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859 results for “Speeches”
Figure 7. Results obtained for the mean, the mean standard deviations and the mean standard errors of the independent speech variable in dyslalic subjects (both the control and the experimental group)
<p>Following the logopedic assessment of the investigated subjects, we moved on to the statistic processing of the gathered data, and we analysed the results obtained from the tests administered to the two groups after one year of speech therapy. The conducted analysis was directed both at the overall effectiveness of the speech therapy, and at the importance of strategies for the language development and stimulation, through the use of the computer-assisted Terapers system. It was noticed that the results obtained for the dyslalic subjects (children with pronunciation disorders) from the experimental group improved significantly due to the computer-based therapeutic program, compared to the subjects in the control group (who underwent classical therapy). The mean, the standard deviations and the standard error of the mean obtained for the independent speech variable in dyslalic children (control group and experimental group) are presented in Figure 7.</p>
Figure 3. Logoped 1.0 program-Modern Tools in Patient-Centred Speech Therapy for Romanian Language
<p>The program was designed for the therapy of logoneurosis, while being equally useful for the therapeutic activities used in the treatment of dyslexic-dysgraphic disorders. Logoped 1.0 provides a vast lexical material, which is organised into several sections: exercises involving reading the syllables and the words, sentences reading, followed by phrase and text reading. The colourful design of the words and sentences, the attractive way in which they are displayed on the monitor, and the fact that it allows choosing the exercises level of difficulty render the reading activity much more attractive for the pupil (Tobolcea, 2001). Its functional schema is presented in figure 3.</p>
Figure 4. The relationship between the functional blocks of the systemModern Tools in Patient-Centred Speech Therapy for Romanian Language
<p>The project’s complexity results from the considerable number of research fields it presupposes: artificial intelligence (pattern recognition, learning expert systems), virtual reality, digital signal processing, digital electronic (VLSI), computer architecture, and psychology (assessment procedures and techniques, therapeutic instructions, validation experimental design). In order to assure an assisted therapy one considers the relationships between six functional blocks: patient, speech therapist, office monitor program, expert system, 3D model and patient monitor program. The information flow of the system is given in Figure 4. There is a close connection between the child, as a patient, and his speech therapist. All the other modules are designed so as to contribute to the therapeutic action of the teacher. The monitor program allows realising a complex assessment and collecting information about the child; equally, it provides the opportunity to periodically track the child’s therapy results. The child is provided instant audio feedback which allows him/her to check the audio recordings history. The home monitor program is designed so as to build a virtual interface between teacher and child in order to allow the patient to continue his therapy at home. This component is designed both for the personal computer, which is placed in the therapist’s office, and personal digital assistant (PDA) which is used at home for independent work.</p>
Figure 9. A model that helps the diagnosis prediction-Modern Tools in Patient-Centred Speech Therapy for Romanian Language
<p>We have already implemented many modules of Logo-DM, such as: data cleaning module, data transformation module, feature extraction module, data clustering module and a classification module for diagnosis prediction. Figure 9 shows the model achieved using a decision tree built on complex examination data that aims to predict the patient’s diagnosis. Currently, we are testing the built models on new cases in order to estimate their quality.</p>
Figure 8. The end-to-end operations in Logo-DM-Modern Tools in Patient-Centred Speech Therapy for Romanian Language
