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901 results for “Modality”
Multi-modal Intelligent Diagnosis System for Multiple Ophthalmic Diseases
ClinicalTrials.gov study NCT07143851. IPD Sharing: NO. Countries: 0. Publications: 0.
Multi-modal control of dendritic cell functions by nociceptors
GEO Series GSE217503. Mus musculus. 104 samples. Type: Expression profiling by high throughput sequencing.
DISTRIBUTED ANOMALY DETECTION USING SATELLITE DATA FROM MULTIPLE MODALITIES
DISTRIBUTED ANOMALY DETECTION USING SATELLITE DATA FROM MULTIPLE MODALITIES KANISHKA BHADURI*, KAMALIKA DAS**, AND PETR VOTAVA*** Abstract. There has been a tremendous increase in the volume of Earth Science data over the last decade from modern satellites, in-situ sensors and different climate models. All these datasets need to be co-analyzed for finding interesting patterns or for searching for extremes or outliers. Information extraction from such rich data sources using advanced data mining methodologies is a challenging task not only due to the massive volume of data, but also because these datasets ate physically stored at different geographical locations. Moving these petabytes of data over the network to a single location may waste a lot of bandwidth, and can take days to finish. To solve this problem, in this paper, we present a novel algorithm which can identify outliers in the global data without moving all the data to one location. The algorithm is highly accurate (close to 99%) and requires centralizing less than 5% of the entire dataset. We demonstrate the performance of the algorithm using data obtained from the NASA MODerate-resolution Imaging Spectroradiometer (MODIS) satellite images.
VEGA1 DUST MASS SPECTROMETER MODAL DATA V1.0
The data from MPI for this dataset were received as text files each containing spectra of a single instrument mode (there were several files for most modes). These spectra were reformatted into binary tables, and all spectra from each mode were combined into a single file. The original order of the spectra has been preserved. Spacecraft time, relative to switch-on of the instrument is specified as 1 clock tick = 0.11852 seconds. The exact equation is:
Distributed Anomaly Detection Using Satellite Data From Multiple Modalities
There has been a tremendous increase in the volume of Earth Science data over the last decade from modern satellites, in-situ sensors and different climate models. All these datasets need to be co-analyzed for finding interesting patterns or for searching for extremes or outliers. Information extraction from such rich data sources using advanced data mining methodologies is a challenging task not only due to the massive volume of data, but also because these datasets are physically stored at different geographical locations. Moving these petabytes of data over the network to a single location may waste a lot of bandwidth, and can take days to finish. To solve this problem, in this paper, we present a novel algorithm which can identify outliers in the global data without moving all the data to one location. The algorithm is highly accurate (close to 99%) and requires centralizing less than 5% of the entire dataset. We demonstrate the performance of the algorithm using data obtained from the NASA MODerate-resolution Imaging Spectroradiometer (MODIS) satellite images.
VEGA2 PUMA DUST MASS SPECTROMETER MODAL DATA V1.0
The data from MPI for this dataset were received as text files each containing spectra of a single instrument mode (there were several files for most modes). These spectra were reformatted into binary tables, and all spectra from each mode were combined into a single file. The original order of the spectra has been preserved. Spacecraft time, relative to switch-on of the instrument is specified as 1 clock tick = 0.11852 seconds. The exact equation is:
Multi-modal transcriptional and chromatin accessibility analysis of brains from mice flown on the RR-3 mission
The Rodent Research-3 (RR-3) mission was sponsored by the pharmaceutical company Eli Lilly and Co. and the Center for the Advancement of Science in Space to study the effectiveness of a potential countermeasure for the loss of muscle and bone mass that occurs during spaceflight. Twenty BALB/c, 12-weeks old female mice (ten controls and ten treated) were flown to the ISS and housed in the Rodent Habitat for 39-42 days. Twenty mice of similar age, and matching sex and strain were used for ground controls housed in identical hardware and matching ISS environmental conditions. Basal controls were housed in standard vivarium cages. Spaceflight, ground controls and basal groups had blood collected, then were euthanized, had one hind limb removed, and finally whole carcasses were stored at -80 C until dissection. All mice in this data set received only the control/sham injection. Brain samples from three flight and three ground control animal groups were cut in half between hemispheres. One hemisphere of each brain was used for generating spatially resolved transcriptional profiling data. Hemispheres were cryosectioned so that 2 consecutive sections from the hippocampus of each brain was placed on Visium Gene Expression arrays. Samples were fixed, stained with Hematoxylin and Eosin and imaged. Imaging was followed by tissue permeabilization to release mRNA molecules from cells for capture onto the array surface. Subsequently, following the 10XGenomics Visium Gene Expression protocol, Spatial Transcriptomics RNA-seq libraries were prepared and sequenced. The other hemisphere of each brain was used for single nuclei RNA-seq and ATAC-seq using the 10X Multiome protocol. In addition, bulk RNA-seq (ribodepleted, target depth of 60 M clusters, PE 150 bp) was performed from a pool of RNA extracted from 10-20 sections from each of 3 flight and 3 ground control samples.
