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1,243 results for “Statistics”
GWAS Summary Statistics from "Sex and statin-related genetic associations at the PCSK9 gene locus – results of genome-wide association meta-analysis"
<p>GWAMA summary statistics of PCSK9 levels stratified by sex and statin useage in Europeans.</p> <p>When using this data, please cite:</p> <p>Pott, J., Kheirkhah, A., Gadin, J.R. <em>et al.</em> Sex and statin-related genetic associations at the <em>PCSK9</em> gene locus: results of genome-wide association meta-analysis. <em>Biol Sex Differ</em> <strong>15</strong>, 26 (2024). https://doi.org/10.1186/s13293-024-00602-6</p> <p>All txt files contain the following columns:</p> <ul> <li>markername (unique SNP ID)</li> <li>chr</li> <li>bp_hg19 (base position according to hg19)</li> <li>EA (effect allele)</li> <li>OA (other allele)</li> <li>EAF (effect allele frequency)</li> <li>info (minimal info score across all used studies)</li> <li>nSamples (sample size per SNP)</li> <li>nStudies (in case of double-stratified data: number of studies; in case of single-stratified data: 2, as it is a meta-analysis of the two double-stratified data sets)</li> <li>beta (effect estimate)</li> <li>SE (standard error)</li> <li>pval (p-value)</li> <li>I2 (SNP heterogeneity across studies)</li> <li>invalidAssoc (TRUE/FALSE flag if this variant was excluded in our analysis)</li> <li>reason4exclusion (reason why this SNP was excluded)</li> <li>phenotype (phenotyp setting)</li> </ul>
Statistical analysis and dataset for: Acute exposure to caffeine improves foraging in an invasive ant
<p>Linked to the journal article published in iScience (https://doi.org/10.1016/j.isci.2024.109935).</p> <p><em><strong>Abstract</strong></em></p> <p>Argentine ants, <em>Linepithema humile</em>, are a particularly concerning invasive species. Control efforts often fall short likely due to a lack of sustained bait consumption. Using neuroactives, such as caffeine, to improve ant learning and navigation could increase recruitment and consumption of toxic baits. Here, we exposed <em>L. humile</em> to a range of caffeine concentrations and a complex ecologically relevant task: an open landscape foraging experiment. Without caffeine, we found no effect of consecutive foraging visits on the time the ants take to reach a reward, suggesting a failure to learn the reward’s location. However, under low to intermediate caffeine concentrations ants were 38% faster with each consecutive visit, implying that caffeine boosts learning. Interestingly, such improvements were lost at high doses. In contrast, caffeine had no impact on the ants’ homing behavior. Adding moderate levels of caffeine to baits could improve ant’s ability to learn its location, improving bait efficacy.</p> <p> </p> <ul> <li><strong>sample_videos.zip</strong>: A subset of the videos used for data extraction. The complete collection of videos is not publicly accessible primarily due to their considerable size (105.35GB). Requests for access to the entire video set are encouraged.</li> <li><strong>Preregistration.pdf</strong>: The preregistration created for data collection and analysis with justifications for deviations from it.</li> <li><strong>OpLan_D1_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments.</li> <li><strong>OpLan_D2_DLC_coordinates.zip</strong>: Cartesian coordinates obtained from DeepLabCut for each of the videos analysed.</li> <li><strong>OpLan_C1_reproject_coordinates.py</strong>: Python code used to standardise the ants' coordinates by ensuring the same corner of the A4 platform was used as the origin of the cartesian referential of all videos. The known dimensions of the A4 were further used to convert coordinates from pixels to millimetres.</li> <li><strong>OpLan_C2_remove_impossibilities.py</strong>: Python code used to account for DeepLabCut tracking errors, with any ant movement exceeding two millimetres per frame being considered implausible and subsequently removed.</li> <li><strong>OpLan_C3_find_changepoints.py</strong>: Python code used to automatically derive the times at which an ant reached and left the reward from the tracking data.</li> <li><strong>OpLan_C4_inward_outward_data.py</strong>: Python code used to calculate relevant measures for the foodward (inward) and nestward (outward) journey such as journey duration, mean instantaneous speed and path tortuosity.</li> <li><strong>OpLan_C5_Figure_2.R</strong>: R code used to produce the raw elements of Figure 2.</li> <li><strong>OpLan_C6_Figure_4.R</strong>: R code used to produce the raw elements of Figure 4.</li> <li><strong>OpLan_C7_Statistical_Analysis.html</strong>: Complete statistical analysis and code for the manuscript.</li> </ul>
Supporting data "Scaling theory for the statistics of slip at frictional interfaces"
<p>Principle data supporting "Scaling theory for the statistics of slip at frictional interfaces"</p> <p>T. W. J. de Geus and M. Wyart (2022), <em>Phys. Rev. E</em>, 106(6):065001.</p> <ul> <li>See code at <a href="../doi/10.5281/zenodo.10723197">doi: 10.5281/zenodo.10723197</a> (and its documentation) for workflow, detailed information of the data, and further dependencies. </li> <li>The files <code>N=*_Run*.zip</code> contain fully restorable events for event-driven athermal quasi-static shear. Sequentually numbered files contain different parts of a single dataset.</li> <li>The file <code>summary.zip</code> contains an extract of the key variables of these runs, and of triggers at different stresses. Finally, it contains "flow" data acquired by driving at finite rate. </li> <li>The files <code>N=3^6x4_Trigger_EnsemblePack.zip</code> contain fully restorable triggers at different stresses in the largest system. The sequentially numbered files correspond to one dataset split in different<em> </em><code>.h5</code> files.</li> <li>Highly specific (and poorly documentated) plotting functions are available upon request.</li> </ul>
