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19 results for “audio sample”
BiVib - Audio-Tactile Piano Sample Library
<p><strong>BiVib</strong> is an extensive piano sample library consisting of <strong>bi</strong>naural sounds and keyboard <strong>vib</strong>ration signals.<br>Samples were acquired with high-quality audio and vibration measurement equipment on two <a href="https://en.wikipedia.org/wiki/Disklavier">Yamaha Disklavier pianos</a> (one grand and one upright model) by means of computer-controlled playback of each key at ten different MIDI velocity values.<br>Project files (<em>instruments</em> and <em>multis</em>) are provided for use with the software sampler <a href="https://www.native-instruments.com/en/products/komplete/samplers/kontakt-6/">Native Instruments Kontakt</a> (version 5 and above, available for Windows and Mac OS).<br>The nominal specifications of the equipment used in the acquisition chain are reported in a companion document, allowing researchers to calculate physical quantities (e.g. acoustic pressure, vibration acceleration) from the recordings.<br>The library is especially suited for acoustic and vibration research on the piano, as well as for research on multimodal interaction with musical instruments.</p>
Path Following in Non-Visual Conditions - Screen shots and audio sample
<p>Screen shots and audio file in addition to the publication "Path Following in Non-Visual Conditions" by Alan Del Piccolo, Davide Rocchesso, and Stefano Papetti. Under revision for IEEE Transaction on Haptics (1 June 2018).</p> <p>Developed in Max (https://cycling74.com).</p> <p>Short description:</p> <ul> <li><em>interface.png</em>: the interface for managing the experiment. Output levels, trial repetitions and feedback conditions can be adjusted from here. The underlying Max patch is depicted in "main patch.png"</li> <li><em>main patch.png</em>: the main patch controlling the experiment. It receives the data from the Soundplane's controller (top left), invokes finger position detection and feedback generation (bottom left), manages the trial repetition and feedback modes (bottom center), and enables the adjustment of the feedback levels (right). </li> <li><em>mapToImage.png</em>: the patch that maps the participant's finger position on the Soundplane to the relative position over the loaded path shape. The position is shown by the white circle on the bottom left of the image.</li> <li><em>rolling_feedback.png</em>: the SDT "rolling model" configured for the use in the experiment. Note that the input levels are generated in the track_detection_SP2 patch.</li> <li><em>sdt_rolling.png</em>: a configuration of the SDT "rolling model" adjusted to output a signal similar to the one used in the experiment (see record.wav) as a stand-alone, namely without using the experiment's patches.</li> <li><em>track_detection_SP2.png</em>: the patch that manages the feedback generation (top left), the recording of execution time (bottom left), and the recording of position and force (center).</li> <li><em>record.wav</em>: a recording of the signal used in the experiment for both audio and vibrotactile feedback.</li> </ul>
Audio samples from generative models trained on the TIMIT speech data.
<p>This is a posting of audio snippets to accompany the paper "Benchmarking Generative Latent Variable Models for Speech".</p> <p>The snippets include samples and reconstructions. All samples are completely unconditional and utilise only the prior internal representations learned by the model. Reconstructions are computed from a given test audio snippet by first encoding it to a learned representation and then decoding that to a reconstruction of the audio.</p> <p>All models are trained on the TIMIT speech dataset (<a href="https://catalog.ldc.upenn.edu/LDC93s1">https://catalog.ldc.upenn.edu/LDC93s1</a>). Some snippets are from models trained at different temporal resolutions denoted by `s1` and `s64`. We refer to the paper for details.</p> <p>The files include:</p> <ul> <li>`clockwork-vae-s64-reconstruction-*` <ul> <li>Four reconstructions using a two-layered Clockwork VAE trained with temporal resolution s=64.</li> </ul> </li> <li>`clockwork-vae-s64-sample-*` <ul> <li>Four samples from the prior of a Clockwork VAE trained with temporal resolution s=64.