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1,389 results for “multimodality”
Multimodal analyses of a non-human primate model harboring mutant amyloid precursor protein transgenes driven by the human EF1α promoter
GEO Series GSE194006. Callithrix jacchus. 6 samples. Type: Expression profiling by high throughput sequencing; Other.
A multimodal cross-species comparison of pancreas development [multiome]
GEO Series GSE262227. Sus scrofa. 12 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
ScRNAseq - Neurotransmitter signaling regulates distinct phases of multimodal human interneuron migration
GEO Series GSE161550. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
A multimodal cross-species comparison of pancreas development [scRNA-seq]
GEO Series GSE262225. Sus scrofa. 18 samples. Type: Expression profiling by high throughput sequencing.
OP3: single-cell multimodal dataset in PBMCs for perturbation prediction benchmarking
GEO Series GSE279945. Homo sapiens. 102 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Round or rectangular tables for collaborative problem solving? A multimodal learning analytics study
<p>This dataset contains the necessary details to reproduce the experiments of the paper :</p> <p>Vujovic M., Hernández-Leo, D., Tassani S., Spikol D. (2020). Round or rectangular tables for collaborative problem solving? A multimodal learning analytics study, (in the process of revision) <em>British Journal of Education Technology</em></p> <p>This data represents measurements collected from two experiments explained in the article:</p> <ul> <li>Qualitative data - observations</li> <li>Experiment_university students - measurements (distance between students, range of movement, level of participation)</li> <li>Experiment_elementary school students - measurements (distance between students, range of movement, level of participation)</li> </ul> <p>We would appreciate it if you cite the paper after using the dataset.</p>
Dataset related to the article "Multimodality imaging assessment of mitral annular disjunction in mitral valve prolapse"
<p>This record contains raw data related to the article "Multimodality imaging assessment of mitral annular disjunction in mitral valve prolapse".</p> <p><strong>Objective </strong>Mitral annular disjunction (MAD) is an abnormality linked to mitral valve prolapse (MVP), possibly associated with malignant ventricular arrhythmias. We assessed the agreement among different imaging techniques for MAD identification and measurement.</p> <p><strong>Methods </strong>131 patients with MVP and significant mitral regurgitation undergoing transthoracic echocardiography (TTE) and cardiac magnetic resonance (CMR) were retrospectively enrolled. Transoesophageal echocardiography (TOE) was available in 106 patients. MAD was evaluated in standard long-axis views (four-chamber, two-chamber, three-chamber) by each technique.</p> <p><strong>Results </strong>Considering any-length MAD, MAD prevalence was 17.3%, 25.5%, 42.0% by TTE, TOE and CMR, respectively (p<0.05). The agreement on MAD identification was moderate between TTE and CMR (κ=0.54, 95% CI 0.49 to 0.59) and good between TOE and CMR (κ=0.79, 95% CI 0.74 to 0.84). Assuming CMR as reference and according to different cut-off values for MAD (≥2 mm, ≥4 mm, ≥6 mm), specificity (95% CI) of TTE and TOE was 99.6 (99.0 to 100.0)% and 98.7 (97.4 to 100.0)%; 99.3 (98.4 to 100.0)% and 97.6 (95.8 to 99.4)%; 97.8 (96.2 to 99.3)% and 93.2 (90.3 to 96.1)%, respectively; sensitivity (95% CI) was 43.1 (37.8 to 48.4)% and 74.5 (69.4 to 79.5)%; 54.0 (48.7 to 59.3)% and 88.9 (85.2 to 92.5)%; 88.0 (84.5 o 91.5)% and 100.0 (100.0 to 100.0)%, respectively. MAD length was 8.0 (7.0-10.0), 7.0 (5.0-8.0], 5.0 (4.0-7.0) mm, respectively by TTE, TOE and CMR. Agreement on MAD measurement was moderate between TTE and CMR (ρ=0.73) and strong between TOE and CMR (ρ=0.86).</p> <p><strong>Conclusions </strong>An integrated imaging approach could be necessary for a comprehensive assessment of patients with MVP and symptoms suggestive for arrhythmias. If echocardiography is fundamental for the anatomic and haemodynamic characterisation of the MV disease, CMR may better identify small length MAD as well as myocardial fibrosis.</p>
MuSe-Topic: Multimodal Emotion-Target Sub-challenge (MuSe2020)
