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226 results for “tone”
Pyen tone frames, Speakers A and B
<p>These are data accompanying the article Hornéy, Christina Scotte. 2019. Tonal variation in Pyen. <em>Journal of the Southeast Asian Linguistics Society, 12</em>(1). They comprise approximately 900 recordings from two Pyen speakers each. Capital letters following underscores after each recording indicate the specific speaker (A or B). A list of words and phrases used as tone frames is provided in the two Excel files. For more details on data collection and analysis, see the article above.</p>
Raw data: Supra-threshold perception and neural representation of tones presented in noise in conditions of masking release
<p>Raw data of three experiments:</p> <p>1) Exp1: Psychoacoustical masked thresholds of tone in noise masker (ASCII format)</p> <p>2) Exp2: 64ch EEG data (Biosemi data format .bdf)</p> <p>3) Exp3: Salience rating of a tone masked by various maskers and levels above masked threshold. (ASCII format)</p> <p>Preprint with details on experiments submitted to BioRxiv: https://doi.org/10.1101/575720</p>
Data and code for figures in "Two-tone optomechanical instability and its fundamental implications for backaction-evading measurements"
<p>Here we prepare the data and process scripts to reconstruct figure 4 of the paper (Two-tone optomechanical instability and its fundamental implications for backaction-evading measurements). The folder contains several subfolders and files:</p> <p>“Raw data”: In this folder, you can find the raw data recorded by measurement devices during the experiment. It follows the hierarchical structure. We have three pairs of folders corresponding to three cooperativities (3.5, 7, 14). One folder of each pair contains raw data files in text format (.dat) and the other one contains plots and a Numpy dictionary of the extracted parameter for each cooperativity (superdict.npy). If you need to redo the extraction process from the raw data you can simply run “181031_CXX_Final_NOQT_BAE_2D_post_processeing.py” (XX: 3.5 or 7 or 14) python code to rewrite superdict.npy files and replot all plots in the Raw data folder.<br> “NRBcodes”: A side package for the circle fit (Lorentzian fitting) in the complex plane.<br> “Dicts”: A folder containing Numpy dictionaries needed for the final plot. “superdictXX.npy” are copies of Numpy dictionaries in the Raw data folder. “powers_XX.npy” and “Ds_nor_XX.npy” are Numpy dictionaries needed for the theory plots. You can reproduce them by the uncommenting first part of the “Final_plot.py” and correcting the corresponding cooperativity.<br> “getdata.py” and “postprocessing_libs.py”: Side packages help to read the raw data files.<br> <br> All post-processes are done on the raw data and you just need to run the “Final_plot.py” to reproduce the plot. If you want to access the post-processed data you can simply read “superdictXX.npy” in the Dicts folder. <br> Please do not hesitate to contact us in case of any questions. <br> amir.youssefi@epfl.ch</p>
Source code: Bilateral human laryngeal motor cortex in perceptual decision of lexical tone and voicing of consonant
<p>Source code (and data) for the paper <em>Bilateral Human Laryngeal Motor Cortex in Perceptual Decision of Lexical Tone and Voicing of Consonant</em>.</p> <p><a href="https://zenodo.org/api/files/d8e78f58-333f-45fb-954c-4e3a343fa5a1/Exp1_datacollection.zip">Exp1_datacollection.zip</a>:code for data collection in Experiment 1.</p> <p><a href="https://zenodo.org/api/files/d8e78f58-333f-45fb-954c-4e3a343fa5a1/Exp2_datacollection.zip">Exp2_datacollection.zip</a>: code for data collection in Experiment 2.</p> <p><a href="https://zenodo.org/api/files/d8e78f58-333f-45fb-954c-4e3a343fa5a1/codes_for_dataprocess.zip">codes_for_dataprocess.zip</a>:data processing code and source data.</p> <p>Exp1 and Exp2 collection codes are provided for reference, but for practice, due to copyright and software environment issues, please contact Baishen (liangbs@psych.ac.cn, liangbs95@gmail.com) for technical assistant. </p>
Simulation dataset of "Particle-in-cell simulations of characteristics of rising-tone chorus waves in the inner magnetosphere"
<p>Simulation dataset of "Particle-in-cell simulations of characteristics of rising-tone chorus waves in the inner magnetosphere", including magnetic fields and parallel and perpendicular temperatures of energetic electrons.</p>
Simulation datum of Full particle simulation of whistler-mode triggered falling-tone emissions in the magnetosphere