</li> </ul> </li> <li>`original-*` <ul> <li>Four original samples from TIMIT corresponding in pairs to the reconstructions.</li> </ul> </li> <li>`vrnn-s64-sample-*` <ul> <li>Two samples from the prior of a VRNN trained with temporal resolution s=64.</li> </ul> </li> <li>`vrnn-s1-sample-*` <ul> <li>Two samples from the prior of a VRNN trained with temporal resolution s=1.</li> </ul> </li> <li>`srnn-s64-sample-*` <ul> <li>Two samples from the prior of a SRNN trained with temporal resolution s=64.</li> </ul> </li> <li>`srnn-s1-sample-*` <ul> <li>Two samples from the prior of a SRNN trained with temporal resolution s=1.</li> </ul> </li> <li>`wavenet-s64-sample-*` <ul> <li>Four samples from a WaveNet trained with temporal resolution s=1.</li> </ul> </li> <li>`wavenet-s1-sample-*` <ul> <li>Two samples from a WaveNet trained with temporal resolution s=64.</li> </ul> </li> </ul>
Sample audio data for JSEALS article "Tonal variation in Pyen"
<p>Audio data samples corresponding to sections 1.1, 5.1 and 6 in the JSEALS article "Tonal variation in Pyen." </p>
Tiny AAM: Sample from the Artificial Audio Multitracks Dataset
<p>This is the tiny demo version with 20 samples from the AAM dataset, which originally contains 3,000 artificial music audio tracks. In contrast to the full version, which has lossless audio, we only provide mp3 tracks for demonstration and testing purposes.</p> <p>For the <strong>full version</strong> and <strong>more information</strong> see: <a href="https://doi.org/10.5281/zenodo.5794629">zenodo.5794629</a></p>
AcousticsAustraliaArticle_dataset: audio samples _ vowel phonation
<p><strong>PaperAcousticsAustralia - data</strong></p> <p> </p> <p>Audio samples were collected from 31 participants, extracted from their recorded reading (31 participants), recorded interviews (28 participants) and vowel phonations (29 participants).</p> <p><strong>ParticipantProfiles spreadsheet</strong> details the age, gender, years smoking and ethnicity per participant.</p> <p> <strong>ReadingsAudioSamples : </strong>audio samples extracted from recorded readings. There are generally two reading audio samples per participant. The measures reported per participant are the mean values read from PRAAT for the two samples extracted for each participant.</p> <p> <strong>InterviewsAudioSamples :</strong> audio samples extracted from recorded interviews. This concerns 28 participants: there is no audio samples available from the interviews of participants P20, P33 and P42. There are generally at least two audio samples per participant. The measures reported per participant are the mean values read from PRAAT for the participant’s audio samples.</p> <p> <strong>VowelPhonationAudioSamples :</strong> audio samples of vowel /a/ sustained phonation for 29 participants. Most samples were recorded by the participants, and eight of them were recorded by phone (look for ‘phone’ in the audio sample name). There is only one vowel phonation sample per participant and no vowel phonation sample was provided for participants P16 and P25. </p>
AcousticsAustraliaArticle_dataset: audio samples _ readings
<p><strong>PaperAcousticsAustralia - data</strong></p> <p> </p> <p>Audio samples were collected from 31 participants, extracted from their recorded reading (31 participants), recorded interviews (28 participants) and vowel phonations (29 participants).</p> <p><strong>ParticipantProfiles spreadsheet</strong> details the age, gender, years smoking and ethnicity per participant.</p> <p> <strong>ReadingsAudioSamples : </strong>audio samples extracted from recorded readings. There are generally two reading audio samples per participant. The measures reported per participant are the mean values read from PRAAT for the two samples extracted for each participant.</p> <p> <strong>InterviewsAudioSamples :</strong> audio samples extracted from recorded interviews. This concerns 28 participants: there is no audio samples available from the interviews of participants P20, P33 and P42. There are generally at least two audio samples per participant. The measures reported per participant are the mean values read from PRAAT for the participant’s audio samples.</p> <p> <strong>VowelPhonationAudioSamples :</strong> audio samples of vowel /a/ sustained phonation for 29 participants. Most samples were recorded by the participants, and eight of them were recorded by phone (look for ‘phone’ in the audio sample name). There is only one vowel phonation sample per participant and no vowel phonation sample was provided for participants P16 and P25. </p>