Statistical analysis and dataset for: A high-throughput and sensitive method for food preference assays in walking insects
<p>Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2024.04.10.588882).</p> <p><em><strong>Abstract</strong></em></p> <p>Insects pose significant challenges in both pest management and ecological conservation. Often, the most effective strategy is employing toxicant-laced baits, which must also be designed to specifically attract and be preferred by the targeted species for optimal species-specific effectiveness. However, traditional methods for measuring bait preference are either non-comparative, meaning that most animals only ever taste one bait, or suffer from methodological or conceptual limitations. Here we demonstrate the value of direct comparison food preference assays using the invasive and pest ant <em>Linepithema humile </em>as a model. We compare the food preference sensitivity of non-comparative (one visit to a food source) and sequential comparative (visiting one type of food then another) assays at detecting low levels of aversive quinine in sucrose solution. We then introduce and test a novel dual-choice feeder method for simultaneous comparative evaluation of bait preferences, testing its effectiveness in discerning between foods with varying quinine or sucrose levels. While the non-sequential assay could not detect aversion to 1.25mM quinine in 1M sucrose, the sequential comparative approach detected aversion to quinine levels as low as 0.94mM. The novel dual feeder method approach could detect aversion to quinine levels as low as 0.31mM, and also preference for 1M sucrose over 0.75M sucrose. The dual-feeder method, combines the sensitivity of comparative evaluation with high throughput, ease of use, and avoidance of interpretational issues. This innovative approach offers a promising tool for rapid and effective testing of bait solutions, contributing to the development of targeted control strategies. Moreover, the method can be easily modified for application to a wide range of walking insects, such as cockroaches, crickets, and beetles.</p>
Data for paper "Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts"
<p>The forecasts and observation datasets are used in the paper "Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts". https://doi.org/10.1016/j.jhydrol.2021.127301</p> <p>The forecast data is a subset of the "ensemble for machine learning dataset (ENS4ML)" from ECMWF. </p> <p>The Python codes are stored in Github: https://github.com/wentao-bnu/LeNet_CSG_Precip</p>
Genome- and transcriptome-wide association summary statistics for outcome from traumatic brain injury
<p>The dataset contains summary statistics for the genome- and transcriptome-wide association studies (GWAS, TWAS) of genetic effects on outcome in traumatic brain injury (TBI). The study participants attended hospital within 24 hours of TBI, and underwent head computed tomography imaging.</p> <p><strong>Study participants</strong></p> <p>European ancestry data set contains 4710 individuals; multi-ethnic cohort 5268 individuals, including Europeans (n = 4710), Africans (n = 245) and Admixed Americans (n = 313).</p> <p>The largest European population contribution was from CENTER-TBI (Collaborative European NeuroTrauma Effectiveness Research, https://www.center-tbi.eu), where each participating center (60 centers from 20 countries in Europe) recruited patients between December 2013 and December 2017. The patients recruited in CENTER-TBI were supplemented by subjects from cohorts recruited at two European centres (Cambridge, UK, and Turku, Finland).</p> <p>The majority of patients in the US cohort were recruited between 2014 and 2018 to TRACK-TBI (Transforming Research and Clinical Knowledge in TBI, https://tracktbi.ucsf.edu) by the 18 US participant sites. The subjects recruited to the US cohort from TRACK-TBI were supplemented by patients recruited to an institutional research initiative at Mass General Brigham (MGB).</p> <p><strong>Outcome definition</strong></p> <p>Outcomes were measured using the extended Glasgow Outcome Scale (GOSE), ranging from 1 (dead) to 8 (upper good recovery), measured 6 months post-TBI. TBI severity was specified using the Glasgow Coma Score (GCS), with TBI classified as mild (GCS 13-15), moderate (GCS 9-12), or severe (GCS 3-8).</p> <p>To account for the effect of injury severity on outcome, sliding dichotomization was used to categorize outcome as favourable or unfavourable. A GOSE ≤ 4 was used to define an unfavourable outcome for patients with either moderate (GCS 9-12) or severe (GCS 3-8) TBI, while the unfavourable group was extended to patients with GOSE ≤ 7 if they had mild (GCS 13-15) TBI.</p> <p><strong>Genotype data and imputation</strong></p> <p>Genotyping was completed at FIMM Technology Center for CENTER-TBI, Cambridge, Turku patients and the Broad Institute for TRACK-TBI, using the Illumina Global Screening Array (GSA-24v2-0 + Multi-Disease). The MGB cohort were genotyped using Illumina’s Multi-Ethnic Global array (MEGA) and the pre-releases forms, including MEGA and MEGA-Ex arrays at Illumina at the MGB Translational Genomics Core.</p> <p>A unified quality control procedure was applied for each study cohort and the array-based genotypes were imputed using the Haplotype Reference Consortium panel. Autosomal chromosomes were considered, post-imputation data was filtered by imputation quality (INFO > 0.4 for CENTER-TBI, Cambridge and Turku; R2 > 0.4 for TRACK-TBI and MGB) and MAF > 1%.