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16
datasets available to search
ShareScore release 0.9.0
Dataset results
16 results for “Random variables”
Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study'
<p>Data for Project 'Test-Retest Reliability and Validity of vagally-mediated Heart Rate Variability to Monitor Internal Training Load in Older Adults: A within-subjects (repeated-measures) randomized study' consisting of (1) the original and complete dataset ('Data_Brain-IT-Reliability-of-HRV-during-Exergaming_for-publication'; and (2) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
Dataset and source code for "Explanation and optimizing multi-model blending algorithm using random variables theory"
<p>this dataset contain: </p> <ol> <li>2m temperature de-biased model forecast data on station location, ECMWF, NCEP, JP and CMA</li> <li>2m temperature observaton data, obs_t2m</li> <li>24H QPF model forecast data on station location, ECMWF, NCEP, CMA-GFS, in raw_data_r24.zip</li> <li>24H precipitation data, in raw_data_r24.zip</li> <li>source code (in python)</li> </ol> <p> </p> <p>how to use it: </p> <p>1. prepare data and python environment<br> 1.1 if you want to run [Station_FCST_MMWB.py] or [Station_FCST_MMWB_r24.py] , please download the station forecast and observation data<br> 1.2 neet meteva package to read/write micaps-3 format data: https://github.com/nmcdev/meteva<br> 1.3 need cartopy to draw picture FigS01. </p> <p>2. try the 2m temperature blending methods <optional><br> 2.1 unzip the [CMA.zip, ECMWF.zip, jp.zip, NCEP.zip, obs_t2m.zip] file into ./raw_data/<br> 2.2 run the Station_FCST_MMWB.py in python environment </p> <p>3. try the 24h QPF multi blending methods <optional><br> 3.1 unzip the [raw_data_r24.zip] file into ./raw_data_r24/<br> 3.2 run the Station_FCST_MMWB_r24.py in python environment</p> <p>4. draw figures<br> 4.1 run Fig01.py in python environment <br> 4.2 run Fig02.py in python environment <br> 4.3 run Fig03.py in python environment <br> 4.4 run FigA01.py in python environment <br> 4.5 run FigS01.py in python environment </p>
Full-coverage 1 km daily ambient PM2.5 and O3 concentrations of China in 2005-2017 based on multi-variable random forest model
<p>The aim of our study was to construct random forest models with high-performance, and estimate daily average PM<sub>2.5</sub> concentration and O<sub>3</sub> daily maximum 8h average concentration (O<sub>3</sub>-8hmax) of China in 2005-2017 at a spatial resolution of 1km×1km. The model variables included meteorological variables, satellite data, chemical transport model output, geographic variables and socioeconomic variables. Random forest model based on ten-fold cross validation was established, and spatial and temporal validations were performed to evaluate the model performance. According to our sample-based division method, the daily, monthly and yearly simulations of PM<sub>2.5</sub> gave average model fitting R<sup>2</sup> values of 0.85, 0.88 and 0.90, respectively; these R<sup>2</sup> values were 0.77, 0.77, and 0.69 for O<sub>3</sub>-8hmax, respectively. The meteorological variables and their lagged values can significantly affect both PM<sub>2.5</sub> and O<sub>3</sub>-8hmax simulations. During 2005-2017, PM<sub>2.5</sub> exhibited an overall downward trend, while ambient O<sub>3</sub> experienced an upward trend. Whilst the spatial patterns of PM<sub>2.5</sub> and O<sub>3</sub>-8hmax barely changed between 2005 and 2017, the temporal trend had spatial characteristic.</p> <p>Each dataset is the annual mean concentration of PM<sub>2.5</sub> or O<sub>3</sub>-8hmax based on the standard grid (Grid.csv) for that year. The coordinate system of the grid is WGS-84.</p>
Data for Bradter, Altringham, Kunin, Thom, O'Connell & Benton: Variable ranking and selection with random forest for unbalanced data. Environmental Data Science
<p>The data are used in 'Bradter, Altringham, Kunin, Thom, O'Connell & Benton: Variable ranking and selection with random forest for unbalanced data. Environmental Data Science' and are described in the ReadMe file and in the manuscript and Supporting information.</p>
Heart Rate Variability Biofeedback for Substance Use Disorder: A Randomized Clinical Trial
ClinicalTrials.gov study NCT05454657. IPD Sharing: NO. Countries: 1. Publications: 1.
