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.
14
datasets available to search
ShareScore release 0.9.0
Dataset results
14 results for “music research”
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Poly Continuous)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Mono Discrete)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Poly Discrete)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
Accelerating Digital Skills for Music Researchers - Processing Text-Based Corpora for Musical Discourse Analysis - Episode 5
<p>Dataset containing four .xlsx and .csv files for the exercises in Episode 5 of the <a href="https://acceleratingdigitalskills.github.io/Processing-Text-Based-Corpora/">Processing Text-Based Corpora for Musical Discourse Analysis</a> lesson of the <a href="https://acceleratingdigitalskills.org/">Accelerating Digital Skills for Music Researchers</a> project. The original data was collected from <a href="https://boomkat.com/">Boomkat.com</a> with permission.</p>
Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)
<p>This platform is a multi-functional music data sharing platform for Computational Musicology research. It contains many music datas such as the sound information of Chinese traditional musical instruments and the labeling information of Chinese pop music, which is available for free use by computational musicology researchers.</p> <p>This platform is also a large-scale music data sharing platform specially used for Computational Musicology research in China, including 3 music databases: Chinese Traditional Instrument Sound Database (CTIS), Midi-wav Bi-directional Database of Pop Music and Multi-functional Music Database for MIR Research (CCMusic). All 3 databases are available for free use by computational musicology researchers. For the contents contained in the database, we will provide audio files recorded by the professional team of the conservatory of music, as well as corresponding labelled files, which have no commodity copyright problem and facilitate large-scale promotion. We hope that this music data sharing platform can meet the one-stop data needs of users and contribute to the research in the field of Computational Musicology.</p> <p> </p> <p>If you want to know more information or obtain complete files, please go to the official website of this platform:</p> <p><a href="https://ccmusic-database.github.io/en/">Music Data Sharing Platform for Academic Research</a></p> <p> </p> <ul> <li> <p><strong>Chinese Traditional Instrument Sound Database (CTIS)</strong></p> </li> </ul> <p>This database is developed by Prof. Han Baoqiang's team for many years, which collects sound information about Chinese traditional musical instruments. The database includes 287 Chinese national musical instruments, including traditional musical instruments, improved musical instruments and ethnic minority musical instruments.</p> <ul> <li> <p><strong>Multi-functional Music Database for MIR Research</strong></p> </li> </ul> <p>This database collects sound materials of pop music, folk music and hundreds of national musical instruments, and makes comprehensive annotation to form a multi-purpose music database for MIR researchers.</p> <ul> <li><strong>Midi-wav Bi-directional Database of Pop Music</strong></li> </ul> <p>This database contains hundreds of Chinese pop songs, and each song contains the corresponding midi-audio-lyric information. Among them, recording the vocal part and accompaniment part of audio independently is helpful to study the MIR task under the ideal situation. In addition, the information of singing techniques consistent with vocal part (such as breath sound, falsetto, breathing, vibrato, mute, slide, etc.) is marked in MuseScore, which constitutes a Midi-Wav bi-direction corresponding pop music database.</p>
Accelerating Digital Skills for Music Researchers - Processing Text-Based Corpora for Musical Discourse Analysis - Episode 2
<p>Dataset containing three subgenre-specific .xlsx files for the exercises in Episode 2 of the <a href="https://acceleratingdigitalskills.github.io/Processing-Text-Based-Corpora/">Processing Text-Based Corpora for Musical Discourse Analysis</a> lesson of the <a href="https://acceleratingdigitalskills.org/">Accelerating Digital Skills for Music Researchers</a> project. The original data was collected from <a href="https://boomkat.com/">Boomkat.com</a> with permission.</p>
jazznet: A Dataset of Fundamental Piano Patterns for Music Audio Machine Learning Research
<p>Jazznet is a dataset of piano patterns for music audio machine learning research. The dataset comprises chords, arpeggios, scales, and chord progressions in all keys of an 88-key piano and in all the inversions, for a total of 162520 labeled piano patterns, resulting in 95GB of data and more than 26k hours of audio. The data is also accompanied by Python scripts to enable the easy generation of new piano patterns beyond those present in the dataset. The data is broken down into small, medium, and large subsets, comprising 21516, 30328, and 52360 patterns, respectively (with all the chords, arpeggios, and scales being present in all subsets). </p> <p>The GitHub page of the dataset, containing details of the dataset and scripts for generating new data is https://github.com/tosiron/jazznet.</p>
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Mono Continuous)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
Saraga: research datasets of Indian Art Music