</p> <p><strong>Genome-wide association analysis and meta-analysis</strong></p> <p>Genome-wide single-marker scans were performed using a penalized likelihood-based Firth logistic regression, and implemented in PLINK v2.0. Using favourable outcome as reference, models were fitted on the basis of imputed allelic dosages. Age, sex, major extracranial injury, pupillary reactivity, and the first 10 principal components were included as covariates. Study cohort (CENTER-TBI, Cambridge, Turku) was an additional covariate in the CENTER-TBI GWAS.</p> <p>Fixed-effects meta-analysis of the three European ancestry GWAS was performed using METAL. For trans-ethnic meta-analysis, summary statistics of five GWASs in patients of European, African and Admixed Americans were aggregated via MR-MEGA.</p> <p><strong>Transcriptome-wide association study</strong></p> <p>Genetically regulated gene expression (GREx) was imputed using a regression model fitted on a separate gene expression database. Elastic net models provided by PrediXcan for all available GTEx brain tissues and whole blood were used. For TWAS, the same sliding dichotomy model for outcome with the same set of covariates as in the GWAS, but PCA components were replaced with the top five principal components of the respective gene expression data. </p> <p><strong>Column headers - GWAS</strong></p> <p>rsID: variant rsID<br> Chrom: chromosome<br> Pos: position (build GRCh38)<br> A1: effect allele<br> A2: reference allele<br> EAF: allele frequency of effect allele<br> Effect: effect size of effect allele<br> StdErr: standard error of effect size<br> P: p value of association (with genomic correction)<br> N: sample size</p> <p>Note. 'Effect' and 'StdErr' are only available for the European ancestry meta-analysis.</p> <p><br> <strong>Column headers - TWAS</strong></p> <p>tissue: GTEx tissue type<br> id: ensembl gene id<br> coef: model coefficient<br> se: model standard error for coefficient<br> p: model-based p value<br> symbol: gene symbol<br> name: gene name written out<br> chr: chromosome<br> start: gene start position (build GRCh38)</p>
Replication data for: 'A First-Order Statistical Exploration of the Mathematical Limits of Micromagnetic Tomography'
<p>This repository contains the random data generated for obtaining results described in "A first-order statistical exploration of the mathematical limits of Micromagnetic Tomography". All tested parameters are systematically divided over different folders and subfolders. This dataset contains only .npy files, generated with python version 3.8.8 and numpy version 1.21.5.</p> <p>Each file can be opened with numpy.load(filename)</p> <p>The resulting figures are constructed with data of at least 15 iterations; each iteration is stored in a separate folder 'test_' followed by the iteration number.</p> <p>The README file inside provides a detailled overview of the files included.</p>
Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors: V4.NA.02.MAPLE
<p>We estimate ground-level fine particulate matter (PM<sub>2.5</sub>) total and compositional mass concentrations over North America by combining Aerosol Optical Depth (AOD) retrievals from the NASA MODIS, MISR, and SeaWIFS instruments with the GEOS-Chem chemical transport model, and subsequently calibrated to regional ground-based observations of both total and compositional mass using Geographically Weighted Regression (GWR) as detailed in the provided reference for V4.NA.02. V4.NA.02.MAPLE further modified the V4.NA.02 GWR method with additional developments as part of the MAPLE (Mortality–Air Pollution Associations in Low-Exposure Environments) project. This adjustment was of particular value over low concentrations. The GWR method of individual components remains unchanged from V4.NA.02, but are provided are percentages to ensure mass closure and recommended to be applied to the V4.NA.02.MAPLE total PM<sub>2.5</sub>.</p> <p>Annual datasets are provided in NetCDF [.nc]. Gridded files use the WGS84 projection. Compositional estimates are provided for sulfate (SO4), nitrate (NO3), ammonium (NH4), organic matter (OM), black carbon (BC), mineral dust (DUST), and sea-salt (SS). Percentages are denoted with a ‘p’ after component identifiers within filenames. A slight change in file name has been included for 2017, corresponding to minor internal changes compared to earlier years. Overall, however, the dataset is consistent throughout its entire time period and can be appropriately used for trend analysis.</p> <p><strong>Reference:</strong><br> van Donkelaar, A., R. V. Martin, et al. (2019). <strong>Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors.</strong> Environmental Science & Technology, 2019, doi:10.1021/acs.est.8b06392.</p>
Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation
<p>Dark matter (DM), if exists, is believed to be cold, collisionless, dissipationless, non-baryonic, barely interacting with baryonic matter except through gravity, and sufficiently smooth on large scales with a fluid-like behavior. The flow of dark matter can be best described by a self-gravitating collisionless fluid dynamics (SG-CFD). The statistics of dark matter density, velocity, acceleration, energy, momentum, and their redshift evolution play essential roles for structure formation and evolution. These information can be systematically extracted from cosmological N-body simulations by either i) a structural (halo-based) or ii) a statistical (correlation-based) approach. In this halo-based statistical dataset, i) all halos in N-body system are identified with all particles divided into halo and out-of-halo particles; ii) halos are grouped into halo groups including all halos of the same mass (m<sub>h</sub>); iii) the redshift (z) and mass scale (m<sub>h</sub>) dependence of all halo properties (momentum, energy, size, shape, velocity, acceleration, etc.) are presented . </p> <p>Applications of cascade and statistical theory for dark matter and bulge-SMBH evolution:</p> <ol> <li>Dark matter particle mass ,size, and properties from energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.07240">arxiv</a> 2) <a href="https://zenodo.org/record/6640353">zenodo slides</a></li> <li>Origin of MOND acceleration & deep-MOND from acceleration fluctuation & energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2203.05606">arxiv</a> 2) <a href="https://zenodo.org/record/6640386">zenodo slides</a></li> <li>The baryonic-to-halo mass relation from mass and energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2203.06899">arxiv</a> 2) <a href="https://zenodo.org/record/6640355">zenodo slides</a></li> <li>Universal scaling laws and density slope for dark matter haloes: 1) <a href="http://doi.org/10.48550/arXiv.2209.03313">arxiv</a> 2) <a href="https://zenodo.org/record/7059193">zenodo slides</a> 3) <a href="http://doi.org/10.1038/s41598-023-31083-z">paper</a></li> <li>Dark matter halo mass functions and density profiles from mass/energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2210.01200">arxiv</a> 2) <a href="https://zenodo.org/record/7146473">zenodo slides</a> 3) <a href="https://doi.org/10.1038/s41598-023-42958-6">paper</a></li> <li>Energy cascade for distribution and evolution of supermassive black holes (SMBHs): 2) <a href="http://doi.org/10.5281/zenodo.7490502">zenodo slides</a></li> </ol> <p>Condensed slides for all applications "<a href="http://doi.org/10.5281/zenodo.7508310">Cascade Theory for Turbulence, Dark Matter, and bulge-SMBH evolution </a>"</p> <p>The two relevant datasets and accompanying presentation can be found at: </p> <ol> <li><a href="https://doi.org/10.5281/zenodo.6541230">Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation</a> </li> <li><a href="https://doi.org/10.5281/zenodo.6569898">Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation</a>.</li> <li><a href="https://doi.org/10.5281/zenodo.6569901">A comparative study of Dark matter flow & hydrodynamic turbulence and its applications</a></li> </ol> <p>The same dataset also available on Github at: <a href="https://github.com/ZhijieXu2022/dark_matter_flow_dataset/">Github: dark_matter_flow_dataset</a> and zenodo at: <a href="http://doi.org/10.5281/zenodo.6586212">Dark matter flow dataset from cosmological N-body simulation</a>.</p> <p>Cascade and statistical theory developed by these datasets:</p> <ol> <li>Inverse mass cascade in dark matter flow and effects on halo mass functions: 1) <a href="http://doi.org/10.48550/arXiv.2109.09985">arxiv</a> 2) <a href="https://zenodo.org/record/6639536">zenodo slides</a> </li> <li>Inverse mass cascade and effects on halo deformation, energy, size, and density profiles: 1) <a href="http://doi.org/10.48550/arXiv.2109.12244">arxiv</a> 2) <a href="https://zenodo.org/record/6640337">zenodo slides</a></li> <li>Inverse energy cascade in dark matter flow and effects of halo shape: 1) <a href="http://doi.org/10.48550/arXiv.2110.13885">arxiv</a> 2) <a href="https://zenodo.org/record/6640331">zenodo slides</a></li> <li>The mean flow, velocity dispersion, energy transfer and evolution of dark matter halos: 1) <a href="http://doi.org/10.48550/arXiv.2201.12665">arxiv</a> 2) <a href="https://zenodo.org/record/6640380">zenodo slides</a></li> <li>Two-body collapse model and generalized stable clustering hypothesis for pairwise velocity 1) <a href="http://doi.org/10.48550/arXiv.2110.05784">arxiv</a> 2) <a href="https://zenodo.org/record/6640306">zenodo slides</a></li> <li>Energy, momentum, spin parameter in dark matter flow and integral constants of motion: 1) <a href="http://doi.org/10.48550/arXiv.2202.04054">arxiv</a> 2) <a href="https://zenodo.org/record/6640322">zenodo slides</a></li> <li>Maximum entropy distributions of dark matter in ΛCDM cosmology: 1) <a href="http://doi.org/10.48550/arXiv.2110.03126">arxiv</a> 2) <a href="https://zenodo.org/record/6640373">zenodo slides</a> 3) <a href="http://doi.org/10.1051/0004-6361/202346429">paper</a></li> <li>Halo mass functions from maximum entropy distributions in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2110.09676">arxiv</a> 2) <a href="https://zenodo.org/record/6640325">zenodo slides</a></li> <li>On the statistical theory of self-gravitating collisionless dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.00910">arxiv</a> 2) <a href="https://zenodo.org/record/6640705">zenodo slides</a> 3) <a href="http://doi.org/10.1063/5.0151129">paper</a></li> <li>High order kinematic and dynamic relations for velocity correlations in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.02991">arxiv</a> 2) <a href="https://zenodo.org/record/6640684">zenodo slides</a></li> <li>Evolution of density and velocity distributions and two-thirds law for pairwise velocity: 1) <a href="http://doi.org/10.48550/arXiv.2202.06515">arxiv</a> 2) <a href="https://zenodo.org/record/6640676">zenodo slides</a></li> </ol>
Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation
