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114
datasets available to search
ShareScore release 0.9.0
Dataset results
114 results for “Multimodal data”
AI-Based Prediction of Lymph Node Metastasis in Gastric Cancer Using Preoperative Multimodal Data
ClinicalTrials.gov study NCT06957678. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Investigative needle core biopsies support multimodal deep-data generation in glioblastoma [BulkRNA-seq]
GEO Series GSE287629. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Integrating multimodal data sets into a mathematical framework to describe and predict therapeutic resistance in cancer
GEO Series GSE154932. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
Investigative needle core biopsies support multimodal deep-data generation in glioblastoma [scRNA-seq]
GEO Series GSE287630. Homo sapiens. 7 samples. Type: Expression profiling by high throughput sequencing.
Multimodal hierarchical classification allows for efficient annotation of CITE-seq data
GEO Series GSE229791. Homo sapiens. 102 samples. Type: Expression profiling by high throughput sequencing; Other.
Investigative needle core biopsies support multimodal deep-data generation in glioblastoma [Spatial Transcriptomics]
GEO Series GSE287631. Homo sapiens. 16 samples. Type: Other.
Spectral data obtained by placing a multimodal interference filter with and without exfoliated graphite coating in a ring-shaped laser cavity
<p>This dataset contains the files obtained from the optical spectrum analyzer, by inserting a multimodal interference filter (MMIF) coated with graphite oxide into the ring-shaped laser cavity.<br> The data, according to the file name, is divided into three parts.<br> In one dataset are the spectrum of the cavity, at different pump powers, before inserting the MMIF. The other data set has the spectrum at different pump powers, with the MMIF without graphite oxide coating in the laser cavity. The last data set contains the spectrum at different pump powers, with the MMIF coated with graphite oxide inside the laser cavity.</p>
AI-assisted Quality Control Study of Multimodal Data in the Epidemiological Survey of Shanghai Nicheng Cohort Study
ClinicalTrials.gov study NCT06961461. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Block-GP: Scalable Gaussian Process Regression for Multimodal Data
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases, regression algorithms such as linear regression or neural networks attempt to fit the target variable as a function of the input variables without regard to the underlying joint distribution of the variables. As a result, these global models are not sensitive to variations in the local structure of the input space. Several algorithms, including the mixture of experts model, classification and regression trees (CART), and others have been developed, motivated by the fact that a variability in the local distribution of inputs may be reflective of a significant change in the target variable. While these methods can handle the non-stationarity in the relationships to varying degrees, they are often not scalable and, therefore, not used in large scale data mining applications. In this paper we develop Block-GP, a Gaussian Process regression framework for multimodal data, that can be an order of magnitude more scalable than existing state-of-the-art nonlinear regression algorithms. The framework builds local Gaussian Processes on semantically meaningful partitions of the data and provides higher prediction accuracy than a single global model with very high confidence. The method relies on approximating the covariance matrix of the entire input space by smaller covariance matrices that can be modeled independently, and can therefore be parallelized for faster execution. Theoretical analysis and empirical studies on various synthetic and real data sets show high accuracy and scalability of Block-GP compared to existing nonlinear regression techniques.
MMIFR: Multimodal Industry Focused Data Repository
<p>The MMIFR data repository consists of three distinct components: MMIFR-D, MMIFR-FS, and MMIFR-P. The MMIFR-D dataset comprises a comprehensive assemblage of 5907 images accompanied by corresponding textual descriptions, notably facilitating the application of industrial equipment classification. In contrast, the MMIFR-FS dataset serves as an alternative variant characterized by the inclusion of 129 distinct classes and 5907 images, specifically catering to the task of few-shot learning within the industrial domain. Additionally, the MMIFR-P dataset consists of 142 textual-visual information pairs, making it suitable for detecting pairs of industrial equipment.</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>
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>
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>
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.