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4,563 results for “correlations”
Accuracy of phylogenetic reconstructions from continuous characters analyzed under parsimony and its parametric correlates
<p>Quantitative traits are a source of evolutionary information often difficult to handle in cladistics. Tools exist to analyze this kind of data without subjective discretization, avoiding biases in the delimitation of categorical states. Nonetheless, the ability of continuous characters to accurately infer relationships is incompletely understood, particularly under parsimony analysis. This study evaluates the accuracy of phylogenetic reconstructions from simulated matrices of continuous characters evolving under alternative evolutionary processes and analyzed by parsimony. We sampled 100 empirical trees to simulate 9,000 matrices, each containing between 25 and 50 taxa and 50 and 150 continuous characters evolving under three evolutionary processes: Brownian-Motion (BM), Ornstein-Uhlenbeck (OU) and Early-Burst (EB) with variable parametrizations. Our cladogram comparisons revealed that continuous character matrices, when discretized objectively and analyzed by parsimony in TNT, carry phylogenetic signals to infer species relationships, regardless of the evolutionary models and parameterization schemes. Interestingly, implementing Equal Weighting (EW) or Implied Weighting (IW) with varying penalization strengths against homoplasies did not affect cladogram reconstructions on the basis of continuous characters. Finally, the accuracy of continuous characters in resolving species relationships is skewed toward apical nodes of the recovered trees. Our findings provide general insights of the utility of quantitative traits in cladistics and demonstrate that their effectiveness in estimating shallower nodes is independent of the underlying evolutionary model, parameters and weighting schemes.</p>
Datasets for "Quantifying nanodiamonds biodistribution in whole cells with correlative iono-nanoscopy"
<p><strong>Readme:</strong></p> <p>These are datasets that were generated during experiments, simulations, and data processing.</p> <p><strong>[1]</strong> Data saved in .xlsx files are data points for plots and can be opened with Microsoft Excel.</p> <p><strong>[2]</strong> Data saved in .txt files are image files recorded in 1024 x 1024 pixel arrays. A custom software IonDAQ-lite (ref: Bettiol, A. A. et al. Nucl. Instrum. Methods Phys. Res. B 267, 2069-2072, 2009) was used to open and process these image files, however some commonly used image processing programs (e.g. Matlab) can also be used to open these files.</p> <p><strong>[3]</strong> The .lsm and .czi files are 2D or 3D confocal image files and can be viewed with ZEN 3.4 (ZEN lite, Carl Zeiss).</p> <p> </p>
Dataset for: Bedding scale correlation on Mars in western Arabia Terra
<p>Dataset for: Bedding scale correlation on Mars in western Arabia Terra</p> <p>A.M. Annex et al.</p> <p>Data Product Overview</p> <p>This repository contains all source data for the publication. Below is a description of each general data product type, software that can load the data, and a list of the file names along with the short description of the data product.</p> <p><strong>HiRISE Digital Elevation Models (DEMs).</strong></p> <p>HiRISE DEMs produced using the Ames Stereo Pipeline are in geotiff format ending with ‘*X_0_DEM-adj.tif’, the “X” prefix denotes the spatial resolution of the data product in meters. Geotiff files are able to be read by free GIS software like QGIS.</p> <p><strong>HiRISE map-projected imagery (DRGs).</strong></p> <p>Map-projected HiRISE images produced using the Ames Stereo Pipeline are in geotiff format ending with ‘*0_Y_DRG-cog.tif’, the “Y” prefix denotes the spatial resolution of the data product in centimeters. Geotiff files are able to be read by free GIS software like QGIS. The DRG files are formatted as COG-geotiffs for enhanced compression and ease of use.</p> <p><strong>3D Topography files (.ply).