<p>Dark matter (DM), if exists, is believed to be cold, collisionless, dissipationless, non-baryonic, barely interacting with baryonic matter except through gravity, and sufficiently smooth on large scales with a fluid-like behavior. The flow of dark matter can be best described by a self-gravitating collisionless fluid dynamics (SG-CFD). The statistics of dark matter density, velocity, acceleration, energy, momentum, and their redshift evolution play essential roles for structure formation and evolution. These information can be systematically extracted from cosmological N-body simulations by either i) a structural (halo-based) or ii) a statistical (correlation-based) approach. In this correlation-based statistical dataset, i) all particle pairs with any given separation r in a N-body system are identified; ii) statistical measures are calculated over all particle pairs with the same separation r (pairwise average); iii) the redshift (z) and scale (r) dependence of all statistical measures (correlation/moment/structure/dispersion/spectrum functions for density, velocity and potential etc.) are presented. </p> <p>Applications of cascade and statistical theory for dark matter and bulge-SMBH evolution:</p> <ol> <li>Dark matter particle mass ,size, and properties from energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.07240">arxiv</a> 2) <a href="https://zenodo.org/record/6640353">zenodo slides</a></li> <li>Origin of MOND acceleration & deep-MOND from acceleration fluctuation & energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2203.05606">arxiv</a> 2) <a href="https://zenodo.org/record/6640386">zenodo slides</a></li> <li>The baryonic-to-halo mass relation from mass and energy cascade in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2203.06899">arxiv</a> 2) <a href="https://zenodo.org/record/6640355">zenodo slides</a></li> <li>Universal scaling laws and density slope for dark matter haloes: 1) <a href="http://doi.org/10.48550/arXiv.2209.03313">arxiv</a> 2) <a href="https://zenodo.org/record/7059193">zenodo slides</a> 3) <a href="http://doi.org/10.1038/s41598-023-31083-z">paper</a></li> <li>Dark matter halo mass functions and density profiles from mass/energy cascade: 1) <a href="http://doi.org/10.48550/arXiv.2210.01200">arxiv</a> 2) <a href="https://zenodo.org/record/7146473">zenodo slides</a> 3) <a href="https://doi.org/10.1038/s41598-023-42958-6">paper</a></li> <li>Energy cascade for distribution and evolution of supermassive black holes (SMBHs): 2) <a href="http://doi.org/10.5281/zenodo.7490502">zenodo slides</a></li> </ol> <p>Condensed slides for all applications "<a href="http://doi.org/10.5281/zenodo.7508310">Cascade Theory for Turbulence, Dark Matter, and bulge-SMBH evolution </a>"</p> <p>The two relevant datasets and accompanying presentation can be found at: </p> <ol> <li><a href="https://doi.org/10.5281/zenodo.6541230">Dark matter flow dataset Part I: Halo-based statistics from cosmological N-body simulation</a> </li> <li><a href="https://doi.org/10.5281/zenodo.6569898">Dark matter flow dataset Part II: Correlation-based statistics from cosmological N-body simulation</a>.</li> <li><a href="https://doi.org/10.5281/zenodo.6569901">A comparative study of Dark matter flow & hydrodynamic turbulence and its applications</a></li> </ol> <p>The same dataset also available on Github at: <a href="https://github.com/ZhijieXu2022/dark_matter_flow_dataset/">Github: dark_matter_flow_dataset</a> and zenodo at: <a href="http://doi.org/10.5281/zenodo.6586212">Dark matter flow dataset from cosmological N-body simulation</a>.</p> <p>Cascade and statistical theory developed by these datasets:</p> <ol> <li>Inverse mass cascade in dark matter flow and effects on halo mass functions: 1) <a href="http://doi.org/10.48550/arXiv.2109.09985">arxiv</a> 2) <a href="https://zenodo.org/record/6639536">zenodo slides</a> </li> <li>Inverse mass cascade and effects on halo deformation, energy, size, and density profiles: 1) <a href="http://doi.org/10.48550/arXiv.2109.12244">arxiv</a> 2) <a href="https://zenodo.org/record/6640337">zenodo slides</a></li> <li>Inverse energy cascade in dark matter flow and effects of halo shape: 1) <a href="http://doi.org/10.48550/arXiv.2110.13885">arxiv</a> 2) <a href="https://zenodo.org/record/6640331">zenodo slides</a></li> <li>The mean flow, velocity dispersion, energy transfer and evolution of dark matter halos: 1) <a href="http://doi.org/10.48550/arXiv.2201.12665">arxiv</a> 2) <a href="https://zenodo.org/record/6640380">zenodo slides</a></li> <li>Two-body collapse model and generalized stable clustering hypothesis for pairwise velocity 1) <a href="http://doi.org/10.48550/arXiv.2110.05784">arxiv</a> 2) <a href="https://zenodo.org/record/6640306">zenodo slides</a></li> <li>Energy, momentum, spin parameter in dark matter flow and integral constants of motion: 1) <a href="http://doi.org/10.48550/arXiv.2202.04054">arxiv</a> 2) <a href="https://zenodo.org/record/6640322">zenodo slides</a></li> <li>Maximum entropy distributions of dark matter in ΛCDM cosmology: 1) <a href="http://doi.org/10.48550/arXiv.2110.03126">arxiv</a> 2) <a href="https://zenodo.org/record/6640373">zenodo slides</a> 3) <a href="http://doi.org/10.1051/0004-6361/202346429">paper</a></li> <li>Halo mass functions from maximum entropy distributions in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2110.09676">arxiv</a> 2) <a href="https://zenodo.org/record/6640325">zenodo slides</a></li> <li>On the statistical theory of self-gravitating collisionless dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.00910">arxiv</a> 2) <a href="https://zenodo.org/record/6640705">zenodo slides</a> 3) <a href="http://doi.org/10.1063/5.0151129">paper</a></li> <li>High order kinematic and dynamic relations for velocity correlations in dark matter flow: 1) <a href="http://doi.org/10.48550/arXiv.2202.02991">arxiv</a> 2) <a href="https://zenodo.org/record/6640684">zenodo slides</a></li> <li>Evolution of density and velocity distributions and two-thirds law for pairwise velocity: 1) <a href="http://doi.org/10.48550/arXiv.2202.06515">arxiv</a> 2) <a href="https://zenodo.org/record/6640676">zenodo slides</a></li> </ol>
Summary statistics accompanying the article "Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency" in Scientific Reports (2022)