<p>The useful data mining tasks for speech therapy fall into three categories: classification, clustering, and association rules. Classification places children with different speech impairments in predefined classes, and makes possible to track the characteristics of various groups. To model different classes we use many predictor variables (e.g. personal or familial anamnesis data or related to lifestyle). By clustering we group people with speech disorders on the basis of similarity of different features. This helps therapists to understand their patients. Clustering aims to find subsets of a predetermined segment, with homogeneous behavior towards various methods of therapy that can be effectively targeted by a specific therapy, but it is not based on the previous definition of groups (Danubianu, Tobolcea, & Pentiuc, 2009). Association rules aim to find out relationships between different data which seem to have no semantic dependence. The built patterns might be very useful to determine why a specific therapy program has been successful on a segment of patients with speech disorders, and on the other was ineffective. The Logo-DM system was designed to help the speech therapists to optimize the personalized therapy of dyslalia. To understand what kind of knowledge we could discover in TERAPERS’ dataset to improve speech therapy, we have to describe the collected data.</p>
Figure .5 Architecture of the Terapers system-Modern Tools in Patient-Centred Speech Therapy for Romanian Language
<p>Shown in Figure 5, the architecture of the Terapers system implies the existence of two main connected components: on the one hand, an intelligent system which is installed on the office computer of each speech therapist and, on the other, a mobile system which is used as a virtual friend in the therapy applied to the child (Danubianu et al., 2008). The intelligent system – which represents the fixed component of the system – is installed on each computer from the office of the speech therapist; it is made up of the following parts: • an information management module for children; • an expert system, able to produce inferences based on the data given by the assessment module; • a mouth virtual module which allows the display of all hidden movements that are likely to occur during speech; • a management module of the exercises uses, which allows creating or modifying the exercises, depending on the various therapy stages, as well as their organization into complex issues.</p>
The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)
<p><strong>Description</strong></p> <p>The Ryerson Audio-Visual Database of Emotional Speech and Song (<a href="https://affectivedatascience.com/datasets.html#ravdess">RAVDESS</a>) contains 7356 files (total size: 24.8 GB). The dataset contains 24 professional actors (12 female, 12 male), vocalizing two lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity (normal, strong), with an additional neutral expression. All conditions are available in three modality formats: Audio-only (16bit, 48kHz .wav), Audio-Video (720p H.264, AAC 48kHz, .mp4), and Video-only (no sound). Note, there are no song files for Actor_18.</p> <p>The RAVDESS was developed by Dr <a href="https://affectivedatascience.com/people/livingstone_sr" target="_blank" rel="noopener">Steven R. Livingstone</a>, who now leads the <a href="https://affectivedatascience.com">Affective Data Science Lab</a>, and Dr <a href="https://www.torontomu.ca/psychology/about-us/our-people/faculty/frank-russo/" target="_blank" rel="noopener">Frank A. Russo</a> who leads the <a href="https://psychlabs.torontomu.ca/smartlab/">SMART Lab</a>.</p> <p><strong>Citing the RAVDESS</strong></p> <p>The RAVDESS is released under a Creative Commons Attribution license, so please cite the RAVDESS if it is used in your work in any form. Published academic papers should use the academic paper citation for our PLoS1 paper. Personal works, such as machine learning projects/blog posts, should provide a URL to this Zenodo page, though a reference to our PLoS1 paper would also be appreciated.</p> <p><em>Academic paper citation</em></p> <blockquote> <p>Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. <a href="https://doi.org/10.1371/journal.pone.0196391">https://doi.org/10.1371/journal.pone.0196391</a>.