Fate restricted stromal fibroblasts and adipocytes demonstrate multi-modal responses to tissue injury
GEO Series GSE175650. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
scHiCAR: a tri-modal single-cell genomics technology for integrated transcriptome, epigenome, and 3D genome analysis in complex tissue [mouse_brain_bulk_HiCAR]
GEO Series GSE267118. Mus musculus. 9 samples. Type: Other.
scHiCAR: a tri-modal single-cell genomics technology for integrated transcriptome, epigenome, and 3D genome analysis in complex tissues [mouse_brain_scHiCAR_2]
GEO Series GSE267126. Mus musculus. 80 samples. Type: Expression profiling by high throughput sequencing; Other.
Integrated multi-modal analysis reveals tumor and immune characteristics that distinguish Epstein-Barr virus-positive and -negative B cell post-transplant lymphoproliferative disorders [scRNA-seq]
GEO Series GSE279989. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Multi-modal Spatial Cellular Taxonomy of Human Hippocampus Reveals Region-Specific Activity and States
GEO Series GSE214144. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
scHiCAR: a tri-modal single-cell genomics technology for integrated transcriptome, epigenome, and 3D genome analysis in complex tissues [cellline_scHiCAR]
GEO Series GSE267117. Homo sapiens; Mus musculus. 34 samples. Type: Other.
scHiCAR: a tri-modal single-cell genomics technology for integrated transcriptome, epigenome, and 3D genome analysis in complex tissues [mouse_brain_scHiCAR_1]
GEO Series GSE305439. Mus musculus. 4 samples. Type: Other.
Multi-modal analysis of Col1a1-GFP in vitro vs in vivo state
GEO Series GSE198185. Mus musculus. 42 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Integrated multi-modal analysis reveals tumor and immune characteristics that distinguish Epstein-Barr virus-positive and -negative B cell post-transplant lymphoproliferative disorders
GEO Series GSE279991. Homo sapiens. 13 samples. Type: Expression profiling by high throughput sequencing; Expression profiling by array.
