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9,302 results for “DETECT”
VENµS raw images for vessel detection
<p>The dataset utilized in this study comprises images capturing maritime vessels. It is a meticulously curated collection designed explicitly for training and assessing models focused on vessel detection and identification.</p> <p>The images within the dataset are sourced from satellite sensors, specifically the VENμS satellite imagery. VENμS offers multispectral data from visible to near-infrared bands, boasting high spatial resolution (5.3 meters at Nadir) for detailed vessel analysis. The dataset encompasses images taken on 12 spectral bands, facilitating the examination of the vessel detection model's performance under diverse spectral conditions. These images depict the port of Ashdod, one of Israel's primary cargo ports, from the beginning of 2018 to the conclusion of October 2020, totalling 284 multispectral images.</p> <p>For each multispectral image, three types of data are provided: raw data (non-coregistered multispectral image), coregistered images, and coregistered images with a mask (pixels set to zero) on the harbour to eliminate anchored vessels from the image. Coregistration involves each band's shift (X and Y) to the fifth band.</p> <p>Most vessels in the dataset are annotated with boundary boxes (upper-left position, height, and width) along with AIS data, including vessel type, length, width, and status. These annotations serve as ground truth labels for training and evaluating models focused on vessel detection and classification.</p> <p>Furthermore, the dataset incorporates samples showcasing variations in environmental conditions, such as diverse sea states, lighting conditions, wake complexities, and the presence of clouds. This variability comprehensively evaluates the model's generalization capability and robustness across various scenarios.</p> <p>Content:</p> <p>- ASH_L0_CoReg_xx.zip: coregistered raw images.</p> <p>- ASH_L0_CoReg_mask_xx.zip: coregistered raw images with a mask (pixels set to zero) on the harbour.</p> <p>- ASH_L0_OriginalBands_xx.zip: raw data images (non-coregistered multispectral image).</p> <p>- COCO_ASH_vessels_final_v02.json: boundary boxes (upper-left position, height, and width; wrt to the fifth band) and AIS data.</p> <p>- annots_12bands.zip: boundary boxes (upper-left position, height, and width for each band) and AIS data.</p> <p> </p>
Processed Sentinel 1, Sentinel 2 and Copernicus Emergency Management Service data for fine tuning and predicting flood extent with IBM's granite-geospatial-uki-flood-detection model
<p>This dataset contains processed Sentinel 1 Sentinel 2 imagery together with flood event labels extracted from the Copernicus Emergency Management Service. It has been assembled to demonstrate fine tuning and inference of flood event segmentation using granite geospatial foundation models developed by IBM Research. Please see <a href="https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection">https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection</a> for more information on models and use.</p> <p>Sentinel-1</p> <p>The European Space Agency. 2014. Sentinel-1 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-1">https://sentinel.esa.int/web/sentinel/missions/sentinel1</a>. Accessed: 2024-11-25.</p> <p>Sentinel-2</p> <p>The European Space Agency. 2015. Sentinel-2 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-2">https://sentinel.esa.int/web/sentinel/missions/sentinel2</a>. Accessed: 2024-11-25.</p> <p>Copernicus Emergency Management Service</p> <p><a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid">https://emergency.copernicus.eu/mapping/list-of-activations-rapid</a>. Accessed: 2024-11-25. </p> <p><strong>Attribution</strong></p> <p>Contains modified Copernicus Sentinel data [2019-2024]</p> <p>Contains modified Copernicus Service information [2019-2023]</p>
Thesis data: Enhancing Vulnerability Detection: A Comparative Study of Change Identification Methods Across Granularity Levels
<p>Starting dataset used within the thesis; Enhancing Vulnerability Detection: A Comparative Study of Change Identification Methods Across Granularity Levels.</p> <p>Results of manual annotation of and extract of nonPatchTaggedCommitLinks within the NVD CVE dataset.</p>
Data: Core-periphery detection in multilayer networks
Open the record for dataset details and reuse information.