<p>Summary statistics for genome-wide association studies reported in:</p> <p>Bell, S., Tozer, D.J., & Markus H.S. (2022). Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency. <em>Scientific Reports</em>, DOI: <a href="https://dx.doi.org/10.1038/s41598-022-19106-7">10.1038/s41598-022-19106-7</a>. </p> <p><strong>Abstract</strong></p> <p>Complex brain networks play a central role in integrating activity across the human brain, and such networks can be identified in the absence of any external stimulus. We performed 10 genome-wide association studies of resting state network measures of intrinsic brain activity in up to 36,150 participants of European ancestry in the UK Biobank. We found that the heritability of global network efficiency was largely explained by blood oxygen level-dependent (BOLD) resting state fluctuation amplitudes (RSFA), which are thought to reflect the vascular component of the BOLD signal. RSFA itself had a significant genetic component and we identified 24 genomic loci associated with RSFA, 157 genes whose predicted expression correlated with it, and 3 proteins in the dorsolateral prefrontal cortex and 4 in plasma. We observed correlations with cardiovascular traits, and single-cell RNA specificity analyses revealed enrichment of vascular related cells. Our analyses also revealed a potential role of lipid transport, store-operated calcium channel activity, and inositol 1,4,5-trisphosphate binding in resting-state BOLD fluctuations. We conclude that that the heritability of global network efficiency is largely explained by the vascular component of the BOLD response as ascertained by RSFA, which itself has a significant genetic component.</p> <p> </p> <p>Further information on the files uploaded here can be found in the README. Users interested in bulk downloading these summary statistics may find <a href="https://github.com/dvolgyes/zenodo_get">zenodo_get</a> helpful.</p>
Statistical analysis and dataset for: Three-dimensional body reconstruction enables quantification of liquid consumption in small invertebrates
<p>Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2024.06.14.599002).</p> <p><em><strong>Abstract</strong></em></p> <p>Quantifying feeding patterns provides valuable insights into animal behaviour. However, small invertebrates often consume incredibly small amounts of food. This renders traditional methods, such as weighing individuals before and after food acquisition, either inaccurate or prohibitively expensive. Here, we present a non-invasive method to quantify food consumption of small invertebrates whose body expands during feeding. Using the markerless pose estimation software DeepLabCut, we three-dimensionally track the body of Argentine ants, <em>Linepithema humile</em>. Using these extracted markers, we developed an algorithm which computationally reconstructs the ant’s body, directly measuring volumetric change over time. Moreover, we provide measures of accuracy and quantify the ant’s feeding response to a range of sucrose concentrations, as well as a gradient of caffeine-laced sucrose solutions. Small invertebrates are often prolific invasive species and disease vectors, causing significant ecological and economical damage. Understanding their feeding behaviour could be an important step towards effective control strategies.</p> <p> </p> <ul> <li><strong>VolEst_C1_volume_calculation_multiprocessing.py</strong>: Takes as input H5 3D DeepLabCut files, calculates the gaster volume at every frame using seven different methods and outputs these as CSV files.</li> <li><strong>VolEst_C2_interactive_GUI.py</strong>: Given a folder with Volume CSV files, interactively plots the volume over time, 3D coordinates tracked by DeepLabCut and the frame of interest for both cameras.</li> <li><strong>VolEst_C3_linear_regression.py</strong>: Applies a linear regression to each feeding event tracked and provides measures of interest such as crop load and consumption rate.</li> <li><strong>VolEst_C4_statistical_analysis</strong>: Complete statistical analysis and code for the manuscript.</li> <li><strong>VolEst_D1_sucrose_density.csv</strong>: Data obtained to quantify the density of sucrose solutions of varying molarity.</li> <li><strong>VolEst_D2_accuracy_weight_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the weight-volume accuracy measurements.</li> <li><strong>VolEst_D3_accuracy_weight.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the weight-volume accuracy measurements.</li> <li><strong>VolEst_D4_accuracy_nanoliter_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the volume-volume accuracy measurements.</li> <li><strong>VolEst_D5_accuracy_nanoliter.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the volume-volume accuracy measurements.</li> <li><strong>VolEst_D6_sucrose_caffeine_consumption_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the sucrose and caffeine dilutions application measurements.</li> <li><strong>VolEst_D7_sucrose_caffeine_consumption.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the sucrose and caffeine dilutions application measurements.</li> <li><strong>VolEst_Camera_A-Henrique-2023-09-20.zip</strong>: DeepLabCut labels and trained network for camera A.</li> <li><strong>VolEst_Camera_B-Henrique-2023-09-20.zip</strong>: DeepLabCut labels and trained network for camera B.</li> <li><strong>VolEst_base.stl</strong>: 3D file for the resin platform used in the experimental validation of the setup.</li> <li><strong>VolEst_platform.stl</strong>: 3D file for the resin platform used in the experimental validation of the setup.</li> </ul>
Statistical analysis and dataset for: Invasive ants fed spinosad collectively recruit to known food faster yet individually abandon food earlier
