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19 results for “multisensor”
Dataset: MultiSensor AI Holdings, Inc. (MSAIW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: MultiSensor AI Holdings, Inc. (MSAI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Multisensor measurement of healthy adult performance during standardised motor function test battery
<p>This dataset contains inertial data from 4 wearable sensor nodes and 1 wearable patch worn by 20 healthy adult participants performing a series of physical functioning tests (including the short physical performance battery, the timed up and go test, a walking test and balance tests). Details of patient demographics, the physical functioning tests and of each sensor are contained in files in the main folder.</p> <p>Inertial data (accelerometer and gyroscope) is contained in two folders relating to each sensor type. The start and end time for each sensor can be taken from the details in each folder structure, as detailed below. The times given are specific to each sensor's monitoring system which are not exactly synchronised. As such, a manual synchronisation shaking protocol was followed where all sensors were strapped together and shaken three times in succession at the start of each data collection period. The physical functioning test times will also need to be synchronised.</p> <p>-> Inertial sensor data / (subject id).zip / (subject id) / (date_time_crossTest_SD_session#) /<br> -> Wearable inertial patch / (subject id) / (date)T(time) /</p>
Multisensor Advanced Climatology (MAC) Daily Ocean-Only Surface Wind Speed
<p>Multisensor Advanced Climatology (MAC) of Over-Ocean Surface Wind produced using the MAC-LWP algorithm (Elsaesser et al., 2017, J. Climate), but with Remote Sensing Systems surface wind products swapped in for liquid water path products, and with the product being provided at daily resolution instead of at monthly resolution. Available for 1 deg grid boxes, from 1998 - 2016. Data in grid-boxes over land are set to missing. Uncertainty estimates also provided, following Elsaesser et al. 2017. </p> <p>Reference:</p> <p>Elsaesser, G.S., C.W. O'Dell, M.D. Lebsock, R. Bennartz, and T.J. Greenwald, 2017: The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP). J. Climate, <strong>30</strong>, no. 24, 10193-10210, doi:10.1175/JCLI-D-16-0902.1.</p>
CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection
<p>In this work, we present the CAS Landslide Dataset, a large-scale and multisensor dataset for deep learning-based landslide detection, developed by the Artificial Intelligence Group at the Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS). The dataset aims to address the challenges encountered in landslide recognition. With the increase in landslide occurrences due to climate change and earthquakes, there is a growing need for a precise and comprehensive dataset to support fast and efficient landslide recognition. In contrast to existing datasets with dataset size, coverage, sensor type and resolution limitations, the CAS Landslide Dataset comprises 20,865 images, integrating satellite and unmanned aerial vehicle data from nine regions. To ensure reliability and applicability, we establish a robust methodology to evaluate the dataset quality. We propose the use of the Landslide Dataset as a benchmark for the construction of landslide identification models and to facilitate the development of deep learning techniques. Researchers can leverage this dataset to obtain enhanced prediction, monitoring, and analysis capabilities, thereby advancing automated landslide detection.</p> <p>If you use our data, please cite our work published in Scientific Data.</p> <p>Xu, Y., Ouyang, C., Xu, Q. <em>et al.</em> CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection. <em>Sci Data</em> <strong>11</strong>, 12 (2024). https://doi.org/10.1038/s41597-023-02847-z</p>
Multisensor monitoring data of Hochebenkar Rock Glacier
<p>HRG_DSM_stack.tif: Area-wide monitoring time series of Hochebenkar Rock Glacier (Oetz valley, Tyrol, Austria) based on multitemporal digital surface models (DSMs) derived from</p> <ul> <li>photogrammetry using historical arerial imagery (1953, 1971, 1977, 1990, 1997)</li> <li>airborne laser scanning (2006, 2009, 2010, 2011, 2017)</li> <li>and unmanned aerial vehicle-based laser scanning (2018, 2019, 2020, 2021).</li> </ul> <p>The airborne laser scanning data from 2006 and 2017 were provided by the Federal Government of Tyrol and are licenced under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/deed.de) and must be used according to the following conditions of use: https://www.tirol.gv.at/data/nutzungsbedingungen/</p> <p>HRG_HS_stack.tif: Shaded reliefs computed from the multitemporal DSMs, which were used for applying the image correlation technique to derive displacement vectors.</p> <p>HRG_DDSM_stack.tif: Differential digital surface models computed by subtracting the subsequent DSMs. The uncertainty of the individual DDSMs are masked.</p> <p>Shapefiles in HRG_velocity_vectors.zip: Mean velocity vectors (m/yr) computed from the displacement vectors by dividing the vector length by the time period between the acquisition campaigns.</p> <p>All datasets are provided in the Austrian GK West projection (EPSG 31254). The spatial resolution of the raster datasets is 1 metre.</p>
Evaluation of Multisensor Data in Heart Failure Patients With Implanted Devices
ClinicalTrials.gov study NCT01128166. IPD Sharing: Not stated. Countries: 12. Publications: 4.
Effectiveness of Multisensoral Nature-based Intervention in Hospitalized Children During Venous Blood Sampling
ClinicalTrials.gov study NCT05494684. IPD Sharing: NO. Countries: 1. Publications: 7.
AIDI - Research & Development of a Multisensor-Based Machine Learning Technology for Real-Time Automated Detection of COVID-19 Decompensation
ClinicalTrials.gov study NCT05220306. IPD Sharing: NO. Countries: 1. Publications: 1.
Link-HF: Multisensor Non-invasive Telemonitoring System for Prediction of Heart Failure Exacerbation
ClinicalTrials.gov study NCT03037710. IPD Sharing: NO. Countries: 1. Publications: 1.
