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10,006 results for “randomness”
Identification of high-wind features within extratropical cyclones using a probabilistic random forest - Part 2: Climatology - Dataset
<p>This dataset provides output of RAMEFI for the wind feature climatology presented in Eisenstein et al. (2023; 10.5194/wcd-2023-10) for the winter months October to March 2000-2019 using COSMO-REA6 (https://reanalysis.meteo.uni-bonn.de/?COSMO-REA6).</p> <p><strong>rf_crea_<yyyymm>.nc</strong> include the unfiltered probabilities for 'no feature' (p0), warm jet (p1), cold-frontal convection (p2), cold jet (p3) and cold-sector winds (p5) for each month.</p> <p>To filter for cyclone tracks, use <strong>cyclone_tracks.csv</strong>. The<strong> </strong>file includes interpolated ERA5 cyclone tracks for the investigated area and time period (see Section 2.4 of the paper).</p> <p><strong>mask.nc</strong> includes a land sea mask, height of surface level and a mask to exclude certain grid points as discussed in the manuscript (e.g., grid points with an altitude over 800m and the Balkans) for further filtering.</p>
American Goshawk habitat data from nest stands and random points within the Minidoka Ranger District, Sawtooth National Forest, USA
This data supported analysis of American Goshawk (Astur atricapillus) nest stand habitat and was collected within the Minidoka Ranger District of the Sawtooth National Forest in southern Idaho and northern Utah from 2017-2020. The central goal of this research was to develop management tools that demonstrate the utility of conducting analyses at multiple spatial scales as well as using both parametric and machine learning approaches. The stand-level dataset includes variables collected by hand in the field at nest stands and paired random forested sites 300 meters away. It also includes some terrain variables based on remote sensing data. Variables included in the stand-level data table include nest, distance to edge, distance to road, distance to water, division, dominant tree species, canopy closure, Stand Density Index (SDI), Trees per hectare, elevation, slope, Topographic Position Index (TPI), northness, eastness, Diameter at Breast Height (DBH), DBH variance, tree height, tree height variance, and crown depth. We recommend that the stand-level data be used to identify relevant variables and their thresholds for forest managers due to its high resolution. The forest-wide dataset includes only variables collected using various remote sensing datasets at nests and random forested points throghout the Minidoka Ranger District of the Sawtooth National Forest. Variables included in the forest-wide data table include nest, canopy closure, elevation, slope, TPI, northness, eastness, distance to road, distance to water, distance to edge, tree height, and crown depth. We recommend that the forest-wide data be used to identify areas of high suitability for goshawk occupancy across the study area along with sites that could become suitable habitat with management intervention. Latitude and longitude data, while used in our analyses, are excluded from the data tables to protect breeding goshawks from disturbance.
Large Uniform Random SAT Samples
<p>Large Random SAT samples generated with the following <em>samplers</em>:</p> <ul> <li>BDDSampler</li> <li>Spur</li> <li>QuickSampler </li> <li>KUS</li> <li>Unigen2</li> <li>Smarch </li> </ul>
A Dataset of Pull Requests and A Trained Random Forest Model for predicting Pull Request Acceptance
<p>A Curated Dataset of 470,925 pull requests for 3349 popular NPM packages, description of the variables, code snippet for creating a Random Forest model for predicting pull request acceptance, and a pre-trained Random Forest model (in R). The dataset is for the ESEM-2020 paper: "Impact of Technical and Social Factors on Pull Request Quality for the NPM Ecosystem" (<a href="https://arxiv.org/abs/2007.04816">https://arxiv.org/abs/2007.04816</a>). </p> <p>Citation:</p> <pre>@inproceedings{dey2020effect, title={Effect of technical and social factors on pull request quality for the npm ecosystem}, author={Dey, Tapajit and Mockus, Audris}, booktitle={Proceedings of the 14th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)}, pages={1--11}, year={2020} }</pre>
Dataset: Random forest models of ultra-low frequency magnetospheric wave power.