</p> </blockquote> <p><em>Personal use citation</em></p> <blockquote> <p>Include a link to this Zenodo page - <a href="../record/1188976">https://zenodo.org/record/1188976</a></p> </blockquote> <p><strong>Commercial Licenses</strong></p> <p>Commercial licenses for the RAVDESS can be purchased. For more information, please visit our <a href="https://psychlabs.torontomu.ca/smartlab/ravdess-commercial-licensing/">license page of fees</a>, or contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20Commercial%20License">ravdess@gmail.com</a>.</p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the RAVDESS, to purchase a commercial license, or if you experience any issues downloading files, please contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20feedback%20from%20Zenodo">ravdess@gmail.com</a>.</p> <p><strong>Example Videos</strong></p> <p>Watch a sample of the RAVDESS <a href="https://www.youtube.com/watch?v=Y7OQoNEu3dY">speech</a> and <a href="https://www.youtube.com/watch?v=XQkmH4oYZkg">song</a> videos.</p> <p><strong>Emotion Classification Users</strong></p> <p>If you're interested in using machine learning to classify emotional expressions with the RAVDESS, please see our new RAVDESS Facial Landmark Tracking data set [<a href="../record/3255102">Zenodo project page</a>].</p> <p><strong>Construction and Validation</strong></p> <p>Full details on the construction and perceptual validation of the RAVDESS are described in our PLoS ONE paper - <a href="https://doi.org/10.1371/journal.pone.0196391">https://doi.org/10.1371/journal.pone.0196391</a>.</p> <p>The RAVDESS contains 7356 files. Each file was rated 10 times on emotional validity, intensity, and genuineness. Ratings were provided by 247 individuals who were characteristic of untrained adult research participants from North America. A further set of 72 participants provided test-retest data. High levels of emotional validity, interrater reliability, and test-retest intrarater reliability were reported. Validation data is open-access, and can be downloaded along with our paper from <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0196391">PLoS ONE</a>.</p> <p><strong>Contents</strong></p> <p><em>Audio-only files</em></p> <p>Audio-only files of all actors (01-24) are available as two separate zip files (~200 MB each):</p> <ul> <li>Speech file (Audio_Speech_Actors_01-24.zip, 215 MB) contains 1440 files: 60 trials per actor x 24 actors = 1440. </li> <li>Song file (Audio_Song_Actors_01-24.zip, 198 MB) contains 1012 files: 44 trials per actor x 23 actors = 1012.</li> </ul> <p><em>Audio-Visual and Video-only files</em></p> <p>Video files are provided as separate zip downloads for each actor (01-24, ~500 MB each), and are split into separate speech and song downloads:</p> <ul> <li>Speech files (Video_Speech_Actor_01.zip to Video_Speech_Actor_24.zip) collectively contains 2880 files: 60 trials per actor x 2 modalities (AV, VO) x 24 actors = 2880.</li> <li>Song files (Video_Song_Actor_01.zip to Video_Song_Actor_24.zip) collectively contains 2024 files: 44 trials per actor x 2 modalities (AV, VO) x 23 actors = 2024.</li> </ul> <p><em>File Summary</em></p> <p>In total, the RAVDESS collection includes 7356 files (2880+2024+1440+1012 files).</p> <p><strong>File naming convention</strong></p> <p>Each of the 7356 RAVDESS files has a unique filename. The filename consists of a 7-part numerical identifier (e.g., 02-01-06-01-02-01-12.mp4). These identifiers define the stimulus characteristics: <br><br><em>Filename identifiers </em></p> <ul> <li>Modality (01 = full-AV, 02 = video-only, 03 = audio-only).</li> <li>Vocal channel (01 = speech, 02 = song).</li> <li>Emotion (01 = neutral, 02 = calm, 03 = happy, 04 = sad, 05 = angry, 06 = fearful, 07 = disgust, 08 = surprised).</li> <li>Emotional intensity (01 = normal, 02 = strong). NOTE: There is no strong intensity for the 'neutral' emotion.</li> <li>Statement (01 = "Kids are talking by the door", 02 = "Dogs are sitting by the door").</li> <li>Repetition (01 = 1st repetition, 02 = 2nd repetition).</li> <li>Actor (01 to 24. Odd numbered actors are male, even numbered actors are female).</li> </ul> <p><br><em>Filename example: 02-01-06-01-02-01-12.mp4 </em></p> <ol> <li>Video-only (02)</li> <li>Speech (01)</li> <li>Fearful (06)</li> <li>Normal intensity (01)</li> <li>Statement "dogs" (02)</li> <li>1st Repetition (01)</li> <li>12th Actor (12)</li> <li>Female, as the actor ID number is even.</li> </ol> <p><strong>License information</strong></p> <p>The RAVDESS is released under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> </p> <p>Commercial licenses for the RAVDESS can also be purchased. For more information, please visit our <a href="https://smartlaboratory.org/ravdess/commercial-licensing/">license fee page</a>, or contact us at <a href="mailto:ravdess@gmail.com?subject=RAVDESS%20Commercial%20License">ravdess@gmail.com.</a></p> <p><strong>Related Data sets</strong></p> <ul> <li>RAVDESS Facial Landmark Tracking data set [<a href="../record/3255102">Zenodo project page</a>].</li> </ul>
