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.
2,371
datasets available to search
ShareScore release 0.7.1
Dataset results
2,371 results for “platform”
Using a Hybrid Kano-Importance Questionnaire in the Acquisition of Data Related to Students' Expectations from Online Educational Platforms
<p>This dataset contains the data collected for the assessment of the quality attributes of a new online educational platform. The questionnaire used for data collection the Kano methodology and was designed as a hybrid Kano-importance questionnaire. The purpose of this data collection consists of the analysis of the students’ expectations regarding the features proposed for a new online educational platform. This analysis facilitates the identification of student needs during times of COVID-19 pandemic and post-pandemic times, while a transition to an online educational system was used throughout the world. </p>
Online Repository of the Study "I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform
<p><strong>Online Repository of the Study </strong><em>“I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform</em></p> <p><strong>Introduction</strong></p> <p>In the Mobility-as-a-Service (MaaS) context, e-bikes are important and environmental-friendly transportation resources providing flexibility, time and cost savings, and reducing traffic congestion. Additional to user satisfaction and marketing advantages, the resolution of user-reported issues is regulated in many cities. In order to efficiently solve the issues, it is essential to quickly identify their types (e.g., software- or hardware-related?) to assign them to the responsible team. But for popular e-mobility services, the manual analysis of the reports is inefficient because of its tediousness, high time requirements, and error-proneness. </p> <p>Our empirical study, carried out in the context of a <em>Mobility as a Service </em>start-up company, proposes an approach for the automated identification of relevant concerns reported by users of e-bike services. The company has more than 20,000 private customers across seven different countries and dedicates considerable effort in analyzing user behavior. However, the current manual process of analyzing and triaging user-reported issues hinders MaaS-company’s ability to grow and expand its services. </p> <p>To help MaaS providers identify relevant user-reported issues, In the study, we (i) manually inspect about 3,000 user-reported issues received by the MaaS company; (ii) design a taxonomy modeling the types of relevant issues reported by users; and (iii) propose MaaS-RIDE, an approach to automatically classify the user-reported issues according to the categories of the devised taxonomy. </p> <p>Our results demonstrate that MaaS-RIDE is able to accurately (F-measure ≥ 93%) identify software and hardware user-reported issues. This result is critical for e-bike sharing companies to address such issues in an agile way and achieve the required user satisfaction.</p> <p><strong>Dataset Overview</strong></p> <p>The dataset is composed of the following different sorts of data: </p> <ul> <li> “<em>Data_and_preprocessing</em>” folder <ul> <li>o the user-reported issues data</li> <li>o the user-reported issues data processed as Bag of Words for Machine Learning training. <ul> <li>For this look at the sub-folder “<em>input_data_for_ML</em>” and the following matrices: <ul> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em></li> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em></li> </ul> </li> <li>Moreover, a sample of selected issues was reported in the replication package: <ul> <li>see file “<em>randomSamples.csv</em>” (due to a non-disclosure agreement with our industrial partner, we are unauthorized to share the whole raw user reports used in our experiments)</li> <li> “RQ1” folder: Types of E-bikes User-reported Issues</li> </ul> </li> </ul> </li> <li> the resulting taxonomy after the analysis of the issues</li> <li> “RQ2” folder: Classifying E-bikes Issue types</li> <li> the trained models </li> <li> the results of the models</li> </ul> </li> </ul> <p>The following sections describe more in detail what each of those folders and files contain.</p> <p><strong>“Data_and_preprocessing” folder</strong></p> <ul> <li><strong>User-reported issues subset.</strong></li> </ul> <p>In an industrial setting, due to privacy reasons, we disclose only an example subset of the user-reported issues, this information is in the file <em>randomSamples.csv</em>.