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107 results for “automated analysis”
Data from Automated plankton image analysis using convolutional neural networks
<p>Datasets and code from Luo et al., "Automated plankton image analysis using convolutional neural networks." Limnology and Oceanography Methods.</p> <p>Data include:</p> <p>1) 42,564 item training library, sorted in 108 classes,</p> <p>2) 42,548 item test set for filtering thresholds, sorted into 38 groups. These images are independent from the training library, and are used for setting the thresholds for post-classification filtering.<br> CSV file: Luo_etal_FT_images_pred.csv contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names.</p> <p>3) 75,000 item fully random, validated set for confusion matrix calculations, sorted into 38 groups. This set is a representation of the full dataset, selected at random after classification. <br> CSV file: Luo_etal_confusionmatrix_images.csv contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names.</p> <p> </p> <p>Scripts and programs:</p> <p>1) Segmentation.zip contains the scripts and executables for the segmentation program.</p> <p>2) Plankton_template.zip contains the archived version of the SparseConvNet program used in manuscript (current version available at: https://github.com/btgraham/SparseConvNet or https://github.com/facebookresearch/SparseConvNet)<br> Note that google-sparsehash is necessary for running SparseConvNet.<br> Also, plankton_epoch-150.cnn are the weights from the training used in the manuscript, and should be placed in the /weights folder if you want to replicate the classifications.</p>
Dataset for Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy
<p>Raw and processed image data resulting from the paper "Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy", by S. Mitchell, F. Parés, D. Faust Akl, S. M. Collins, D. M. Kepaptsoglou, Q. M. Ramasse, D. Garcia-Gasulla, J. Pérez-Ramírez, and N. López (JACS, 2021). </p> <p>The corresponding code can be found under: <a href="https://github.com/HPAI-BSC/AtomDetection_ACSTEM">GitHub - HPAI-BSC/AtomDetection_ACSTEM</a></p>
Combined unsupervised and semi-automated supervised analysis of flow cytometry data reveals cellular fingerprint associated with newly diagnosed pediatric type 1 diabetes
<p>Type 1 diabetes is a chronic autoimmune disease resulting in an immune-mediated loss of pancreatic β-cells; however, an unbiased and reproducible profiling of type 1 diabetes-specific circulating immunome at disease onset has yet to be explored. In this study, fresh whole blood was collected from a pediatric cohort of 107 patients with new-onset type 1 diabetes, 85 relatives of patients with type 1 diabetes with 0-1 islet autoantibodies, 58 patients with celiac disease or autoimmune thyroiditis and 76 healthy controls. Up to 6 mL of blood was collected from each subject into a VACUETTE® TUBE 6 ml ACD-B (Greiner). Fresh whole blood underwent red blood cell lysis, was washed and stained with specific monoclonal antibodies. Fresh whole blood samples were stained with five panels of antibodies labelled as T cells, T&NK cells, B cells, Tregs and DCs/monos encompassing main subsets of T cells, NK cells, B cells, Tregs, DCs and monocytes detected using 26 surface markers and the intracellular marker forkhead box P3 (FoxP3); for the Treg panel, intracellular staining was performed after fixation and permeabilization. Cells were acquired on a BD FACSCanto-II flow cytometer equipped with FACSDiva software (Becton Dickinson, Franklin Lakes, NJ). </p>
Semi-automated Quantitative Morphometric Analysis of E18 Rat Hippocampal Neurons from 0.5 to 6 Days In Vitro
<p>This is the dataset presented in "Semi-automated quantitatve evaluation of neuron developmental morphology <em>in vitro</em> using the change-point test" by AS Liao, W Cui, VS Webster-Wood, and YJ Zhang (submitted to Neuroinformatics 2022).</p>
nNPipe: A neural network pipeline for automated analysis of morphologically diverse catalyst systems - Resources
<p>This dataset comprises of resources required to replicate the results described in "<em>nNPipe</em>: A neural network pipeline for automated analysis of morphologically diverse catalyst systems". <em>nNPipe </em>is a deep learning based method in which two deep convolutional neural networks are used for the automated analysis of 2048x2048 HRTEM images.</p> <p>The file contains:<br> - Relevant experimental images as well as ground truth for Pd/C and Au/Ge systems.<br> - A workflow file explaining the nNPipe workflow.<br> - Mathematica 12.1 code for the generation of computational models.<br> - MATLAB code for HRTEM multislice simulations using MULTEM, as well as code required to form respective training datasets.<br> - Weights and files required for training the YOLOv5x module.<br> - Weights and files required for training the SegNet module.<br> - Mathematica 12.1 code required for reconstruction of 2048x2048 binary segmented maps of HRTEM images. </p>
Quality Assessment in DevOps: Automated Analysis of a Tax Fraud Detection System
<p>The dataset includes the results of the performance analysis of Big Blu case study under different workloads, number of available resources and execution demand of activities</p>
Dataset from "A user-friendly method to get automated pollen analysis from environmental samples". New Phytologist.