Changes in neuronal representations of phonemes in the ascending auditory system and their role speech recognition
<p>This dataset comprises neural responses to a set of speech sounds from several brain regions. Auditory nerve data was simulated using a computational model of the auditory nerve. Also included are multi-unit extracellular recordings or responses to the same stimuli in the inferior colliculus and auditory cortex of anaethetised guinea pigs.</p>
The speed-curvature power law in tongue movements of repetitive speech [dataset]
<p>Files in this record contain data used to produce results presented in the<br> paper:</p> <p>Title: The speed-curvature power law in tongue movements of repetitive speech<br> Authors: Stephan R. Kuberski and Adamantios I. Gafos<br> DOI: <a href="https://doi.org/10.1371/journal.pone.0213851">https://doi.org/10.1371/journal.pone.0213851</a></p> <p>For further details refer to the included file README.txt.</p>
Fitts' law in tongue movements of repetitive speech [dataset]
<p>Files in this record contain data used to produce results presented in the paper:</p> <p> </p> <p>Title: Fitts' law in tongue movements of repetitive speech</p> <p>Authors: Stephan R. Kuberski and Adamantios I. Gafos</p> <p>DOI: TBA</p> <p> </p> <p>For further details refer to the included file README.txt.</p>
Detecting weak and strong Islamophobic hate speech on social media
<p>Data, code and annotation guidelines for our publication, 'Detecting weak and strong Islamophobic hate speech on social media' (2019).</p>
Speech and noise mixtures used in Modelling Auditory Processing and Organisation
<p>Speech and noise signals used in Cooke, M (1991) Modelling Auditory Processing and Organisation, Ph. D. Thesis, Department of Computer Science, University of Sheffield</p>
Data from: FMRI speech tracking in primary and non-primary auditory cortex while listening to noisy scenes
<p>This data set was analysed for the publication "FMRI speech tracking in primary and non-primary auditory cortex while listening to noisy scenes" by Hausfeld, Hamers, and Formisano (<em>Communications Biology</em>, 2024). Anatomical and functional MRI was acquired at 7 Tesla. Participants listened to speech of 1 or 2 (concurrent) audiobooks. To analyze fMRI-based speech tracking, participants were asked to listen to one speaker by performing a task. </p> <p>The dataset is arranged as follows:</p> <p>- MRI data [single particpant folders S1-15] (preprocessed) and individual speech tracking maps are contained in the participant-specific files S[participant_ID].zip in folder "MRI"</p> <p>- Stimulus descriptions (i.e., envelopes) are included in the folder "ENVELOPES"</p> <p>- Individual results (tracking map similarities and behavioral outcomes) are included in "INDIV_RESULTS"</p> <p>- Code to recreate figures is provided in folder "CODE" </p> <p>- the README contains information on the repository's content</p> <p> </p> <p>Please note additional information in the original publication</p> <p> </p> <p>Abstract of corresponding manuscript</p> <p>Invasive and non-invasive electrophysiological measurements during “cocktail-party”-like listening indicate that neural activity in the human auditory cortex (AC) “tracks” the envelope of relevant speech. However, due to limited coverage and/or spatial resolution, the distinct contribution of primary and non-primary areas remains unclear. Here, using 7-Tesla fMRI, we measured brain responses of participants attending to one speaker, in the presence and absence of another speaker. Through voxel-wise modeling, we observed envelope tracking in bilateral Heschl’s gyrus (HG), right middle superior temporal sulcus (mSTS) and left temporo-parietal junction (TPJ), despite the signal’s sluggish nature and slow