Replication Package for "Mitigating Automated Obfuscation Attacks on Software Plagiarism Detection Systems"
<p>This is the replication package for the doctoral dissertation titled "<em>Mitigating Automated Obfuscation Attacks on Software Plagiarism Detection Systems</em>".</p> <p>The contributions of the dissertation were also integrated into the source code plagiarism detection system <a href="https://github.com/jplag/JPlag/">JPlag</a> to ensure they are widely accessible.</p> <p><strong>Contents Overview:</strong></p> <p>- <strong>Datasets</strong>: the artifacts of the evaluation datasets.<br>- <strong>Raw Results</strong>: the measured results of our evaluation.<br>- <strong>Evaluation Scripts</strong>: the evaluation code for plotting and statistical tests.<br>- <strong>Implementation</strong>: the source code of the JPlag-based implementation and the prebuilt application as a JAR file.<br>- <strong>Other</strong>: additional plots.</p>
Development of a HPLC Method for Improved Detection and Analysis in Glycerol Electrooxidation Studies
<p># Dataset of "Development of a HPLC Method for Improved Detection and Analysis in Glycerol Electrooxidation Studies"</p> <p>---</p> <p>## GENERAL INFORMATION<br>----------------------</p> <p>1. Dataset title: "Development of a HPLC Method for Improved Detection and Analysis in Glycerol Electrooxidation Studies"</p> <p>2. Authorship: <br> Name: Eva Ng <br> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006 Castelló, Spain<br> ORCID: 0009-0003-4184-5579</p> <p> Name: Camilo A. Mesa<br> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006 Castelló, Spain<br> ORCID: 0000-0002-8450-2563</p> <p> Name: Sixto Giménez<br> Institution: Institute of Advanced Materials (INAM), Universitat Jaume I, 12006 Castelló, Spain<br> Email: <sjulia@uji.es> <br> ORCID: 0000-0002-4522-3174</p> <p>## FILE DESCRIPTION<br>--------------<br>### Figure 1<br>- Fig1a.txt : Chromatogram obtained from the analysis of the analytical standards containing glycerol electrooxidation products previously reported. <br>- Fig1b.txt : Chromatogram obtained from the analysis of the analytical standards containing glycerol electrooxidation products in this work.</p> <p><br>### Figure 2<br>- Fig2a.txt : Referential chromatograms from analytical standards that can be produced during Glycerol Electrooxidation Reaction (GEOR). Reported method.<br>- Fig2b.txt : Referential chromatograms from analytical standards that can be produced during Glycerol Electrooxidation Reaction (GEOR). New developed method.</p> <p><br>### Figure 3<br>- Fig3a.txt : Calibration curve for oxalic acid analytical standard from 0.1 mM to 5 mM and its standard deviation. Each point was measured by triplicate.<br>- Fig3b.txt : Calibration curve for tartronic acid analytical standard from 0.1 mM to 5 mM and its standard deviation. Each point was measured by triplicate.<br>- Fig3c.txt : Calibration curve for L-sodium glycerate analytical standard from 0.1 mM to 5 mM and its standard deviation. Each point was measured by triplicate.<br>- Fig3d.txt : Calibration curve for glycolic acid analytical standard from 0.1 mM to 5 mM and its standard deviation. Each point was measured by triplicate.<br>- Fig3e.txt : Calibration curve for formic acid analytical standard from 0.1 mM to 5 mM and its standard deviation. Each point was measured by triplicate.<br>- Fig3f.txt : Calibration curve for dihydroxyacetone analytical standard from 0.1 mM to 5 mM and its standard deviation. Each point was measured by triplicate.<br>- Fig3g.txt : Calibration curve for glyceraldehyde analytical standard from 0.1 mM to 5 mM and its standard deviation. Each point was measured by triplicate.<br>- Fig3h.txt : Calibration curve for glycerol analytical standard from 0.1 mM to 5 mM and its standard deviation. Each point was measured by triplicate.</p> <p>### Figure 4<br>- Fig4.txt : Glyceraldehyde standard concentrations as a function of different wavelengths. Each measurement was performed by triplicate and it is shown with its standard deviation.</p> <p>### Figure 5<br>- Fig5.txt : Experimental concentrations at different calibration levels for glyceraldehyde and formic acid quantification with their standard deviation. Each measurement was performed by triplicate.</p> <p>### Figure 6<br>-Fig6a.txt : Chromatograms obtained from a potentiostatic measurement performed at 1.62 V vs RHE in a solution containing LiOH 0.1 M and glycerol 50 mM and analytical standards as a reference. <br>-Fig6b.txt : Product distribution represented in terms of Faradaic Efficiency (FE). The percentages above the bars represent the total (FE) while inside the bars, they refer to the different products. </p>