<p><strong>Overview</strong></p> <p>This dataset is obtained from <a href="http://space.rish.kyoto-u.ac.jp/software/">KEMPO1</a> code with minor modifications. This dataset consists of raw data of simulation, an example of the python code, and Dockerfile to construct a drawing environment using Python. For more detail, please refer README.md.</p> <p><strong>Command example using <a href="https://www.docker.com/">Docker</a></strong></p> <p>If you draw figures using Docker, please download <strong>ALL the files</strong> in the dataset.<br> Make sure that all files are put at the same directory.</p> <pre><code class="language-bash">cd (path to downloaded files) docker build -t kempo1data . docker run -v ${PWD}:/data -it kempo1data python plot_data.py <data_file>.h5</code></pre>
The Impacts of Sentiments and Tones in Community-Generated Issue Discussions
<p>The dataset, data analysis code, and complete results accompanying the paper published in CHASE2021, titled <em>The Impacts of Sentiments and Tones in Community-Generated Issue Discussions</em>.</p>
Data from: Pure-tone thresholds in adults aged 18 to 97 years and hearing aid use
<p>On an international level, estimates of the prevalence of hearing loss are often based on the criterion of the world health organization (WHO), but other criteria have also been applied. Both the prevalence of hearing loss and the number of hearing-aid fittings can be used to estimate the adoption rate, which is often regarded as being in need of improvement. To illustrate the effect of the prevalence criteria on the assessments, epidemiological data for hearing abilities in Oldenburg, Emden, and Aalen were used. The criteria were either based on the pure-tone audiogram, on speech recognition in noise, or on the subjective indication of hearing difficulties. The results showed a strong dependency of the adoption rate on the prevalence criterion. Criteria based on speech recognition in noise led to very high prevalence, but low adoption rates. Age-independent analysis resulted in similar adoption rates of approx. 25% for subjective hearing difficulties, for the common WHO criterion, and for the four-frequency-table of Röser. However, age-dependent analysis revealed large differences between the subjective indication and the criteria based on pure-tone audiometry. Overall, statements regarding the prevalence of hearing impairment and rate of hearing-aid adoption should always include the applicable criterion, and should either be viewed as age-dependent, or related to a standard population.</p>
TONES Ontology Repository (corpus)
<p>The <em>TONES Ontology Repository</em> was designed to be a central location for ontologies that might be of use to tools developers for testing purposes. It includes various metrics for the contained ontologies, such as DL expressiveness and the number of different axioms. The web service is now deprecated, the corpus is stored here for future reference.</p>
Beat Pilot Tone (BPT): Simultaneous MR Imaging and RF Motion Sensing at Arbitrary Frequencies
<p>This data is for the manuscript "Beat Pilot Tone (BPT): Simultaneous MR Imaging and Radio-Frequency Motion Sensing at Arbitrary Frequencies" submitted to the journal Magnetic Resonance in Medicine. It is divided into one subfolder for each figure, with each folder containing the data necessary to reproduce the figure. Most of the data are saved in "cfl" format as used by the BART Computational Magnetic Resonance Imaging Toolbox (https://mrirecon.github.io/bart/). Other data are saved as Comma Separated Values (csv) or plain text files.</p> <p>Keywords and Categories: Magnetic Resonance Imaging, MRI, medical imaging, radio frequency, microwave, motion sensing, motion correction</p>
HiRISE and MOC-NA Images Used in "Mapping of Western Valles Marineris Light-toned Layered Deposits and Newly Classified Rim Deposits"
<p>These files contain the images used in the paper "Mapping of Western Valles Marineris Light-toned Layered Deposits and Newly Classified Rim Deposits"</p> <p>All images examined, and all images used in the creation of figures are listed within these documents. They are seperated by region, and type of image (HiRISE, MOC-NA)</p>
SHARAD Tracks Mapped in "Mapping of Western Valles Marineris Light-toned Layered Deposits and Newly Classified Rim Deposits"
<p>These three .CSV files contain the SHARAD tracks, as exported by JMARS.</p> <p>This data contains overlaps in each file, as they are sorted by the location in which they were examined, for example, if the SHARAD track crosses all three plateaus examined in the study, that SHARAD track will be listed in all three files. </p> <p>The Radar_Track_LOCATION_and_DETECTIONS.xlxs file contains information on if a basal detection was located in a particular track, and on what plateau the detection was located. </p>
Vascular tone monitoring for subject1