</strong></p> <p>Traingular Mesh versions of the HiRISE/CTX topography data used for 3D figures in “.ply” format. Meshes are greatly geometrically simplified from source files. Topography files can be loaded in a variety of open source tools like ParaView and Meshlab. Textures can be applied using embedded texture coordinates.</p> <p><strong>3D Geological Model outputs (.vtk)</strong></p> <p>VTK 3D file format files of model output over the spatial domain of each study site. VTK files can be loaded by ParaView open source software. The “block” files contain the model evaluation over a regular grid over the model extent. The “surfaces” files contain just the bedding surfaces as interpolated from the “block” files using the marching cubes algorithm.</p> <p><strong>Geological Model geologic maps (geologic_map.tif).</strong></p> <p>Geologic maps from geological models are standard geotiffs readable by conventional GIS software. The maximum value for each geologic map is the “no-data” value for the map. Geologic maps are calculated at a lower resolution than the topography data for storage efficiency.</p> <p><strong>Beds Geopackage File (.gpkg).</strong></p> <p>Geopackage vector data file containing all mapped layers and associated metadata including dip corrected bed thickness as well as WKB encoded 3D linestrings representing the sampled topography data to which the bedding orientations were fit. Geopackage files can be read using GIS software like QGIS and ArcGIS as well as the OGR/GDAL suite. A full description of each column in the file is provided below.</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>uuid</td> <td>String</td> <td>unique identifier</td> </tr> <tr> <td>stratum_order</td> <td>Real</td> <td>0-indexed bed order</td> </tr> <tr> <td>section</td> <td>Real</td> <td>section number</td> </tr> <tr> <td>layer_id</td> <td>Real</td> <td>bed number/index</td> </tr> <tr> <td>layer_id_bk</td> <td>Real</td> <td>unused backup bed number/index</td> </tr> <tr> <td>source_raster</td> <td>String</td> <td>dem file path used</td> </tr> <tr> <td>raster</td> <td>String</td> <td>dem file name</td> </tr> <tr> <td>gsd</td> <td>Real</td> <td>ground sampling distant for dem</td> </tr> <tr> <td>wkn</td> <td>String</td> <td>well known name for dem</td> </tr> <tr> <td>rtype</td> <td>String</td> <td>raster type</td> </tr> <tr> <td>minx</td> <td>Real</td> <td>minimum x position of trace in dem crs</td> </tr> <tr> <td>miny</td> <td>Real</td> <td>minimum y position of trace in dem crs</td> </tr> <tr> <td>maxx</td> <td>Real</td> <td>maximum x position of trace in dem crs</td> </tr> <tr> <td>maxy</td> <td>Real</td> <td>maximum y position of trace in dem crs</td> </tr> <tr> <td>method</td> <td>String</td> <td>internal interpolation method</td> </tr> <tr> <td>sl</td> <td>Real</td> <td>slope in degrees</td> </tr> <tr> <td>az</td> <td>Real</td> <td>azimuth in degrees</td> </tr> <tr> <td>error</td> <td>Real</td> <td>maximum error ellipse angle</td> </tr> <tr> <td>stdr</td> <td>Real</td> <td>standard deviation of the residuals</td> </tr> <tr> <td>semr</td> <td>Real</td> <td>standard error of the residuals</td> </tr> <tr> <td>X</td> <td>Real</td> <td>mean x position in CRS</td> </tr> <tr> <td>Y</td> <td>Real</td> <td>mean y position in CRS</td> </tr> <tr> <td>Z</td> <td>Real</td> <td>mean z position in CRS</td> </tr> <tr> <td>b1</td> <td>Real</td> <td>plane coefficient 1</td> </tr> <tr> <td>b2</td> <td>Real</td> <td>plane coefficient 2</td> </tr> <tr> <td>b3</td> <td>Real</td> <td>plane coefficient 3</td> </tr> <tr> <td>b1_se</td> <td>Real</td> <td>standard error plane coefficient 1</td> </tr> <tr> <td>b2_se</td> <td>Real</td> <td>standard error plane coefficient 2</td> </tr> <tr> <td>b3_se</td> <td>Real</td> <td>standard error plane coefficient 3</td> </tr> <tr> <td>b1_ci_low</td> <td>Real</td> <td>plane coefficient 1 95% confidence interval low</td> </tr> <tr> <td>b1_ci_high</td> <td>Real</td> <td>plane coefficient 1 95% confidence interval high</td> </tr> <tr> <td>b2_ci_low</td> <td>Real</td> <td>plane coefficient 2 95% confidence