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1,243 results for “Statistics”
QUIQI II Dataset - Statistical analyses of motion-corrupted MRI relaxometry data
<p>QUIQI II package includes supporting material for the scientific article by Corbin et al. entitled ‘Statistical analyses of motion-corrupted MRI relaxometry data’.</p> <p>The complete support package for the QUIQI method includes:</p> <ol> <li>A copy of the original analysis code used to compile the results presented in the original scientific publication (doi: 10.5281/zenodo.7612032) </li> <li>A subset of the data used in the original publication for computation of the results. This data also includes a set of analysis results obtained by running the code described in 1. on the provided data.</li> </ol> <p><br> The combination of 1. and 2. allows users to replicate the computation of the provided analysis results.<br> <strong>The material provided here only concerns part 2. of the QUIQI support package described above - subset of the data used in the original publication.</strong></p>
A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR: accompanying dataset
<p>Dataset containing surface temperature and surface mass balance datasets generated from the MAR regional climate model over Greenland over two test areas using statistical downscaling tools from 6 km to 100m. The abstract of the accompanying submitted paper follows: </p> <p> </p> <p>The Greenland Ice Sheet (GrIS) has been contributing directly to sea level rise and this contribution is projected to accelerate over next decades. A crucial tool for studying the evolution surface mass loss (e.g., surface mass balance, SMB) consists of regional climate models (RCMs) which can provide current estimates and future projections of sea level rise associated with such losses. However, one of the main limitations of RCMs is the relatively coarse horizontal spatial resolution at which outputs are currently generated. Here, we report results concerning the statistical downscaling of the SMB modeled by the Modèle Atmosphérique Régional (MAR) RCM from the original spatial resolution of 6 km to 100 m building on the relationship between elevation and mass losses in Greenland. To this goal, we developed a geospatial framework that allows the parallelization of the downscaling process, a crucial aspect to increase the computational efficiency of the algorithm. The results obtained in the case of the SMB, assessed through the comparison of the modeled outputs with in-situ SMB measurements, show a considerable improvement in the case of the downscaled product with respect to the original, coarse output. In the case of the downscaled MAR product, the coefficient of determination (R<sup>2</sup>) increases from 0.868 for the original MAR output to 0.935 for the downscaled product. Moreover, the value of the slope and intercept of the linear regression fitting modeled and measured SMB values shifts from 0.865 for the original MAR to 1.015 for the downscaled product in the case of the intercept and from the value -235mm (original) to -57 mm (downscaled) in the case of the slope, considerably improving upon results previously published in the literature.</p>
Zonal Statistics of Weather Indicators for Brazilian Municipalities from the TerraClimate Project
<p>This dataset contains 14 parquet-format files with monthly data.</p> <table align="center"> <tbody> <tr> <td>File</td> <td>Indicator</td> <td>Unit</td> </tr> <tr> <td>aet.parquet</td> <td>Actual Evapotranspiration</td> <td>mm</td> </tr> <tr> <td>def.parquet</td> <td>Climate Water Deficit</td> <td>mm</td> </tr> <tr> <td>pdsi.parquet</td> <td>Palmer Drought Severity Index (PDSI)</td> <td>unitless</td> </tr> <tr> <td>pet.parquet</td> <td>Precipitation</td> <td>mm</td> </tr> <tr> <td>ppt.parquet</td> <td>Potential evapotranspiration</td> <td>mm</td> </tr> <tr> <td>q.parquet</td> <td>Runoff</td> <td>mm</td> </tr> <tr> <td>soil.parquet</td> <td>Soil Moisture</td> <td>mm</td> </tr> <tr> <td>srad.parquet</td> <td>Downward surface shortwave radiation</td> <td>W/m2</td> </tr> <tr> <td>swe.parquet</td> <td>Snow water equivalent</td> <td>mm</td> </tr> <tr> <td>tmax.parquet</td> <td>Maximun Temperature</td> <td>°C</td> </tr> <tr> <td>tmin.parquet</td> <td>Minimum Temperature</td> <td>°C</td> </tr> <tr> <td>vap.parquet</td> <td>Vapor pressure</td> <td>kPa</td> </tr> <tr> <td>vpd.parquet</td> <td>Vapor Pressure Deficit</td> <td>kpq</td> </tr> <tr> <td>ws.parquet</td> <td>Wind speed</td> <td>m/s</td> </tr> </tbody> </table> <p> </p>