</p> <p>The <em>randomSamples.csv </em>a subset that was generated randomly adding 20 examples using a stratified sampling from the High-level categories and 20 from the Low-level categories. This subset is not exhaustive but serves the purpose of showing the reviewers the kind of issues that this particular industrial set is confronted with. The file contains:</p> <ul> <li> <ul> <li> the Id of the user report; </li> <li> the column "comment_final"<strong> </strong>contains the issue text after the replacement of information that needed anonymization (e.g., vehicle-plates, personal names, addresses and timestamps); </li> <li> the column "High_level_category" contains the selected category from the 5 first level categories of the presented <em>Three-level taxonomy of e-bike user reported issues</em>; </li> <li>• the columns ‘Low_level_category" and "Fine_grained_topic" contain the assigned, if existing, respective category. </li> </ul> </li> <li><strong>Bag of Words Term by Document matrix.</strong></li> </ul> <p>An important input for training the ML models is the Bag of Words representation generated after processing the 2,989 manually-labeled user issues. The result of this process is a Term-by-Document matrix. We share this matrix in the files in the sub-folder <em>input_data_for_ML </em>where they are labeled for High- and Low-level categories. </p> <p>In the <em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em> and <em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em> files, the first column refers to the issue “Id”, the last column “oracle” is the labeled category, the rest of the columns represent the terms contained in the 2,989 user-reported issues and in each row the weight of the i−𝑡ℎ term contained in the j−𝑡ℎ user issue by using the tf-idf score.</p> <p><strong>“RQ1” folder</strong></p> <ul> <li><strong>“Three-level taxonomy of e-bike user-reported issues.pdf<em>” file</em></strong></li> </ul> <p>The taxonomy derives from the manual analysis of the 2,989 user issues. We found that a three-level taxonomy provides significant granularity to the MaaS-company. The taxonomy encompasses 5 High-level categories, 16 Low-level categories, and 15 Low-level subcategories of e-bike user-reported issues. The file <em>Three-level taxonomy of e-bike user-reported issues.pdf</em> presents the taxonomy categories and in the columns “Nr.” and “%” it shows the number of occurrences within the analyzed dataset, and the corresponding percentages.</p> <p><strong>“RQ2” folder</strong></p> <ul> <li><strong>“Trained Models” folder</strong></li> </ul> <p>We provide the trained machine and deep learning models in the sub-folder <em>ML_DL_models</em>. Our approach experimented with classic machine learning models based on the Bag-of-Words approach using SVM, on Word Embeddings using FastText, and Language models leveraging BERT. The SVM and BERT models were trained using the open source low-code data analytics platform KNIME and were used to classify issues corresponding to the first and second levels of the taxonomy from the “RQ1” folder. A 10-fold cross validation strategy was used to assess the classification performance. </p> <p>The fastText model was trained by using default values of parameters (https://fasttext.cc/docs/en/options.html) and a 10-fold cross-validation strategy. With fastText, we classified issues corresponding only to the first level of the taxonomy from “RQ1” folder, since fastText is more effective when more data points are available in the training set (i.e., lower levels in the taxonomy have fewer well-represented issue types).</p> <ul> <li><strong>“Model results” folder</strong></li> </ul> <p>In the sub-folder model_results we provide the tables summarizing the results of using the proposed MaaS-RIDE approach, with which we automatically identify and categorize user-reported issues according to the High-level and Low-level categories of the taxonomy devised in RQ1, which are relevant for the MaaS-company. </p>
Replication Package of the study "Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms"