interval low</td> </tr> <tr> <td>b2_ci_high</td> <td>Real</td> <td>plane coefficient 2 95% confidence interval high</td> </tr> <tr> <td>b3_ci_low</td> <td>Real</td> <td>plane coefficient 3 95% confidence interval low</td> </tr> <tr> <td>b3_ci_high</td> <td>Real</td> <td>plane coefficient 3 95% confidence interval high</td> </tr> <tr> <td>pca_ev_1</td> <td>Real</td> <td>pca explained variance ratio pc 1</td> </tr> <tr> <td>pca_ev_2</td> <td>Real</td> <td>pca explained variance ratio pc 2</td> </tr> <tr> <td>pca_ev_3</td> <td>Real</td> <td>pca explained variance ratio pc 3</td> </tr> <tr> <td>condition_number</td> <td>Real</td> <td>condition number for regression</td> </tr> <tr> <td>n</td> <td>Integer64</td> <td>number of data points used in regression</td> </tr> <tr> <td>rls</td> <td>Integer(Boolean)</td> <td>unused flag</td> </tr> <tr> <td>demeaned_regressions</td> <td>Integer(Boolean)</td> <td>centering indicator</td> </tr> <tr> <td>meansl</td> <td>Real</td> <td>mean section slope</td> </tr> <tr> <td>meanaz</td> <td>Real</td> <td>mean section azimuth</td> </tr> <tr> <td>angular_error</td> <td>Real</td> <td>angular error for section</td> </tr> <tr> <td>mB_1</td> <td>Real</td> <td>mean plane coefficient 1 for section</td> </tr> <tr> <td>mB_2</td> <td>Real</td> <td>mean plane coefficient 2 for section</td> </tr> <tr> <td>mB_3</td> <td>Real</td> <td>mean plane coefficient 3 for section</td> </tr> <tr> <td>R</td> <td>Real</td> <td>mean plane normal orientation vector magnitude</td> </tr> <tr> <td>num_valid</td> <td>Integer64</td> <td>number of valid planes in section</td> </tr> <tr> <td>meanc</td> <td>Real</td> <td>mean stratigraphic position</td> </tr> <tr> <td>medianc</td> <td>Real</td> <td>median stratigraphic position</td> </tr> <tr> <td>stdc</td> <td>Real</td> <td>standard deviation of stratigraphic index</td> </tr> <tr> <td>stec</td> <td>Real</td> <td>standard error of stratigraphic index</td> </tr> <tr> <td>was_monotonic_increasing_layer_id</td> <td>Integer(Boolean)</td> <td>monotonic layer_id after projection to stratigraphic index</td> </tr> <tr> <td>was_monotonic_increasing_meanc</td> <td>Integer(Boolean)</td> <td>monotonic meanc after projection to stratigraphic index</td> </tr> <tr> <td>was_monotonic_increasing_z</td> <td>Integer(Boolean)</td> <td>monotonic z increasing after projection to stratigraphic index</td> </tr> <tr> <td>meanc_l3sigma_std</td> <td>Real</td> <td>lower 3-sigma meanc standard deviation</td> </tr> <tr> <td>meanc_u3sigma_std</td> <td>Real</td> <td>upper 3-sigma meanc standard deviation</td> </tr> <tr> <td>meanc_l2sigma_sem</td> <td>Real</td> <td>lower 3-sigma meanc standard error</td> </tr> <tr> <td>meanc_u2sigma_sem</td> <td>Real</td> <td>upper 3-sigma meanc standard error</td> </tr> <tr> <td>thickness</td> <td>Real</td> <td>difference in meanc</td> </tr> <tr> <td>thickness_fromz</td> <td>Real</td> <td>difference in Z value</td> </tr> <tr> <td>dip_cor</td> <td>Real</td> <td>dip correction</td> </tr> <tr> <td>dc_thick</td> <td>Real</td> <td>thickness after dip correction</td> </tr> <tr> <td>dc_thick_fromz</td> <td>Real</td> <td>z thickness after dip correction</td> </tr> <tr> <td>dc_thick_dev</td> <td>Integer(Boolean)</td> <td>dc_thick <= total mean dc_thick</td> </tr> <tr> <td>dc_thick_fromz_dev</td> <td>Integer(Boolean)</td> <td>dc_thick <= total mean dc_thick_fromz</td> </tr> <tr> <td>thickness_fromz_dev</td> <td>Integer(Boolean)</td> <td>dc_thick <= total mean thickness_fromz</td> </tr> <tr> <td>dc_thick_dev_bg</td> <td>Integer(Boolean)</td> <td>dc_thick <= section mean dc_thick</td> </tr> <tr> <td>dc_thick_fromz_dev_bg</td> <td>Integer(Boolean)</td> <td>dc_thick <= section mean dc_thick_fromz</td> </tr> <tr> <td>thickness_fromz_dev_bg</td> <td>Integer(Boolean)</td> <td>dc_thick <= section mean thickness_fromz</td> </tr> <tr> <td>slr</td> <td>Real</td> <td>slope in radians</td> </tr> <tr> <td>azr</td> <td>Real</td> <td>azimuth in radians</td> </tr> <tr> <td>meanslr</td> <td>Real</td> <td>mean slope in radians</td> </tr> <tr> <td>meanazr</td> <td>Real</td> <td>mean azimuth in radians</td> </tr> <tr> <td>angular_error_r</td> <td>Real</td> <td>angular error of section in radians</td> </tr> <tr> <td>pca_ev_1_ok</td> <td>Integer(Boolean)</td> <td>pca_ev_1 < 99.5%</td> </tr> <tr> <td>pca_ev_2_3_ratio</td> <td>Real</td> <td>pca_ev_2/pca_ev_3</td> </tr> <tr> <td>pca_ev_2_3_ratio_ok</td> <td>Integer(Boolean)</td> <td>pca_ev_2_3_ratio > 15</td> </tr> <tr> <td>xyz_wkb_hex</td> <td>String</td> <td>hex encoded wkb geometry for all points used in regression</td> </tr> </tbody> </table> <p><strong>Geological Model input files (.gpkg).