Data for: BetaScan2: Standardized Statistics to Detect Balancing Selection Utilizing Substitution Data
<p>Genome-wide scan using BetaScan2 in 1KG populations report in:</p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/32011695/">BetaScan2: Standardized Statistics to Detect Balancing Selection Utilizing Substitution Data.</a></p> <p>Siewert KM, Voight BF.Genome Biol Evol. 2020 Feb 1;12(2):3873-3877. doi: 10.1093/gbe/evaa013.</p> <p>PMID: 32011695 </p> <p>Code available at: https://github.com/ksiewert/BetaScan</p>
Wind2Loads 9D load simulation database statistics
<p>1. An Excel file providing a list of the environmental conditions used to generate the load simulations. Column names correspond to the variables that are used as load simulation inputs. An "F" prefix in the variable means this is the cumulative distribution function (CDF) of the distribution used to generate the random sample. If there is no F-prefix, the column corresponds to the physical value of the variable. </p> <p>2. Files containing the 10-minute statistics of load simulations. Files are delimited with semicolon ";"</p> <p>- Filenames specify details about the simulation: "PointNo" corresponds to the sample number listed in the "conditions list" Excel file. "SetNo" corresponds to the random realization (there are 5-8 random realization at each sample point). Each sample realization corresponds to 1h simulations, but the simulation data are split in parts of 10min (so there are 6 "parts" in each realization) - the "part" in the filename indicates which part of the realization this is.</p> <p>- The columns of each stats file are organized as follows (first column considered to have index 1):</p> <p>Column 1: File name</p> <p>For load channel number j, j = 1: n_channels:</p> <p>Column 7*(j-1) + 2: mean value of channel j</p> <p>Column 7*(j-1) + 3: standard deviation of channel j</p> <p>Column 7*(j-1) + 4: minimum of channel j</p> <p>Column 7*(j-1) + 5: maximum of channel j</p> <p>Column 7*(j-1) + 6: DEL4 - fatigue damage-equivalent load (DEL) with S-N curve slope m = 4, for channel j</p> <p>Column 7*(j-1) + 7: DEL8 - fatigue damage-equivalent load (DEL) with S-N curve slope m = 8, for channel j</p> <p>Column 7*(j-1) + 8: DEL12 - fatigue damage-equivalent load (DEL) with S-N curve slope m = 12, for channel j</p>
Wind2Loads 6D load simulation database statistics
<p>1. An Excel file providing a list of the environmental conditions used to generate the load simulations. Column names correspond to the variables that are used as load simulation inputs. An "F" prefix in the variable means this is the cumulative distribution function (CDF) of the distribution used to generate the random sample. If there is no F-prefix, the column corresponds to the physical value of the variable. </p> <p>2. Files containing the 10-minute statistics of load simulations. Files are delimited with semicolon ";"</p> <p>- Filenames specify details about the simulation: "PointNo" corresponds to the sample number listed in the "conditions list" Excel file. "SetNo" corresponds to the random realization (there are 5-8 random realization at each sample point). Each sample realization corresponds to 1h simulations, but the simulation data are split in parts of 10min (so there are 6 "parts" in each realization) - the "part" in the filename indicates which part of the realization this is.</p> <p>- The columns of each stats file are organized as follows (first column considered to have index 1):</p> <p>Column 1: File name</p> <p>For load channel number j, j = 1: n_channels:</p> <p>Column 7*(j-1) + 2: mean value of channel j</p> <p>Column 7*(j-1) + 3: standard deviation of channel j</p> <p>Column 7*(j-1) + 4: minimum of channel j</p> <p>Column 7*(j-1) + 5: maximum of channel j</p> <p>Column 7*(j-1) + 6: DEL4 - fatigue damage-equivalent load (DEL) with S-N curve slope m = 4, for channel j</p> <p>Column 7*(j-1) + 7: DEL8 - fatigue damage-equivalent load (DEL) with S-N curve slope m = 8, for channel j</p> <p>Column 7*(j-1) + 8: DEL12 - fatigue damage-equivalent load (DEL) with S-N curve slope m = 12, for channel j</p> <p> </p>
Genome-wide association statistics of Hearing Problems