Optimizing Deep Learning Models for Aflatoxin Detection: A Case of Artificial Intelligence-Driven Classified Groundnut Image Datasets for Postharvest Management
<p><strong>DATASET DESCRIPTION </strong><br>This dataset comprises a curated collection of classified groundnut images, specifically designed for deep learning applications in aflatoxin detection. The dataset is organized into four distinct categories: Healthy, Moldy, Insect-Infested, and Physiological Disorder, making it a vital resource for training AI and machine learning models aimed at advancing agricultural research. These classifications are crucial for the development of AI-driven solutions addressing aflatoxin contamination, enhancing crop quality assessments, and improving postharvest management practices.<br>The dataset has been developed to support research in agricultural Artificial Intelligence (AI), machine learning (ML), and food safety, with a focus on aiding resource-constrained regions in combating postharvest losses due to contamination. By leveraging this dataset, researchers can contribute to safeguarding public health, promoting food security, and supporting smallholder farmers.</p> <p><strong>POTENTIAL APPLICATIONS</strong><br>This dataset provides numerous opportunities for innovation in agriculture through AI and deep learning technologies. Its key applications include:<br><strong>Early Aflatoxin Detection</strong>: Facilitates the development of AI-powered models for prompt identification of aflatoxins in groundnuts, helping mitigate associated health risks.<br><strong>Postharvest Management Improvement</strong>: Enables the creation of innovative solutions to enhance storage, handling, and processing, reducing contamination and losses.<br><strong>Food Safety and Quality Assurance</strong>: Strengthens agricultural value chains by supporting the production of safe and high-quality food products.</p> <p><strong>BROADER IMPACT</strong><br>This resource is invaluable for fostering AI innovation in agriculture, particularly in resource-limited environments. It addresses critical challenges such as postharvest losses and food contamination while contributing to global efforts in sustainable agricultural development. By utilizing this dataset, researchers can improve food security, support smallholder farmers, and drive advancements in agricultural practices that benefit both local and global communities.</p>
Detection of black rat inhabitation using environmental DNA
<p>The all raw data of this study including expeirment 1, 2 and primer specificitiy as well as LOD.</p> <p> </p> <p>Pest management is essential for safeguarding agricultural productivity, public health, and ecological balance, especially as the global demand for sustainable pest control strategies rises with population growth. Among emerging technologies, environmental DNA (eDNA) analysis has gained prominence as a powerful tool, enabling the detection of pest species by isolating their genetic material from environmental samples. This study explores the innovative application of eDNA analysis in black rat (<a name="_Hlk184139956"></a><em>Rattus</em><em> rattus</em>) management, utilizing wipe sampling to recover eDNA from the air. Key experiments were found to determine the minimum surface area (~5 inch area) required for effective eDNA collection and to analyze the temporal persistence of black rat eDNA over two months. Our findings demonstrate the potential of eDNA techniques to transform pest management strategies, offering a highly efficient, cost-effective, and environmentally sustainable approach to monitoring and controlling black rat populations. This work represents a significant advancement in integrating molecular tools into pest control practices.</p> <p> </p>
Label-free multiplexed detection of diabetic retinopathy biomarkers using fiber optic biosensors: towards lab-in-the-tear
<p>Raw experimental data on label-free detection of diabetic retinopathy biomarkers using fiber optic biosensors. This data contains information on the multiplexed and separate detection of LCN1 and VEGF diabetic retinopathy biomarkers in artificial tears. </p>