Using Data From a Multisensor Rapid Health Assessment Device to Predict Decompensation in Long COVID (AIDI)
ClinicalTrials.gov study NCT05713266. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Multisensor Array for the Localisation of Coronary Artery Stenosis (MALCAS)
ClinicalTrials.gov study NCT04647448. IPD Sharing: NO. Countries: 0. Publications: 5.
Multisensor Advanced Climatology Mean Liquid Water Path L3 Monthly 1 degree X 1 degree V1 (MACLWP_mean) at GES DISC
The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP) data set contains monthly 1.0-degree ocean-only estimates of cloud liquid water path (MACLWP_mean), total water path (MACTWP_mean) which includes both cloud and rain water, and monthly climatologies of cloud liquid water path diurnal cycle amplitudes and phases (MACLWP_diurnal). The MACTWP_mean field can also be used as a quality-control screen for the MACLWP_mean field as discussed in Elsaesser et al. (2017), where uncertainty increases as the ratio of cloud to total water path increases. The MAC-LWP algorithm uses as input the Remote Sensing Systems (RSS) Version 7 0.25 degree-resolution retrieval products (produced using the SSM/I, AMSR-E, TMI, AMSR-2, GMI, SSMIS, and WindSat satellite sensors), and performs a bias correction on all input RSS cloud water path products based on AMSR-E matchups to clear-sky MODIS scenes. The MAC-LWP algorithm ensures that spurious trends and variability in the cloud fields arising from drifting satellite overpass times are mitigated by simultaneously solving for the monthly average cloud and total water paths and monthly-mean diurnal cycles, as discussed in O’Dell et al. (2008). Additional details on the algorithm and data fields can be found in Elsaesser et al. (2017).
Multisensor Advanced Climatology Mean Liquid Water Path Diurnal Cycle L3 Monthly 1 degree x 1 degree V1 (MACLWP_diurnal) at GES DISC
The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP) data set contains monthly 1.0-degree ocean-only estimates of cloud liquid water path (MACLWP_mean), total water path (MACTWP_mean) which includes both cloud and rain water, and monthly climatologies of cloud liquid water path diurnal cycle amplitudes and phases (MACLWP_diurnal). The MACTWP_mean field can also be used as a quality-control screen for the MACLWP_mean field as discussed in Elsaesser et al. (2017), where uncertainty increases as the ratio of cloud to total water path increases. The MAC-LWP algorithm uses as input the Remote Sensing Systems (RSS) Version 7 0.25 degree-resolution retrieval products (produced using the SSM/I, AMSR-E, TMI, AMSR-2, GMI, SSMIS, and WindSat satellite sensors), and performs a bias correction on all input RSS cloud water path products based on AMSR-E matchups to clear-sky MODIS scenes. The MAC-LWP algorithm ensures that spurious trends and variability in the cloud fields arising from drifting satellite overpass times are mitigated by simultaneously solving for the monthly average cloud and total water paths and monthly-mean diurnal cycles, as discussed in O’Dell et al. (2008). Additional details on the algorithm and data fields can be found in Elsaesser et al. (2017).
Multisensor Advanced Climatology Total Liquid Water Path L3 Monthly 1 degree x 1 degree V1 (MACTWP_mean) at GES DISC
The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP) data set contains monthly 1.0-degree ocean-only estimates of cloud liquid water path (MACLWP_mean), total water path (MACTWP_mean) which includes both cloud and rain water, and monthly climatologies of cloud liquid water path diurnal cycle amplitudes and phases (MACLWP_diurnal). The MACTWP_mean field can also be used as a quality-control screen for the MACLWP_mean field as discussed in Elsaesser et al. (2017), where uncertainty increases as the ratio of cloud to total water path increases. The MAC-LWP algorithm uses as input the Remote Sensing Systems (RSS) Version 7 0.25 degree-resolution retrieval products (produced using the SSM/I, AMSR-E, TMI, AMSR-2, GMI, SSMIS, and WindSat satellite sensors), and performs a bias correction on all input RSS cloud water path products based on AMSR-E matchups to clear-sky MODIS scenes. The MAC-LWP algorithm ensures that spurious trends and variability in the cloud fields arising from drifting satellite overpass times are mitigated by simultaneously solving for the monthly average cloud and total water paths and monthly-mean diurnal cycles, as discussed in O’Dell et al. (2008). Additional details on the algorithm and data fields can be found in Elsaesser et al. (2017).
A multisensor high-temperature signaling framework for triggering daytime thermomorphogenesis in Arabidopsis
GEO Series GSE275012. Arabidopsis thaliana. 18 samples. Type: Expression profiling by high throughput sequencing.
Multisensor Technology for Beat to Beat Fetal Heart Rate Measurement
ClinicalTrials.gov study NCT03741569. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Detecting Syncope by an Integrated Multisensor Patch-type Recorder
ClinicalTrials.gov study NCT05782699. IPD Sharing: YES. Countries: 1. Publications: 0.
Multisensor Analyzed Sea Ice Extent - Northern Hemisphere (MASIE-NH), Version 1
The Multisensor Analyzed Sea Ice Extent - Northern Hemisphere (MASIE-NH) products provide measurements of daily sea ice extent and sea ice edge boundary for the Northern Hemisphere and 16 Arctic regions in a polar stereographic projection at both 1 km and 4 km grid cell sizes. MASIE products include an ASCII text file of sea ice extent values in square km for each Arctic region, time series plots, and image files that visually show where the sea ice is.Note: MASIE may look like several other sea ice products distributed at NSIDC and elsewhere, but its source data from the U.S. National Ice Center (USNIC) and intended uses are different. If intended and appropriate uses of the data are not clear after reading the documentation, please contact <a href="mailto:nsidc@nsidc.org">NSIDC User Services</a>.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.