<p>Vascular tone monitoring for subject1.<br> Subject 1; non-smoker; 29 years old, 73 kg Play speed is 0.6 second for each ECG cycle. Green: Normotonic, Red: Hypertonic, Blue: Hypotonic, and White: vascular tone doesn't detected</p>
Neural processing and perception of Schroeder‐phase harmonic tone complexes in the gerbil: Relating single‐unit neurophysiology to behavior
<p><span>Schroeder-phase harmonic tone complexes have been used in physiological and psychophysical studies in several species to gain insight into cochlear function. Each pitch period of the Schroeder stimulus contains a linear frequency sweep; the duty cycle, sweep velocity, and direction are controlled by parameters of the phase spectrum. Here, responses to a range of Schroeder-phase harmonic tone complexes were studied both behaviorally and in neural recordings from the auditory nerve and inferior colliculus of Mongolian gerbils. Gerbils were able to discriminate Schroeder-phase harmonic tone complexes based on sweep direction, duty cycle, and/or velocity for fundamental frequencies up to 200 Hz. Temporal representation in neural responses based on the van Rossum spike-distance metric, with time constants of either 1 ms or related to the stimulus' period, was compared to average discharge rates. Neural responses and behavioral performance were both expressed in terms of sensitivity, d', to allow direct comparisons. Our results suggest that in the auditory nerve, stimulus fine structure is represented by spike timing while envelope is represented by rate. In the inferior colliculus, both temporal fine structure and envelope appear to be represented best by rate. However, correlations between neural d' values and behavioral sensitivity for sweep direction were strongest for both temporal metrics, for both auditory nerve and inferior colliculus. Furthermore, the high sensitivity observed in the inferior colliculus neural rate-based discrimination suggests that these neurons integrate across multiple inputs arising from the auditory periphery.</span></p>
Simulation data : Triggering of whistler-mode rising and falling tone emissions in a homogeneous magnetic field
<p>This dataset is obtained from <a href="http://space.rish.kyoto-u.ac.jp/software/">KEMPO1</a> code with minor modifications. This dataset contains simulation data and Python3 code examples. For more details, please refer to README.md.</p>
Cooperative cortical network for categorical processing of Chinese lexical tone
<p><strong>This dataset contains the ECoG, CT and MRI data for the six subjects associated with the manuscript, "Cooperative cortical network for categorical processing of Chinese lexical tone", as well as stimulus sound files used in the study.</strong></p> <p>data_MRI.rar - MRI scan before the ECoG electrode implantation</p> <p>data_CT.rar - CT images with ECoG electrode implanted</p> <p>data_ECoG.rar - ECoG recording of six patients reported in the manuscript</p> <p>stimuli_BehaviorContinumm.rar - Chinese tone continuum stimuli (Token 1-13 as in Fig 1 of the manuscript)</p> <p>stimuli_ECoG_MMN.rar - Chinese tone stimuli used for oddball paradigm in ECoG experiment (Token 2, 5 and 8, see Fig 1 of the manuscript)</p> <p>Upon email request (hongbo@tsinghua.edu.cn), the authors may provide analysis code. </p> <p> </p> <p><strong>Original manuscript:</strong></p> <p>Si, Zhou and Hong. <em>PNAS</em>, 2017</p> <p><strong>Cooperative cortical network for categorical processing of Chinese lexical tone</strong></p> <p><strong>Abstract:</strong> In tonal languages such as Chinese, lexical tone with varying pitch contours serves as a key feature to provide contrast in word meaning. Similar to phoneme processing, behavioral studies have suggested that Chinese tone is categorically perceived. However, its underlying neural mechanism remains poorly understood. By conducting cortical surface recordings in surgical patients, we revealed a cooperative cortical network along with its dynamics responsible for this categorical perception. Based on an oddball paradigm, we found amplified neural dissimilarity between cross-category tone pairs, rather than between within-category tone pairs, over cortical sites covering both the ventral and dorsal streams of speech processing. The bilateral superior temporal gyrus (STG) and the middle temporal gyrus (MTG) exhibited increased response latencies and enlarged neural dissimilarity, suggesting a ventral hierarchy that gradually differentiates the acoustic features of lexical tones. In addition, the bilateral motor cortices were also found to be involved in categorical processing, interacting with both the STG and the MTG and exhibiting a response latency in between. Moreover, the motor cortex received enhanced Granger causal influence from the semantic hub, the anterior temporal lobe, in the right hemisphere. These unique data suggest that there exists a distributed cooperative cortical network supporting the categorical processing of lexical tone in tonal language speakers, not only encompassing a bilateral temporal hierarchy that is shared by categorical processing of phonemes but also involving intensive speech-motor interactions over the right hemisphere, which might be the unique machinery responsible for the reliable discrimination of tone identities.</p>