</strong></p> <p>Four geopackage (.gpkg) files represent the input dataset for the geological models, one per study site as specified in the name of the file. The files contain most of the columns described above in the Beds geopackage file, with the following additional columns. The final seven columns (azimuth, dip, polarity, formation, X, Y, Z) constituting the actual parameters used by the geological model (GemPy). </p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>azimuth_mean</td> <td>String</td> <td>Mean section dip azimuth </td> </tr> <tr> <td>azimuth_indi</td> <td>Real</td> <td>Individual bed azimuth</td> </tr> <tr> <td>azimuth</td> <td>Real</td> <td>Azimuth of trace used by the geological model</td> </tr> <tr> <td>dip</td> <td>Real</td> <td>Dip for the trace used by the geological mode</td> </tr> <tr> <td>polarity</td> <td>Real</td> <td>Polarity of the dip vector normal vector </td> </tr> <tr> <td>formation</td> <td>String</td> <td>String representation of layer_id required for GemPy models</td> </tr> <tr> <td>X</td> <td>Real</td> <td>X position in the CRS of the sampled point on the trace</td> </tr> <tr> <td>Y</td> <td>Real</td> <td>Y position in the CRS of the sampled point on the trace</td> </tr> <tr> <td>Z</td> <td>Real</td> <td>Z position in the CRS of the sampled point on the trace</td> </tr> </tbody> </table> <p><strong>Stratigraphic Column Files (.gpkg).</strong></p> <p>Stratigraphic columns computed from the Geological Models come in three kinds of Geopackage vector files indicated by the postfixes <code>_sc</code>, <code>rbsc</code>, and <code>rbssc</code>. File names include the wkn site name.</p> <p><strong>sc (_sc.gpkg).</strong></p> <p>Geopackage vector data file containing measured bed thicknesses from Geological Model joined with corresponding Beds Geopackage file, subsetted partially. The columns largely overlap with the the list above for the Beds Geopackage but with the following additions</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>X</td> <td>Real</td> <td>X position of thickness measurement</td> </tr> <tr> <td>Y</td> <td>Real</td> <td>Y position of thickness measurement</td> </tr> <tr> <td>Z</td> <td>Real</td> <td>Z position of thickness measurement</td> </tr> <tr> <td>formation</td> <td>String</td> <td>Model required string representation of bed index</td> </tr> <tr> <td>bed thickness (m)</td> <td>Real</td> <td>difference of bed elevations</td> </tr> <tr> <td>azimuths</td> <td>Real</td> <td>azimuth as measured from model in degrees</td> </tr> <tr> <td>dip_degrees</td> <td>Real</td> <td>dip as measured from model in degrees</td> </tr> <tr> <td>Dip corrected bed thickness (m)</td> <td>Real</td> <td>dip corrected bed thickness in meters</td> </tr> <tr> <td>lower_point</td> <td>Real</td> <td>lower bed elevation in meters</td> </tr> <tr> <td>upper_point</td> <td>Real</td> <td>upper bed elevation in meters</td> </tr> <tr> <td>_formation</td> <td>Real</td> <td>integer number of formation string</td> </tr> <tr> <td>layer_iid</td> <td>Integer64</td> <td>integer number of layer_id</td> </tr> <tr> <td>bascom_baryte_diff_bt</td> <td>Real</td> <td>diff. in thickness from geomodel measurements</td> </tr> <tr> <td>bascom_baryte_diff_dcbt</td> <td>Real</td> <td>diff. in dip cor. thicknesses ’’</td> </tr> </tbody> </table> <p><strong>rbsc (rbsc.gpkg)</strong></p> <p>Geopackage vector file containing virtual boreholes with high resolution vertical sampling placed in a regular grid in the spatial extent of the DEM with the following columns.