<p><strong>[Post-challenge] MuSe-Topic of MuSe2020: </strong>Predicting 10-class domain-specific topics as the target of 3-class (low, medium, high) emotions of valence and arousal. This package includes only MuSe-Topic features (all partitions) and annotations of the training and development set (test scoring via the MuSe website). </p> <p><strong>General: </strong>The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge and Workshop (MuSe) is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), and the sentiment analysis community (symbol-based). </p> <p>We introduce the novel dataset MuSe-CAR that covers the range of aforementioned desiderata. MuSe-CAR is a large (>36h), multimodal dataset that has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity.</p> <p>We have designed MuSe-CAR to be of high voice and video quality, as informative video social media content, as well as everyday recording devices have improved in recent years. This enables robust learning, even with a high degree of novel, in-the-wild characteristics, for example as related to: i) Video: Shot size (a mix of closeup, medium, and long shots), face-angle (side, eye, low, high), camera motion (free, free but stable, and free but unstable, switch, e.g., zoom, fixed), reviewer visibility (full body, half-body, face only, and hands only), highly varying backgrounds, and people interacting with objects (car parts). ii) Audio: Ambient noises (car noises, music), narrator and host diarisation, diverse microphone types, and speaker locations. iii) Text: Colloquialisms, and domain-specific terms.</p>
MuSe-Wild: Multimodal Sentiment in-the-Wild Sub-challenge (MuSe2020)
<p><strong>MuSe-Wild of MuSe2020: </strong>Predicting the level of emotional dimensions (arousal, valence) in a time-continuous manner from audio-visual recordings. <strong>This package includes only MuSe-Wild features (all partitions) and annotations of the training and development set </strong>(test scoring via the MuSe website). </p> <p><strong>General: </strong>The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge and Workshop (MuSe) is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), and the sentiment analysis community (symbol-based). </p> <p>We introduce the novel dataset MuSe-CAR that covers the range of aforementioned desiderata. MuSe-CAR is a large (>36h), multimodal dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity.</p> <p>We have designed MuSe-CAR to be of high voice and video quality, as informative video social media content, as well as everyday recording devices have improved in recent years. This enables robust learning, even with a high degree of novel, in-the-wild characteristics, for example as related to: i) Video: Shot size (a mix of close-up, medium, and long shots), face-angle (side, eye, low, high), camera motion (free, free but stable, and free but unstable, switch, e.g., zoom, fixed), reviewer visibility (full body, half-body, face only, and hands only), highly varying backgrounds, and people interacting with objects (car parts). ii) Audio: Ambient noises (car noises, music), narrator and host diarisation, diverse microphone types, and speaker locations. iii) Text: Colloquialisms, and domain-specific terms.</p> <p> </p>
MuSe-Trust: Multimodal Trustworthiness Sub-challenge (MuSe2020)
<p><strong>MuSe-Trust of MuSe2020:</strong> Predicting the level of trustworthiness of user-generated audio-visual content in a sequential manner utilising a diverse range of features and (optional) emotional (arousal and valence) predictions. This package includes only MuSe-Trust features (all partitions) and annotations of the training and development set (test scoring via the MuSe website). </p> <p><strong>General:</strong> The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge and Workshop (MuSe) is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), and the sentiment analysis community (symbol-based). </p> <p>We introduce the novel dataset MuSe-CAR that covers the range of aforementioned desiderata. MuSe-CAR is a large (>36h), multimodal dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity.</p> <p>We have designed MuSe-CAR to be of high voice and video quality, as informative video social media content, as well as everyday recording devices have improved in recent years. This enables robust learning, even with a high degree of novel, in-the-wild characteristics, for example as related to: i) Video: Shot size (a mix of closeup, medium, and long shots), face-angle (side, eye, low, high), camera motion (free, free but stable, and free but unstable, switch, e.g., zoom, fixed), reviewer visibility (full body, half-body, face only, and hands only), highly varying backgrounds, and people interacting with objects (car parts). ii) Audio: Ambient noises (car noises, music), narrator and host diarisation, diverse microphone types, and speaker locations. iii) Text: Colloquialisms, and domain-specific terms.</p>