<p>Genome-wide Association Statistics of Hearing Problems</p> <p>Citation: De Angelis F, Zeleznik OA, Wendt FR, Pathak GA, Tylee DS, De Lillo A, Koller D, Cabrera-Mendoza B, Clifford RE, Maihofer AX, Nievergelt CM, Curhan GC, Curhan SG, Polimanti R. Sex differences in the polygenic architecture of hearing problems in adults. Genome Med. https://doi.org/10.1186/s13073-023-01186-3</p> <p>COLUMN HEADERS<br> chromosome: chromosome<br> base_pair_location: position<br> effect_allele: effect allele (corresponds to the effect size’s sign; may not be the alternate allele)<br> other_allele: non-effect allele<br> beta: effect measured as beta, sign corresponds to the effect of the effect allele<br> standard_error: standard error of the effect<br> effect_allele_frequency: effect allele frequency in UK Biobank participants of European descent<br> p_value: p value of the association statistic<br> variant_id: variant identifier<br> rs_id: rsID of the variant<br> n: sample size per variant</p> <p> </p>
Parameters for Statistical Evaluation of Time to Fixate Efectiveness for Assessment of Fitness to Drive
<p>This repository contains a table with parameters (including time to fixate - TTF parameter) and an R script for statistical analysis. This is supplementary material for the paper titled "<a href="https://doi.org/10.3758/s13428-023-02177-3">Effectiveness of a Time to Fixate for Fitness to Drive Evaluation in Neurological Patients</a>" and authored by Nadica Miljković and Jaka Sodnik (published in <a href="https://www.springer.com/journal/13428">Behavior Research Methods</a> and previously shared on <a href="https://arxiv.org/ftp/arxiv/papers/2205/2205.08942.pdf">arXiv</a>).</p> <p>TTF parameter was calculated in overall 56 patients during selected scenario with pedestrian collision in a driving simulator produced by <a href="https://www.nervtech.com/">Nervtech</a>. Together with other parameters, ST parameters are stored in <a href="https://zenodo.org/record/7963337/files/tableParametersAll.csv?download=1">tableParametersAll.csv</a>, while R programming code for statistical analysis of all parameters is placed in <a href="https://zenodo.org/record/7963337/files/statisticalAnalysisAll.R?download=1">statisticalAnalysisAll.R</a>.</p> <p><strong>Dataset contents</strong></p> <ol> <li><a href="https://zenodo.org/record/7963337/files/tableParametersAll.csv?download=1">tableParametersAll.csv</a>, table with parameters, csv (comma-separated values) format</li> <li><a href="https://zenodo.org/record/7963337/files/statisticalAnalysisAll.R?download=1">statisticalAnalysisAll.R</a>, code in R programming language for statistical analysis</li> </ol> <p><strong>Table with parameters has the following structure</strong></p> <ol> <li>column - no which is ordinary number in consecutive order from 1 to 56</li> <li>column - id presents an internal patient's id</li> <li>column - ttf presents TTF parameter in ms</li> <li>column - fitness presents a categorical variable and can be either fit-, unfit-, or conditionally-fit-to-drive (cond fit)</li> <li>column - speed at the collision onset in km/h</li> <li>column - ttc presents time-to-collision in s</li> <li>column - manual_correction is categorical variable: 0 means that no manual correction was required for ST calculation, while 1 means that manual correction was required</li> <li>column - igd presents initial gaze distance in pixels</li> <li>column - R1 presents the first measurement of perception response time (PRT)</li> <li>column - R2 presents the second measurement of PRT</li> <li>column - R3 presents the third measurement of PRT</li> </ol> <p>Missing data are presented with NA (Not Available).</p> <p>NOTE: Python code for ST calculation and sample eye tracker video are available on GitHub repository <a href="https://github.com/NadicaSm/Time-To-Fixate-Calculation-from-the-Eye-Tracker-Videos">https://github.com/NadicaSm/Time-To-Fixate-Calculation-from-the-Eye-Tracker-Videos</a> under GNU GPL license and released on Zenodo with doi (<a href="https://doi.org/10.5281/zenodo.6560419">https://doi.org/10.5281/zenodo.6560419</a>).