Citation Tone of Lugang dialect in Chaonan District of Shantou City in Guangdong Province, China
<p>Citation Tone of Lugang dialect in Chaonan District of Shantou City in Guangdong Province, China </p>
Online Tone Manipulation in Violin Performance: An ERP and ERSP study
<p><br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>Data and Software used in <br> Online Tone Manipulation in Violin Performance: An ERP and ERSP Study.<br> <em>Ángel David Blanco, Jordi Costa-Faidella, Alfonso Pérez, David Dalmazzo, Rafael Ramirez, Iria SanMiguel</em><br> (not published at this moment)</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>FILES:</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>1. Online_Tone_Manipulation_Violin_DATA.rar</strong></p> <p>In this compressed file we found 3 folders:</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>1.1 Raw_data</strong>: raw data of the participants of the experiment.</p> <p>Inside we find 16 folders. Each one contains the raw data of each participant: SXX (where XX is the code assigned to each subject).<br> Data from participants S01 and S11 are missing due to technical problems.</p> <p>Each folder contains:</p> <p>audio: This folder contains the audio recorded during each block of the session.<br> sXX: This folder contains the EEG files recorded during each block of the session.<br> tony: This folder contains the tony and excel files with the information about the audio onsets and the onsets of corrective movements.</p> <p>We also find 2 matlab scripts:</p> <p>main_final.m: This script creates one *.set file per block with the EEG data and the audio markers for each event. <br> main_EEG.m This script creates the Merged_Datasets.set file with the data from all the blocks. It also cleans the data from noise artifacts that were previously visual inspected.<br> It also computes the average reference, filters the Data, computes ICA and removes those components related with ocular activity. <br> It also creates te SXX_MergedDatasets_filt25_ICprun.set and the SXX_MergedDatasets_filt50_ICprun_TF.set</p> <p>Those files can already be found inside each folder. </p> <p>SXX_MergedDatasets_filt25_ICprun.set: This file contains the data for the ERPs already processed (pass band filter 1-25Hz). <br> SXX_MergedDatasets_filt25_ICprun_TF.set: This file contains the data for the ERSPs already processed (pass band filter 1-50Hz).</p> <p>RECODED TRIGGERS <br> (Based on audio onsets and logfiles)<br> Hundreds: TASK<br> Tens: FEEDBACK<br> Units: ORDER<br> 0: Reference<br> 100: Active<br> 200: Replayed<br> 300: Manipulated Active<br> 400: Post-error manipulation Active<br> 500: Non-manipulated active<br> 600: Manipulated Replayed<br> 700: Post-error manipulated Replayed<br> 800: Non-manipulated Replayed<br> 900: Onset End Correction Active<br> 1000: Onset End Correction Passive<br> 10: Open-String Note<br> 20:In Tune ONSET<br> 30: Mistuned ONSET <br> 40: In Tune STABLE<br> 50: Mistuned STABLE<br> 60: Notes with correction ONSET (All)<br> 70: Mistuned notes with correction ONSET<br> 80: Mistuned notes without correction ONSET<br> 1: Low (15-30c)<br> 2: LowHigh(30-50c)<br> 3: Middle (50-70c)<br> 4: MiddleHigh(70-100)<br> 5: High (>100)</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>In Raw_data we can also found two scripts</p> <p>load_participants.m: This script executes the main_final.m script for each participant.<br> load_participants_EEG.m: This script executes the main_EEG.m script for each participant</p> <p><br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>1.2 ERPs:</strong></p> <p>Inside this folder we find 3 more folders:</p> <p>Active: Contains the *.set files with the ERPs for each event of interest inside the Active condition.<br> Replayed (Passive): Contains the *.set files with the ERPs for each event of interest inside the Replayed condition.<br> Reference Melody: Contains the *.set files with the ERPs of the reference melody.</p> <p>Events of interest in the names of each Folder:<br> XXXX_Tuned: tuned notes<br> XXXX_Mistuned: notes with an error higher than 30 cents.