<p>Predictive models of ground-based ultra-low frequency (ULF, 1-15 mHz) wave power, corresponding to magnetospheric waves. The series of decision tree ensembles (random forests) are dependent on solar wind properties, latitude and azimuthal angle around the Earth (magnetic local time, MLT).</p>
N30 Random Machine
<p>Minimum sample calculation methods for continuous and discrete data in non-time series scenarios. The data came from randomly generated values by using Excel command: rand()*100 for test sample sizes of <em>n = 5, 10, 20, 30, 50, 100, 200, 300, 400, 500, </em>and<em> 1,000</em>. We proposed a new minimum sample size method that consistently produces <em>n = 30.</em></p>
Cluster randomized controlled trial evaluating the impact on children's linear growth of an unconditional cash transfer program implemented in rural areas of North Togo
<p>A parallel cluster randomized controlled trial was implemented in North Togo. 162 rural landlocked villages were randomized into either an intervention arm (cash transfers + package of community activities, <em>n</em>=82) or a control arm (package of community activities only, <em>n</em>=80). </p> <p>Two different representative samples of children aged 6-29 months and their mothers were surveyed, one before the intervention (2014, <em>n</em>=2,658), the other two years afterwards (2016, <em>n</em>=2,031).</p> <p>You will found here:</p> <ul> <li>Two datasets, fully anonymized (French, CSV files): one for observations on households and the other for observations on mother-child pairs</li> <li>Two dictionaries of variables (French, Excel files): one for the household database and the other for the mother-child pairs database</li> <li>The study protocol as submitted to the Ministry of Health of Togo (French, PDF)</li> <li>The technical and financial proposal (to evaluate the CT program) as submitted to funders (English, PDF)</li> </ul> <p> </p>
Data and code for the manuscript: "Varying richness need not imply non-random species co-occurrence: implications for specifying null models"
<p>Data and R code for the manuscript "Varying richness need not imply non-random species co-occurrence: implications for specifying null models".</p>
Dataset used for making conclusions in article Gesture-controlled image management for operating room: A randomized crossover study.
<p>Dataset used for article titled:</p> <p>Gesture-controlled image management for operating room: A randomized crossover study.</p>
WiggleZ Dark Energy Survey Baryon Acoustic Oscillation Random Catalogues
<p>Data products associated with the WiggleZ Dark Energy survey measurement of the Baryon Acoustic Oscillations (BAO), as described in Blake et al. arXiv:1108.2635 (2011, MNRAS, 418, 1707)</p> <p>Data is given for 6 WiggleZ regions (01, 03, 09, 11, 15, 22 hrs) in 3 different overlapping redshift slices (z=0.2-0.6, 0.4-0.8, 0.6-1.0), matching the datasets analyzed in the final WiggleZ baryon acoustic peak paper Blake et al. (2011, MNRAS, 418, 1707). For each sub-region the following files are given:</p> <p><strong>Correlation function:</strong> xi_**hr*.dat - correlation function measurement and covariance matrix for each sub-region. Format of the file:</p> <ul> <li>1st line: nbin, ngalaxy</li> <li>nbin lines: mean separation [Mpc/h], xi(s), sqrt[Cov(i,i)]</li> <li>nbin x nbin lines: i, j, Cov(i,j)</li> </ul> <p>The covariance matrix is from lognormal realizations, not jack-knife regions.</p> <p><strong>Combined correlation function:</strong> xi_combined*.dat - correlation function measurement for each redshift range combining the measurements in each sub-region, in the same format as above.</p> <p><strong>Integral constant:</strong> wigglez_ic.dat - integral constraint correction which should be added to the measured correlation function, format of the file is:</p> <ul> <li>Region [hr],</li> <li>zmin,</li> <li>zmax,</li> <li>integral constraint delta-xi</li> </ul> <p>We are considering adding another 90 random catalogues in the near future. For now the random catalogues used in the previous analysis are available below.</p>
Sequence data for the article "Targeted reduction of highly abundant transcripts with pseudo-random primers"
<p>Sequence data (Illumina MiSeq runs) for the article "Targeted reduction of highly abundant transcripts with pseudo-random primers". File names indicate unique run identifiers. In the manuscript, shorter names are used:</p> <ul> <li>NC12: 140602_M00528_0019_000000000-A88YD</li> <li>NC17: 140918_M00528_0047_000000000-A8GHU</li> <li>NC22b: 141105_M00528_0062_000000000-AAPC2</li> <li>NCki: 140207_M00528_0069_000000000-A5TY9</li> </ul> <p> </p>
Genome alignments for the article "Targeted reduction of highly abundant transcripts with pseudo-random primers"
<p>Sequence alignment (Moirai workflow management) for the article "Targeted reduction of highly abundant transcripts with pseudo-random primers". File names indicate unique run identifiers. In the manuscript, shorter names are used:</p> <ul> <li>NC12: NC12_1.CAGEscan_short-reads.20150629125015</li> <li>NC17: NC16-17_1.CAGEscan_short-reads.20150625154740</li> <li>NC22b: NC22b.CAGEscan_short-reads.20150625152335</li> <li>NCki: NCms10058_1.CAGEscan_short-reads.20150625154711</li> </ul>
