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135 results for “multimodal dataset”
ERIT Lightweight Multimodal Dataset for Elderly Emotion Recognition
<p><span>ERIT is a novel multimodal dataset designed to facilitate research in elderly emo</span><span>tion recognition. The dataset contains lightweight text and image data collected</span><br><span>from videos of elderly individuals reacting to various stimuli. The dataset includes </span><span>transcriptions of the speech and emotion labels extracted from the video frames. It was created for multimodal fusion evaluation, but can be also used for elderly emotion recognition from the image.<br></span></p>
Multimodal Dataset for Predicting the Interruptibility of Office Workers
<p>This is a multimodal dataset for predicting the interruptibility of office workers. </p>
Dataset related to article "Multimodal single-cell profiling of intrahepatic cholangiocarcinoma defines hyperactivated Tregs as a potential therapeutic target"
<p>This record contains raw data related to article “Multimodal single-cell profiling of intrahepatic cholangiocarcinoma defines hyperactivated Tregs as a potential therapeutic target"</p> <p>Abstract</p> <p><strong>Background & aims: </strong> The landscape and function of the immune infiltrate of intrahepatic cholangiocarcinoma (iCCA), a rare, yet aggressive tumor of the biliary tract, remains poorly characterized, limiting development of successful immunotherapies. Herein, we aimed to define the molecular characteristics of tumor-infiltrating leukocytes with a special focus on CD4+ regulatory T cells (Tregs).</p> <p><strong>Methods: </strong> We used high-dimensional single-cell technologies to characterize the T-cell and myeloid compartments of iCCA tissues, comparing these with their tumor-free peritumoral and circulating counterparts. We further used genomics and cellular assays to define the iCCA-specific role of a novel transcription factor, mesenchyme homeobox 1 (MEOX1), in Treg biology.</p> <p><strong>Results: </strong> We found poor infiltration of putative tumor-specific CD39+ CD8+ T cells accompanied by abundant infiltration of hyperactivated CD4+ Tregs. Single-cell RNA-sequencing identified an altered network of transcription factors in iCCA-infiltrating compared to peritumoral T cells, suggesting reduced effector functions by tumor-infiltrating CD8+ T cells and enhanced immunosuppression by CD4+ Tregs. Specifically, we found that expression of MEOX1 was highly enriched in tumor-infiltrating Tregs, and demonstrated that MEOX1 overexpression is sufficient to reprogram circulating Tregs to acquire the transcriptional and epigenetic landscape of tumor-infiltrating Tregs. Accordingly, enrichment of the MEOX1-dependent gene program in Tregs was strongly associated with poor prognosis in a large cohort of patients with iCCA.</p> <p><strong>Conclusions: </strong> We observed abundant infiltration of hyperactivated CD4+ Tregs in iCCA tumors along with reduced CD8+ T-cell effector functions. Interfering with hyperactivated Tregs should be explored as an approach to enhance antitumor immunity in iCCA.</p> <p><strong>Lay summary: </strong> Immune cells have the potential to slow or halt the progression of tumors. However, some tumors, such as intrahepatic cholangiocarcinoma, are associated with very limited immune responses (and infiltration of cancer-targeting immune cells). Herein, we show that a specific population of regulatory T cells (a type of immune cell that actually suppresses the immune response) are hyperactivated in intrahepatic cholangiocarcinoma. Targeting these cells could enable cancer-targeting immune cells to act more effectively and should be looked at as a potential therapeutic approach to this aggressive cancer type.</p> <p> </p>
ROS2 bag multimodal perception dataset acquired in Portuguese forest with Modular-E robot for line vegetation clearing
<p>Rosbag data acquired:</p> <ul> <li>RGB-D data from OAK-D facing sideways;</li> <li>Pointcloud from Hokuyo 2D Lidar facing sideways;</li> <li>Pointcloud from Robosense 360º Lidar facing forward;</li> <li>RTK fix.</li> </ul>
Offroad UGV environment sensing : a multimodal dataset for farming and forestry operations
<p>The aim of this dataset is to provide a multimodal dataset for environmental perception tasks in agricultural and forestry environments, in the case of autonomous land vehicle navigation. This evolving dataset will be updated regularly throughout 2023.</p> <p><br> This dataset is provided by Technology & Strategy Engineering SAS and is jointly funded by the Agence Nationale de la Recherche et de la Technologique (ANRT). This work was carried out in collaboration with the University of Haute Alsace, and more specifically the MIAM-FOTI team of the IRIMAS EA7499 laboratory.</p>
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.
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>
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>
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>
EmpathicSchool: A multimodal dataset for real-time facial expressions and physiological data analysis under different stress conditions
<p>EmpathicSchool is a novel dataset that captures facial expressions and the associated physiological signals, such as heart rate, electrodermal activity, and skin temperature, under different stress levels. The data was collected from 30 participants at different sessions for 42 hours. The data includes 7 different signal types, including both computer vision and physiological features that can be used to detect stress. In addition, various experiments were conducted to validate the signal quality.<br><br>The data will be available upon signing the Data Usage Agreement.</p>
dataset related to article "Sporadic and von Hippel–Lindau Related Hemangioblastomas of Brain and Spinal Cord: Multimodal Imaging for Intraoperative Strategy"
<p>Datasheet containing clinicla anonimyzed information on patients involved in study at title</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.