Effect of hyperparameters on variable selection in random forests
<p>These are data used to generate the results presented in simulation studies conducted in Fouodo et al. 2023. Each dataset is an R object in RDS format with 100 lists. For each element of the list, parameters used to generate the data are presented, followed by the simulated data. <a href="https://github.com/imbs-hl/RF-hyperparameters-and-variable-selection">Here</a> is the git repository of the R code used to conduct simulations in the manuscript. The files <a href="https://github.com/imbs-hl/RF-hyperparameters-and-variable-selection/tree/main/R-code/01-scenario1">01-data-only.R</a> and <a href="https://github.com/imbs-hl/RF-hyperparameters-and-variable-selection/tree/main/R-code/02-scenario2">02-data-only.R</a> contain the functions and more details about how the data have been simulated for studies 1 and 2.</p>
The Effect of Energy Drink on Cardiovascular Variables: A Randomized Controlled Trial
ClinicalTrials.gov study NCT03841838. IPD Sharing: NO. Countries: 1. Publications: 6.
Randomized Control Trial to Compare Standard Monitoring and Plethysmographic Variability Index-Guided Fluid Therapy In 64 Severe Traumatic Brain Injury Patients, Aged 18 to 60 Years Old Undergoing Eme
ClinicalTrials.gov study NCT04664699. IPD Sharing: NO. Countries: 1. Publications: 15.
Effects of Inspiratory Muscle Warm-up on Respiratory Variables, Physiological Indices, and 400-meter Performance in Elite Male Runners - Randomized Crossover Trial
ClinicalTrials.gov study NCT06886503. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Effect of Brain Training Through Visual Mirror Feedback, Action Observation Training and Motor Imagery on Orofacial Sensorimotor Variables in Asymptomatic Subjects: A Single-blind Randomized Controlle
ClinicalTrials.gov study NCT04021147. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Effect of Sensory Integration Therapy on Gait Variability and Quality Of Life in Patients With Multiple Sclerosis: A Randomized Controlled Trial
ClinicalTrials.gov study NCT07390201. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Perfusion Index and Pleth Variability Index an Early Indicator of the Success of Brachial Plexus Block; Randomized Clinical Triall
ClinicalTrials.gov study NCT05208632. IPD Sharing: NO. Countries: 1. Publications: 0.
Short-term Effects of Osteopathic Manipulations on Heart Rate Variability in Lung Cancer Patients - A Randomized Pilot Study
ClinicalTrials.gov study NCT06822348. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Randomized Controlled Trial of Treatment to Optimize Heart Rate Variability for Persistent Post-Concussion Symptoms
ClinicalTrials.gov study NCT07071350. IPD Sharing: NO. Countries: 1. Publications: 0.
Randomized Study of Polyethylene-Glycol-Conjugated Interleukin 2 in Patients With Common Variable Immunodeficiency
ClinicalTrials.gov study NCT00004695. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Randomized Battery Usage 3: Room Temperature Variable Recharge Random Walk
This dataset is part of a series of datasets, where batteries are continuously cycled with randomly generated current profiles. Reference charging and discharging cycles are also performed after a fixed interval of randomized usage to provide reference benchmarks for battery state of health. In this dataset, four 18650 Li-ion batteries (Identified as RW1, RW2, RW7 and RW8) were continuously operated by repeatedly discharging them to 3.2V using a randomized sequence of discharging currents between 0.5A and 4A. This type of discharging profile is referred to here as random walk (RW) discharging. After each discharging cycle the batteries were charged for a randomly selected duration between 0.5 hours and 3 hours. After every fifty RW cycles a series of reference charging and discharging cycles were performed in order to provide reference benchmarks for battery state health.
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.