temporal sampling. Neurovascular activity correlated positively (HG) or negatively (mSTS, TPJ) with the envelope. Further analyses comparing the similarity between spatial response patterns in the <em>single speaker </em>and<em> concurrent speakers</em> conditions and envelope decoding indicated that tracking in HG reflected both relevant and (to a lesser extent) non-relevant speech, while mSTS represented the relevant speech signal. Additionally, in mSTS, the similarity strength correlated with the comprehension of relevant speech. These results indicate that the fMRI signal tracks cortical responses and attention effects related to continuous speech and support the notion that primary and non-primary AC process ongoing speech in a push-pull of acoustic and linguistic information.</p> <p> </p> <p>Author contact: lars.hausfeld@maastrichtuniversity.nl</p> <p> </p>
Hachidaishu Part-of-Speech Dataset
<p><strong>Full Changelog</strong>: https://github.com/yamagen/hachidaishu-pos/commits/1.0.1</p>
Amharic Hate Speech Detection Dataset
<p>Amharic Hate Speech Detection Dataset V1</p> <p>To contribute for the research and development of hate speech detection in Amharic language from social media, we are glad to release our hate speech dataset we prepared from the Ethiopian Broadcasting Corporation (EBC) Facebook page (<a href="https://www.facebook.com/EBCzena">https://www.facebook.com/EBCzena</a>), and some chosen Facebook page (<a href="https://www.facebook.com/604407519910492">https://www.facebook.com/604407519910492</a>) that we found potential hateful comments.</p> <p>We extracted comments/posts pertaining to race, religion, and ethnicity using the Facepager API, resulting in a set of 30,000 comments between April 15, 2019 and December 15, 2019. A total of 5,000 comments/posts were chosen at random for annotation. Three annotators (two candidate PhD. in Linguistics and one MSc. in Law) manually annotated the selected samples as “<strong>Hate</strong>” or “<strong>not</strong>-<strong>Hate</strong>” resulting 2,000 (1000 hate and 1000 non-hate) labeled comments because of majority vote among the annotators.</p> <p>For the labeling procedure, the annotators used Ethiopian government’s hate speech and misinformation prevention and suppression proclamation <a href="https://www.accessnow.org/cms/assets/uploads/2020/05/Hate-Speech-and-Disinformation-Prevention-and-Suppression-Proclamation.pdf">https://www.accessnow.org/cms/assets/uploads/2020/05/Hate-Speech-and-Disinformation-Prevention-and-Suppression-Proclamation.pdf</a>, as well as our definition of hate speech and the hate speech characterization lists proposed in Fino (2020) <a href="https://doi.org/10.1093/jicj/mqaa023">https://doi.org/10.1093/jicj/mqaa023</a>) were provided to the annotators.</p> <p>Accordingly, a speech is labeled as “<strong>Hate</strong>” when:</p> <ul> <li>“the speech targets a group or individual as a member of a group (ethnicity, race, religion)”</li> <li>“the speech content in the message expresses hatred”</li> <li>“the speech causes a harm”</li> <li>“the speaker intends harm or bad activity”</li> <li>“the speech incites bad actions”</li> <li>“the speech is either public and directed at a member of the group”</li> <li>“the context makes violent response possible”</li> </ul>
Cross-Register Projection for Headline Part of Speech Tagging
<p>POSH: The POS-tagged HeadlIne corpus was created for the paper “Cross-Register Projection for Headline Part of Speech Tagging” published in EMNLP 2021. This dataset contains headlines with gold annotated POS tags.</p> <p>The <em>GSCh</em> evaluation set is here: GSCh/gsc-headline-gold.test.conllu</p> <p>The smaller evaluation set of GSC headlines sampled uniformly at random (described in section 2.3) is here: gold_unconstrained_headlines/unifrand_gsc.test.conllu</p> <p>The POS-tagged NYT headlines described in section 2.3 are not shared directly as this text was drawn from the New York Times Annotated Corpus (LDC2008T19), and subject to license constraints. However, if you have access to and have untarred LDC2008T19, you can recover this evaluation set with:</p> <p> TAG_PATH="./unifrand_onlynyt.tags.json" # mapping from NYT headline span to gold POS tag</p> <p> python build_gold_nyt_headlines.py --nyt_dir /PATH/TO/ANNOTATED/NYT/CORPUS/ --tag_path ${TAG_PATH} --num_proc 4</p> <p>Increase the argument to --num_procs to process more shards from the NYT corpus in parallel and reduce build time.</p> <p>Under GSCproj we also share the <em>GSCproj</em> folds which we used to train and validate our models. These are not gold POS tags, and are shared purely for reproducibility sake.</p>
