "Selection rules in symmetry-broken systems by symmetries in synthetic dimensions" by Matan Even Tzur, Ofer Neufeld, Eliyahu Bordo, Avner Fleischer, and Oren Cohen.</p>
Single-Writer Synthetic Strikethrough Dataset
<p>This dataset is based on the train set from https://doi.org/10.5281/zenodo.4765062 and contains registered pairs of clean and struck-through handwritten. The text, a passage from Bram Stoker's Dracula, was written by a single writer, using a blue ballpoint pen on regular white paper.</p> <p>The 126 word images have been synthetically struck through (generated via: https://doi.org/10.5281/zenodo.4767062), using one of the following stroke types: single horizontal line, double horizontal lines, diagonal, cross, wave, zigzag, scratch.</p>
Virus Finding Tools: current solutions and limitations - Synthetic datasets
<p>The simulated RNA-seq datasets were built using FluxSimulator to generate paired-end reads. The first one contains thirty viral genomes from NCBI and the human genome (version GRCh38). The second dataset comprises five Human Rhinovirus A1, five Human Papillomavirus 16, and the human genome (version GRCh38).</p> <p>Concerning the first dataset, we chose the genomes to mix human, animal, and vegetable viruses for a fair analysis of each tool's reliability. Instead, the second dataset was created to understand the capability of each tool to assess the taxonomic level and the specificity of viral identification.</p> <p>We downloaded the genomes of the selected viruses from NCBI nucleotide, getting the FASTA file and the GFF3 annotation. Next, we used AGAT to transform the GFF3 files into GTF files. After that, we used FluxSimulator to generate a synthetic RNA-seq dataset for each species. Finally, we joined each simulated dataset to build a single RNA-seq sample. The number of reads generated for each species was chosen to act as a real sample.</p> <p>For each dataset, we include two compressed archives. The "*_fastq_files.tar" files contain the synthetic fastq files generated by FluxSimulator. The "*_raw_results.tar" files contain all the raw output produced by the tools employed in our benchmarking.</p> <p>The selected viruses for the first dataset are Human rhinovirus 1 strain ATCC VR-1559, Human Rhinovirus 3, Tomato mosaic virus, Molluscum contagiosum virus subtype 1, Apple mosaic virus RNA 3, Encephalomyocarditis virus, Human papillomavirus 52 isolate 52HB20, Hepatitis C virus genotype 1, Human papillomavirus type 31, Human papillomavirus type 54, JC polyomavirus, Marine RNA virus SF-2, Marine RNA virus JP-B, Hepatitis A virus, Human immunodeficiency virus 1, Anguillid herpes virus strain UK N080, Apis mellifera virus 14 isolate BFH508NG, Human enterovirus, Escherichia phage T7 isolate T7, Human herpesvirus 6B, Human measles virus, Cyprinid herpesvirus 3, Rotavirus C segment 8, Japanese encephalitis virus, Human papillomavirus 116, Influenza A virus (A/New York/392/2004(H3N2)) segment 4, Rotavirus RCU, Human parvovirus B19, Rous Sarcoma, Human papillomavirus 16.</p> <p>The selected strains and isolates for the second dataset are Human rhinovirus 1 strain ATCC VR-1559, Rhinovirus A1 strain 7A2, Rhinovirus A1 strain 5Q1, Rhinovirus A1 strain RvA1B/USA/2021/RJ9JKH, Rhinovirus A1 strain RvA1/USA/2021/L8MQLH, Human papillomavirus type 16, Human papillomavirus type 16 isolate 16CN37, Human papillomavirus type 16 isolate 16CN34, Human papillomavirus type 16 strain MML8, Human papillomavirus type 16 strain MML20.</p> <p>The tools employed in our benchmarking are VirusFinder, VirusSeq, VirTect, viGEN, VirDetect, DAMIAN, Metamap, Kraken2, Centrifuge.</p>
Training dataset for "A deep learned nanowire segmentation model using synthetic data augmentation"
<p>This image dataset contains synthetic structure images used for training the deep-learning based nanowire segmentation model presented in our work "A deep learned nanowire segmentation model using synthetic data augmentation" to be published in <em>npj Computational materials. </em>Detailed information can be found in the corresponding article.</p>
Supporting Dataset for "A synthetic C4 shuttle via the β-hydroxyaspartate cycle in C3 plants"