</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>formation</td> <td>String</td> <td>Model required string representation of bed index</td> </tr> <tr> <td>pred_z</td> <td>Real</td> <td>Z value of bedding plane</td> </tr> <tr> <td>layer_id</td> <td>Integer64</td> <td>Bed Index from Beds Geopackage file</td> </tr> <tr> <td>section</td> <td>Real</td> <td>section number</td> </tr> <tr> <td>thickness</td> <td>Real</td> <td>thickness of the layer predicted by model</td> </tr> <tr> <td>geom</td> <td>Point</td> <td>contains X,Y,Z of point in dem CRS</td> </tr> </tbody> </table> <p><strong>rbssc (rbssc.gpkg)</strong></p> <p>Geopackage vector file containing virtual boreholes with high resolution vertical sampling placed at the centroids for each section within the spatial extent of the DEM with the following columns.</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>formation</td> <td>String</td> <td>Model required string representation of bed index</td> </tr> <tr> <td>pred_z</td> <td>Real</td> <td>Z value of bedding plane</td> </tr> <tr> <td>layer_id</td> <td>Integer64</td> <td>Bed Index from Beds Geopackage file</td> </tr> <tr> <td>section</td> <td>Real</td> <td>section number</td> </tr> <tr> <td>thickness</td> <td>Real</td> <td>thickness of the layer predicted by model</td> </tr> <tr> <td>geom</td> <td>Point</td> <td>contains X,Y,Z of point in dem CRS</td> </tr> </tbody> </table> <p><strong>crescent_shapes.gpkg</strong></p> <p>Geopackage vector file containing the measurements of the crescent features.</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>azimuth</td> <td>Real</td> <td>Azimuth of the crescent feature in degrees</td> </tr> </tbody> </table>
Dataset: Correlative Light, Electron Microscopy and Raman Spectroscopy Workflow to Detect and Observe Microplastic Interactions with Whole Jellyfish
<p>ABSTRACT</p> <p>Many researchers have turned their attention to understanding microplastic interaction with marine fauna. Efforts are being made to monitor exposure pathways and concentrations, and to assess the impact such interactions may have. To answer these questions, it is important to select appropriate experimental parameters and analytical protocols. This study focuses on medusae of <em>Cassiopea andromeda</em> jellyfish: a unique benthic jellyfish known to favor (sub-)tropical coastal regions which are potentially exposed to plastic waste from land-based sources. Juvenile medusae were exposed to fluorescent poly(ethylene terephthalate) and polypropylene microplastics (< 300 µm), resin embedded, and sectioned before analysis with confocal laser scanning microscopy as well as transmission electron microscopy and Raman Spectroscopy. Results show the fluorescent microplastics were stable enough to be detected with the optimized analytical protocol presented, and that their observed interaction with medusae occurs in a manner which is likely driven by the microplastic properties (<em>e.g.</em> density, hydrophobicity).</p>
Discrimination of textures with spatial correlations and multiple gray levels
<p>This upload contains supporting psychophysical and modeling data for Victor, J.D., Rizvi, S.M, Bush, J.W., and Conte, M.M. (2023) Discrimination of textures with spatial correlations and multiple gray levels. J. Opt. Soc. Am. A 40, 237-258, and Victor, J.D., Thengone, D.J., Rizvi, S.M., and Conte, M.M. (2015) A perceptual space of local image statistics. Vision Research 117, 117-135. Please see the docx files in this upload, and the above papers, for further information.</p>
Data for: Detecting Long-Term Balancing Selection Using Allele Frequency Correlation
<p>Genome-wide and top 1% scores for 1KG project data output from BetaScan reported in:</p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/28981714/">Detecting Long-Term Balancing Selection Using Allele Frequency Correlation.</a></p> <p>Siewert KM, Voight BF. Mol Biol Evol. 2017 Nov 1;34(11):2996-3005. doi: 10.1093/molbev/msx209.</p> <p>PMID: 28981714</p> <p>Code available at: https://github.com/ksiewert/BetaScan</p>