Data from: Structured Detection for Simultaneous Super-Resolution and Optical Sectioning in Laser Scanning Microscopy
<p>This repository contains the raw data of the experimental ISM dataset used to make the figures and supplementary figures for the paper entitled <em>Structured Detection for Simultaneous Super-Resolution and Optical Sectioning in Laser Scanning Microscopy.<br></em></p>
Tiny Robotics Dataset and Benchmark for Continual Object Detection
<p>Dataset for <strong>TiROD</strong>: Tiny Robotics Dataset and Benchmark for Continual Object Detection<br><br>Official Website -> <a href="https://pastifra.github.io/TiROD/">https://pastifra.github.io/TiROD/</a></p> <p>Code -> <a href="https://github.com/pastifra/TiROD_code">https://github.com/pastifra/TiROD_code</a></p> <p>Video -> <a href="https://www.youtube.com/watch?v=e76m3ol1i4I">https://www.youtube.com/watch?v=e76m3ol1i4I</a></p> <p>Paper -> <a href="https://arxiv.org/abs/2409.16215">https://arxiv.org/abs/2409.16215</a></p>
PCLMM: A multimodal dataset for Patronizing and Condescending Language detection
<p>The PCLMM dataset is sourced from Bilibili, the largest online community for young people in China. Our work aims to uncover microaggressions targeted at vulnerable groups, including discriminatory and patronizing language expressions, as well as facial expression features. PCLMM contains six main vulnerable communities :</p> <ol> <li>Disabled individuals</li> <li>Women</li> <li>The elderly</li> <li>Children</li> <li>Single-parent families</li> <li>Low-income groups</li> </ol> <p>The PCLMM dataset contains 715 videos, totaling 21 hours of content, with an average video length of 1.80 mins and a frame rate of 30 FPS, comprising 2.3M frames. Approximately 27.4% of the videos were labeled as patronizing (label-1), aligning with the distribution of PCL data on internet platforms.</p> <p>If you would like to learn more about our PCL field (a branch of toxic speech detection) and PCLMM dataset, please refer to this paper, accepted by ICASSP 2025: </p> <p><a href="https://ieeexplore.ieee.org/abstract/document/10890580">https://ieeexplore.ieee.org/abstract/document/10890580</a></p> <p>The code implementation of the paper can be found in our repository: <a href="https://github.com/dut-laowang/PCLMM" target="_new" rel="noopener">https://github.com/dut-laowang/PCLMM</a></p> <p><strong>Updation</strong></p> <p><em><strong>February 9, 2025</strong></em> – The Annotation_Subset.csv containing the original video links has been updated.</p>
Natural noise affects conspecific signal detection and territorial defense behaviors in songbirds
<p>Recent research suggests that anthropogenic noise can substantially alter animal behavior. Although there are many sources of natural background noise, the relative influence of these sounds on behavior has received much less attention. Using landscape-scale playbacks of rushing rivers and crashing ocean surf, we investigated how habitat appropriate natural noise alters territorial defense behaviors in lazuli buntings (<i>Passerina amoena</i>) occupying riparian areas and spotted towhees (<i>Pipilo maculatus</i>) in riparian and coastal areas when exposed to simulated intruder song. We also incorporated naturally occurring cicada noise as an acoustic source influencing lazuli bunting behavior. Both songbird species possess songs that share substantial spectral overlap with low-frequency, water-generated noise, and lazuli bunting song shares an additional high-frequency overlap with cicada calls. Thus, there is potential for background acoustic conditions to mask conspecific signals. We found that detection and discrimination of conspecific playback occurred more slowly for both species as background sound level increased. Lazuli buntings also exhibited complex flight behavior in noise, suggesting they respond differently depending on the amplitude and type (with versus without cicada calls) of background noise. Our results suggest natural noise can impair territorial defense behaviors in songbirds, highlighting natural soundscapes as an under-appreciated axis of the environment.</p>
Long short-term memory (LSTM) recurrent neural network for muscle activity detection