T1DiabetesGranada: a longitudinal multi-modal dataset of type 1 diabetes mellitus
<h2><strong>T1DiabetesGranada</strong></h2> <p>A longitudinal multi-modal dataset of type 1 diabetes mellitus</p> <p>Documented by:</p> <p><strong>Rodriguez-Leon, C., Aviles-Perez, M. D., Banos, O., Quesada-Charneco, M., Lopez-Ibarra, P. J., Villalonga, C., & Munoz-Torres, M. (2023). T1DiabetesGranada: a longitudinal multi-modal dataset of type 1 diabetes mellitus.<em> Scientific Data, 10(1), 916.</em> <a href="https://doi.org/10.1038/s41597-023-02737-4">https://doi.org/10.1038/s41597-023-02737-4</a></strong></p> <p> </p> <h3><strong>Background</strong></h3> <p>Type 1 diabetes mellitus (T1D) patients face daily difficulties in keeping their blood glucose levels within appropriate ranges. Several techniques and devices, such as flash glucose meters, have been developed to help T1D patients improve their quality of life. Most recently, the data collected via these devices is being used to train advanced artificial intelligence models to characterize the evolution of the disease and support its management. The main problem for the generation of these models is the scarcity of data, as most published works use private or artificially generated datasets. For this reason, this work presents T1DiabetesGranada, a open under specific permission longitudinal dataset that not only provides continuous glucose levels, but also patient demographic and clinical information. The dataset includes 257780 days of measurements over four years from 736 T1D patients from the province of Granada, Spain. This dataset progresses significantly beyond the state of the art as one the longest and largest open datasets of continuous glucose measurements, thus boosting the development of new artificial intelligence models for glucose level characterization and prediction.</p> <p> </p> <h3><strong>Data Records</strong></h3> <p>The data are stored in four comma-separated values (CSV) files which are available in <em><strong>T1DiabetesGranada.zip</strong></em>. These files are described in detail below.</p> <p> </p> <h4><strong>Patient_info.csv</strong></h4> <p><em>Patient_info.csv</em> is the file containing information about the patients, such as demographic data, start and end dates of blood glucose level measurements and biochemical parameters, number of biochemical parameters or number of diagnostics. This file is composed of 736 records, one for each patient in the dataset, and includes the following variables:</p> <p><strong>Patient_ID</strong> – Unique identifier of the patient. Format: LIB19XXXX.</p> <p><strong>Sex</strong> – Sex of the patient. Values: F (for female), masculine (for male) </p> <p><strong>Birth_year</strong> – Year of birth of the patient. Format: YYYY.</p> <p><strong>Initial_measurement_date</strong> – Date of the first blood glucose level measurement of the patient in the <em>Glucose_measurements.csv</em> file. Format: YYYY-MM-DD.</p> <p><strong>Final_measurement_date</strong> – Date of the last blood glucose level measurement of the patient in the <em>Glucose_measurements.csv</em> file. Format: YYYY-MM-DD.</p> <p><strong>Number_of_days_with_measures</strong> – Number of days with blood glucose level measurements of the patient, extracted from the <em>Glucose_measurements.csv</em> file. Values: ranging from 8 to 1463.</p> <p><strong>Number_of_measurements</strong> – Number of blood glucose level measurements of the patient, extracted from the <em>Glucose_measurements.csv</em> file. Values: ranging from 400 to 137292.</p> <p><strong>Initial_biochemical_parameters_date</strong> – Date of the first biochemical test to measure some biochemical parameter of the patient, extracted from the <em>Biochemical_parameters.csv</em> file. Format: YYYY-MM-DD.</p> <p><strong>Final_biochemical_parameters_date</strong> – Date of the last biochemical test to measure some biochemical parameter of the patient, extracted from the <em>Biochemical_parameters.csv</em> file. Format: YYYY-MM-DD.</p> <p><strong>Number_of_biochemical_parameters</strong> – Number of biochemical parameters measured on the patient, extracted from the <em>Biochemical_parameters.csv</em> file. Values: ranging from 4 to 846.</p> <p><strong>Number_of_diagnostics</strong> – Number of diagnoses realized to the patient, extracted from the <em>Diagnostics.csv</em> file. Values: ranging from 1 to 24.