Raw and post-processed data for the study of prosodic cues to word boundaries in a segmentation task using reverse correlation
<p>The current dataset provides all the stimuli (folder <strong>../01-Stimuli/</strong>), raw data (folder <strong>../02-Raw-data/</strong>) and post-processed data (<strong>../03-Post-proc-data/</strong>) used in a prosody reverse correlation study with the title "prosodic cues to word boundaries in a segmentation task using reverse correlation" by the same authors. The listening experiment was implemented using one-interval trials with target words of the structure l'aX (option 1) and la'X (option 2). The experiment was designed and implemented using the <a href="https://github.com/aosses-tue/fastACI">fastACI toolbox</a> under the name 'segmentation'. A between-subject design was used with a total of 47 participants, who evaluated one of five conditions, LAMI (N=16), LAPEL (N=18), LACROCH (N=5), LALARM (N=5), and LAMI_SHIFTED (N=3). More details are given in the related publication (to be submitted to JASA-EL in May 2023).</p>
Raw and post-processing data for using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox
<p><strong>Description</strong>: The current dataset provides all the stimuli (folder ../01-Stimuli/), raw data (folder ../02-Raw-data/) and post-processed data (../03-Post-proc-data/) used in the Forum Acusticum 2013 paper titled "Using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox" by the same authors. In this paper, we replicated the tone-in-noise experiment by Ahumada et al. (1975) but using an artificial listener instead of collecting data from real participants. The behavioural data were mimicked using an artificial listener based on 'king2019' (King et al., 2019) as a front-end model using a template-matching decision to indicate whether a 500-Hz tone was (or not) present in each of the noisy trials. This study offers a step-by-step guide of how can be an artificial listener integrated into fastACI.</p> <p><strong>Use these data</strong>: Download all these data, locate them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="https://github.com/aosses-tue/fastACI">https://github.com/aosses-tue/fastACI</a>) you can recreate the figures of our paper. After downloading and initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB), run the script <strong>g20230501_FA_Artificial_listener_paper_figs.m</strong> (provided in this dataset) and follow the instructions on the screen to generate one of the four study figures. This script calls the function <strong>publ_osses2023b_FA_figs.m</strong> from the toolbox. </p> <p> </p>
Simulation output for Improving the stability of bivariate correlations using informative Bayesian priors: A Monte Carlo simulation study
<p>This repository contains the (compressed) simulation output from <em>Improving the stability of bivariate correlations using informative Bayesian priors: A Monte Carlo simulation study</em>. On a Linux-based system, extract the contents with:</p> <pre><code class="language-bash">tar -xvzf raw_output_compressed.tar.gz </code></pre> <p>Please refer to the published article (https://doi.org/10.3389/fpsyg.2023.1253452) and the associated GitHub (<a href="https://github.com/carldelfin/stability-of-bivariate-correlations">github.com/carldelfin/stability-of-bivariate-correlations</a>) for additional information.</p>
AgrImOnIA: Open Access dataset correlating livestock and air quality in the Lombardy region, Italy
