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10,006 results for “randomness”
Repeatable randomness, invariant properties, and the design of biological signatures of identity
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Non-random mating within an island rookery of Hawaiian hawksbill turtles: demographic discontinuity at a small coastline scale
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Randomized controlled clinical trials with tagged information regarding the number of participants
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Randomized controlled oncology trials with tumor stage inclusion criteria
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Random forest climatic modeling of agricultural insurance loss across the inland Pacific Northwest region of the United States
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Data from: Predicting primate-parasite associations with exponential random graph models
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Code from: Enhancing data-limited assessments with random effects: A case study on Korea chub mackerel (Scomber japonicus)
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ASTRAL-MP: scaling ASTRAL to very large datasets using randomization and parallelization
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The effects of a clinically feasible application of low-level laser therapy on the rate of orthodontic tooth movement: A triple-blinded split-mouth randomized controlled clinical trial.
<p>Dataset for all study's analyses.</p>
CLIC Calorimeter 3D images: Photon showers at Random Angle
<p>Energy deposits from single-particle showers in the ECAL+HCAL calorimeters of the CLIC detector</p> <p>Simulation performed with GEANT4 (https://geant4.web.cern.ch) and DD4HEP software (https://dd4hep.web.cern.ch/dd4hep/)</p> <p>Photons entering the detector at variable energy and direction</p> <p>See https://arxiv.org/abs/1912.06794 for details</p>
Serial correlation of temporal pain intensities with beta-endorphin and meta-enkephalin concentrations, following Transcutaneous Electrical Nerve Stimulation among patients with chronic low back pain: a randomized controlled trial
<p>This is a dataset for the research that evaluate the responses of plasma beta-endorphin (βE), met-enkephalin (ME) and pain intensity (PI) among patients with chronic low back pain exposed to transcutaneous electrical nerve stimulation (TENS) = Group 1 or sham-TENS = Group 2.</p> <p>The dataset codes: TENS group = 1, Sham-TENS = 2, Sex: male = 1, female = 2, Baseline = before trial, Zero_Hr = immediately after trial, One_Hr = an hour after trial, TwentyFour_Hrs = 24 hrs after trial, FortyEight_Hrs = 48 hrs after trial.</p> <p>Variables labels: group, sex, age, pain duration, systolic blood pressure (SBP), diastolic blood pressure (DBP), resting heart rate (RHR), body temperature (Temp), beta-endorphin (BE), meta-enkephalin (ME), pain intensity (PI) measured with numeric pain scale.</p> <p>To describe time series, codes were combined with variable labels E.g. TwentyFour_Hrs_BE = bete endorphin concentration after 24 hrs of intervention.</p>
Spatial Distribution of the International Food Prices: Unexpected Heterogeneity and Randomness
<p>Global <a href="https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/food-prices">food prices</a> are typically analysed in a time-series framework. We complement this approach by focusing on the spatial <a href="https://www.sciencedirect.com/topics/economics-econometrics-and-finance/price-dispersion">price dispersion</a> of the country-pair bilateral trade in the international food trade network (<em>IFTN</em>), for ten relevant commodities. The main purposes are to verify if the <a href="https://www.sciencedirect.com/topics/economics-econometrics-and-finance/price-convergence">Law of One Price</a> (<em>LOP</em>) holds and to investigate the emergence of randomness in the price-formation mechanism.</p> <p>We distinguish between the “internal” variance, which indicates the magnitude of price discrimination, and the “external” variance, that is a measure of price dispersion. We find that, for some commodities, spatial price dispersion is remarkable and persistent over time (i.e., failure of the <em>LOP</em>) and that there exists a strict correlation between price spikes and peaks in spatial price variability.</p> <p>We test whether the price distribution can be replicated through a <a href="https://www.sciencedirect.com/topics/economics-econometrics-and-finance/stochastic-process">stochastic process</a> of extraction. Surprisingly, the actual distribution of prices, for several commodities, is well described by a random distribution. Then, the process of data aggregation is not neutral because the information at the micro-level scale might be lost at the macro-scale, due to the complexity of the <em>IFTN</em>. Finally, we discuss some possible economic explanations of these outcomes and the main methodological, environmental, and policy consequences.</p>
