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1,389 results for “multimodality”
Wheat Breeding Multimodal Dataset
<p>This dataset is a wheat breeding multimodal dataset, including wheat germplasm data, wheat phenotypic data, wheat cultivation technique data, wheat plant protection technique data, wheat seed price data, UAV remote sensing data, and experimental site weather data. The data sources are field acquisition and online public data.</p> <p>The wheat germplasm data comes from the Chinese Crop Germplasm Information Network (https://www.cgris.net/). The data on wheat cultivation technique and wheat plant protection technique come from search engines (Google, Bing, Baidu), and the search terms include "wheat cultivation technique, wheat plant protection technique, 小麦栽培技术, 小麦植保技术". The wheat seed historical price data comes from the National Seed Market Monitoring Information Release Platform (http://202.127.45.18/) - China. UAV remote sensing data is the result of further processing after being obtained from field experiments. Weather data comes from meteorological equipment at various agricultural experimental bases and meteorological observation stations of the China Meteorological Administration.</p> <p>The acquisition and processing of data are described in the relevant part of the manuscript.</p> <p>This dataset will be continuously updated in the future to help breeding work be carried out efficiently and accelerate the breeding process of excellent varieties.</p>
Multimodal Argument Mining: A Case Study in Political Debates
<p>MM-USED supplementary files for building the corpus</p>
The TUFI dataset used in research paper "Assessing Large Multimodal Models for Urban Floodwater Depth Estimation"
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The DUFI dataset used in research paper "Assessing Large Multimodal Models for Urban Floodwater Depth Estimation"
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Data for: Meningioangiomatosis- Multimodal analysis and insights from a systematic review
<p class="CxSpFirst"><span><span><span><span><span><span><span><span><span><span><span><b>Background:</b>Meningioangiomatosis is a poorly studied, rare, benign, and epileptogenic brain lesion.</span></span></span></span></span></span></span></span></span></span></span></p> <p class="CxSpMiddle"><span><span><span><span><span><span><span><span><span><span><span><b>Objective: </b><span>To demonstrate that surgical resection and a short time interval to surgery improves epileptic seizure control, we </span>performed a systematic review and meta-analysis of meningioangiomatosis cases<span>.</span></span></span></span></span></span></span></span></span></span></span></span></p> <p class="CxSpMiddle"><span><span><span><span><span><span><span><span><span><span><span><b>Methods: </b>Using PRISMA-IPD guidelines, the authors performed a systematic review and meta-analysis of histopathologically proven meningioangiomatosis cases.Literature search in the French and English languages (PubMed, Embase, the Cochrane Library and the Science Citation Index) including all studies (January 1981 to June 2020)dealing with histopathologically proven meningioangiomatosis, without age restriction.We assessed clinical, imaging, histomolecular, management, and outcome findings of meningioangiomatosis patients.</span></span></span></span></span></span></span></span></span></span></span></p> <p class="CxSpMiddle"><span><span><span><span><span><span><span><span><span><span><span><b>Results: </b>Two-hundred and seven cases of meningioangiomatosis from 78 studies were included.The majority of meningioangiomatosis was sporadic, preferentially concerned male patients, younger than 20-year-old, and allowed a functionally independent status. Epileptic seizure was the main symptom, with 81.4%of patients having uncontrolled seizures at the time of surgery. Meningioangiomatosis mainly had frontal (32.3%) or temporal (30.7%) locations. Imaging presentation was heterogeneous, and the diagnosis was often missed pre-operatively. The histopathologic pattern was similar whatever the clinical presentation, and immunohistochemistry had limited diagnostic value. On molecular analysis, allelic loss at 22q12 was more frequent in samples of meningioangiomatosis-associated meningioma (37.5%) than in isolated meningioangiomatosis (23.1%). Time interval from diagnosis to surgery (p=0.011) and lack of surgical resection of the meningioangiomatosis (p=0.009) were independent predictors of post-operative seizure control.</span></span></span></span></span></span></span></span></span></span></span></p> <p class="CxSpMiddle"><span><span><span><span><span><span><span><span><span><span><span><b>Conclusions: </b>Due to low scientific evidence, a multicentric prospective study should help refining the management of meningioangiomatosis.</span></span></span></span></span></span></span></span></span></span></span></p> <p><br> </p>