<p><strong>Description of the Dataset of the work "Automated Identification and Qualitative Characterization of Safety<br> Concerns Reported in UAV Software Platforms"</strong></p> <p><strong><em>"1_Safety-Dataset" folder: </em></strong>This folder contains the bugs data and row data of all analyzed projects.<br> Specifically, this folder contains the following relevant entries<br> <br> - "bugs" folder: It contains the bugs of all analyzed projects (PX4-merged.json.gz, dDronin-merged.json.gz, ardupilot-merged.json.gz)<br> of all sentences extracted from the project issues<br> - "Dataset-safety-bugs.csv": For all projects, it contains the raw data of the set of sentences classified as safety and non-safety related.<br> </p> <p><em><strong>"2_Scripts-and-generated-data (RQ1)" folder:</strong> </em>This folder contains the scripts and code used to preprocess and analyze the issue data in <br> the context of RQ1<br> Specifically, this folder contains the following relevant entries<br> <br> - "main-program.py" file: Main program executing all subscripts generating the data required for RQ1 (detailed in the following line)<br> - "utilities.R" file: (Utility) R script containing relevant functions for pre-processing/indexing text and issue data<br> - "1_Script-to-create-test-dataset.r" file: R script containing simple code for analyzing issue data<br> - "2_MainScript.r" file: Main R program orchestrating the scripts "utilities.R" and "1_Script-to-create-test-dataset.r" execution<br> - "files-setDirectory" folder: Folder where data are generated and stored from the "main-program.py"<br> - "fasttext" folder: Folder where data used as input from fastText (by "main-program.py") are reported<br> - "cross-project-analysis" folder: Folder with data used for the cross-project analysis</p> <p> - "main-program-grid-search.py" file: Main program executing all experiments for the grid search analysis</p> <p><em><strong>"3_Results" folder: </strong></em>This folder contains the results, scripts and figures used to discuss results of the study.<br> Specifically, this folder contains the following relevant entries<br> <br> - "RQ1" folder: This folder contains the results, scripts and figures used to discuss results of RQ1.<br> - "RQ2" folder: This folder contains the results, scripts and Tables used to discuss results of RQ2.</p>
Interdisciplinary Composition of E-Learning Platforms based on Reusable Low-Code Adapters
<p>E-learning platforms are becoming increasingly popular and, accordingly, are also being used more and more by teachers at schools and professors at universities. They are used to distribute educational material digitally to students, but also to offer the possibility to upload and collect assignments, solve tasks, and view grades. This thesis addresses the problems of the inflexibility of established platforms, assists lecturers in designing their courses, and motivates and supports students in their learning. Under the aspect of generalization, a concept for a software product line for the demands of various fields of study is designed, which provides lecturers with a basic platform and allows them to use low-code adapters to design and modify their courses according to their needs.</p>
CT Dataset associated with the paper: (PLOSONE) Modular robotic platform for precision neurosurgery with a bio-inspired needle: system overview and first in-vivo deployment
<p>Imaging dataset associated with the work entitled "Modular robotic platform for precision neurosurgery with a bio-inspired needle: system overview and first in-vivo deployment.", published in the journal PLOS ONE</p>
SURCOM pH and temperature data collection registered at Cortegada Platform (Ria de Arousa - Spain) from 2020 to 2021
<p>This dataset comprises the data recorded by the SURCOM production prototype deployed from 2020 to 2021 at Cortegada Platform (42º 37' 40" N, 8º 46' 57" W), located in the National Park of Atlantic Islands at Ria de Arousa, in the NW of Spain.</p> <p>The measurements were taken continuously with pH and temperature collected at a frecuency of 10.3 minutes<sup>-1</sup>.</p> <p>Real-time adquired data is displayed and can be also downloaded from the following web panel:</p> <ul> <li><a href="https://co2.iim.csic.es/monitoring/d/surcom_cortegada/surcom-cortegada">https://co2.iim.csic.es/monitoring/d/surcom_cortegada/surcom-cortegada</a></li> </ul> <p>These results have been obtained thanks to the support of <a href="http://pti-waterios.csic.es/">CSIC PTI WATER:iOS</a> and are, therefore, a contribution to its efforts.</p>
Figure 1 in The Tydeoidea (Ereynetidae, Iolinidae, Triophtydeidae and Tydeidae) - An online database in the Wikispecies platform
Figure 1 Diachronic classification of Tydeoidea. Abbreviations: pcp = post-cunliffean period, Pseudot. = Pseudotydeinae, R = Riccardoellinae, s = synonymy, subfam. = subfamilies, Trioph. = Triophtydeidae.
