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1,243 results for “Statistics”
Summary statistics for association tests between human and Plasmodium falciparum genetic variants in 3,346 severe malaria cases from The Gambia and Kenya
<p>This dataset contains summary statistics for association tests between human and<br> <em>Plasmodium falciparum</em> malaria parasite genetic variants, using data from 3,346 severe malaria cases from The Gambia and Kenya. These results underlie the analysis described in our paper:</p> <p><strong>"Malaria protection due to sickle haemoglobin depends on parasite genotype"</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy Nguyen, Sónia M. Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor, David J. Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>:: <a href="https://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a><br> <br> The genotype data underlying these summary statistics has also been deposited on Zenodo<br> (<a href="https://zenodo.org/record/4973477">doi:10.5281/zenodo.4973477</a>). The <a href="https://www.well.ox.ac.uk/~gav/hptest)">HPTEST software</a> used to generate these results has also been deposited (<a href="https://doi.org/10.5281/zenodo.5685580">doi:10.5281/zenodo.5685580</a>). Please see the <a href="https://www.malariagen.net/resource/32">MalariaGEN website</a> for a full list of datasets which have been released with this manuscript.</p> <p><strong>Data contents.</strong></p> <p>The dataset consists of a single <a href="http://sqlite.org">sqlite database file</a> containing the results, and an accompanying README file in markdown and html format. Please see the README file for full details of the data contents.</p> <p> </p>
Supporting data for the AI education publication statistics in "An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates"
<p>Supporting data for the AI education publication statistics presented in the paper "An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates" to be published at the Twelfth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-22). The data was used to plot the figure showing the cumulative number of publications from 1976 to 2020 relating to AI education.</p>
Statistical data for "Van Allen Probes Observations of Oxygen Ion Cyclotron Harmonic Waves: Statistical Study "
<p>This file contains parameters for the identified oxygen ion cyclotron harmonic (OCH) waves observed by Van Allen Probes. The meaning of each column is shown as follows.</p> <p>Column #1: the name of Van Allen Probe, A or B.</p> <p>Column #2: event start day</p> <p>Column #3: event end day</p> <p>Column #4: event start hour</p> <p>Column #5: event strat minute</p> <p>Column #6: event end hour</p> <p>Column #7: event end minute</p> <p>Column #8: MLT, columns(t, MLT)</p> <p>Column #9: MLAT, columns(t, MLAT)</p> <p>Column #10: L-shell, columns(t, L-shell)</p> <p>Column #11: lower frequency limit</p> <p>Column #12: upper frequency limit</p> <p>Column #13: distance to the plasmapause, the positive and negative values correspond to outside and inside the plasmapause, respectively.</p> <p>Column #14: wave normal angle, columns (t, WNA)</p> <p>Column #15: the ratio of the frequency spacing between two consecutive wave harmonics to the local oxygen ion gyrofrequency. </p> <p>Column #16: root-mean-square amplitude, columns(t, Bw)</p> <p>Column #17: AE*</p>
InnoRate_Social_media_statistics_Dataset14_2021.12.28_v1
<p>This dataset contains the final statistics of (i) the social media accounts (Facebook, Twitter, LinkedIn) and (ii) the InnoRate web portal, which both have been created in the frame of the InnoRate Project (H2020 GA 821518).</p>
Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning"
<p>Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning". Predictors from the ERA5 reanalysis and predictand from ReKIS (https://rekis.hydro.tu-dresden.de). Data is saved in ".rda" format, to be read from R.</p>
Capturing features of hourly-resolution energy models through statistical annual indicators