<p><strong>Background: </strong>The accurate temporal analysis of muscle activation is of great interest in many research areas, spanning<br> from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological<br> patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors<br> is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to<br> detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle<br> activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks.<br> </p> <p><strong>Methods: </strong>First, the applicability of the proposed LSTM-based muscle activity detector (LSTM-MAD) is studied<br> through simulated sEMG signals, comparing the LSTM-MAD performance against other two widely used approaches,<br> i.e., the standard approach based on Teager–Kaiser Energy Operator (TKEO) and the traditional approach, used in<br> clinical gait analysis, based on a double-threshold statistical detector (Stat). Second, the effect of the Signal-to-Noise<br> Ratio (SNR) on the performance of the LSTM-MAD is assessed considering simulated signals with nine different SNR<br> values. Finally, the newly introduced approach is validated on real sEMG signals, acquired during both physiological<br> and pathological gait. Electromyography recordings from a total of 20 subjects (8 healthy individuals, 6 orthopedic<br> patients, and 6 neurological patients) were included in the analysis.</p> <p><strong>Results</strong>: The proposed algorithm overcomes the main limitations of the other tested approaches and it works<br> directly on sEMG signals, without the need for background-noise and SNR estimation (as in Stat). Results demonstrate<br> that LSTM-MAD outperforms the other approaches, revealing higher values of F1-score (F1-score > 0.91) and Jaccard<br> similarity index (Jaccard > 0.85), and lower values of onset/offset bias (average absolute bias < 6 ms), both on simulated<br> and real sEMG signals. Moreover, the advantages of using the LSTM-MAD algorithm are particularly evident for<br> signals featuring a low to medium SNR.</p> <p><strong>Conclusions</strong>: The presented approach LSTM-MAD revealed excellent performances against TKEO and Stat. The<br> validation carried out both on simulated and real signals, considering normal as well as pathological motor function<br> during locomotion, demonstrated that it can be considered a powerful tool in the accurate and effective recognition/<br> distinction of muscle activity from background noise in sEMG signals.</p> <p> </p>
Data from: The stochastic dynamics of early epidemics: probability of establishment, initial growth rate, and infection cluster size at first detection
<p>Emerging epidemics and local infection clusters are initially prone to stochastic effects that can substantially impact the epidemic trajectory. While numerous studies are devoted to the deterministic regime of an established epidemic, mathematical descriptions of the initial phase of epidemic growth are comparatively rarer. Here, we review existing mathematical results on the epidemic size over time, and derive new results to elucidate the early dynamics of an infection cluster started by a single infected individual. We show that the initial growth of epidemics that eventually take off is accelerated by stochasticity. These results are critical to improve early cluster detection and control. As an application, we compute the distribution of the first detection time of an infected individual in an infection cluster depending on the testing effort, and estimate that the SARS-CoV-2 variant of concern Alpha detected in September 2020 first appeared in the United Kingdom early August 2020. We also compute a minimal testing frequency to detect clusters before they exceed a given threshold size. These results improve our theoretical understanding of early epidemics and will be useful for the study and control of local infectious disease clusters.</p>
Whole-genome resequencing confirms reproductive isolation between sympatric demes of brown trout (Salmo trutta) detected with allozymes