Figure 4 A-B in The Tydeoidea (Ereynetidae, Iolinidae, Triophtydeidae and Tydeidae) - An online database in the Wikispecies platform
Figure 4 A-B – Tetranychus urticae; C – T. viburni; D – Tydeus goetzi; A – Dissecting microscope view; B-C – Facsimile of Koch's figures (same magnification) with some dorsal setae notation added; D – Compound microscope view, Agroscope Changins [Switzerland], routine black chlorazol coloration by Marc Baillod, scale bar = 100 µm. Koch's "Schulterborsten" correspond to scapular setaesc(1 andsc2) plus the subhumeral seta (c3). A – photoghraphy by Gilles San Martin. CC-BY.
Figure 3 in The Tydeoidea (Ereynetidae, Iolinidae, Triophtydeidae and Tydeidae) - An online database in the Wikispecies platform
Figure 3 The number of ereynetid mites described by Fain and by other acarologists (data grouped by decade).
A DNA biosensors-based microfluidic platform for attomolar real-time detection of unamplified SARS-CoV-2 virus
<p>Raw data associated to the study entitled:</p> <p><em>A DNA biosensors-based microfluidic platform for attomolar real-time detection of unamplified SARS-CoV-2 virus</em><strong> </strong></p> <p><em>- </em>Metadata file</p> <p>- Computational data</p> <p>- Extraction data</p> <p>- Fluorescence detection</p> <p>- Fluorescence imaging</p> <p>- Labbooks</p> <p>- Surface characterization</p>
Processed and annotated yeast gene expression data from yeast2 and ygs98 platforms
<p>This dataset contains the following files:</p> <ul> <li><em>yeast2_processed_rds.tar.gz -</em> processed gene expression matrices from the yeast2 platform. The data is stored in binary R format (.rds).</li> <li><em>ygs98_processed_rds.tar.gz </em>- processed gene expression matrices from the yeast2 platform. The data is stored in binary R format (.rds).</li> <li><em>yeast2-curated-annotations.txt</em> - metadata for the yeast2 platform.</li> <li><em>ygs98-curated-annotations.txt</em> - metadata for the ygs98 platform.</li> </ul> <p> </p>
Survey for online registered users of HistoricGraves platform
<div>This survey is being conducted by Eachtra Archaeological Projects as part of INCULTUM (2021-2024), a tourism-oriented HORIZON2020 funded project. The main goal of this survey was to better understand users and usage of the Historic Graves website and how to improve the visitor experience.</div>
Рис. 7. ГнезΑо ΑаΛьневосточного аиста, распоΛоженное на гнезΑовой пΛатформе, установΛенной на опоре ΑЭП-500 в Серышевском районе Амурской обΛасти Fig. 7. The Oriental stork nest, built on the special platform installed on a 500 kV power transmission pole in the Seryshevsky district of the Amur region in Oriental stork (Ciconia boyciana Swinhoe) breeding population survey in the Amur region in 2018-2019
Рис. 7. ГнезΑо ΑаΛьневосточного аиста, распоΛоженное на гнезΑовой пΛатформе, установΛенной на опоре ΑЭП-500 в Серышевском районе Амурской обΛасти Fig. 7. The Oriental stork nest, built on the special platform installed on a 500 kV power transmission pole in the Seryshevsky district of the Amur region
High-Speed Video Recordings of Wheel-Rail Traction Enhancement Using a Full-Scale Testing Platform - Sander Position & Angle Aimed at Nip & Wheel
<p>A database of 5 high-speed video recordings of rail-sanding process using a full-scale testing platform is provided in this data note. The videos are recorded for various case studies, namely different positioning of the sander nozzle aiming at the rail, nip, and wheel with various angles, and different materials used as rail-sand. The particle velocities can be extracted from these high-speed videos using particle image velocimetry software. The spread angle of the particles as they flow out of the nozzle can also be measured with the use of image processing software. The data extracted from these high-speed recording can be utilised for calibration, validation, and verification of experimental and numerical set-ups, as well as for training artificial intelligence models.</p>
High-Speed Video Recordings of Wheel-Rail Traction Enhancement Using a Full-Scale Testing Platform - Sander Position & Angle Aimed at Rail