<p>Dear colleagues,</p> <p>This is the official repository of the Task 7.4 of H2020 Locomotion project. Feel free to use our data by citing this work and comment about our work by referencing the main authors of it. The article explaining this work is under revision. it will be referenced as soon as posible.</p> <p><strong>Python scripts </strong>("create_inputs.txt" and "run_simulations.txt") creates the input files for EnergyPLAN. The second one runs iteratively EnergyPLAN to generate the outputs of combinations (which are saved in the "EU_Iterate_case.xlsx" file). Hourly distributions of demands and supply technologies are contained in the RAR file ("EUdist.rar") and "EU_start_v2_noFlex.txt" initialize the starting configuration of the European energy system. Those files are required to run EnergyPLAN. The <strong>PowerPoint file</strong> ("EnergyPLAN_instructions.pptx") explains the procedure to carry out the runs of combinations in Python/Excel.</p> <p>In case you couldn't properly do the combinations, the<strong> Excel file</strong> ("EU.xlsx") saves this information, so the steps of the approach could be followed from this point with the Excel file. We have used Power Query (Excel) to prepare the data for the next step of building the regression models.</p> <p>The <strong>Matlab file (</strong>"CreateRegressionModels.m"<strong>)</strong> automatically generates the regression models for the European region of WILIAM (official model of the Locomotion project).</p> <p>Best regards,</p> <p>Gonzalo.</p>
Natural Image Statistics for Mouse Vision
<p>This repository contains 232 "mouse-view" UV/Green natural scene images for the mouse vision, acquired by a custom multi-spectral camera. For details, please see the following paper:</p> <p> Natural Image Statistics for the Mouse Vision<br> Luca Abballe and Hiroki Asari<br> PLOS One (accepted)</p> <p>The preprint is also available from bioRxiv 2021.04.08.438953.</p>
Summary Statistics from "Genetically regulated gene expression and proteins revealed discordant effects" (LWAS of biomarker)
<p>Summary statistics of 92 blood protein levels. The corresponding publication is currently under revision.</p> <p> The zipped txt file is tab-delimited and contains the following columns:</p> <ul> <li>protein: protein name abbreviation</li> <li>cytoband: genomic region</li> <li>gene: gene name abbreviation</li> <li>setting: either "combined" (adj. for sex & age) or sex-stratified ("males", "females"; adj. for age)</li> <li>variant_id_hg19: SNP ID according to hg19</li> <li>variant_id_hg38: SNP ID according to hg19</li> <li>chr: chromosome</li> <li>pos_hg19: base position according to hg19</li> <li>pos_hg38: base position according to hg19</li> <li>effect_allele: also known as counted allele in additive model</li> <li>other_allele: not-counted allele</li> <li>eaf: effect allele frequency</li> <li>maf: minor allele frequency</li> <li>info: imputation info score</li> <li>n_samples: number of samples</li> <li>beta: effect estimate</li> <li>se: standard error</li> <li>zscore: Z-statistic</li> <li>pvalue: p-value</li> <li>FDR: FDR by gene and setting</li> <li>BBFDR: hierarchical FDR by setting</li> <li>hierFDR: TRUE if SNP is significant after hierarchical FDR</li> </ul>
ESMARConf2022 evaluation responses and interaction statistics
<p>Responses to the ESMARConf2022 post-event evaluation for participants and presenters, statistics for the ESMARConf website (https://esmarconf.github.io), and YouTube channel viewing data for ESMARConf2021.</p>
NBA Player Statistics 2020-2021
<p>The dataset contains data for each of the players who have interacted with the NBA during a specific period of time (last season) and collects all the accumulated statistics.<br> In addition, it summarizes the performance of each player through the rest of the data by means of the player efficiency rating (PER) variable, a metric that takes into account all the data extracted from a player.</p>
Chronogram or phylogram for ancestral state estimation? Model-fit statistics indicate the branch lengths underlying a binary character's evolution: R scripts and simulated trees
<p>All R scripts used in this study, and the set of simulated phylogenetic trees used in the study.</p> <p>1. Modern methods of ancestral state estimation (ASE) incorporate branch length information, and it has been demonstrated that ASEs are more accurate when conducted on the branch lengths most correlated with a character's evolution; however, a reliable method for choosing between alternate branch length sets for discrete characters has not yet been proposed.<br><br>2. In this study, we simulate paired chronograms and phylograms, and generate binary characters that evolve in correlation with one of these. We then investigate (1) the effect of alternate branch lengths on ASE error, and (2) whether phylogenetic signal statistics and/or model-fit statistic can be used to select the branch lengths most correlated with a binary character.