<p>Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2024.06.20.599949).</p> <p><em><strong>Abstract</strong></em></p> <p>Current management strategies applied to invasive ants rely on slow-acting insecticides which aim to delay the ant’s ability to detect the poison until its effects are noticeable. Despite this, most control efforts are unsuccessful, likely due to bait abandonment and insufficient sustained consumption. Conditioned taste aversion, a learned avoidance of a particular taste, is a crucial survival mechanism which prevents animals from repeatedly ingesting toxic substances. However, whether ants are capable of this delayed association between food taste and subsequent illness remains largely unexplored. Here, we exposed colonies of the highly invasive Argentine ant, <em>Linepithema humile</em>, to a sublethal dose of the slow-acting insecticide spinosad. We combined measurements of individual-level feeding patterns with quantification of collective preferences and foraging dynamics to investigate the potential effects of the toxicant on behaviour. Collectively, ants preferred an odour associated with a previously experienced food, even if this contained spinosad, over a novel one. However, at the individual-level, previous exposure to spinosad resulted in reduced food consumption, as a consequence of earlier food abandonment. Moreover, while control-treated colonies recruited slower to a food source which tasted like a previously experienced one, spinosad-exposed colonies recruited equally fast to both novel and familiar foods. Although it appears that ants are unable to develop a conditioned taste aversion to sublethal doses of spinosad, ingestion of even small amounts of the toxicant strongly influences foraging behaviour. Understanding the subtle effects of slow-acting pesticides on ant cognition and behaviour can ultimately inspire the development of more efficient control methodologies.</p>
Supporting Data for the paper titled "The Intensity, Directionality and Statistics of Underwater Noise from Melting Icebergs"
<p>The dataset contains:</p> <p>a) 13 audio files, with names including date, track number and channel; format: WAV files</p> <p>b) data from magnetic compass used to calculate noise directionality; format: txt files with lines containing date, time and magnetic direction (degrees)</p> <p>c) GPS tracks of the boat and attached acoustic buoy; format: txt files with NMEA codes</p> <p>d) GPS tracks around each iceberg tracked; format: txt files with UTM coordinates</p> <p>The study was founded by National Science Centre Poland grant no. 2013/11/N/ST10/01729 and partially supported within statutory activities No 3841/E-41/S/2018 of the Ministry of Science and Higher Education of Poland. Partial support for this work was also provided by US Office of Naval Research, Grant No. N00014-17-1-2633.</p> <p>Corresponding author: Oskar Glowacki, oglowacki@igf.edu.pl</p>
Dataset for: Statistical properties of meso-scale plasma flows in the nightside high-latitude ionosphere
<p>This dataset is a compilation of statistical results from Gabrielse et al. [2018] (<a href="https://doi.org/10.1029/2018JA025440">https://doi.org/10.1029/2018JA025440</a>). If you would like to use the dataset, please contact Christine Gabrielse (cgabrielse@ucla.edu, cgabrielse@gmail.com). Depending on how the results are used, the main authors request co-authorship on publications. </p> <p>The following list describes the columns in each data file labeled, ***_FLOW-DATA-PCvsAO_YYYY.txt <br> Files named ***_FLOW-DATA-PCvsAO_YYYY_poleward.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> flagAO [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> flagPC [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> FWHMavg_AO [degrees]<br> FWHMkmavg_AO=[km]<br> longtestranges=[ignore]<br> Velmaxavg_AO=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_AO=[m/s, determined from the Gaussian fits]<br> FWHMavg_PC=[degrees]<br> FWHMkmavg_PC=[km]<br> Velmaxavg_PC=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_PC=[m/s, determined from the Gaussian fits]<br> ;;For the bearings/orientation, see the orientation text files. The following four variables were calculated in a first step but are not<br> ;;those used in the paper. They were not found with the strict selection criteria. Please do not use.<br> mbearingAO=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> mbearingPC=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)] <br> gbearingAO=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> gbearingPC=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> ;;;;;;;;;;;;;;;<br> minlatAO=[degrees, min geographic latitude of the flow]<br> maxlatAO=[degrees, max geographic latitude of the flow]<br> minlatPC=[degrees, min geographic latitude of the flow]<br> maxlatPC=[degrees, max geographic latitude of the flow]<br> mltAO=[degrees (MLT)]<br> mltPC=[degrees (MLT)]<br> AE=[nT]<br> AL=[nT]<br> SYMH=[nT]<br> IMFBz=[nT]<br> IMFBy=[nT]<br> F107=[sfu]</p> <p>The following list describes the columns in each data file labeled, ***_orientation_YYYY.txt <br> Files named ***_orientation_YYYY_poleward.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> The orientation was determined when enough bearings between RGs were available. See Gabrielse et al. [2018] for description. <br> https://doi.org/10.1029/2018JA025440 <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> mbearingAO [degrees clockwise from magnetic North]<br> gbearingAO [degrees clockwise from geographic North]<br> mbearingPC [degrees clockwise from magnetic North]<br> gbearingPC [degrees clockwise from geographic North]</p> <p>The following list describes the columns in each data file labeled, ***_SPEC_TEST_***_noRG1-2.txt</p> <p> time [YYYYMMDDhhmmss]<br> RG [the range gate number at which the polar cap boundary was determined at RNK, or the auroral oval's equatorial boundary at SAS]</p>