USPTO patent data: 250k random sample and NPEs' patents
<p>This package includes:</p> <ol> <li>Two Stata .dta files consisting of information on patents assigned by the United States Patent and Trademark Office between 1976 and 2014: a random sample of 250 000 US patents, and data on patent owned by Intellectual Ventures, RPX, and several other companies. The variables for example include: grant date, application date, forward and backward citations, renewals, claims and others.</li> <li>Source codes and methods used in generating and analyzing the two data files.</li> </ol> <p>A bachelor thesis with further information will be linked here.</p>
Spatial Evolve Algorithm Results for Random Topology Median Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topologies used for the spatial tournaments has been random between, small world, random and complete. The objective function taken into account has been the median normalized rank. Three files are contained here based on the list of strategies,deterministic and non, and on the sample size. </p>
Spatial Evolve Algorithm Results for Random Topology Minimum Normalized Rank - MSc Dissertation
<p>A data set containing the results of the spatial evolve lookup algorithm. The topologies used for the spatial tournaments has been random between, small world, random and complete. The objective function taken into account has been the minimum normalized rank. Two files are contained here based on the sample size. All 132 strategies of the Axelrod have been used. </p>
System Call Logs with Natural Random Faults: Experimental Design and Application
<p>In this work we present a large dataset for use in embedded systems research which has been gathered from a realistic development environment operating in the path of an accelerated neutron beam. The dataset contains traces with events from a real-time operating system as as user events from a safety critical application. All data is carefully timestamped and in human understandable form.</p>
Numerical simulation of the equatorial plasma bubble: the effect of seeding by the vertical winds and random background noise perturbations.
<p>A wide variety of small-amplitude waves widely exist in the ionosphere and have significant effects on the evolution of equatorial plasma bubbles. In this paper, we simulated equatorial plasma bubbles (EPB) seeded by vertical neutral wind perturbations with wavelengths of 125 km and 250 km, and compared the morphology characteristics of plasma bubble structures with those under random noise perturbations in the background density. The numerical results showed that both vertical winds and random background noise perturbations can contribute to the growth of plasma bubbles, and the perturbations under additional random background noise can promote the growth of the plasma bubble structures faster. Additionally, several processes of the nonlinear behavior of bifurcated EPB structures, including bifurcation, pinching, and small-scale turbulent structures, were successfully obtained. Our simulation captured supersonic flows within the low-density plasma structures characterized by vertical velocities of about 1.5 km/s, which is consistent with experimental studies found in the literature.</p>
Test Dataset for Random Document access
Open the record for dataset details and reuse information.
Generation of Random laser from Dye-Derived Red-Emitting Carbon Dots
<p>Carbon dots are carbon-based nanoparticles renowned for their intense light-emitting capabilities covering the whole visible light range. Here, we overcome these problems by solvothermally synthesizing carbon dots starting from Neutral Red, a common red-emitting dye, as a molecular precursor. The obtained nanoparticles are highly luminescent in the red region, with a quantum yield comparable to that of the starting dye. Most importantly, the nanoparticle carbogenic matrix protects the Neutral Red molecules from photobleaching under ultraviolet excitation while preventing aggregation-induced quenching, thus allowing solid-state emission inside PVA. Finally, the dye-based carbon dots demonstrate stable and efficient random lasing emission in the red region.</p>
Figure 7 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies
Figure 7. Summary of Random Forests classifications for each empirical comparison. Each row shows results from the stratum with the smallest fraction of individuals correctly classified, with comparisons labeled by their taxonomic codes as listed in Table 2. Colors identify comparison type as species (blue), subspecies (green), and populations (red). Points show the fraction of individuals correctly classified with probabilities> 50% (PD50, circles), and> 95% (PD95, triangles). Thin colored lines show 95% confidence intervals (CI) around PD50 estimates. Gray bars show range of a priori random classification rates based on individual size (left) to maximum possible classification rates based on shared haplotypes (right).
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.