<br><br>3. In agreement with previous studies, we find that ASEs are more accurate when conducted on the branch lengths most correlated with the character. Phylogenetic signal statistics show limited utility for selecting the correct branch lengths, but model-fit statistics are found to be more accurate, with the correct branch lengths generally returning greater model-fit (lower AICc and BIC values). Using this method to choose between alternate branch length sets is more accurate when tree and character properties are more favorable for model optimization, and when shape differences between alternate phylogenies are greater.<br><br>4. Our results indicate that researchers conducting ASEs on discrete characters should carefully consider which branch lengths are appropriate, and, in the absence of other evidence, we suggest estimating model-fit values over alternate branch length sets and evolutionary models and choosing the branch length/model combination that returns better model fit.</p>
Statistically Determined Global Fire Regimes (GFRs) Empirically Characterized Using Historical MODIS Hotspots
<p><strong>Statistically Determined Global Fire Regimes (GFRs) Empirically Characterized Using Historical MODIS Hotspots</strong></p> <p>Fire regimes are areas having similar fire characteristics, and show the spatial pattern, frequency and intensity of fires that prevail in that area over long periods of time. Fire regimes are created and maintained by multivariate interactions between climate, vegetation/fuels, and ignitions. Like ecoregions, fire regimes indicate the extent and overlap of particular vegetative/fuel communities and climatic conditions, and are important for understanding, monitoring, predicting and managing fire.</p> <p>More than 83M MODIS “hotspot” thermal detections from 2002-2019 were grouped into 10km cells, and 21 derived variables describing fire characteristics of fire intensity, return frequency, and seasonality within each cell were developed and subjected to unsupervised Multivariate Geographic Clustering to produce world maps of Global Fire Regimes (GFRs), each having similar fire intensity and timing characteristics.</p> <p>Methodology behind these datasets are described in manuscript currently in review.</p> <p><strong>W. W. Hargrove, Jitendra Kumar, Steven P. Norman, Forrest M. Hoffman (2022), "Empirical Characterization of Global Fire Regimes Show Shared Fire Relationships" 2022 (in review)</strong></p> <p>This data collection includes:</p> <p>1. Multivariate Geographic Clustering Global Fire Regimes at 3000, 1000, 500, 100, 50, 20, 10 levels of divisions in form of geospatial raster in IMG formats, and associated color tables.</p> <p>2. Characteristics of GFRs</p> <p>3. Location groups</p> <p>4. Geospatial maps of global fire frequency modes, global seasonality strength, and 12 types of global fires.</p> <p>5. PNG maps for all data products </p> <p>6. Description and script for global date transform algorithm.</p>
Dataset, statistical analysis code, and supplementary material of juvenile ravens' responses towards acoustic cues of different social categories
<p>Social competence i.e., defined as the ability to adjust the expression of social behaviour to the available social information, is known to be influenced by early-life conditions. Brood size might be one of the factors determining such early conditions, particularly in species with extended parental care. We here tested in ravens, whether growing up in families of different sizes affects the chicks' responsiveness to social information. We experimentally manipulated the brood size of 20 captive raven families, creating either small or large families. Simulating dispersal, juveniles were separated from their parents and temporarily housed in one of two captive non-breeder groups. After five weeks of socialization, each raven was individually tested in a playback setting with food-associated calls from three social categories: sibling, familiar unrelated raven they were housed with, and unfamiliar unrelated raven from the other non-breeder aviary. We found that individuals reared in small families were more attentive than birds from large families, in particular towards the familiar unrelated peer. These results indicate that variation in family size during upbringing can affect how juvenile ravens value social information. Whether the observed attention patterns translate into behavioural preferences under daily life conditions remains to be tested in future studies.</p>