Uniform Random SAT Samples
<p>Random SAT samples generated with the following <em>samplers</em>:</p> <ul> <li>Spur</li> <li>QuickSampler </li> <li>Unigen2</li> <li>Smarch </li> </ul>
Global soil saturated hydraulic conductivity map using random forest in a Covariate-based GeoTransfer Functions (CoGTF) framework at 1 km resolution
<p>The global Ksat map at 1 km resolution was developed by harnessing the technological advances in machine learning and availability of remotely sensed surrogate information such as terrain, climate, vegetation, and soil covariates. We merge concepts of predictive soil mapping with a large data set of Ksat measurements and local information (soil, vegetation, climate) into covariate-based “Geo Transfer Functions'' (CoGTFs) to generate global estimates of Ksat values (to highlight the impact of Geo-referenced covariates including various remote sensing maps, we use the term Geotransfer function GTF and not pedotransfer function PTF; in the latter case, typically only soil properties are used to estimate Ksat).</p> <p>The Ksat dataset is provided in GeoTIFF format. A total of 4 files that represent different soil depths (0, 30, 60, and 100 cm) are provided. The Ksat values are log-transformed (log10 Ksat) and cm/day was selected as a standardized unit.</p> <p>The Global Ksat training dataset used for this study is available here:<br> <a href="https://doi.org/10.5281/zenodo.3752721">https://doi.org/10.5281/zenodo.3752721</a></p> <p>The R code used for this study is available here:<br> <a href="https://github.com/ETHZ-repositories/Ksat_mapping_2020">https://github.com/ETHZ-repositories/Ksat_mapping_2020</a></p> <p>For more details / to cite this dataset please use:</p> <ul> <li>Gupta, S., Lehmann, P., Bonetti, S., Papritz, A., and Or, D., (2020): <strong>Global prediction of soil saturated hydraulic conductivity using random forest in a Covariate-based Geo Transfer Functions (CoGTF) framework</strong>. Journal of Advances in Modeling Earth Systems,<strong> </strong>13(4), e2020MS002242. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002242</li> </ul> <p>Other datasets related to this project:</p> <p>The Global vG training dataset is available here:</p> <p><a href="https://doi.org/10.5281/zenodo.5547338">10.5281/zenodo.5547338</a></p> <p>Examples of using this dataset to generate van Genuchten parameters maps can be found in <a href="https://doi.org/10.5281/zenodo.6343570">10.5281/zenodo.6343570</a>.</p> <p>The study was supported by ETH Zurich (Grant ETH-18 18-1). We would like to thank Zhongwang Wei, Samuel Bickel and Simone Fatichi (ETH Zurich) for insightful discussions.</p> <p> </p> <p> </p>
Switch to second-line versus continued first-line antiretroviral therapy for patients with low-level HIV-1 viremia: an open-label randomized controlled trial in Lesotho
<p>These are pseudo-anonymised data from the SESOTHO randomised trial: "Switch to second-line versus WHO-guided standard of care for unsuppressed patients on first-line ART with viremia below 1000 copies/mL – a multicenter, parallel-group, open-label, randomized clinical study in rural Lesotho". There are two datasets, one containing baseline data (one row per participant) and one containing longitudinal viral load data (one row per participant per visit). The data dictionary explains the data available in each dataset. Between August 2017 and August 2019, 80 eligible and consenting participants were enrolled from eight health facilities in four districts of Lesotho, and followed up for nine months. The protocol was published, DOI: 10.1186/s12879-018-2979-y</p>
Three‐dimensional reconstruction of porous polymer films from FIB‐SEM nanotomography data using random forests
<p>Dataset and code used in M. Röding, et al, "Three-dimensional reconstruction of porous polymer films from FIB-SEM nanotomography data using random forests", published in Journal of Microscopy, 2020. In this work, we develop a segmentation method for focused ion beam scanning electron microscopy (FIB-SEM) data acquired by volumetric imaging of porous polymer films made from ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. This type of polymer films are used for controlled release applications. Based on manual segmentation of a fraction of the data, a random forest classifier is trained and applied to the full data set. Here, raw data, manual segmentations, and the Matlab code used for all steps in the analysis are supplied.</p>
Seafloor Density Measurements, Prediction, and Associated Uncertainty for "Predicting global marine sediment density using the random forest regressor machine learning algorithm"