AcousticsAustraliaArticle_dataset: audio samples _ Interviews
<p><strong>PaperAcousticsAustralia - data description</strong></p> <p> </p> <p>Audio samples were collected from 31 participants, extracted from their recorded reading (31 participants), recorded interviews (28 participants) and vowel phonations (29 participants).</p> <p><strong>ParticipantProfiles spreadsheet</strong> details the age, gender, years smoking and ethnicity per participant.</p> <p> <strong>ReadingsAudioSamples: </strong>audio samples extracted from recorded readings. There are generally two reading audio samples per participant. The measures reported per participant are the mean values read from PRAAT for the two samples extracted for each participant.</p> <p> <strong>InterviewsAudioSamples:</strong> audio samples extracted from recorded interviews. This concerns 28 participants: there is no audio samples available from the interviews of participants P20, P33 and P42. There are generally at least two audio samples per participant. The measures reported per participant are the mean values read from PRAAT for the participant’s audio samples.</p> <p> <strong>VowelPhonationAudioSamples: </strong>audio samples of vowel /a/ sustained phonation for 29 participants. Most samples were recorded by the participants, and eight of them were recorded by phone (look for ‘phone’ in the audio sample name). There is only one vowel phonation sample per participant and no vowel phonation sample was provided for participants P16 and P25. </p> <p> </p> <p> </p>
JalCommunDisorderAssistTechnol_dataset: audio samples_interviews
<p><strong>PaperJalCommunDisorderAssistTechnol - data</strong></p> <p> </p> <p>Audio samples were collected from 59 participants, either extracted from recorded reading (34 participants) or from a recorded interview (the remaining 25 participants).</p> <p><strong>ParticipantProfiles spreadsheet </strong>details the age, gender, and years smoking per participant.</p> <p> <strong>ReadingsAudioSamples :</strong> audio samples extracted from recorded readings. This concerns 34 participants. There are generally two reading audio samples per participant. The measures reported per participant are the mean values read from PRAAT for the two samples per participant.</p> <p> <strong>InterviewsAudioSamples :</strong> audio samples extracted from recorded interviews. This concerns 25 participants. There is generally only one audio sample per participant. When there is more than one audio sample for a same participant, the measures reported for the participant are the mean values read from PRAAT for the participant’s audio samples.</p>
JalCommunDisorderAssistTechnol_dataset: audio samples_readings
<p><strong>PaperJalCommunDisorderAssistTechnol - data</strong></p> <p> </p> <p>Audio samples were collected from 59 participants, either extracted from recorded reading (34 participants) or from a recorded interview (the remaining 25 participants).</p> <p><strong>ParticipantProfiles spreadsheet </strong>details the age, gender, and years smoking per participant.</p> <p> <strong>ReadingsAudioSamples :</strong> audio samples extracted from recorded readings. This concerns 34 participants. There are generally two reading audio samples per participant. The measures reported per participant are the mean values read from PRAAT for the two samples per participant.</p> <p> <strong>InterviewsAudioSamples :</strong> audio samples extracted from recorded interviews. This concerns 25 participants. There is generally only one audio sample per participant. When there is more than one audio sample for a same participant, the measures reported for the participant are the mean values read from PRAAT for the participant’s audio samples.</p>
The Aliased Complex Oscillator as a Paradigm for Analog Physical Modeling Sound Synthesis --- Audio Samples