AARCHIVED - Data for Defining the function of disease variants with CRISPR editing and multimodal single cell sequencing: PTEN, FBXO11, HLA-DQB1
<p>Please refer to the more recent Zenodo record here: <a href="https://zenodo.org/records/15935857">https://zenodo.org/records/15935857</a>.</p>
multimodal data testing EI
<p>The zip files are the compressed multimodal data tested in:</p> <p>Integrating multimodal data through interpretable heterogeneous ensembles Yan-Chak Li, Linhua Wang, Jeffrey Law, T. M. Murali, Gaurav Pandey bioRxiv 2020.05.29.123497; doi: <a href="https://doi.org/10.1101/2020.05.29.123497">https://doi.org/10.1101/2020.05.29.123497</a></p>
Enhancing consent forms to support participant decision making in multimodal learning research
<p>Advances in the field of multimodal learning research is often accomplished by actively exploring new technologies and techniques related to the collection and analysis of data. However, the exploration of the ethical principles and procedures for governing the usage of the new technologies and techniques is not as eagerly pursued. As multimodal learning data grows in complexity, and to an extent invasiveness, a need is arising to scrutinize the ethical aspects of multimodal learning research. The process of informed consent in multimodal learning research may be an appropriate starting point as the results of bioethical studies suggest that many informed consent processes do not adequately support participant comprehension of the studies they consent to. Studies have shown that enhancing consent forms can improve participant comprehension and contribute to validating the consent received. The following data set include two types of enhanced consent forms: one form is written from a researcher perspective and the other from a participant perspective. Results of the study involving first year undergraduate students suggest that the overall level of comprehension did not differ between conditions. Yet, the participant-oriented consent form had significantly lower rates of enrollment. </p> <p>The data set is composed by:</p> <p>Anonymized Data Sheet: participant responses to the Informed Consent Test for Educational Technology and survey questions</p> <p>Enhanced Consent Forms: Enhanced consent forms for educational technology research for each condition in the study and in both English and Spanish. Form A is written from a research perspective (researcher-oriented) – which is how most forms are written. Form B is written from a participant perspective (participant-oriented).</p> <p> </p>
Multimodal OCTA and Fundus Image dataset for detection of Diabetic Retinopathy
<p>This is a Multimodal OCTA and Fundus Image dataset curated for detection of Diabetic Retinopathy at Natasha Eye care and Research Institute. In all 224 images from 74 persons were captured in Version-1 and 338 images from 111 person in Version-2 Two modalities for every individual were taken simultaneously using Eidon camera for Fundus images and Optovue Avanti edition for OCTA images. we have three categories of classification in this dataset 1. NO DR 2. MILD DR 3. MODERATE DR</p> <p> </p> <p>CONSENT Forms are taken for every Individual.</p>
Multimodal monitoring data
<p>Multimodal monitoring data of TBI patients (anonymized)</p> <p>Data format: Matlab data files (mat).</p> <p>Raw data downsampled to sampling frequency 10 Hz.</p> <p>mat files include variables:</p> <p>abp (arterial blood pressure in mmHg, fs=10 Hz)</p> <p>icp (intracrania blood pressure in mmHg, fs=10 Hz) </p> <p> </p>
PERSIST: A Multimodal Dataset for the Prediction of Perceived Exertion During Resistance Training