<p>Dataset used in publication "A user-friendly method to get automated pollen analysis from environmental samples". New Phytologist.</p> <p><br>This repository contains images from annual pollen trap samples mounted on slides and scanned under light microscopy; image annotation metadata; and the weights of the trained models from the YOLOv5 algorithm, saved after the last training epoch.</p> <p>More details can be found in the README file.</p>
Supplementary Material for 'Leveraging the GIDAS Database for the Criticality Analysis of Automated Driving Systems'
<p>This repository contains the supplementary material for the publication 'Leveraging the GIDAS Database for the Criticality Analysis of Automated Driving Systems'.<br> It consists of four files:</p> <ol> <li>Criticality-Phenomena-Catalog.CSV: The catalog of criticality phenomena (CP)</li> <li>Criticality-Phenomena-Phi-Coefficient.CSV: The calculation of the Phi coefficient between all pairs of CP</li> <li>Criticality-Phenomena-Risk-Calculation.CSV: The case-phenomenon relation matrix, including the calculated values for the risk of each CP for all three severity levels</li> <li>Criticality-Phenomena-Sorted-By-Risk.CSV: A list of the CP from the CP catalog sorted by risk for all three severity classes</li> </ol>
Data Set for the Journal Article "Automated Preparation of Nanoscopic Structures: Graph-Based Sequence Analysis, Mismatch Detection, and pH-Consistent Protonation with Uncertainty Estimates"
<p>This repository containes the data generated by ASAP and discussed in the journal article [Csizi, K.-S. and Reiher, M., 2023, arXiv:2307.16344], including Cartesian coordinates of training and test set molecules, and MD trajectories. </p>
dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
<p>dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning</p>
Digitalisation to improve automated agro-export logistics: Comprehensive bibliometric analysis
<p><strong>Introduction/objective</strong>: Digitalization in logistics transcended in the search for continuous improvement of good process optimization. This study aims to know the effectiveness of digitization implemented by companies to improve the automated logistics of cross-border trade in the agricultural sector.</p> <p><strong>Methodology</strong>: A bibliometric analysis was generated, exploring the evolution of the state of the art through Scopus, WOS and Dimensions databases, in order to select relevant empirical studies on digitization and automated logistics, using quality criteria and the application of the Prisma 2020 flowchart.</p> <p><strong>Results:</strong> Since 2017, there were signs of increased interest from researchers, highlighting authors such as Zoubek, Kumar and Ghobakhloo. This review provided insight into how digitization contributes to cost and time optimization in the logistics chain. Designing public policies allows a better integration of technology, such as IoT and AI. It identified 3 important blocks that have contributed to the effectiveness of digitization in automated logistics, they refer to “Impact of digitization on logistics efficiency and supply chain”, “Technology integration and automation in cross-border logistics” and “Governance, policy and social considerations in logistics digitization”.</p> <p><strong>Conclusions</strong>: Digitalization has been a fundamental element to improve logistics and make it autonomous within cross-border trade, allowing technology to get involved, integrating digital technologies such as artificial intelligence (AI), which reduced obstacles affecting the supply chain.</p>
SynActJ: Easy-to-use automated analysis of synaptic activity
<p>Neuronal synapses are highly dynamic communication hubs that mediate chemical neurotransmission via the exocytic fusion and subsequent endocytic recycling of neurotransmitter-containing synaptic vesicles (SVs). Functional imaging tools allow for the direct visualization of synaptic activity by detecting action potentials, pre- or postsynaptic calcium influx, SV exo- and endocytosis, and glutamate release. Fluorescent organic dyes or synapse-targeted genetic molecular reporters, such as calcium, voltage or neurotransmitter sensors and synapto-pHluorins reveal synaptic activity by undergoing rapid changes in their fluorescence intensity upon neuronal activity on timescales of milliseconds to seconds, which typically are recorded by fast and sensitive widefield live cell microscopy.</p> <p>The analysis of the resulting time-lapse movies in the past has been performed by either manually picking individual structures, custom scripts that have not been made widely available to the scientific community, or advanced software toolboxes that are complicated to use. For the precise, unbiased and reproducible measurement of synaptic activity, it is key that the research community has access to bio-image analysis tools that are easy-to-apply and allow the automated detection of fluorescent intensity changes in active synapses.