</p> <p> </p> <h4><strong>Glucose_measurements.csv</strong></h4> <p><em>Glucose_measurements.csv</em> is the file containing the continuous blood glucose level measurements of the patients. The file is composed of more than 22.6 million records that constitute the time series of continuous blood glucose level measurements. It includes the following variables:</p> <p><strong>Patient_ID</strong> – Unique identifier of the patient. Format: LIB19XXXX.</p> <p><strong>Measurement_date</strong> – Date of the blood glucose level measurement. Format: YYYY-MM-DD. </p> <p><strong>Measurement_time</strong> – Time of the blood glucose level measurement. Format: HH:MM:SS.</p> <p><strong>Measurement</strong> – Value of the blood glucose level measurement in mg/dL. Values: ranging from 40 to 500.</p> <p> </p> <h4><strong>Biochemical_parameters.csv</strong></h4> <p><em>Biochemical_parameters.csv</em> is the file containing data of the biochemical tests performed on patients to measure their biochemical parameters. This file is composed of 87482 records and includes the following variables:</p> <p><strong>Patient_ID</strong> – Unique identifier of the patient. Format: LIB19XXXX.</p> <p><strong>Reception_date</strong> – Date of receipt in the laboratory of the sample to measure the biochemical parameter. Format: YYYY-MM-DD.</p> <p><strong>Name</strong> – Name of the measured biochemical parameter. Values: 'Potassium', 'HDL cholesterol', 'Gammaglutamyl Transferase (GGT)', 'Creatinine', 'Glucose', 'Uric acid', 'Triglycerides', 'Alanine transaminase (GPT)', 'Chlorine', 'Thyrotropin (TSH)', 'Sodium', 'Glycated hemoglobin (Ac)', 'Total cholesterol', 'Albumin (urine)', 'Creatinine (urine)', 'Insulin', 'IA ANTIBODIES'.</p> <p><strong>Value</strong> – Value of the biochemical parameter. Values: ranging from -4.0 to 6446.74.</p> <p> </p> <h4><strong>Diagnostics.csv</strong></h4> <p><em>Diagnostics.csv</em> is the file containing diagnoses of diabetes mellitus complications or other diseases that patients have in addition to type 1 diabetes mellitus. This file is composed of 1757 records and includes the following variables:</p> <p><strong>Patient_ID</strong> – Unique identifier of the patient. Format: LIB19XXXX.</p> <p><strong>Code</strong> – ICD-9-CM diagnosis code. Values: subset of 594 of the ICD-9-CM codes (https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes).</p> <p><strong>Description</strong> – ICD-9-CM long description. Values: subset of 594 of the ICD-9-CM long description (<a href="https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes">https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes</a>).</p> <p> </p> <h3><strong>Technical Validation</strong></h3> <p>Blood glucose level measurements are collected using FreeStyle Libre devices, which are widely used for healthcare in patients with T1D. Abbott Diabetes Care, Inc., Alameda, CA, USA, the manufacturer company, has conducted validation studies of these devices concluding that the measurements made by their sensors compare to YSI analyzer devices (Xylem Inc.), the gold standard, yielding results of 99.9% of the time within zones A and B of the consensus error grid. In addition, other studies external to the company concluded that the accuracy of the measurements is adequate.</p> <p>Moreover, it was also checked in most cases the blood glucose level measurements per patient were continuous (i.e. a sample at least every 15 minutes) in the <em>Glucose_measurements.csv </em>file as they should be.</p> <p> </p> <h3><strong>Usage Notes</strong></h3> <p>For data downloading, it is necessary to be authenticated on the Zenodo platform, accept the Data Usage Agreement and send a request specifying full name, email, and the justification of the data use. This request will be processed by the Secretary of the Department of Computer Engineering, Automatics, and Robotics of the University of Granada and access to the dataset will be granted.</p> <p>The files that compose the dataset are CSV type files delimited by commas and are available in <em><strong>T1DiabetesGranada.zip</strong></em>. A Jupyter Notebook (Python v. 3.8) with code that may help to a better understanding of the dataset, with graphics and statistics, is available in <em><strong>UsageNotes.zip</strong></em>.</p> <p> </p> <h4><strong>Graphs_and_stats.ipynb</strong></h4> <p>The Jupyter Notebook generates tables, graphs and statistics for a better understanding of the dataset. It has four main sections, one dedicated to each file in the dataset. In addition, it has useful functions such as calculating the patient age, deleting a patient list from a dataset file and leaving only a patient list in a dataset file.