MOJITOO: a fast and universal method for integration of multimodal single cell data
<p>MOJITOO benchmarking seurat Robjects.</p>
Multimodal Analysis for Human ex vivo Studies Shows Extensive Molecular Changes from Delays in Blood Processing
<p>This is an assembled raw data in Seurat object. Expression profiling data can be accessed from <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE156989">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE156989</a>.</p>
UMONS-TAICHI: A multimodal motion capture dataset of expertise in Taijiquan gestures
<p><strong>Presentation</strong></p> <p>UMONS-TAICHI is a large 3D motion capture dataset of Taijiquan martial art gestures (n = 2200 samples) that includes 13 classes (relative to Taijiquan techniques) executed by 12 participants of various skill levels. Participants levels were ranked by three experts on a [0-10] scale. The dataset was captured using two motion capture systems simultaneously: 1) Qualisys, a sophisticated motion capture system of 11 Oqus cameras that tracked 68 retroreflective markers at 179 Hz, and 2) Microsoft Kinect V2, a low-cost markerless sensor that tracked 25 locations of a person’s skeleton at 30 Hz. Data from both systems were synchronized manually. Qualisys data were manually corrected, and then processed to complete any missing data. Data were also manually annotated for segmentation. Both segmented and unsegmented data are provided in this database. The data were initially recorded for gesture recognition and skill evaluation, but they are also suited for research on synthesis, segmentation, multi-sensor data comparison and fusion, sports science or more general research on human science or motion capture. A preliminary analysis has been conducted by Tits et al. (2017) on a part of the dataset to extract morphology-independent motion features for gesture skill evaluation and presented in: “Morphology Independent Feature Engineering in Motion Capture Database for Gesture Evaluation” (<a href="https://doi.org/10.1145/3077981.3078037">https://doi.org/10.1145/3077981.3078037</a>).</p> <p><strong>Processing</strong></p> <p><em><strong>Qualisys</strong></em></p> <p>Qualisys data were processed manually with <a href="http://www.qualisys.com/software/qualisys-track-manager/">Qualisys Track Manager</a>.</p> <p>Missing data (occluded markers) were then recovered with an automatic recovery method: <a href="https://github.com/titsitits/MocapRecovery">MocapRecovery</a>.</p> <p>Data were annotated for gesture segmentation, using the <a href="https://github.com/numediart/ofxMotionMachine">MotionMachine</a> framework (C++ <a href="http://openframeworks.cc/">openFrameworks</a> addon). The code for annotation can be found <a href="https://github.com/numediart/ofxMotionMachine/tree/master/mmTutorial_4_Annotation">here</a>. Annotations were saved as ".lab" files (see Download section).</p> <p><em><strong>Kinect</strong></em></p> <p>The Kinect data were recorded with <a href="https://msdn.microsoft.com/en-us/library/hh855389.aspx">Kinect Studio</a>. Skeleton data were then extracted with <a href="https://www.microsoft.com/en-us/download/details.aspx?id=44561">Kinect SDK</a> and saved into “.txt” files which contain several lines corresponding to each captured frame. Each line contains one integer number (ms), relative to the moment when the frame was captured, followed by 3 x 25 float numbers corresponding to the 3-dimentional locations of the 25 body joints.</p> <p>For more information please visit <a href="https://github.com/numediart/UMONS-TAICHI">https://github.com/numediart/UMONS-TAICHI</a></p> <p>PS: All files can be used with the <a href="https://github.com/numediart/ofxMotionMachine">MotionMachine</a> framework. Please use the parser provided in this github repository for kinect (.txt) data.</p> <p> </p>
ALFABETIZAÇÃO MULTIMODAL NO SÉCULO XXI: PERSPECTIVAS EMERGENTES E DESAFIOS CONTEMPORÂNEOS
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Dataset related to article "Evolution of brain injury and neurological dysfunction after cardiac arrest in the rat – a multimodal and comprehensive model."
<p>Excel file related to the article</p>
Supplementary methods and dataset related to the manuscript: Temporal integration of multimodal signals in crocodiles
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Multimodal single-cell analyses reveal distinct fusion-regulated transcriptional programs in Ewing sarcoma.