</p> <p>If you find these parameters and R code useful for your own research and teaching class, please cite the following references:</p> <ol> <li>Miljković, N., & Sodnik, J. (2023). NadicaSm/Time-To-Fixate-Calculation-from-the-Eye-Tracker-Videos: v2. [Software code], Zenodo. <a href="https://doi.org/10.5281/zenodo.6560419">https://doi.org/10.5281/zenodo.6560419</a></li> <li>Miljković, N., & Sodnik, J. (2023). Effectiveness of a time to fixate for fitness to drive evaluation in neurological patients. Behavior Research Methods. <a href="https://doi.org/10.3758/s13428-023-02177-3">https://doi.org/10.3758/s13428-023-02177-3</a></li> <li>Motnikar, L., Stojmenova, K., Štaba, U. Č., Klun, T., Robida, K. R., & Sodnik, J. (2020). Exploring driving characteristics of fit-and unfit-to-drive neurological patients: A driving simulator study. Traffic Injury Prevention, 21(6), 359-364. <a href="https://doi.org/10.1080/15389588.2020.1764547">https://doi.org/10.1080/15389588.2020.1764547</a></li> </ol> <p><strong>Acknowledgements</strong></p> <p>J.S. kindly acknowledges University Rehabilitation Institute Soča employees and the Nervtech team. Authors gratefully appreciate the support from Nenad B. Popović, PhD from University of Belgrade – School of Electrical Engineering for his valuable assistance in design of illustrations and for provided feedback for the initial manuscript structure. Also, both Authors thank Nebojša Jovanović, MSc from University of Belgrade - School of Electrical Engineering for his kind contribution to earlier stages of the project, especially for his work on developing Python code to capture time to fixate parameter. Last but not least, we are very thankful to Damjan Krstajić, founder and director of the Research Centre for Cheminformatics for his precious advices on statistical analysis in a retrospective study and to student Gregor Kovač from Faculty of Electrical Engineering, University of Ljubljana for his diligent work on YOLO application in driving simulation.</p>
Extra-tropical cyclone statistics from OpenIFS aquaplanet simulations
<p>This data set is derived from three idealised modelling experiments that were performed with the global numerical weather prediction model, OpenIFS. Three aqua-planet experiments were performed that differed only in their sea surface temperatures. There was a control simulation, case where the sea surface temperatures were warmed everywhere by 4K and a case where the polar sea surface temperatures were increased by 5K. In all three experiments, the extra-tropical cyclones were identified. The data presented in this data set is the maximum vorticity and precipitation of all of these extra-tropical cyclones. This data set is used by Sinclair and Catto (2023) in their study.</p> <p>Sinclair, V. A. and Catto, J. L.: The relationship between extra-tropical cyclone intensity and precipitation in idealised current and future climates, Weather Clim. Dynam. Discuss. [preprint], https://doi.org/10.5194/wcd-2022-62, in review, 2022.</p>
Statistical blending of global-gridded climatological products: an approach to inverse hydrological model
<p>The growing use of global-scale environmental products in hydro-climatic modeling (with different assumptions, resolutions, and precisions) has increased the variety of their applications and the complications of their uncertainties and evaluations. Researchers have recently turned to statistical blending (fusion) of these products to achieve optimal modeling while avoiding difficulties. The proposed statistical blending in this study includes five large-scale and satellite precipitation (Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), ERA5-Land of ECMWF (ERA), Integrated Multi-Satellite Retrievals for GPM (IMERG), Tropical Rainfall Measuring Mission (TRMM), and Terra) and evapotranspiration (Global Land Evaporation Amsterdam Model (GLEAM), SSEBop, Moderate Resolution Imaging Spectroradiometer (MODIS), Terra, and ERA) products committed in three modeling scenarios. The blending procedures are organized using a conceptual water balance model to achieve the best precipitation and evapotranspiration results for the conceptual production of streamflow using hydrological inverse modeling. Based on the results, the proposed blending procedures of precipitation and evapotranspiration improved the performance of the model using different statistical metrics. In addition, the results show the conformity of the pattern and behavior of the blended precipitation calculated using the moving least square method in the study area. This happened by changing the estimation based on <em>in situ</em> values, particularly in cold months considering the orographic/snow effects. The combining method provides a good fusion procedure to improve the realistic estimation of precipitation and evapotranspiration in ungagged watersheds as well<strong>.</strong></p>