</p> <p> </p> <h3><strong>Code Availability</strong></h3> <p>The dataset was generated using some custom code located in <em><strong>CodeAvailability.zip</strong></em>. The code is provided as Jupyter Notebooks created with Python v. 3.8. The code was used to conduct tasks such as data curation and transformation, and variables extraction.</p> <p> </p> <h4><strong>Original_patient_info_curation.ipynb</strong></h4> <p>In the Jupyter Notebook is preprocessed the original file with patient data. Mainly irrelevant rows and columns are removed, and the sex variable is recoded.</p> <p> </p> <h4><strong>Glucose_measurements_curation.ipynb</strong></h4> <p>In the Jupyter Notebook is preprocessed the original file with the continuous glucose level measurements of the patients. Principally rows without information or duplicated rows are removed and the variable with the timestamp is transformed into two new variables, measurement date and measurement time.</p> <p> </p> <h4><strong>Biochemical_parameters_curation.ipynb</strong></h4> <p>In the Jupyter Notebook is preprocessed the original file with patient data of the biochemical tests performed on patients to measure their biochemical parameters. Mainly irrelevant rows and columns are removed and the variable with the name of the measured biochemical parameter is translated.</p> <p> </p> <h4><strong>Diagnostic_curation.ipynb</strong></h4> <p>In the Jupyter Notebook is preprocessed the original file with patient data of the diagnoses of diabetes mellitus complications or other diseases that patients have in addition to T1D.</p> <p> </p> <h4><strong>Get_patient_info_variables.ipynb</strong></h4> <p>In the Jupyter Notebook it is coded the feature extraction process from the files <em>Glucose_measurements.csv</em>, <em>Biochemical_parameters.csv</em> and <em>Diagnostics.csv</em> to complete the file <em>Patient_info.csv</em>. It is divided into six sections, the first three to extract the features from each of the mentioned files and the next three to add the extracted features to the resulting new file.</p> <p> </p> <h3><strong>Data Usage Agreement</strong></h3> <p>The conditions for use are as follows:</p> <ol> <li>You confirm that you will not attempt to re-identify research participants for any reason, including for re-identification theory research.</li> <li>You commit to keeping the T1DiabetesGranada dataset confidential and secure and will not redistribute data or Zenodo account credentials.</li> <li>You will require anyone on your team who utilizes these data to comply with this Data Use Agreement by accessing the data themselves through Zenodo. Each user wishing to access controlled data must individually agree to the Conditions for Use.</li> <li>You understand that these data may not be used for commercial use or to re-contact research participants.</li> <li>You agree to report any misuse, unauthorized access, or data release, intentional or inadvertent within 5 business days. For this purpose, please write an email to icarsecretaria@ugr.es explaining the detected data breach.</li> <li>You agree to cite the research paper under which T1DiabetesGranada dataset was published on all publications or presentations which result from using the T1DiabetesGranada dataset.</li> </ol> <p> </p> <p><strong>Please note that in order to request access to the dataset you need to be authenticated on Zenodo, accept the Data Usage Agreement and specify in the request your full name, email, and the justification of the data use.</strong> </p>
Machining Simulated Data, Modal 0, 6 Cutting Teeth
<p>This is a simulated data set for machining data based on the work done by T. Schmitz, K. Smith, <em>Machining Dynamics: Frequency Response to Improved Productiv</em><em>ity,</em> Second Edition (Springer, New York, NY, 2019).</p> <p>This is Modal 0, with 6 cutting teeth across forces from 600-800</p>
Machining Simulated Data, Modal 0, 3 Cutting Teeth
<p>This is a simulated data set for machining data based on the work done by T. Schmitz, K. Smith, <em>Machining Dynamics: Frequency Response to Improved Productiv</em><em>ity,</em> Second Edition (Springer, New York, NY, 2019).</p> <p>This is Modal 0, with 3 cutting teeth acorss forces from 600-800</p>
Machining Simulated Data, Modal 0, 2 Cutting Teeth
<p>This is a simulated data set for machining data based on the work done by T. Schmitz, K. Smith, <em>Machining Dynamics: Frequency Response to Improved Productiv</em><em>ity,</em> Second Edition (Springer, New York, NY, 2019).</p> <p>This is Modal 0, with 2 cutting teeth across forces from 600-800</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.