<p>Additional material to the paper with the title: "The Aliased Complex Oscillator as a Paradigm for Analog Physical Modeling Sound Synthesis"</p>
SASS-E: The Steelpan Audio Sample Set for Evaluation
<p>SASS-E is an audio dataset containing 13,313 one-hit audio samples from three distinct steelpans.</p> <p><strong>Citation</strong></p> <p>This dataset is part of paper that will be published in the NIME 2023 proceedings in June, 2023. This section will be updated to reflect that citation then.</p> <p><strong>Data curator</strong></p> <p>Colin Malloy</p> <p><strong>Contact</strong></p> <p>You can contact Colin regarding this dataset at malloyc@uvic.ca.</p> <p> </p> <p><strong>About SASS-E</strong></p> <p>The SASS-E dataset is an audio dataset curated as part of Colin's PhD research. It consists of over 13,000 one-hit audio samples from three tenor steelpans totaling over 9 hours and 25 minutes of audio The samples were recorded in a professional quality recording studio at 48 kHz/ 24-bit depth. Approximately 50 strikes were recorded per note per instrument at a wide variety of dynamic levels and beating areas. This allows for comprehensive coverage of minute details and fluctuations in timbre.</p> <p>The audio samples are pre-split into training, validation, and test sets with 7,931 samples in the training set, 2,680 samples in the validation set, and 2,702 samples in the test set.</p> <p>The audio files are have filenames in the following format: <MIDI note number>_<set>_<instrument label>_sample_<number>.wav. They are each labeled with the MIDI note number for the given note struck. The instrument label is in the format "ctenor-0x" where x is the number assigned to the instrument.</p> <p> </p> <p><strong>Future of SASS-E</strong></p> <p>In the future, we plan to add samples from a wider variety of steelpans. The goal is to create a comprehensive dataset that covers all major varieties of steelpans from the soprano to bass voices of the family. This will include other kinds of tenor steelpans (such as low D tenors, Invader tenors, and 3rds/4ths tenors).</p> <p> </p> <p><strong>License</strong></p> <p>All of the samples in SASS-E are released under Creative Commons (CC) CC-BY-NC. This license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. Specifically, these samples are not to be used in a commercial sample library (such as a virtual instrument).</p>
Audio Rendered Using Symbolic Music Data Sampled From DMelodies
<p>This is a dataset of audio files rendered using a subset of symbolic music data from <a href="https://github.com/ashispati/dmelodies_dataset">DMelodies</a>.</p>
Mmm Whatcha say? Audio samples
<p>Example audio for Mmm whatcha say? Uncovering distal and proximal context effects in first and second-language word perception using psychophysical reverse correlation, Interspeech 2024</p>
Audio samples from generative models trained on the TIMIT speech data.
<p>The snippets include samples and reconstructions. All samples are completely unconditional and utilise only the prior internal representations learned by the models. Reconstructions are computed from a given test audio snippet by first encoding it to a learned representation and then decoding that to a reconstruction of the audio.</p> <p>All models are trained on the TIMIT speech dataset (<a href="https://catalog.ldc.upenn.edu/LDC93s1">https://catalog.ldc.upenn.edu/LDC93s1</a>). Some snippets are from models trained at different temporal resolutions denoted by `s1` and `s64`. We refer to the paper for details.</p> <p>The files include:</p> <ul> <li>`clockwork-vae-s64-reconstruction-*` <ul> <li>Four reconstructions using a two-layered Clockwork VAE trained with temporal resolution s=64.</li> </ul> </li> <li>`clockwork-vae-s64-sample-*` <ul> <li>Four samples from the prior of a Clockwork VAE trained with temporal resolution s=64.</li> </ul> </li> <li>`original-*` <ul> <li>Four original samples from TIMIT corresponding in pairs to the reconstructions.</li> </ul> </li> <li>`vrnn-s64-sample-*` <ul> <li>Two samples from the prior of a VRNN trained with temporal resolution s=64.</li> </ul> </li> <li>`vrnn-s1-sample-*` <ul> <li>Two samples from the prior of a VRNN trained with temporal resolution s=1.</li> </ul> </li> <li>`srnn-s64-sample-*` <ul> <li>Two samples from the prior of a SRNN trained with temporal resolution s=64.</li> </ul> </li> <li>`srnn-s1-sample-*` <ul> <li>Two samples from the prior of a SRNN trained with temporal resolution s=1.</li> </ul> </li> <li>`wavenet-s64-sample-*` <ul> <li>Four samples from a WaveNet trained with temporal resolution s=1.</li> </ul> </li> <li>`wavenet-s1-sample-*` <ul> <li>Two samples from a WaveNet trained with temporal resolution s=64.</li> </ul> </li> </ul>