Curated GWAS summary statistics on African ancestry on 19 blood count traits and glycemic traits (hg38)
<p>Genome wide curated summary statistics on 19 blood count traits and glycemic traits</p> <p>File format is the inittable format intended to be used with the Joint Analysis of Summary Statistics (JASS), which allows to perform multi-trait GWAS:</p> <p>https://gitlab.pasteur.fr/statistical-genetics/jass</p> <p>GWAS of hematological traits originate from Chen et al paper and were downloaded from the GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/32888493#study_panel">https://www.ebi.ac.uk/gwas/publications/32888493#study_panel</a>). GWAS of glycemic traits come from the <a href="https://www.zotero.org/google-docs/?S1MIfx">(18)</a> study downloadable from GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/34059833">https://www.ebi.ac.uk/gwas/publications/34059833</a>).</p> <p> </p>
Curated GWAS summary statistics on East Asian ancestry on 19 blood count traits and glycemic traits
<p>Genome wide curated summary statistics on 19 blood count traits and glycemic traits</p> <p>File format is the inittable format intended to be used with the Joint Analysis of Summary Statistics (JASS), which allows to perform multi-trait GWAS:</p> <p>https://gitlab.pasteur.fr/statistical-genetics/jass</p> <p>GWAS of hematological traits originate from Chen et al paper and were downloaded from the GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/32888493#study_panel">https://www.ebi.ac.uk/gwas/publications/32888493#study_panel</a>). GWAS of glycemic traits come from the <a href="https://www.zotero.org/google-docs/?S1MIfx">(18)</a> study downloadable from GWAS Catalog (<a href="https://www.ebi.ac.uk/gwas/publications/34059833">https://www.ebi.ac.uk/gwas/publications/34059833</a>).</p> <p>Full description of the method used to derive this dataset can be found in </p>
Simulated discharge statistics in Central and Southwestern Europe considering water use under 2K global warming
<p>We provide a novel, high-resolution hydrological modelling dataset using pseudo-global warming climate data as forcing to the Community Water Model (CWatM). CWatM is a state-of-the-art large-scale rainfall-runoff and channel routing water resources model that is process-based and used to quantify water supply, as well as human water withdrawals from different sectors (industry, domestic, agriculture) and multiple sources representing the effects of water infrastructure, including reservoirs, groundwater pumping and irrigation canals. CWatM is forced by a pseudo-global warming (PGW) experiment from 1981 to 2010. PGW simulations resemble historical weather patterns and events under globally warmer conditions (here, 2 K global warming) by perturbing historical, reanalysis-driven regional climate simulations. We performed simulations considering regular incremental adjustments of the historical water withdrawals (ranging between +/- 50% of historic water withdrawals) under PGW conditions. That range represents an ad hoc and simplified representation of multiple possible future water management scenarios across Southwestern and Central Europe. The approach allows us to investigate the effects of changing water withdrawals under 2 K global warming. Especially in Western and Central Europe, the projected impacts on low flows highly depend on the chosen water withdrawal assumption. The data highlights the importance of accounting for future water withdrawals in discharge projections.