<br> XXXX_nonman: nonmanipulated<br> XXXX_man: manipulated<br> XXXX_postman: postmanipulated<br> XXXX_Corr_Low: Trials with slow corrective movements (>350 ms) <br> XXXX_Corr_Medium: Trials with medium corrective movements (250-350ms)<br> XXXX_Corr_High: Trials with fast corrective movements (<250ms)<br> XXXX_Low: low error (15-30 cents)<br> XXXX_Medium: Medium error (30-50 cents)<br> XXXX_Medium_High: Medium High error (50-70 cents)<br> XXXX_High_High: High errors (>70 cents)</p> <p><br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> <br> <strong>1.3 ERSPs:</strong></p> <p>ERSPs of Active Tuned and Mistuned and Replayed Tuned and Mistuned in MATLAB Data files.</p> <p>Inside each file we can find the ERSPs and the ITC for different electrodes:</p> <p>ersp_XX: where XX is the name of the electrode (C3,C4,CP3,CP4)..<br> itc_XX: where XX is the name of the electrode (C3,C4,CP3,CP4)..</p> <p>both the ersp_XX and the itc_XX are three-dimensional matrices:</p> <p>frequencies (30 points) X time (200 points) X participants (15 subjects).</p> <p>The frequencies and times variables contain an array with the information of the frequency value (Hz) and time value (ms) for each point.</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2. Online_Tone_Manipulation_Violin_STIMULI_and_MAX_software.rar</strong></p> <p>In this compressed file we found two folders:</p> <p><br> <strong>2.1 Online_Tone_Manipulation_System_in_Max folder</strong></p> <p>This folder contains the system in Max that allows us to manipulate the pitch of the played note in the melody.</p> <p>recording_session.maxpat: open this file to access the system.<br> random_file.csv: file which contains the order of the melodies reproduced to the participants, the note which has to receive the manipulation, and the direction of the manipulation (1 up, 0 down).</p> <p>We can also find two folders:</p> <p>audio: the audio of the participant for each block is recorded and saved inside this folder<br> New_generated_melodies: This folder needs to contain the melodies reproduced to the participant during the experiment</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2.2 Stimuli folder</strong></p> <p>This folder contains three folders:</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2.2.1 Generated_Scores folder</strong></p> <p>This folder contains the code and which generates the score images used during the experiment.</p> <p>Inside the folder we can find:</p> <p>generate_scores.m: Script used to generate the score images</p> <p>New_generated_scores folder: This folder contains the XML code and the *.jpg file for each score.</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2.2.2 Screen</strong></p> <p>This folder contains the code used to deliver the visual information to the participant during the session and also the clicks sent to the DSP computer and the markers to the EEG computer via parallel port.<br> The random_file.csv inside this folder has to be the same that the one contained inside the Online_Tone_Manipulation_System_in_Max.</p> <p>Inside this folder we can find:</p> <p>Violin_screen_Brainlab.m: script with the code which has to be executed to start delivering the visual instructions to the participants</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2.2.3 Violin_Sample_sounds</strong></p> <p>Inside this folder we can find two folders:</p> <p>New_generated_melodies: contains the final generated melodies of the experiment<br> Original_Sounds: contains the original sounds used to generate the rest of the melodies of the experiment</p> <p>We can also find two important scripts:</p> <p>Generate_audios: this script generates the different melodies of the experiment from the original sounds.<br> Randomize_audios_new: this script generates the random_file.csv with the random order of the melodies together</p> <p><br> </p>
Supplementary audio files for "Artistic adaptation of Seenku tone"
<p>Audio files for all examples in "Artistic adaptation of Seenku tone: Musical surrogates vs. vocal music", published in the Selected Proceedings of ACAL 50. </p>
Simulation data for Upstream shift of generation region for whistler-mode rising-tone emission in the magnetosphere
<p>Simulation data for the article "Upstream shift of generation region for whistler-mode rising-tone emission in the magnetosphere" by Nogi and Omura (2023).</p> <p>Nogi, T., & Omura, Y. (2023). Upstream shift of generation region of whistler-mode rising-tone emissions in the magnetosphere. <em>Journal of Geophysical Research: Space Physics</em>, <em>128</em>, e2022JA031024. https://doi. org/10.1029/2022JA031024</p>
ScienceDex guides
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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.