<p><strong>Dataset introduction</strong></p> <p>This repository contains time aligned melody, rhythm, and structural annotations for two large open corpora of Indian Art Music (Carnatic and Hindustani music).</p> <p>The repository contains Carnatic and Hindustani collections in separated zip files, and each collection is organized by songs grouped by artist concerts/live performances. This organization follows the structure generated by downloading the data using the scripts available at the dataset Github repository: <a href="https://github.com/MTG/saraga">https://github.com/MTG/saraga</a>.</p> <p>Moreover, there is a part of the Carnatic collection, 168 tracks to be specific, that counts with multitrack audio files apart from the mix audio. The considered instruments are: Ghatam, Mridangam, Violin, Voice and Secondary Voice.</p> <p> </p> <p><strong>Annotations in the dataset</strong></p> <p>Section and tempo annotations stored as start and end timestamps together with the name of the section and tempo during the section (in a separate file). Sama annotations referring to rhythmic cycle boundaries stored as timestamps. Phrase annotations stored as timestamps and transcription of the phrases using solfège symbols ({S, r, R, g, G, m, M, P, d, D, n, N}). Audio features automatically extracted and stored: pitch and tonic.</p> <p>For more information about the dataset tracks and annotations, please refer to the Saraga website: <a href="https://mtg.github.io/saraga/">https://mtg.github.io/saraga/</a></p> <p> </p> <p><strong>Using this dataset</strong></p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p>*Please note that you can also use this dataset through the MIRDATA library (<a href="https://github.com/mir-dataset-loaders/mirdata">https://github.com/mir-dataset-loaders/mirdata</a>), where this dataset is in the list of available datasets.</p>
User Base of rOpenGov's Music Research Software
<p>The Digital Music Observatory has already created open-source software for the music industry that had been tested in real-life policy advocacy and business cases and scientific uses related to piracy research. While developed with a clear music industry focus, they have found thousands of users in the open research community worldwide for other purposes, too. We aim to further improve them to work as as a software ecosystem, and whenever possible, add web-based application interfaces with the ambition to make them useable for music organization that do not possess in-house R&D, IT, or data science capacities.<br> <br> The datasets contains the download statistics of these packages from CRAN.</p>
Cognitive Health Research on Musical Arts
ClinicalTrials.gov study NCT04137913. IPD Sharing: NO. Countries: 1. Publications: 95.
Research on the factors influencing tourist loyalty to outdoor music festivals: An application of stimulus-organism-response paradigm
<p>The Strawberry Music Festival, a local event in China, has been hosted in major cities nationwide since early 2024. Its growth has not only increased brand recognition but also stimulated economic and cultural development in its host cities. The festival’s themes of "spring, romance, and love" reflect attitudes and cultural ideals that resonate with many music enthusiasts, particularly the youth.</p> <p>The research was conducted in Shenyang, Liaoning Province, a city known for its rich history, modern urban style, and thriving music scene. The 2023 Shenyang Strawberry Music Festival, branded as "Hello, Shenyang!" spanned two days and attracted over 40,000 attendees with its impressive lineup, striking stage design, and four vibrant performances. Approximately half of the visitors traveled from other cities, bringing a substantial boost to Shenyang's cultural, tourism, and economic sectors. This study focused on participants of the 2023 Shenyang Strawberry Music Festival. At the festival's end, researchers used convenience sampling at the exits to recruit departing attendees for data collection, a process that took 20 to 25 minutes per participant. Only individuals aged 18 and older were included in the study. Following the recommendation of a minimum of 200 samples for structural equation modeling, we collected 673 usable responses, exceeding the suggested sample size for robust data analysis.</p>
Language and Music, Speech and the Human Beatbox: Theoretical Issues for Research in General and Applied Linguistics
ClinicalTrials.gov study NCT04526041. IPD Sharing: NO. Countries: 1. Publications: 0.
MedleyDB Audio: A Dataset of Multitrack Audio for Music Research
<p>Audio files for the MedleyDB multitrack dataset. <strong>Annotation and Metadata files are version controlled and are available in the <a href="https://github.com/marl/medleydb">MedleyDB github</a> repository: </strong><em>Metadata</em> can be found <a href="https://github.com/marl/medleydb/tree/master/medleydb/data/Metadata">here</a>, <em>Annotations</em> can be found <a href="https://github.com/marl/medleydb/tree/master/medleydb/data/Annotations">here</a>.</p> <p>For detailed information about the dataset, please visit MedleyDB's <a href="http://medleydb.weebly.com">website</a>.</p> <p> </p> <p>If you make use of MedleyDB for academic purposes, please cite the following publication:<br> <br> <em>R. Bittner, J. Salamon, M. Tierney, M. Mauch, C. Cannam and J. P. Bello, "<a href="http://marl.smusic.nyu.edu/medleydb_webfiles/bittner_medleydb_ismir2014.pdf">MedleyDB: A Multitrack Dataset for Annotation-Intensive MIR Research</a>", in 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan, Oct. 2014.</em></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.