</p> <p> </p> <p><strong>Discharge statistics</strong> based on daily output from CWatM within 1981-2010:</p> <ul> <li><strong>Q1</strong> - 1st percentile</li> <li><strong>Q5</strong> - 5th percentile</li> <li><strong>Q10</strong> - 10th percentile</li> <li><strong>Qavg</strong> - average discharge</li> <li><strong>Q90</strong> - 90th percentile</li> <li><strong>Q95</strong> - 95th percentile</li> <li><strong>Q99</strong> - 99th percentile</li> </ul> <p><strong>Files:</strong></p> <ul> <li><strong>Qxx_reference</strong>: CWatM considering historical water use forced by RACMO-ERA5</li> <li><strong>Qxx_PGW:</strong> CWatM considering historical water use forced by RACMO-ERA5 + climate pertubations under 2 K global warming. In the reference experiment, RACMO is forced at the lateral and sea surface boundaries of the model domain by unperturbed ERA5 reanalysis data, while in the pseudo-global warming experiment, the forcing data consist of perturbed reanalysis data. Perturbations are added to the ERA5 reference data corresponding to climate change patterns of surface pressure and sea surface temperature, and atmospheric profiles of temperature, relative humidity, and wind speed components that are retrieved from a 16-member single model initial condition ensemble of EC-EARTH global climate simulations.</li> <li><strong>Qxx_PGW_adjusted_demand:</strong> We have performed 11 additional hydrological simulations adjusting the historical water demand (ranging between +/- 50% of historic water withdrawals) to enable sensitivity assessments of low and high flows under 2 K global warming.</li> </ul> <p>An upcoming publication will be made available and linked to this research very soon.</p> <p> </p>
Supplementary data and summary statistics - Genetic influences on circulating retinol and its relationship to human health
<p><strong>Summary statistics from the circulating retinol GWAS</strong></p> <p>See -<em><strong> GWAS_summary_stats_README.txt </strong></em>for details of these files and the header names. METSIM+INTERVAL meta-analyses have a sample size of 17268. The full meta-analysis that includes ATBC+PLCO has a sample size of 22274.</p> <p><strong>Please cite the following if you use any of these data </strong>- Reay, W.R. et al. Genetic influences on circulating retinol and its relationship to human health. Nature Communications (2024).</p> <p>By downloading these summary statistics, investigators agree to the following:</p> <ol> <li>Investigators acknowledge that these data are provided on an “as-is” basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose.</li> <li>Investigators will not cross-post these data or make them available elsewhere – this website is the definitive source for these data without express written permission from the study corresponding authors.</li> <li>Investigators will never attempt to identify any participant who contributed to these data.</li> <li>Any commercial or for-profit use of these data is forbidden unless express permission is sought from the study corresponding authors.</li> <li>Investigators will cite the associated manuscript when using these data.</li> </ol> <p><strong>Supplementary data from the circulating retinol GWAS phenome-wide Mendelian randomisation study</strong></p> <p>1. MR_retinol_as_exp - full output from the MR-pheWAS using circulating retinol as the exposure</p>
Zonal Statistics of Climate Indicators from ERA5-Land for Brazilian Municipalities, 1950-2022
<p>Climate indicators are used in several statistical models for many research areas and are specially important for modelling Climate Sensitive Diseases (CSD) incidence. Those models usually adopts a lattice structure, where its data is aggregated at administrative boundaries (e.g. disease incidence), but climate indicators are usually presented in a continuous regular grid format.</p><p>To make climate indicators compatible with lattice structures, zonal statistics may be adopted. Zonal statistics are descriptive statistics calculated using a set of cells that spatially intersects a given spatial boundary. For each boundary in a map, statistics like average, maximum value, minimum value, standard deviation, and sum are obtained to represent the cell's values that intersect the boundary.</p><p>This dataset present zonal statistic of climate indicators computed from Copernicus ERA5-Land daily aggregates for the Brazilian municipalities, from 1950 to 2022.</p><p> </p><p> </p>
Statistically downscaled future precipitation for the Luquillo Mountains, Puerto Rico