Listening Samples for Paper "Score to Audio: Integrating Systems to Transform Music Scores into Expressive Piano Audio"
<p>Listening Samples for Paper "Score to Audio: Integrating Systems to Transform Music Scores into Expressive Piano Audio".</p> <p>Please check the README.md for more details.</p>
Audio samples from "A Physical Intelligent Instrument using Recurrent Neural Networks"
<p>These are the audio samples from chapter 3 and 4 of my thesis "A Physical Intelligent Instrument using Recurrent Neural Networks".</p>
A workflow and digital filters for correcting speed and equalisation errors on digitised audio open-reel magnetic tapes - Audio Samples
<p>This repository makes available the audio samples related to the paper:</p> <p>Niccolò Pretto, Nadir Dalla Pozza, Alberto Padoan, Anthony Chmiel, Kurt James Werner, Alessandra Micalizzi, Emery Schubert, Antonio Rodà, Simone Milani and Sergio Canazza, <em>A workflow and digital filters for compensating speed and equalisation errors on digitised audio open-reel magnetic tapes</em>, Journal of the Audio Engineering Society, Special Issue on Audio Filter Design, 2022.</p> <p>The experiment and the three case studies are described in the publication above.</p> <p>This repository contains two main directories (<strong>bold</strong> indicates directory names):</p> <ul> <li> <p><strong>Experiment Samples</strong>: the 10 seconds long samples used in the experiment;</p> </li> <li> <p><strong>Long Samples</strong>: the original 6 minutes long samples, one for each of the identified cases.</p> </li> </ul> <p>Here is the notation of the file naming:</p> <ul> <li> <p>W: recording (writing);</p> </li> <li> <p>R: reproducing;</p> </li> <li> <p>3: 3.75 NAB;</p> </li> <li> <p>7N: 7.5 NAB;</p> </li> <li> <p>7C: 7.5 CCIR;</p> </li> <li> <p>15C: 15 CCIR.</p> </li> </ul> <p>For what concerns the <strong>Experiment Samples</strong> directory, here is the summary of the recording/reproducing standards of the adopted samples and their notation:</p> <ul> <li> <p>SET A: Recording 3.75 NAB (W3) - Reproducing 7.5 CCIR (R7C);</p> </li> <li> <p>SET B: Recording 3.75 NAB (W3) - Reproducing 15 CCIR (R15C);</p> </li> <li> <p>SET C: Recording 7.5 NAB (W7N) - Reproducing 15 CCIR (R15C).</p> </li> </ul> <p>Here are the variants of the samples:</p> <ul> <li> <p>REFERENCE: produced by using the correct equalization standard;</p> </li> <li> <p>ANCHOR: the "Reference" altered with a low-pass filter, with pass band set at 7 kHz for music and 3.5 kHz for speech;</p> </li> <li> <p>INCORRECT: produced by using an intentionally incorrect equalization, created by mismatching the recording and reading curves and resampled to the correct speed;</p> </li> <li> <p>MATLAB: the “Incorrect” variant corrected by means of a Matlab script;</p> </li> <li> <p>API: the “Incorrect” variant corrected by means of an <em>ad hoc</em> web interface adopting Web Audio API, for simulating real-time correction in web applications.</p> </li> </ul> <p>Here is the samples list:</p> <ul> <li> <p>SET A:</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample4: Richard Wagner - <em>Ride of the Valkyries</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample1: Taylor Swift - <em>Shake It Off</em>;</p> </li> <li> <p>sample5: Queen - <em>We Will Rock You</em>;</p> </li> <li> <p>sample8: Bruno Maderna - <em>Continuo</em>;</p> </li> <li> <p>sample9: Luciano Berio - <em>Différences</em>.</p> </li> </ul> </li> </ul> </li> <li> <p>SET B (the track title reflects the name of the file from which the track itself was extracted, from the CLIPS project of the University of Napoli: <a href="http://www.clips.unina.it/en/index.jsp">http://www.clips.unina.it/en/index.jsp</a>):</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample22: CLIPS project - <em>LP4m18bZ</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample15: CLIPS project - <em>LP1f20bZ</em>;</p> </li> <li> <p>sample16: CLIPS project - <em>LP4m20bZ</em>;</p> </li> <li> <p>sample17: CLIPS project - <em>LP1f19bZ</em>;</p> </li> <li> <p>sample18: CLIPS project - <em>LP4m19bZ</em>.