<p>All processed data for analysis at <a href="https://github.com/furlan-lab/EwS_multiome">https://github.com/furlan-lab/EwS_multiome</a></p>
OMuSense-23: A Multimodal dataset for contactless breathing pattern recognition and biometric analysis
<p>OMuSense-23 is a multimodal dataset for non-contact biometric and breathing analysis. <br>This database comprises RGBD and mmWave radar data collected from 50 participants. <br>The data capture process involves participants engaging in four breathing pattern activities <br>(normal breathing, reading, guided breathing, and breath holding to simulate apnea) <br>each one performed in three distinct static poses: standing (A), sitting (B), and lying down (C). <br> <br> For citations please refer to the paper:<br> Manuel Lage Cañellas, Le Nguyen, Anirban Mukherjee, Constantino Álvarez Casado,<br> Xiaoting Wu, Nhi Nguyen, Praneeth Susarla, Sasan Sharifipour, Dinesh B. Jayagopi, Miguel Bordallo López,<br> "OmuSense-23: A Multimodal Dataset For Contactless Breathing Pattern Recognition And Biometric Analysis",<br> arXiv:2407.06137, 2024 </p>
Automated electrosynthesis reaction mining with multimodal large language models - raw data and prompts
<p>Compilation of the prompts used, together with the raw response files from the tested MLMM models. </p>
Multimodal fish feeding behavior dataset Part 1 (You need download all dataset from Part 2 (https://zenodo.org/records/11060370) and Part 3 (https://zenodo.org/records/11060195))
<p>Multimodal Fish Feeding Intensity Assessment in Aquaculture (The paper you can find on Arxiv: <a href="https://arxiv.org/pdf/2309.05058.pdf" rel="nofollow">https://arxiv.org/pdf/2309.05058.pdf</a>) .</p>
The effect of a 6-month tablet-based multimodal training for cognitive functioning in Alzheimer's disease in a randomized controlled trial
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VISEM: A Multimodal Video Dataset of Human Spermatozoa
<p>Real multimedia datasets that contain more than just images or text are rare. Even more so are open multimedia datasets in medicine. Often, clinically related datasets only consist of image or videos. We present a dataset that is novel in two ways. Firstly, it is a multi-modal dataset containing different data sources such as videos, biological analysis data, and participant data. Secondly, it is the first dataset of that kind in the field of human reproduction. It consists of anonymized data from 85 different participants. We hope this dataset will inspire people to apply their knowledge in this important field, generate shareable results in the domain, and ultimately improve human infertility investigation and treatment.</p>
Bottom-up anytime discovery of generalised multimodal graph patterns for knowledge graphs
<p>These resources were generated during the research performed for a research paper with title "Bottom-up anytime discovery of generalised multimodal graph patterns for knowledge graphs", submitted in 2024. The research was performed by Xander Wilcke (first author), Rick Mourits, Auke Rijpma, and Richard Zijdeman, and was funded by the NWO CLARIAH-PLUS.</p> <p>File list</p> <p>This is the dataset that was used as input to our experiments. It contains the civil records of 100,000 Dutch citizen in RDF format (gzipped N-Triples), and was randomly sampled from the nationwide civil registry. All individuals in this dataset have been deceased for at least 100 years.</p> <p> - dataset_sample-100000.nt.gz</p> <p>This data was generated during the experiments. In other words, these are the (unfiltered) patterns that were founds during our research, and which are thus present in the 100,000 people sample that was used as input. The patterns are encoded as RDF (N-Triples) with provenance and metadata. </p> <p> - socchist-100K_output.tar.xz</p> <p>This file contains the results from the survey (cleaned) held during the evaluation of our research, and lists the Likert scores and free-form responses from the participants of the survey. The responses have been anonymised.</p> <p> - survey_results_240713_cleaned.csv</p>
Multimodal transcriptomics reveal neurogenic aging trajectories and age-related regional inflammation in the dentate gyrus
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Generating two-mode squeezing with multimode measurement-induced nonlinearity
<p>The zip file includes the data on which the figures of <a href="https://iopscience.iop.org/article/10.1088/2399-6528/abeec2">Journal of Physics Communications 5, 045002 (2021)</a> ( <a href="https://doi.org/10.1088/2399-6528/abeec2">https://doi.org/10.1088/2399-6528/abeec2</a> ) are based and a sample plot file for Figure 1.</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.