Meningitis hGWAS results (summary statistics)
<p>Summary statistics for association between genetic variation and meningitis phenotypes. Contains human genome association and interaction effects (pGWAS.tar.bz2).</p> <p>Unpack with `tar xf `. Contents are described in the README.</p>
Voxel-level summary statistics of hippocampus shape, white matter microstructure, and cortical surface curvature in UK Biobank (n=33,324)
<p>This deposit hosts GWAS summary statistics of hippocampus shape (n=33,324), white matter microstructure (n=33,324), and cortical surface curvature (n=15,752) using UKB unrelated white subjects. The data was generated by using the highly efficient imaging genetics (<a href="https://github.com/Zhiwen-Owen-Jiang/heig">HEIG v1.1.0</a>) framework where only the triplets - summary statistics of low-dimensional representations (LDRs), the functional bases, and the variance-covariance matrix LDRs - are shared, which is sufficient to recover all voxel-variant pairs as well as to conduct voxel-level heritability and (cross-trait) genetic correlation analysis. Check the <a href="https://github.com/Zhiwen-Owen-Jiang/heig/wiki">tutorial</a> and the <a href="../records/13770930">example data</a> used in the tutorial. </p> <p>The shared data includes:</p> <p>1. Triplets for hippocampus shape measured by the radial distance from the medial model for each vertex. The original images contain 30,000 vertices while the shared data contains 49 LDRs. Left and right hemispheres were analyzed separately, each with 15,000 vertices.</p> <p>2. Triplets for 21 white matter tracts measured by fractional anisotropy. The original images contain 32,217 voxels and each tract contains 88 ~ 3503 voxels while the shared data contains 1,034 LDRs. Tracts were analyzed separately.</p> <p>3. Triplets for cortical surface curvature. The original images contain 59,412 vertices while the shared data contains 1,750 LDRs. The entire brain was analyzed as a whole.</p> <p>4. LD matrix and its inverse for 22 chromosomes including 460k genotyped SNPs. LD matrix and its inverse were estimated by using two separate datasets each containing 8.4k white unrelated subjects in UKB. Two regularization levels are provided: {85%, 80%} for heritability and genetic correlations within images and {75%, 70%} for cross-trait genetic correlations.</p> <p>5. LD matrix and its inverse for 22 chromosomes including 1.2 million imputed HapMap3 SNPs. LD matrix and its inverse were estimated by using two separate datasets each containing 42k white unrelated subjects in UKB. Two regularization levels are provided: {98%, 95%} for heritability and genetic correlations within images and {90%, 85%} for cross-trait genetic correlations.</p>
Marine heatwaves statistics for the tropical western and central Pacific Ocean
<p>Processed marine heatwave metrics are provided for the tropical western and central Pacific Ocean region (120°E-140°W, 40°S-15°N). The metrics are computed from daily sea surface temperature (SST) data, from both observations and models. The observed marine heatwave data are calculated from NOAA 0.25° daily Optimum Interpolation Sea Surface Temperature (OISST) over the period 1982-2019. The modelled marine heatwave data are from analysis of 18 model simulations as part of the Coupled Model Intercomparison Project, Phase 6 (CMIP6) over the period 1982-2100, where two future scenarios have been analysed. Marine heatwaves are computed with respect to the 1995-2014 climatology. The marine heatwave data are provided on a grid point basis across the domain. Marine heatwave timeseries metrics are also provided for three case study regions: Fiji, Samoa, and Palau.</p>
GWAS summary statistics for waist-to-hip ratio and body principal components
<p>This dataset contains genome-wide association summary statistics for waist-to-hip ratio (WHR), as well as those for body principal components (PCs). A subset of 387,139 unrelated, white British individuals were analyzed for WHR. PCs were combined from the summary statistics for WHR and 13 other anthropometric traits (body mass index, standing height, weight, hip circumference, waist circumference, arm lean mass (left), arm fat mass (left), leg lean mass (left), leg fat mass (left), trunk lean mass, trunk fat mass, body fat percentage, basal metabolic rate) provided by the Neale lab (http://www.nealelab.is/uk-biobank). All traits were inverse-rank normal transformed (by the Neale lab or ourselves for WHR).</p> <p>All effect sizes, including those for PCs, are standardized, i.e. they represent the effects on a trait with variance 1.</p> <p>The zip files contain the data to run the sample pipeline and the shiny app, both available from <a href="https://github.com/JonSulc/PCA_Cross-sex_MR">https://github.com/JonSulc/PCA_Cross-sex_MR</a>.</p>
QTL summary statistics from the DIRECT consortium
<p>These are the complete summary statistics for DIRECT genotype-phenotypes associations (QTLs). The project performed genotype-phenotype associations for gene expression (RNAseq), targeted proteins (Olink), targeted metabolites (Biocrates) and untargeted metabolites (Metabolon) derived from 3,029 blood and plasma samples from the DIRECT cohort. This submission includes supplementary files and nominal pvalues (as uncorrected pvalues) for all associations included in the manuscript. Trans associations included are typically limited to pvalues <1e-04. Network tables are also included, with information to load and use Cytoscape to visualize them. This is version 2, some files were missing on version 1.</p>
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.