This dataset contains climate predictions that serve as the basis for the analysis in Ramseyer et al. (2019), which projected a trend toward drier conditions in eastern Puerto Rico during the mid- and late-21st century. The analysis was informed by computing nine atmospheric variables, which had been shown by previous research to related to precipitation in Puerto Rico (Ramseyer and Mote 2016) from four GCMs. These nine variables were used to train an artificial neural network (ANN) to predict the binary occurrence of a wet (>= 5 mm of precipitation) versus dry (<5 mm) day using in-situ daily precipitation observations from El Verde Field Station in northeast Puerto Rico. The nine atmospheric variables used to train the ANN were: 1000- 850-, 700-, and 500-hPa daily specific humidity, 1000–700-hPa bulk wind shear (BWS), the Gálvez-Davison Index (GDI), and the GDI's three component terms (the column buoyancy index, mid-level warming index, and a trade-wind inversion index). These same nine variables were then extracted on a daily basis from four GCMs for the eastern Caribbean early rainfall season (April-July) between 2041-2060 and 2081-2100, and fed through the ANN. These data are the daily predicted values of wet (1) or dry (0) conditions for each of the four GCMs in the ensemble. Because ERS total precipitation at El Verde is strongly correlated with the percentage of ERS dry days (R2=0.95 for years with <10% missing data), the GCM predictions were used to estimate future ERS precipitation using the following formula: ERS precipitation (mm) = 3373-37.6*(ERS dry-day percentage) Applying this formula to each of the GCM dry-day projections yielded an ensemble mean ERS precipitation total of 771 mm by 2041-2060 and 974 mm by 2081-2100. See Ramseyer et al. (2019) for a complete description of the neural network and its predictions. Ramseyer, C., P. Miller, and T. Mote, 2019: Future precipitation variability during the early rainfall season in the El Yunque National Fore
The US LTER Thesaurus: Contents and Keyword Use Statistics in LTER Data Packages in 2006 and 2018
This dataset contains raw data and statistical summaries that reflect use of keywords in LTER Datasets in May 2018 and 2006. Specific summaries include: Number of uses and number sites by keyword (LTERVocabKeywordSummary.csv), Summary of keyword use by data package (LTERVocabDataPackageSummary.csv), Summary of Keyword Use by LTER Site in 2018(LTERVocabSiteSummary.csv), Summary of Keyword Use by LTER Site in 2006(KeyStats2006.csv). Raw data includes XML files containing the US LTER Thesaurus in Moodle format and the ResultSet containing the information for each dataset from the Environmental Data Initiative PASTA repository.
LiDAR Cluster Statistic of Wind Turbine Wakes
<p>Mean and standard deviation of the wake velocity field generated by utility-scale wind turbines for different turbulence intensity of the incoming wind and rotor thrust coefficient. Statistics are retrieved from wind LiDAR measurements. More details in this paper https://onlinelibrary.wiley.com/doi/full/10.1002/we.2430 </p>
Education statistics 1970 - 2012 province of Uusimaa, Finland
<p>Excel worksheet for project internal use.</p> <p>(http://tilastokeskus.fi/meta/til/kjarj.html TARGET=_blank) Kuvaus <br> (http://tilastokeskus.fi/til/kjarj/kas.html TARGET=_blank) Käsitteet</p> <p>määritelmät <br> (http://tilastokeskus.fi/til/kjarj/laa.html TARGET=_blank) <br> <br> Laatuseloste<br> <br> </p>
JJIF Development & Statistics in the years 2010 - 2019
<p>The following dataset is a digest from the JJIF world ranking list curated by the software Sportdata: http://setopen.sportdata.org/jjifranking/ranking_main.php and older hand-curated ranking lists. </p> <p>Used data:</p> <p>JJIF Raking lists from 31.12 of each year<br> - Not corrected for athletes participating in more than one category/discipline<br> (If an athlete participates in Fighting & Jiu-Jitsu he/she will be counted double)<br> - Duo & Show couples count as one (1) athlete</p> <p>Abbreviations for categories<br> -Discipline: FS = Fighting System; JJ = Jiu-Jitsu/Ne-Waza; DS= Duo System; SS = Show System; CS = Contact Ju-Jitsu</p> <p>-Gender: W = Women; M = Men; X = Mixed</p> <p>- Weight: +94kg is called 95; +70 is called 71</p> <p> </p> <p>Disclaimer:<br> The information provided here is for general informational purposes only. All information on is provided in good faith, however, we make no representation or warranty of any kind, express or implied, regarding the accuracy, adequacy, validity, reliability, availability or completeness of any information.<br> <br> Rightfull owner of the data is the Ju-Jitsu International Federation (JJIF)</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.