<p>The sympatric existence of genetically distinct populations of the same species remains a puzzle in ecology. Coexisting salmonid fish populations are known from over 100 freshwater lakes. Most studies of sympatric populations have used limited numbers of genetic markers making it unclear if genetic divergence involves only certain parts of the genome. We return to the first reported case of salmonid sympatry, initially detected through contrasting homozygosity at a single allozyme locus (coding for lactate dehydrogenase A) in brown trout in the small Lakes Bunnersjöarna, Sweden. First, we verify the existence of the two coexisting demes using a 96-SNP fluidigm array. We then apply whole-genome resequencing of pooled DNA to explore genome-wide diversity within and between these demes; nucleotide diversity is higher in Deme I than in Deme II. Furthermore, strong genetic divergence is observed with genome-wide <i>F</i><sub>ST</sub>≈0.2. Comparing with similar data from other lakes, this divergence is of similar magnitude as that between reproductively isolated populations. Individual whole-genome resequencing of two individuals per deme suggests higher inbreeding in Deme II vs. Deme I, indicating different degree of isolation. Finally, we located two gene-copies for LDH-A<i> </i>and find divergence between demes in a regulatory section of one of these genes. However, we did not find a perfect fit between the sequence data and previous allozyme results, and this will require further research. Our data demonstrates genome-wide divergence governed mostly by genetic drift but also by diversifying selection in coexisting populations. This type of hidden biodiversity needs consideration in conservation management.</p>
Detection and characterization of small-sized microplastics (≥ 5 µm) in milk products
<p><strong>Data used in the scientific article to be published in Nature Scientific Reports.</strong></p>
GitTables benchmark - column type detection
<p><strong>Note: the download page of the entire GitTables corpus is here: <a href="https://zenodo.org/record/4943312">https://zenodo.org/record/4943312</a>.</strong></p> <p>This dataset represents a <strong>small subset</strong> of tables from <a href="https://gittables.github.io">GitTables</a> curated for benchmarking column type detection methods. This benchmark evaluates systems that match table columns to semantic types from the <a href="http://wikidata.dbpedia.org/services-resources/ontology">DBpedia</a> and <a href="https://schema.org/docs/schemas.html">Schema.org</a> ontologies. It is featured in the <a href="https://www.cs.ox.ac.uk/isg/challenges/sem-tab/2021/index.html">SemTab 2021 challenge</a> (CTA task).</p> <p>This dataset consists of the following files:</p> <ul> <li>“tables.zip”: directory with a sample of 1101 tables from GitTables. Filenames correspond to table IDs, the first column (without column name) corresponds to row indices, column names are replaced with "col_0", "col_1", etc. which match to the targets and labels (semantic types).</li> <li>“<ontology>_targets.csv”: target columns per table ID, 1 file per ontology (DBpedia or Schema.org): columns are “table_id” (ignore the "_<ontology>" suffix) and “target_column” (i.e. the column that should be annotated).</li> <li>“<ontology>_gt.csv”: ground truth column annotations per table ID, 1 file per ontology: columns are “table_id” (ignore the _<ontology> suffix), “target_column”, “annotation_id”, “annotation_label”.</li> <li>“<ontology>_labels.csv”: unique labels present in the annotated tables, 1 file per ontology: columns are “annotation_id” and “annotation_label”.</li> </ul> <p>The labels (semantic types) from each ontology come from:</p> <ul> <li>DBpedia: only properties from the <a href="http://wikidata.dbpedia.org/services-resources/ontology">DBpedia ontology</a>.</li> <li>Schema.org: properties and types from <a href="https://schema.org/docs/schemas.html">Schema.org</a>.</li> </ul> <p>For the entire GitTables corpus, please refer to <a href="https://zenodo.org/record/4943312#.YZQUWr3ML6Y.">this dataset</a>. Visit <a href="https://gittables.github.io">https://gittables.github.io</a> for more background and contact details.</p>
Polarization lidar data for detecting dust orientation
<p>Polarization lidar data used in publication:</p> <p>Tsekeri, A., Amiridis, V., Louridas, A., Georgoussis, G., Freudenthaler, V., Metallinos, S., Doxastakis, G., Gasteiger, J., Siomos, N., Paschou, P., Georgiou, T., Tsaknakis, G., Evangelatos, C., and Binietoglou, I.: Polarization lidar for detecting dust orientation: System design and calibration, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2021-30, in review, 2021.</p>
Datasets for "Automated Detection of Portal Fields and Central Veins in Whole-Slide Images of Liver Tissue"
<p>Datasets and results for the manuscript “Automated Detection of Portal Fields and Central Veins in Whole-Slide<br> Images of Liver Tissue” (Journal of Pathology Informatics 13 (2022) 100001, https://doi.org/10.1016/j.jpi.2022.100001)</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.