<p>Plants depend on the enzyme ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) for CO<sub>2</sub> fixation. However, especially in C3 plants, photosynthetic yield is reduced by the formation of 2-phosphoglycolate, a toxic oxygenation product of Rubisco, which needs to be recycled in a high-flux–demanding metabolic process called photorespiration. Canonical photorespiration dissipates energy and causes carbon and nitrogen losses. Reducing photorespiration through carbon-concentrating mechanisms, such as C4 photosynthesis, or bypassing photorespiration through metabolic engineering is expected to improve plant growth and yield. The β-hydroxyaspartate cycle (BHAC) is a recently described microbial pathway that converts glyoxylate, a metabolite of plant photorespiration, into oxaloacetate in a highly efficient carbon-, nitrogen-, and energy-conserving manner. Here, we engineered a functional BHAC in plant peroxisomes to create a photorespiratory bypass that is independent of 3-phosphoglycerate regeneration or decarboxylation of photorespiratory precursors. While efficient oxaloacetate conversion in <em>Arabidopsis thaliana</em> still masks the full potential of the BHAC, nitrogen conservation and accumulation of signature C4 metabolites demonstrate the proof of principle, opening the door to engineering a photorespiration-dependent synthetic carbon–concentrating mechanism in C3 plants.</p> <p>Data analysis was performed in R. For analysis of gas exchange measurements, the “plantecophys” package was used (<a href="https://www.pnas.org/doi/full/10.1073/pnas.2022307118#core-r55">55</a>). The data are summarized in <a href="http://www.pnas.org/lookup/doi/10.1073/pnas.2022307118#supplementary-materials">Datasets S1–S10</a>. All other study data are included in the article and/or supporting information, available at <a href="https://doi.org/10.1073/pnas.2022307118">https://doi.org/10.1073/pnas.2022307118</a></p> <p>Dataset S1: Enzymatic activity of BHAC enzymes in Arabidopsis rosette leaves. For ISR the rate of percentual 15N label enrichment in aspartate was quantified. Shown mean and standard deviation (SD).</p> <p>Dataset S2: Metabolome of BHAC plants. Shown is mean and standard deviation (SD) of the calculated relative amount per mg fresh weight of four biological replicates per genotype for each condition.</p> <p>Dataset S3: Ammonium quantification in BHAC plants. Shown is mean and standard deviation (SD) for four biological replicates per genotype per condition measured in technical triplicates.</p> <p>Dataset S4: Phenotyping of BHAC plants. Shown is mean and standard deviation (SD) of five biological replicates per genotype per condition.</p> <p>Dataset S5: A/Ci curve measurements of BHAC plants. Shown is mean of four biological replicates per genotype.</p> <p>Dataset S6: Light response measurements of BHAC plants. Shown is mean of four biological replicates per genotype.</p> <p> Dataset S7: Metabolite levels of phosphorylated intermediates and glyoxylate in air-grown plants. Shown is mean and standard deviation of ≥ 3 replicates.</p> <p>Dataset S8: Metabolome of ggt1-1 complementation lines with AGAT. Shown is mean and standard deviation (SD) of four biological replicates.</p> <p>Dataset S9: Enzymatic activity of AGAT and GGT in Arabidopsis rosette leaves of the ggt1-1 complemention lines. Shown mean and standard deviation (SD) of three biological replicates measured in technical triplicates.</p> <p>Dataset S10: O2-Dependency of CCP was measured at 4% O2. Shown is the mean ±SD of n ≥ 3.</p>
A comprehensive and synthetic dataset for global, regional and national greenhouse gas emissions by sector 1970-2018 with an extension to 2019
<p>Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO<sub>2</sub> emissions from fossil fuel combustion and industry (FFI), CH<sub>4</sub> emissions, N<sub>2</sub>O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO<sub>2</sub> emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.</p>
Synthetic P-wave first motion dataset (20171208ver.)
<p>This is a dataset of synthetic P-wave first motions (polarities). The format is shown in README file.</p>
Synthetic temporal dataset for temporal trend analysis and retrieval
<p>This repository contains a synthetic, temporal data set that was generated by the authors by sampling values from the Gaussian distribution. The dataset contains eight nontemporal dimensions, a temporal dimension, and a numerical measure attribute. The data set was generated according to the scheme and procedure detailed in this source paper: Kaufmann, M., Fischer, P.M., May, N., Tonder, A., Kossmann, D. (2014). TPC-BiH: A Benchmark for Bitemporal Databases. In: Performance Characterization and Benchmarking. TPCTC 2013. Lecture Notes in Computer Science, vol 8391. Springer, Cham. https://doi.org/10.1007/978-3-319-04936-6_2. The data set can be used for analyzing and locating temporal trends of interest, where a temporal trend is generated by selecting the desired values of the nontemporal dimensions, and then selecting the corresponding values of the temporal dimension and the numerical measure attribute. Locating temporal trends of interest, e.g., unusual trends, is a common task in many applications and domains. It can also be of interest to understand which nontemporal dimensions are associated with the temporal trends of interest. To this end, the data set can be used for analyzing and locating temporal trends in the data cube induced by the data set.</p>
TUBERS - Ultrasonic and Synthetic Crack Datasets
Open the record for dataset details and reuse information.