</p> <p>Here we present SynActJ (<strong>Syn</strong>aptic <strong>Act</strong>ivity in Image<strong>J</strong>), an easy-to-use fully open-source workflow that enables automated image and data analysis of synaptic activity. The workflow consists of a Fiji plugin performing the automated image analysis of active synapses in time-lapse movies via an interactive seeded watershed segmentation that can be easily adjusted and applied to a dataset in batch mode. The extracted intensity traces of each synaptic bouton are automatically processed, analyzed, and plotted using a R Shiny workflow. We validate the workflow on time-lapse images of stimulated synapses expressing the presynaptic pH reporter Synaptophysin-pHluorin or a synapse-targeted calcium sensor, Synaptophysin-RGECO. We compare the automatic workflow to manual analysis and compute calcium-influx and SV exo-/ endocytosis kinetics and other parameters for synaptic vesicle recycling under different conditions. We predict SynActJ to become an important tool for the analysis of synaptic activity and synapse properties.</p>
Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR)
<p>This dataset is related to the paper <strong>“Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR)”.</strong></p> <p>https://www.sciencedirect.com/science/article/pii/S2001037021003792?via%3Dihub</p> <p>The dataset comprises horizontal and vertical frequencies of 2D NMR TOCSY of breast cancer-tissue sample. 2D TOCSY was acquired by employing a broadband high resolution 600.13 MHz (B0 = 14.1 T) NMR Bruker spectrometer (AVANCE III 600 with the Bruker magnet ASCEND 600) supported with the room temperature probe (BBO model-Bruker) and Magic Angle Spinning (MAS) probehead. 1D and 2D NMR spectra acquisition and processing were achieved by using the TopSpin software package 3.6.</p> <p>There are two files:</p> <p><strong>BreastCancerMetabolites.csv:</strong></p> <p>First column: numerical labels of the metabolites. Each number represent a metabolite. In total, there are 27 metabolites with multiple multiplets per metabolite.</p> <p>Second and third column: Horizontal and vertical frequencies for each metabolite.</p> <p><strong>Labels.csv:</strong></p> <p>The corresponding metabolites names.</p> <p> </p>
How to Build an Image Processing Pipeline for Automating Multiparameter Histocytometry Analysis
<p>Image files for evaluation of an upcoming Current Protocols submission, as well as associated reference files.</p>
Automated Qualitative and Quantitative Analysis of Complex Forensic Drug Samples using 1H NMR
<p>Dataset to accompany the manuscript "Automated Qualitative and Quantitative Analysis of Complex Forensic Drug Samples using <sup>1</sup>H NMR"</p>
Supplementary GIS data - Potential and implications of automated pre-processing of LiDAR-based digital elevation models for large-scale archaeological landscape analysis
<p>A supplementary dataset related to the paper discussing preparation of a digital elevation model derived from DMR 5G (LiDAR-based DEM of the Czech Republic) cleaned of modern artificial features. It includes data used as a clipping mask and data produced during the testing phase.</p> <p>Contents:</p> <ul> <li>..\clipping_buffers.gdb\ - Clipping buffers based on ZABAGED dataset used for masking the original data stored as ESRI geodatabase.</li> <li>..\drainages\ - Drainages with Strahler order higher than four (potential watercourses) for the original and filtered DEMs. <ul> <li>drainages_filtered - Drainges identified in the filtered DEM stored as GeoTIFF.</li> <li>drainages_original - Drainges identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\LSC\ - Locations with significant land surface curvature for the original and filtered DEMs. <ul> <li>LSC_filtered - Significant LSC identified in the filtered DEM stored as GeoTIFF. </li> <li>LSC_original - Significant LSC identified in the original DEM stored as GeoTIFF. </li> </ul> </li> <li>..\visibility\ - Viewsheds computed over the original and filtered DEMs. <ul> <li>Libice\ - Sample viewsheds computed for the early medieval hillfort of Libice. <ul> <li>Libice_visibility_filtered - Viewshed based on the filtered DEM stored as GeoTIFF. </li> <li>Libice_visibility_original - Viewshed based on the original DEM stored as GeoTIFF. </li> <li>observer_points - Observer points used for calculating the viewsheds.</li> </ul> </li> <li>regular_grid\ - Cumulative viewsheds calculated for regularly spaced points in a 10 x 10 km grid with a visibility radius of 5 km and an observer height of 2 m; a total of 574 viewsheds. <ul> <li>visibility_filtered - Cumulative viewshed for the filtered DEM stored as GeoTIFF.</li> <li>visibility_original - Cumulative viewshed for the original DEM stored as GeoTIFF. </li> <li>visibility_test_buffers - Buffers used for the viewshed calculations stored as ESRI shapefile.</li> <li>visibility_test_observers - Observer points used for the viewshed calculations stored as ESRI shapefile.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>Preprint version of the related paper:</p> <p>Novák, David and Pružinec, Filip, Potential and Implications of Automated Pre-Processing of Lidar-Based Digital Elevation Models for Large-Scale Archaeological Landscape Analysis. Available at SSRN: <a href="https://ssrn.com/abstract=4063514">https://ssrn.com/abstract=4063514</a></p>