Graphic Novel Character Networks and Statistics
<p><strong>Description.</strong> This dataset contains the character networks extracted from the graphic novel Thorgal, as well as the statistics and plots produced when analyzing these networks.</p> <p><strong>Source code. </strong>The source code used to produce these files is available on GitHub: <a href="https://github.com/CompNet/NaNet">https://github.com/CompNet/NaNet</a></p> <p><strong>Citation. </strong>If you use these data, please cite the following article:</p> <ul> <li>V. Labatut, “Complex Network Analysis of a Graphic Novel: The Case of the Bande Dessinée <em>Thorgal</em>,” Advances in Complex Systems, p. 22400033, 2022. ⟨<a href="https://hal.archives-ouvertes.fr/hal-03694768">hal-03694768</a>⟩ - DOI: <a href="http://doi.org/10.1142/S0219525922400033">10.1142/S0219525922400033</a></li> </ul> <p><br><code>@Article{Labatut2022,</code><br><code> author = {Labatut, Vincent},</code><br><code> title = {Complex Network Analysis of a Graphic Novel: The Case of the Bande Dessinée {T}horgal},</code><br><code> journal = {Advances in Complex Systems},</code><br><code> year = {2022},</code><br><code> volume = {25},</code><br><code> number = {5\&6},</code><br><code> pages = {2240003},</code><br><code> doi = {10.1142/S0219525922400033},</code><br><code>}</code></p>
CONTENT -- Multi-context genetic modeling TWAS and eAssociation summary statistics
<p>We provide the summary statistics of running CONTENT, the context-by-context approach, and UTMOST on over 22 phenotypes. The phenotypes are listed in the manuscript, and their respective studies and sample size can be found in a table under the supplementary section of the manuscript. All 3 methods were trained on GTEx v7 as well as CLUES, a single-cell RNA sequencing dataset of PBMCs. The data include the gene name, model, cross-validated R^2, prediction pvalue, TWAS p value, TWAS Z score, and a column titled "hFDR" indicating whether the association was statistically significant while employing hierarchical FDR. The benefits of employing such an approach for all methods can be found in the manuscript.</p> <p> </p> <p>We also include the eAssociations that we obtain by training prediction models on GTEx and CLUES alone. For the CxC and UTMOST approaches, these files contain the gene, context, pvalue and adjusted R^2. For CONTENT, these include the gene, context and pvalue and adjusted R^2 for each CONTENT model--the column names are described like a regression of y~x, rsq_y_x, so rsq_observed_full is the adjusted R^2 from regressing the observed expression onto the cross-validated full model predictions. In cases where the R^2 is higher from the specific or shared models, it's best to use either of those rather than the full model for out of sample prediction.</p>
Data associated with the manuscript "Simple statistical models can be sufficient for testing hypotheses with population time series data"
<p>This is a revised version of the archive of R code and data used in the manuscript, <em>Simple statistical models can be sufficient for testing hypotheses with population time series data. </em>The data are in three files. <em>etodata1.csv</em> and <em>etodata2.csv</em> contain two versions of the same data for shoal-dwelling fishes in the Etowah River and associated environmental covariates. <em>knz_dat</em> contains data for small mammals collected in the Konza Prairie Biological Station and associated environmental covariates. The R code consists of four primary files that call nine auxiliary files. CaseStudy1-main_code and CaseStudy2-main_code are the primary files for running the two case studies. Simulations1 and Simulations2 are the files for running the two batteries of simulations. We thank the Konza Prairie Biological Station and Konza Prairie Long-Term Ecological Research Program supported by the National Science Foundation (DEB-1440484) for collecting and providing access to mammal community data. More details are in the manuscript and supporting information. </p>
Applied Statistics data-sets