<p>The AgrImOnIA dataset is a comprehensive dataset relating air quality and livestock (expressed as the density of bovines and swine bred) along with weather and other variables. The AgrImOnIA Dataset represents the first step of the <a href="http://www.agrimonia.net">AgrImOnIA project</a>. The purpose of this dataset is to give the opportunity to assess the impact of agriculture on air quality in Lombardy through statistical techniques capable of highlighting the relationship between the livestock sector and air pollutants concentrations.</p> <p>The building process of the dataset is detailed in the <strong>companion paper:</strong></p> <p>A. Fassò, J. Rodeschini, A. Fusta Moro, Q. Shaboviq, P. Maranzano, M. Cameletti, F. Finazzi, N. Golini, R. Ignaccolo, and P. Otto (2023). Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy. <em>SCIENTIFIC DATA</em>, 1-19.</p> <p>available <a href="https://rdcu.be/c7T9H">here</a>.</p> <p>This dataset is a collection of estimated daily values for a range of measurements of different dimensions as: air quality, meteorology, emissions, livestock animals and land use. Data are related to Lombardy and the surrounding area for 2016-2021, inclusive. The surrounding area is obtained by applying a 0.3° buffer on Lombardy borders.</p> <p>The data uses several aggregation and interpolation methods to estimate the measurement for all days.</p> <p>The files in the record, renamed according to their version (es. .._v_3_0_0), are:</p> <ul> <li> <p>Agrimonia_Dataset.csv(.mat and .Rdata) which is built by joining the daily time series related to the AQ, WE, EM, LI and LA variables. In order to simplify access to variables in the Agrimonia dataset, the variable name starts with the dimension of the variable, i.e., the name of the variables related to the AQ dimension start with 'AQ_'. This file is archived also in the format for MATLAB and R software. </p> </li> <li> <p>Metadata_Agrimonia.csv which provides further information about the Agrimonia variables: e.g. sources used, original names of the variables imported, transformations applied.</p> </li> <li> <p>Metadata_AQ_imputation_uncertainty.csv which contains the daily uncertainty estimate of the imputed observation for the AQ to mitigate missing data in the hourly time series. </p> </li> <li> <p>Metadata_LA_CORINE_labels.csv which contains the label and the description associated with the CLC class. </p> </li> <li> <p>Metadata_monitoring_network_registry.csv which contains all details about the AQ monitoring station used to build the dataset. Information about air quality monitoring stations include: station type, municipality code, environment type, altitude, pollutants sampled and other. Each row represents a single sensor.</p> </li> <li> <p>Metadata_LA_SIARL_labels.csv which contains the label and the description associated with the SIARL class.</p> </li> <li> <p>AGC_Dataset.csv(.mat and .Rdata) that includes daily data of almost all variables available in the Agrimonia Dataset (excluding AQ variables) on an equidistant grid covering the Lombardy region and its surrounding area. </p> </li> </ul> <p>The Agrimonia dataset can be reproduced using the code available at the GitHub page: <a href="https://github.com/AgrImOnIA-project/AgrImOnIA_Data">https://github.com/AgrImOnIA-project/AgrImOnIA_Data</a></p> <p><strong>UPDATE 31/05/2023</strong> <strong>- NEW RELEASE - V 3.0.0</strong></p> <p>A new version of the dataset is released: Agrimonia_Dataset_v_3_0_0.csv (.Rdata and .mat), where variable <em>WE_rh_min, WE_rh_mean and WE_rh_max </em>have been recomputed due to some bugs<em>.</em></p> <p>In addition, two new columns are added, they are <em>LI_pigs_v2 and LI_bovine_v2 </em>and represents the density of the pigs and bovine (expressed as animals per kilometer squared) of a square of size ~ 10 x 10 km centered at the station localisation.</p> <p>A new dataset is released: the Agrimonia Grid Covariates (AGC) that includes daily information for the period from 2016 to 2020 of almost all variables within the Agrimonia Dataset on a equidistant grid containing the Lombardy region and its surrounding area. The AGC does not include AQ variables as they come from the monitoring stations that are irregularly spread over the area considered.</p> <p><strong>UPDATE 11/03/2023</strong> <strong>- NEW RELEASE - V 2.0.2</strong></p> <p>A new version of the dataset is released: Agrimonia_Dataset_v_2_0_2.csv (.Rdata), where variable <em>WE_tot_precipitation </em>have been recomputed due to some bugs<em>.