Combining dynamic and static analysis for automated grading SQL statements
<p><strong>Introduction</strong></p> <p>Our experiment was conducted in an undergraduate Relational Database course at the Australian National University. The experiment was conducted on August 10th 2018 when students enrolled in the Relational Database course started to learn relational data model and SQL. The experiment was carried out fully online for three weeks and a total of 393 students were enrolled. The students were asked to login in an online assessment platform and complete 15 exercises. This platform provided an SQLite environment in students browsers by compiling the SQLite C code with Emscripten.</p> <p>Students were allowed to submit and execute their answers in the form of SQL statements. If the execution result of the statement submitted by the student is the same as that of the reference statement, the online assessment platforms will return a feedback message indicating that the execution result is correct. During the interaction with the assessment platform, statements submitted by students were recorded and archived. Overall, our experiment had collected 12,899 statements submitted by students. To create a benchmark dataset that can be used to evaluate different grading approaches, we randomly selected 45 SQL statements submitted by students for each exercise, and asked three teaching assistants to grade them manually. Finally, we average the scores provided by the three assistants and take it as the final score of each statement. The dataset collected in this experiment is ready for public release.</p> <p>All experimental data are stored in Submission.sqlite, which is an SQLite database file. It is recommended to use software such as DB browser or SQLite expert to explore the database.</p> <p> </p> <p><strong>Datatable description</strong></p> <p> </p> <p><em><strong>exercises_result</strong></em></p> <p>This datatable stores the statements submitted by students. Based on the execution result of statement, statements were divided into three categories.</p> <ul> <li>noninterpretable: the statement is non-executable.</li> <li>partially correct: the execution result of statement is different from the expected result.</li> <li>correct: the execution result of the SQL statement is the same as the expected result.</li> </ul> <p>After analyzing the correct statements, we found that the correct set contains some statements carefully constructed by students to deceive the examination system.</p> <p>Take exercise 1 as an example, the task is to answer the following questions using SQL statements.</p> <p>Question: Assume persons who were born in the same year are the same age and there is only one youngest person (with no ties/draws) in this database, who is/are the second youngest person(s) in the database? List the id(s) of the person(s).</p> <p>The reference statement to this exercise is:</p> <pre><code class="language-sql">SELECT p.id FROM person p WHERE p.year_born = (SELECT MAX(year_born) FROM person WHERE year_born < (SELECT MAX(year_born) FROM person)); </code></pre> <p>By exploring the database or trying to execute different statements, some students found that the ID of the person who met the conditions was '00000842', so the following statement was submitted.</p> <pre><code class="language-sql">select id from person where id ='00000842'; </code></pre> <p>The execution result of the above code was correct, but it was obviously not what the tutor expected. Therefore, we identified such statements as 'cheating'.</p> <p>Table 1 Description of exercises_result table.</p> <table> <thead> <tr> <th> <p><strong>field</strong></p> </th> <th> <p><strong>desc</strong></p> </th> <th> <p><strong>datatype</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>submission_id</p> </td> <td> <p>Submission ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>submitted_answer</p> </td> <td> <p>statement submitted by student</p> </td> <td> <p>TEXT</p> </td> </tr> <tr> <td> <p>submission_time</p> </td> <td> <p>Submission time</p> </td> <td> <p>NUM</p> </td> </tr> <tr> <td> <p>exercise_id</p> </td> <td> <p>Exercise ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>is_correct</p> </td> <td> <p>Mark whether the statement is correct</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>student_id</p> </td> <td> <p>Student ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>category</p> </td> <td> <p>categories of statement</p> </td> <td> <p>TEXT</p> </td> </tr> </tbody> </table> <p> </p> <p><em><strong>exercises_benchmark</strong></em></p> <p>This datatable stores the scores provided by different assistants. We randomly selected 45 SQL statements submitted by students for each exercise, and asked three teaching assistants to grade them manually. Finally, we averaged the scores provided by the three assistants as the final score of each statement.