<p>PERSIST is a novel dataset for predicting PERceived exertion during reSIStance Training. It includes multiple sensor modalities, including inertial measurement units (IMU), electrocardiography (ECG), and motion capture (MoCap). The MoCap data has been synchronized to the IMU and ECG data. Also, it provides heart rate variability (HRV) parameters obtained from the ECG signal. The dataset contains data from twelve young and healthy male participants with at least one year of resistance training experience. Subjects performed twelve sets of squats on a Flywheel platform with twelve repetitions per set. After each set, subjects reported their current RPE.</p>
COSMOS: a dataset for Classification Of Stress and workload using multiMOdal wearable Sensors
<p>Prolonged stress and high mental workload can have deteriorating long-term effects developing several stress-related diseases. The existing stress detection techniques are often uni-modal and limited to controlled setups. One sensing modality could be unobtrusive but mostly results in unreliable sensor readings, especially in uncontrolled environments. Our study recorded multi-modal physiological signals from twenty-five participants in controlled and uncontrolled environments by performing given and self-chosen tasks of high and low mental demand. In this version, we processed and published a subset of the dataset from six participants while working on the rest. The subset of the data is used to check the feasibility of our study by engineering features from electroencephalography (EEG), photoplethysmography (PPG), electrodermal activity (EDA), and temperature sensor data. Machine learning methods were used for the binary classification of the tasks. Personalized models in the uncontrolled environment achieved a mean classification accuracy of up to 83% while using one of the four labels, unveiling some unintentional mislabeling by participants. In controlled environments, multi-modality improved the accuracy by at least 7%. Generalized machine learning models achieved close to chance-level performances. This work underlines the importance of multi-modal recordings and provides the research community with an experimental paradigm to take studies of mental workload and stress workload and stress out of controlled into uncontrolled environments<br> </p>
The Indonesian Young-Adult Attachment (IYAA) Dataset: A Multimodal Behavioral Young-Adult Attachment Assessment
<p>IYAA is an Indonesian language-based facial expression and speech for attachment style classification dataset. IYAA consists of Indonesian young-adult audio-video data from diverse age groups between 18-29 years old, with 20 male and 67 female subjects. The data was developed during the COVID-19 pandemic in an online setting by using the Zoom video conference application. Each subject participated in a projective-based attachment assessment which consists of two stages: exposure and response of 14 attachment-based stimuli. The data obtained for each subject includes 14 exposure videos, 14 response videos, and 14 speech response audios. However, two subjects' data are incomplete due to technical issues during data collection. The first subject loses an exposure video, and the second one loses data for a stimulus (an exposure video, a response video, and a speech response audio). So, in total, there are 3650 files, including 1216 exposure videos, 1217 response videos, and 1217 speech response audios. Each data has a varying duration; for exposure video, the minimum duration is 30 seconds, while for response video and speech response audio, the minimum duration is 40 seconds. The data is annotated into two kinds of annotation: attachment and emotion. First, each subject is annotated into one label among three classes of attachment style: secure, insecure anxious, insecure avoidance. Second, each stimulus's data (exposure videos, response videos, and speech response audios) are annotated into one or more labels among eight basic emotions: neutral, happy, sad, contempt, anger, disgust, surprise, and fear. Subject's responses on ECR-RS self-report questionnaire are also extracted to support holistic adult attachment assessment regarding non-verbal behaviors and subjective responses. The IYAA dataset supports multidisciplinary research, specifically on attachment, in Psychology (Behavioral, Social, and Developmental Psychology) and Computer Science (Social Signal Processing).</p>
IndEmoVis: An Indian Multimodal Audio-Visual Emotion Dataset for Conversational Interactions