<p>A database of 4 high-speed video recordings of rail-sanding process using a full-scale testing platform is provided in this data note. The videos are recorded for various case studies, namely different positioning of the sander nozzle aiming at the rail, nip, and wheel with various angles, and different materials used as rail-sand. The particle velocities can be extracted from these high-speed videos using particle image velocimetry software. The spread angle of the particles as they flow out of the nozzle can also be measured with the use of image processing software. The data extracted from these high-speed recording can be utilised for calibration, validation, and verification of experimental and numerical set-ups, as well as for training artificial intelligence models.</p>
Strain gauge platforms: Time-lapse microscopy dataset of engineered cardiac microbundles
<p>This dataset is a "part I" extension of the "<a href="https://doi.org/10.5061/dryad.5x69p8d8g">Engineered cardiac microbundle time-lapse microscopy image dataset</a>" and contains 732 experimental time-lapse image sequences of beating hiPSC-based cardiac microbundles using microbundle strain gauge platforms [1] ("Type1"). In the "part II" extension, we included 808 experimental time-lapse image sequences of beating hiPSC-based cardiac microbundles using FibroTUG platforms [2] ("Type2"). </p> <p>References:</p> <p>[1] Zhang, K., Cloonan, P. E., Sundaram, S., Liu, F., Das, S. L., Ewoldt, J. K., ... & Chen, C. S. (2021). Plakophilin-2 truncating variants impair cardiac contractility by disrupting sarcomere stability and organization. <em>Science Advances</em>, <em>7</em>(42), eabh3995.</p> <p>[2] DePalma, S. J., Davidson, C. D., Stis, A. E., Helms, A. S., & Baker, B. M. (2021). Microenvironmental determinants of organized iPSC-cardiomyocyte tissues on synthetic fibrous matrices. <em>Biomaterials science</em>, <em>9</em>(1), 93-107.</p>
Comparing coronary stent material performance on a common geometric platform through simulated bench testing: Supporting data
<p>Data including UMATs and Abaqus input files related to the paper 'Comparing coronary stent material performance on a common geometric platform through simulated bench testing' <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jmbbm.2012.02.013" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.jmbbm.2012.02.013</span></a></p>
A model-based approach to acoustic reflector localization with a robotic platform
<p>Constructing a spatial map of an indoor environment, e.g., a typical office environment with glass surfaces, is a difficult and challenging task. Current state-of-the-art, e.g., camera- and laser-based approaches are unsuitable for detecting transparent surfaces. Hence, the spatial map generated with these approaches are often inaccurate. In this paper, a method that utilizes echolocation with sound in the audible frequency range is proposed to robustly localize the position of an acoustic reflector, e.g., walls, glass surfaces etc., which could be used to construct a spatial map of an indoor environment as the robot moves. The proposed method estimate the acoustic reflector’s position, using only a single microphone and a loudspeaker that are present on many socially assistive robot platforms such as the NAO robot. The experimental results show that the proposed method could robustly detect an acoustic reflector up to a distance of 1.5 m in more than 60% of the trials and works efficiently even under low SNRs. To test the proposed method, a proof-of-concept robotic platform was build to construct a spatial map of an indoor environment.</p> <p>This dataset is made available with IROS 2020 paper: https://ieeexplore.ieee.org/abstract/document/9341437</p> <p>code could be found on Github: https://github.com/irtiq7/iROS2020</p>
The Challenges of Implementing Digital Learning Platforms in the Ministry of Information and Digitalization in Malawi
<p>This dataset was collected as part of a study exploring the implementation challenges and opportunities of digital learning platforms within the Ministry of Information and Digitalization in Malawi. The study employs a mixed-methods approach to reveal significant barriers such as internet connectivity issues, technological access limitations, and insufficient support that hinder the effective utilization of these platforms. The data includes responses from ministry personnel on their experiences with digital learning platforms, focusing on factors like support availability, time management, and motivation.</p>