<p>This collection of data-sets is a companion to the <em><strong>Applied Statistics eBook</strong></em>, which is available to download at <a href="https://zenodo.org/record/6783846#.Yr3EQ3bMKUk">https://zenodo.org/record/6783846#.Yr3EQ3bMKUk</a> . The datasets have been selected for illustrative purposes and should not be relied upon as a basis for substantive research. </p>
Integrated Statistical Indicators from Scottish Linked Open Government Data
<p>Integrated statistical indicators that were retrieved from the official Scottish data portal in order to facilitate the exploitation of Machine Learning methods in Open Government Data. Data include 60 statistical indicators from seven categories such as health and social care, housing, and crime and justice. The indicators refer to the 6,976 “2011 data zones” of Scotland, while the year of reference is 2015. Data are ready to be used by the research community, students, policy makers, and journalists and give rise to plenty of social, business, and research scenarios that can be solved using Machine Learning technologies and methods.</p>
Yearly CDIP:MOP-alongshore modeled wave statistics for California, January 2000 - July 2022
<p><strong>Overview</strong></p> <ul> <li>Yearly wave averages for all 11,594 CDIPS-MOPS alongshore sites in California.</li> <li>Sites are defined in the files "CDIP_Transects.csv" and "CDIP_Transects.geojson". The bounds of each site are listed in the file "CA_region_bounds.csv"</li> <li>Data are described here: https://cdip.ucsd.edu/documents/index/product_docs/mops/mop_intro.html</li> <li>Data are obtained from here: https://thredds.cdip.ucsd.edu/thredds/catalog.html</li> </ul> <p><strong>Methods</strong></p> <ul> <li>Data are computed from hourly inshore wave hindcasts and nowcasts. Data download script is the file "CDIP_MassDownloader.ipynb"</li> <li>wave summary statistics have been computed using the file "Create_stats.ipynb". All yearly data are simple averages (i.e. mean values) of the hourly data</li> </ul> <p><strong>Data files</strong><br> Data have been split into 25 regions, defined in "CA_regions.json"</p> <p>Data are provided in geoJSON format, in the form of one file per region, and one file for all regions</p> <p><strong>Data fields</strong></p> <ul> <li>Hs: significant wave height [meters]</li> <li>Tp: peak wave period [seconds]</li> <li>Ta: average wave period [seconds]</li> <li>Dp: peak wave direction [degrees]</li> <li>Da: average wave direction [degrees]</li> <li>Ea: wave energy density, averaged over wave frequencies</li> <li>Es: wave energy density, summed over wave frequencies</li> <li>QC: quality flag</li> <li>waveTime: UTC time string</li> <li>metaWaterDepth: water depth of modeled wave data (range is 10-15m)</li> </ul>
Monthly CDIP:MOP-alongshore modeled wave statistics for California, January 2000 - July 2022
<p><strong>Overview</strong></p> <ul> <li>Monthly wave averages for all 11,594 CDIPS-MOPS alongshore sites in California.</li> <li>Jan 2000 to July 2022 inclusive</li> <li>Sites are defined in the files "CDIP_Transects.csv" and "CDIP_Transects.geojson". The bounds of each site are listed in the file "CA_region_bounds.csv"</li> <li>Data are described here: https://cdip.ucsd.edu/documents/index/product_docs/mops/mop_intro.html</li> <li>Data are obtained from here: https://thredds.cdip.ucsd.edu/thredds/catalog.html</li> </ul> <p><strong>Methods</strong></p> <ul> <li>Data are computed from hourly inshore wave hindcasts and nowcasts. Data download script is the file "CDIP_MassDownloader.ipynb"</li> <li>wave summary statistics have been computed using the file "Create_stats.ipynb". All monthly data are simple averages (i.e. mean values) of the hourly data</li> </ul> <p><strong>Data files</strong></p> <ul> <li>Data have been split into 25 regions, defined in "CA_regions.json"</li> <li>Data are provided in geoJSON format, in the form of one file per region, and one file for all regions</li> </ul> <p><strong>Data fields</strong></p> <ul> <li>Hs: significant wave height [meters]</li> <li>Tp: peak wave period [seconds]</li> <li>Ta: average wave period [seconds]</li> <li>Dp: peak wave direction [degrees]</li> <li>Da: average wave direction [degrees]</li> <li>Ea: wave energy density, averaged over wave frequencies</li> <li>Es: wave energy density, summed over wave frequencies</li> <li>QC: quality flag</li> <li>waveTime: UTC time string</li> <li>metaWaterDepth: water depth of modeled wave data (range is 10-15m)</li> </ul>
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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.