<p>The dataset comprises a total of 122 videos, involving 61 participants, consisting of 25 females and 36 males, with ages ranging from 18 to 21 years. Each video captures a conversational interaction between two participants, with two cameras mounted on the table to record separate videos of the speaker and the listener. These recorded videos are subsequently segmented into clips, each showcasing different emotions expressed by the participants. The dataset comprises a total of 122 videos, involving 61 participants, consisting of 25 females and 36 males, with ages ranging from 18 to 21 years. Each video captures a conversational interaction between two participants, with two cameras mounted on the table to record separate videos of the speaker and the listener. These recorded videos are subsequently segmented into clips, each showcasing different emotions expressed by the participants. The data collection process involved conversational interactions between pairs of participants within a controlled environment. The recordings captured a combination of spontaneous and guided conversations. The dataset consists of 45 recordings of guided conversations and 77 recordings of natural conversations, resulting in a total of 216 video clips portraying various emotions from the guided interactions and 379 video clips displaying emotions from the natural conversations. To facilitate further analysis and research, the video clips were further processed to extract image frames. From each clip, the frame with the highest intensity, capturing the peak expression of the corresponding emotion, was selected for subsequent analysis. These peak images serve as representative snapshots of the emotional expressions and can be used for facial emotion recognition tasks. IndEmoVis dataset is annotated for six basic emotion categories (Happiness, Sadness, Surprise, Disgust, Anger, Fear), along with complex emotion categories of Awe and Sympathy, and a Neutral emotion class. Additionally, the dataset is annotated for the presence of braces, spectacles, and prominent hand gestures, providing valuable context for emotion analysis. Furthermore, intensity and confidence levels are annotated on a scale of 0 to 5 and 0 to 7, respectively, by the clinical psychologist to further enrich the dataset with nuanced emotional expressions.</p>
Dataset related to article "Multimodal Treatments for Brain Metastases from Renal Cell Carcinoma: Results of a Multicentric Retrospective Study "
<p>This record contains raw data related to article "Multimodal Treatments for Brain Metastases from Renal Cell Carcinoma: Results of a Multicentric Retrospective Study"</p><p>Abstract</p><p>The aim of this study was to evaluate the clinical outcomes of a large series of brain metastatic renal cell carcinoma (BMRCC) patients treated in three Italian centers.</p><p><strong>Methods: </strong>A total of 120 BMRCC patients with a total of 176 lesions treated were evaluated. Patients received surgery plus postoperative HSRS, single-fraction SRS, or hypofractionated SRS (HSRS). Local control (LC), brain distant failure (BDF), overall survival (OS), toxicities, and prognostic factors were assessed.</p><p><strong>Results: </strong>The median follow-up time was 77 months (range 16-235 months). Surgery plus HSRS was performed in 23 (19.2%) cases, along with SRS in 82 (68.3%) and HSRS in 15 (12.5%). Seventy-seven (64.2%) patients received systemic therapy. The main total dose and fractionation used were 20-24 Gy in single fraction or 32-30 Gy in 4-5 daily fractions. Median LC time and 6 month and 1, 2 and 3 year LC rates were nr, 100%, 95.7% ± 1.8%, 93.4% ± 2.4%, and 93.4% ± 2.4%. Median BDF time and 6 month and 1, 2 and 3 year BDF rates were n.r., 11.9% ± 3.1%, 25.1% ± 4.5%, 38.7% ± 5.5%, and 44.4% ± 6.3%, respectively. Median OS time and 6 month and 1, 2 and 3 year OS rates were 16 months (95% CI: 12-22), 80% ± 3.6%, 58.3% ± 4.5%, 30.9% ± 4.3%, and 16.9% ± 3.6, respectively. No severe neurological toxicities occurred. Patients with a favorable/intermediate IMDC score, a higher RCC-GPA score, an early occurrence of BMs from primary diagnosis, absence of EC metastases, and a combined local treatment (surgery plus adjuvant HSRS) had a better outcome.</p><p><strong>Conclusions: </strong>SRS/HSRS is proven to be an effective local treatment for BMRCC. A careful evaluation of prognostic factors is a valid step to manage the optimal therapeutic strategy for BMRCC patients.</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.