Survey on Speech-and-Language Therapists' attitudes and approaches towards multilingualism across four European countries
<p>The file contains all the responses of 300 Speech-and-Language Therapists (SLTs) from Germany, Austria, Italy and Switwerland to a series of questions concerning the provision of speech and language therapy to multilingual children with Developmental Language Disorders. Both the original responses and numerically coded versions are included. Two excel sheets are provided: in the first sheet all responses to the general questionnaire are reported, whereas in the second sheet a subset of the responses obtained from 154 German-speaking SLT respondents are included (more in-depth analyses will be performed on an extended dataset concerning German-speaking SLTs only). These responses have been analysed and presented in the paper titled "Speech and Language Therapy service for multilingual children: Attitudes and approaches across four European countries".</p>
ParlamentParla - Speech corpus of Catalan Parliamentary sessions
<p>This is the <a href="http://github.com/CollectivaT-dev/ParlamentParla">ParlamentParla</a> speech corpus for Catalan prepared by <a href="https://collectivat.cat/">Col·lectivaT</a>. The audio segments were extracted from recordings the Catalan Parliament (<a href="https://www.parlament.cat/">Parlament de Catalunya</a>) plenary sessions, which took place between 2007/07/11 - 2018/07/17. We aligned the transcriptions with the recordings and extracted the corpus. The content belongs to the Catalan Parliament and the data is released conforming their <a href="https://www.parlament.cat/pcat/serveis-parlament/avis-legal/">terms of use</a>.</p> <p>Preparation of this corpus was partly supported by the <a href="http://cultura.gencat.cat/">Department of Culture</a> of the Catalan autonomous government, and the v2.0 was supported by the Barcelona Supercomputing Center, within the framework of the project <a href="http://aina.gencat.cat/">AINA</a> of the <a href="https://politiquesdigitals.gencat.cat/">Departament de Polítiques Digitals</a>.</p> <p>As of v2.0 the corpus is separated into 211 hours of clean and 400 hours of other quality segments. Furthermore, each speech segment is tagged with its speaker and each speaker with their gender. The statistics are detailed in the readme file.</p> <p>For more information, go to <a href="https://github.com/CollectivaT-dev/ParlamentParla">https://github.com/CollectivaT-dev/ParlamentParla</a> or mail info@collectivat.cat.</p> <p><strong>Revision log:</strong></p> <ul> <li> <p><em>2.0:</em> Major changes in the file structure; speaker ids with respective<br> genders added. The speakers of train, test and dev corpora do not overlap.<br> A major increase in size with a total time of 611 hours 43 minutes.</p> </li> <li> <p><em>1.0:</em> Much better quality due to improved segmentation, corpus separated<br> into clean and other.</p> </li> <li> <p><em>0.2:</em> First public release of approx. 320 hours.</p> </li> </ul>
Lada: Ukrainian High-Quality Female Text-to-Speech Dataset
<p>The dataset has high-quality data recorded in a professional studio. </p> <p>Archives with a <strong>trimmed</strong> tag are having removed silence (aligned) using <a href="https://github.com/proger/uk">https://github.com/proger/uk</a> </p> <p><strong>Features</strong></p> <ul> <li>Quality: high</li> <li>Duration: 10h37m</li> <li>Audio formats: OPUS/WAV</li> <li>Text format: JSONL (a <code>metadata.jsonl</code> file)</li> <li>Frequency: 16000/22050/48000 Hz</li> </ul>
Voice of America: Ukrainian ASR Dataset of Broadcast Speech
<p>The dataset is based on public recordings of Voice of America (<a href="https://ukrainian.voanews.com">https://ukrainian.voanews.com</a>) extracted from their videos.</p> <p>The dataset contains 398 hours of speech.</p> <p>The dataset is created by the ASR Corpus Creator (<a href="https://zenodo.org/record/7396705">https://zenodo.org/record/7396705</a>).</p> <p>The format of files: WAV with 16 kHz.</p> <p> </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.