A synthetic dataset for the exploration of survival and classification models: prediction of heart attack or stroke within a 10-year follow-up period
<div> <div></div> </div> <div> <div> <div> <p><span>Machine learning methodologies are increasingly popular in health care research. This shift to integrated data science approaches necessitates professional development of the existing health care data analyst workforce. To enhance a smooth transition, educational resources need to be developed. Barriers to accessing real healthcare datasets, vital for health care data analyses methodologies training purposes, include financial, ethical and patient confidentiality concerns. Synthetic datasets mimicking real-world complexities offer a simpler solution.</span></p> <p>We present a synthetic dataset which mirrors routinely collected primary care data on heart attack and stroke among the adult population. The data incorporates much of the practical challenges encountered in routinely collected primary care systems such as missing data, informative censoring, interactions, variable irrelevance, and noise and can be used for training in methods which handle these difficulties. The intent is for the user to build models of heart/stroke risk using survival-based methodologies.</p> <p>By sharing this synthetic dataset openly, our goal is to contribute a transformative asset for professional training in health and social care data analysis. The dataset covers demographics, lifestyle variables, comorbidities, systolic blood pressure, hypertension treatment, family history of cardiovascular diseases, respiratory functioning, and experience of heart-attack and/or stroke. This initiative aims to bridge the gap in sophisticated healthcare datasets for training, fostering professional development of the health and social care research workforce.</p> <p>This study is funded by the National Institute for Health and Care Research ARC Wessex and the National Centre for Research Methods. The views expressed in this summary are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.</p> <p> </p> </div> </div> </div>
Synthetic dataset of 2342 earthquake/tsunami scenarios targeting the Nankai trough subduction zone
<h2>Summary:</h2> <ul> <li>Data of 2342 hypothetical Nankai Trough events</li> <li>Tsunami propagation is simulated by the GeoClaw software (Clawpack Development Team, 2021).</li> <li>Earthquake realizations generated by the Mudpy software (Melgar, 2020).</li> <li>Data composition is as follows:<br><code>nankai_data</code><br><code>├── quakes</code><br><code>│ ├── dtopofiles</code><br><code>│ │ ├── nankai_xy_zzzzzz.dtt3</code><br><code>│ │ ├── ...</code><br><code>│ ├── png</code><br><code>│ │ ├── nankai_xy_zzzzzz_slip_dtopo.png</code><br><code>│ │ ├── ...</code><br><code>│ └── ruptures</code><br><code>│ ├── nankai_xy_zzzzzz.log</code><br><code>│ ├── nankai_xy_zzzzzz.rupt</code><br><code>│ ├── ...</code><br><code>├── waves</code><br><code>│ ├── nankai_xy_zzzzzz.csv </code><br><code>│ ├── ...</code><br><code>├── gauge_loc.csv</code><br><code>└── wave_seq.npy</code></li> </ul> <h2>Details of each file:</h2> <p>Unzipping `nankai_data.tar.gz` creates two directories and two file: `quakes/`, `waves/`, `gauge_loc.csv` and `wave_seq.npy`.<br>Note that 8 GB of additional storage is required. </p> <ul> <li> <h3>quakes/</h3> Earthquake data of 2342 scenarios. <ul> <li>quakes/dtopofiles/nankai_xy_zzzzzz.dtt3<br>Seafloor deformation of each scenario<br>Mw: x.y, ID: zzzzzz</li> <li>quakes/ruptures/<br>nankai_xy_zzzzzz.rupt stores the rupture for each scenario.<br>nankai_xy_zzzzzz.log has information about the rupture.</li> <li>quakes/png/nankai_xy_zzzzzz_slip_dtopo.png<br>The slip distribution and seafloor deformation due to fault rupture are visualized in PNG format.<br><br></li> </ul> </li> <li> <h3>waves/</h3> <p>2342 .csv files contain time series data of simulated tsunami wave heights<br>The left column has the time (minute) elapsed from the fault rupture<br>The following columns have the wave sequences recorded at each gauge<br>Each file has 2160 rows corresponding to the simulation time steps, 3 [hr] x 3600 [sec/hr] / 5[sec] = 2160.<br><br></p> </li> <li> <h3>gauge_loc_all.csv</h3> The locations of 62 synthetic gauges<br>Gauge IDs are in the left column named ID.<br>The gauge locations are in Longitude and Latitude columns.<br>Some of the synthetic gauges are set by referring to the locations of existing gauges, as shown in the Instruments column.<br><br></li> <li> <h3>wave_seq.npy</h3> Data matrix containing the wave sequences. <br> Loading this binary data by <br> numpy.load('wave_seq.npy') <br> gives a 2342 x 62 x 2160-shaped 3d array.</li> </ul> <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.