iDRAMA-Scored-2024: A Dataset of the Scored Social Media Platform from 2020 to 2023
<p>ABSTRACT<br>---------------<br>Online web communities often face bans for violating platform policies, encouraging their migration to alternative platforms. This migration, however, can result in increased toxicity and unforeseen consequences on the new platform. In recent years, researchers have collected data from many alternative platforms, indicating coordinated efforts leading to offline events, conspiracy movements, hate speech propagation, and harassment. Thus, it becomes crucial to characterize and understand these alternative platforms. To advance research in this direction, we collect and release a large-scale dataset from Scored -- an alternative Reddit platform that sheltered banned fringe communities, for example, c/TheDonald (a prominent right-wing community) and c/GreatAwakening (a conspiratorial community). Over four years, we collected approximately 57M posts from Scored, with at least 58 communities identified as migrating from Reddit and over 950 communities created since the platform's inception. Furthermore, we provide sentence embeddings of all posts in our dataset, generated through a state-of-the-art model, to further advance the field in characterizing the discussions within these communities. We aim to provide these resources to facilitate their investigations without the need for extensive data collection and processing efforts.</p> <ul> <li>Scored platform: <a href="https://scored.co">https://scored.co</a></li> <li>Link to paper: <a href="https://arxiv.org/abs/2405.10233">https://arxiv.org/abs/2405.10233</a></li> <li>License: <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en">CC BY-NC-SA 4.0</a></li> </ul> <h1>Repository links</h1> <ul> <li><strong>Zenodo:</strong> From Zenodo, researchers can download `lite` version of this dataset, which includes only 57M posts from Scored (not the sentence embeddings).</li> <li><strong>Github:</strong> The main repository of this dataset, where we provide code-snippets to get started with this dataset. <ul> <li>Link here: <a href="https://github.com/idramalab/iDRAMA-scored-2024">https://github.com/idramalab/iDRAMA-scored-2024</a></li> </ul> </li> <li><strong>Huggingface:</strong> On Huggingface, we provide complete dataset with senetence embeddings.<br> <ul> <li>Link here: <a href="https://hf.co/datasets/iDRAMALab/iDRAMA-scored-2024">https://hf.co/datasets/iDRAMALab/iDRAMA-scored-2024</a></li> </ul> </li> </ul> <h1>Dataset Info</h1> <table> <tbody> <tr> <td><strong>File-name</strong></td> <td><strong>Data-points</strong></td> </tr> <tr> <td>comments-2020</td> <td>12,774,203</td> </tr> <tr> <td>comments-2021</td> <td>16,097,941</td> </tr> <tr> <td>comments-2022</td> <td>12,730,301</td> </tr> <tr> <td>comments-2023</td> <td>8,919,159</td> </tr> <tr> <td>submissions-2020-to-2023</td> <td>6,293,980</td> </tr> </tbody> </table> <h1>Authorship</h1> <p>This dataset is published at "AAAI ICWSM 2024 (INTERNATIONAL AAAI CONFERENCE ON WEB AND SOCIAL MEDIA)" hosted at Buffalo, NY, USA.</p> <ul> <li><strong>Academic Organization: </strong><a href="https://idrama.science/people/">iDRAMA Lab</a></li> <li><strong>Affiliation:</strong> Binghamton University, Boston University, University of California Riverside</li> </ul> <h1>Licensing</h1> <p>This dataset is available for free to use under terms of the non-commercial license <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en">CC BY-NC-SA 4.0</a>.</p> <h1>Citation</h1> <blockquote> <p>@inproceedings{patel2024idrama,<br> title={iDRAMA-Scored-2024: A Dataset of the Scored Social Media Platform from 2020 to 2023},<br> author={Patel, Jay and Paudel, Pujan and De Cristofaro, Emiliano and Stringhini, Gianluca and Blackburn, Jeremy},<br> booktitle={Proceedings of the International AAAI Conference on Web and Social Media},<br> volume={18},<br> pages={2014--2024},<br> year={2024},<br> issn = {2334-0770},<br> doi = {10.1609/icwsm.v18i1.31444},<br>}</p> </blockquote>
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.