</p> <p>Table 2 Description of exercises_benchmark table.</p> <table> <thead> <tr> <th> <p><strong>Field</strong></p> </th> <th> <p><strong>comment</strong></p> </th> <th> <p><strong>datatype</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>Submission_id</p> </td> <td> <p>Submission ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>grade</p> </td> <td> <p>grade provided by tutor</p> </td> <td> <p>REAL</p> </td> </tr> <tr> <td> <p>tutor</p> </td> <td> <p>tutor</p> </td> <td> <p>TEXT</p> </td> </tr> </tbody> </table> <p> </p> <p><em><strong>exercises_exercise</strong></em></p> <p>This datatable stores the exercises provided by tutor.</p> <p>Table 3 Description of exercises_exercise table.</p> <table> <thead> <tr> <th> <p><strong>Field</strong></p> </th> <th> <p><strong>comment</strong></p> </th> <th> <p><strong>datatype</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>id</p> </td> <td> <p>Exercise ID</p> </td> <td> <p>INT</p> </td> </tr> <tr> <td> <p>title</p> </td> <td> <p>Title of exercise</p> </td> <td> <p>TEXT</p> </td> </tr> <tr> <td> <p>preamble</p> </td> <td> <p>Description of exercise</p> </td> <td> <p>TEXT</p> </td> </tr> <tr> <td> <p>difficulty</p> </td> <td> <p>Coefficient of difficulty</p> </td> <td> <p>integer</p> </td> </tr> <tr> <td> <p>ref</p> </td> <td> <p>Reference statement</p> </td> <td> <p>integer</p> </td> </tr> </tbody> </table> <p> </p> <p><em><strong>database schema</strong></em></p> <p>Please refer to db_schema.pdf for the database schema used in the experiment.</p> <p> </p> <p><strong>BibTex</strong></p> <p>if you want to cite our paper:</p> <p> </p> <blockquote> <pre>@article{wang2020combining, title={Combining dynamic and static analysis for automated grading SQL statements}, author={Wang, Jinshui and Zhao, Yunpeng and Tang, Zhengyi and Xing, Zhenchang}, journal={J Netw Intell}, volume={5}, number={4}, pages={179--190}, year={2020} }</pre> </blockquote>
Automated Segmentation of Large Image Datasets using Artificial Intelligence for Microstructure Characterisation and Damage Analysis
<p>Many properties of commonly used materials are driven by their microstructure, which can be influenced<br>by the composition and manufacturing processes. To optimise future materials, understanding the<br>microstructure is critically important. Here, we present two novel approaches based on artificial intelligence<br>that allow the segmentation of the phases of a microstructure for which simple numerical approaches, such<br>as thresholding, are not applicable: One is based on the nnU-Net neural network, and the other on generative<br>adversarial networks (GAN).<br>Using scanning electron microscopy images collected from large areas (~1 mm²) of dual-phase steels as a<br>case study, we demonstrate how both methods effectively segment intricate microstructural details,<br>including martensite, ferrite, and damage sites, for subsequent analysis.<br>Either method shows substantial generalizability across a range of image sizes and conditions, including<br>heat-treated microstructures with different phase configurations. The nnU-Net excels in mapping large<br>image areas. Conversely, the GAN-based method performs reliably on smaller images, providing greater<br>step-by-step control and flexibility over the segmentation process.<br>This study highlights the benefits of segmented microstructural data for various purposes, such as<br>calculating phase fractions, modelling material behaviour through finite element simulation, and<br>conducting geometrical analyses of damage sites and the local properties of their surrounding<br>microstructure.</p> <p>https://doi.org/10.1016/j.matdes.2024.113031</p>
Figure 9. Performance analysis of FCM-PSO, GPC-PSO and GFCM-PSO-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>All scans obtained from different image clustering models are manually ranked based on<br> values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and<br> GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO)<br> are applied on a dataset of 208 images and ranking is done in terms of under detected, over<br> detected, properly detected as shown in figure 8 and figure 9.</p>
Figure 8. Performance analysis of FCM, GPC and GFCM Figure 9.-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>All scans obtained from different image clustering models are manually ranked based on<br> values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and<br> GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO)<br> are applied on a dataset of 208 images and ranking is done in terms of under detected, over<br> detected, properly detected as shown in figure 8 and figure 9. The number of images detected<br> properly in GFCM is comparatively high than FCM and GPC. The optimized result of GFMC<br> provides accurate detection of WMLs and it properly detects 195 images.</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.