</p> </li> </ul> </li> </ul> </li> <li> <p>SET C:</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample3: Carl Orff - <em>Carmina Burana - O Fortuna</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample2: The Weeknd - <em>Save Your Tears</em>;</p> </li> <li> <p>sample6: Eagles - <em>Hotel California</em>;</p> </li> <li> <p>sample10: Bruno Maderna - <em>Musica su Due Dimensioni</em>;</p> </li> <li> <p>sample12: Bruno Maderna - <em>Syntaxis</em>.</p> </li> </ul> </li> </ul> </li> </ul>
A workflow and novel digital filters for compensating speed and equalization errors on digitized audio open-reel tapes: audio samples
<p>This repository makes available the audio samples related to the experiments described in the paper: </p> <p><em>Niccolò Pretto, Nadir Dalla Pozza, Alberto Padoan, Anthony Chmiel, Kurt James Werner, Alessandra Micalizzi, Emery Schubert, Antonio Rodà, Simone Milani and Sergio Canazza. 2021. A workflow and novel digital filters for compensating speed and equalization errors on digitized audio open-reel tapes. In Proceedings of the 16th International Conference on Audio Mostly (AM '21). Association for Computing Machinery, New York, NY, USA</em></p> <p>The experiment and the three case studies are described in the publication below. Here is the summary of their recording/reproducing standards and their notation.</p> <ul> <li>SET A: Recording 3.75 NAB (W3) - Reproducing 7.5 CCIR (R7C);</li> <li>SET B: Recording 3.75 NAB (W3) - Reproducing 15 CCIR (R15C);</li> <li>SET C: Recording 7.5 NAB (W7N) - Reproducing 15 CCIR (R15C).</li> </ul> <p>Here is the notation of the file naming: </p> <ul> <li>W: recording (writing);</li> <li>R: reproducing;</li> <li>3: 3.75 NAB;</li> <li>7N: 7.5 NAB;</li> <li>7C: 7.5 CCIR;</li> <li>15C: 15 CCIR.</li> </ul> <p>Here are the variants of the samples:</p> <ul> <li>REFERENCE: produced by using the correct equalization standard;</li> <li>ANCHOR: the "Reference" altered with a low-pass filter, with pass band set at 7 kHz for music and 3.5 kHz for speech;</li> <li>INCORRECT: produced by using an intentionally incorrect equalization, created by mismatching the recording and reading curves and resampled to the correct speed;</li> <li>MATLAB: the “Incorrect” variant corrected by means of a Matlab script;</li> <li>API: the “Incorrect” variant corrected by means of an <em>ad hoc</em> web interface adopting Web Audio API, for simulating real-time correction in web applications.</li> </ul> <p>Here is the samples list:</p> <p>SET A:</p> <ul> <li>Training: <ul> <li>sample4: Richard Wagner - <em>Ride of the Valkyries</em>;</li> </ul> </li> <li>Test: <ul> <li>sample1: Taylor Swift - <em>Shake It Off</em>;</li> <li>sample5: Queen - <em>We Will Rock You</em>;</li> <li>sample8: Bruno Maderna - <em>Continuo</em>;</li> <li>sample9: Luciano Berio - <em>Différences</em>.</li> </ul> </li> </ul> <p>SET B (the track title reflects the name of the file from which the track itself was extracted, from the CLIPS project of the University of Napoli: <a href="http://www.clips.unina.it/en/index.jsp">http://www.clips.unina.it/en/index.jsp</a>):</p> <ul> <li>Training: <ul> <li>sample22: CLIPS project - <em>LP4m18bZ</em>;</li> </ul> </li> <li>Test: <ul> <li>sample15: CLIPS project - <em>LP1f20bZ</em>;</li> <li>sample16: CLIPS project - <em>LP4m20bZ</em>;</li> <li>sample17: CLIPS project - <em>LP1f19bZ</em>;</li> <li>sample18: CLIPS project - <em>LP4m19bZ</em>.</li> </ul> </li> </ul> <p>SET C:</p> <ul> <li>Training: <ul> <li>sample3: Carl Orff - <em>Carmina Burana - O Fortuna</em>;</li> </ul> </li> <li>Test: <ul> <li>sample2: The Weeknd - <em>Save Your Tears</em>;</li> <li>sample6: Eagles - <em>Hotel California</em>;</li> <li>sample10: Bruno Maderna - <em>Musica su Due Dimensioni</em>;</li> <li>sample12: Bruno Maderna - <em>Syntaxis</em>.</li> </ul> </li> </ul> <p>Supplementary material can be found at the following DOI: 10.5281/zenodo.5118708</p> <p> </p>
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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.