<p>Global seafloor density prediction results using the random forest regressor machine learning algorithm. </p> <p>Dataset S1. Seafloor density measurements. Columns are labeled with a header and include associated drilling project and measurement type for each sample. File format: CSV text file</p> <p>Dataset S2. Seafloor density prediction results from the random forest regressor machine learning algorithm at 5×5-arc minute resolution. Units are g/cm^3. File format: netCDF (.nc)</p> <p>Dataset S3. Seafloor density prediction standard deviation from the random forest regressor machine learning algorithm at 5×5-arc minute resolution. Units are g/cm^3. File format: netCDF (.nc)</p>
Data of "A network-ready random-access qubits memory"
<p>Data published in "<em>A network-ready random-access qubits memory</em>"</p> <p><em>npj Quantum Information</em></p>
Randomized trial of AKI alerts in hospitalized patients
<p><b>Objective: </b>To determine whether electronic health record (EHR) alerts for Acute Kidney Injury (AKI) would improve patient outcomes of mortality, dialysis and progression of AKI. </p> <p><b>Design: </b>Double-blinded, multicenter, parallel, randomized, controlled trial of an electronic AKI alert versus usual care (no alert). Participants were electronically identified and randomized via a best practice alert build using simple randomization with allocation concealment.</p> <p><b>Setting:</b> Six diverse hospitals (four teaching and two non-teaching) ranging from small community hospitals to large tertiary care centers.</p> <p><b>Participants:</b> 6,030 adult inpatients with AKI, as defined by the Kidney Disease: Improving Global Outcomes (KDIGO) creatinine criteria.</p> <p><b>Interventions:</b> An EHR-based "pop-up" alert for AKI with an associated AKI order set upon provider opening of the patient's medical record.</p> <p><b>Main Outcome Measures: </b>A composite of AKI progression, receipt of dialysis, or death within 14 days of randomization. Pre-specified secondary outcomes included per-hospital outcome rates and rates of various AKI care practices. </p> <p><b>Results: </b>6,030 patients were randomized over 22 months. The primary outcome occurred in 653 (21.4%) patients in the alert group and 622 (20.9%) in the usual care group (relative risk 1.02, 95% confidence interval [CI] 0.93 to 1.13, p=0.67). Per-hospital analysis revealed worse outcomes in the two non-teaching hospitals (N=765, 13%), where alerts were associated with a relative risk of the primary outcome of 1.49 (95% CI, 1.12 to 1.98, p=0.006). More deaths (15.6% in the alert group vs. 8.6% in the usual care group) occurred at these centers (p=0.003). Certain AKI care practices were increased in the alert group but did not appear to mediate these outcomes.</p> <p><b>Conclusions: </b>Alerts did not reduce rates of our primary outcome among hospitalized patients with AKI. The overall lack of clinical benefit and signals of harm in non-teaching hospitals should lead to a re-evaluation of existing AKI alerting systems.</p> <p><b>Trial Registration: </b>ClinicalTrials.gov NCT02753751.</p>
Random forests for predicting species identity of forensically important blow flies (Diptera: Calliphoridae) and flesh flies (Sarcophagidae) using geometric morphometric data: proof of concept
<p>Wing shape variation has been shown to be useful for delineating forensically important fly species in two Diptera families: Calliphoridae and Sarcophagidae. Compared to DNA-based identification, the cost of geometric morphometric data acquisition and analysis is relatively much lower because the tools required are basic, and stable softwares are available. However, to date, an explicit demonstration of using wing geometric morphometric data for species identity prediction in these two families remains lacking. Here, geometric morphometric data from 19 homologous landmarks on the left wing of males from seven species of Calliphoridae (<em>n</em>=55), and eight species of Sarcophagidae (<em>n</em>=40) were obtained and processed using Generalized Procrustes Analysis. Allometric effect was removed by regressing centroid size (in log10) against the Procrustes coordinates. Subsequently, principal component analysis of the allometry-adjusted Procrustes variables was done, with the first 15 principal components used to train a random forests model for species prediction. Using a real test sample consisting of 33 male fly specimens collected around a human corpse at a crime scene, the estimated percentage of concordance between species identities predicted using the random forests model and those inferred using DNA-based identification was about 80.6% (approximate 95% confidence interval = [68.9%, 92.2%]). In contrast, baseline concordance using naive majority class prediction was 36.4%. The results provide proof of concept that geometric morphometric data has good potential to complement morphological and DNA-based identification of blow flies and flesh flies in forensic work. </p>
ScienceDex guides
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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.