</em></p> <p>A new version of the metadata is available: Metadata_Agrimonia_v_2_0_2.csv where the spatial resolution of the variable <em>WE_precipitation_t </em>is corrected.</p> <ul> </ul> <p><strong>UPDATE 24/01/2023</strong> <strong>- NEW RELEASE - V 2.0.1</strong></p> <p>minor bug fixed</p> <p><strong>UPDATE 16/01/2023</strong> <strong>- NEW RELEASE - V 2.0.0</strong></p> <p>A new version of the dataset is released, Agrimonia_Dataset_v_2_0_0.csv (.Rdata) and Metadata_monitoring_network_registry_v_2_0_0.csv. Some minor points have been addressed:</p> <ul> <li>Added values for <em>LA_land_use</em> variable for Switzerland stations (in Agrimonia Dataset_v_2_0_0.csv)</li> <li>Deleted incorrect values for <em>LA_soil_use</em> variable for stations outside Lombardy region during 2018 (in Agrimonia Dataset_v_2_0_0.csv)</li> <li>Fixed duplicate sensors corresponding to the same pollutant within the same station<em> </em>(in Metadata_monitoring_network_registry_v_2_0_0.csv)</li> </ul>
Data and code for "Phase transitions in inorganic halide perovskites from machine learning potentials: The impact of size, rate, and the underlying exchange-correlation functional"
<p>This record contains databases with data from density functional theory calculations used for training a series of neuroevolution potentials (NEPs), which are also included here. Information is also included for how to access the databases and run the NEP models.</p> <p><strong>Databases</strong><br> The <code>*.db</code> files are databases with the results from density functional theory (DFT) calculations. These are sqlite databases in ase format, see <a href="https://wiki.fysik.dtu.dk/ase/tutorials/tut06_database/database.html">here</a> for more information. The <code>demo-database-access.py</code> script illustrates the most basic access.</p> <p><strong>Models</strong><br> The neuroevolution potential (NEP) models described in the publication can be found in the <code>nep-*.txt</code> files. They can be used in conjunction with the <a href="https://gpumd.org">GPUMD package</a>. The <a href="https://calorine.materialsmodeling.org">calorine package</a> provides a Python interface to GPUMD.</p> <p><strong>Primitive structures</strong><br> Several primitive structures in extended xyz format can be found in the <code>*.xyz</code> files. These structures have been relaxed using the NEP models included here. The <code>demo-for-using-structures-and-models.py</code> script illustrates how to access the structures and models.</p>
Supplementary Tables for Expression of cell-wall related genes is highly variable and correlates with sepal morphology
<p>Supplementary Information and script for "Expression of cell-wall related genes is highly variable and correlates with sepal morphology"</p> <p>R scripts for analysis</p> <p>Data necessary to run analyses</p> <p>Generated data</p> <p>Supplementary Tables</p> <p> </p>
Data of the publication "Real-time broadening of bath-induced density profiles from closed-system correlation functions"
<p>The Lindblad master equation is one of the main approaches to open quantum systems. While it has been<br> widely applied in the context of condensed matter systems to study properties of steady states in the limit<br> of long times, the actual route to such steady states has attracted less attention yet. Here, we investigate the<br> nonequilibrium dynamics of spin chains with a local coupling to a single Lindblad bath and analyze the transport<br> properties of the induced magnetization. Combining typicality and equilibration arguments with stochastic<br> unraveling, we unveil for the case of weak driving that the dynamics in the open system can be constructed<br> on the basis of correlation functions in the closed system, which establishes a connection between the Lindblad<br> approach and linear response theory at finite times. In this way, we provide a particular example where closed and<br> open approaches to quantum transport agree strictly. We demonstrate this fact numerically for the spin-1/2 XXZ<br> chain at the isotropic point and in the easy-axis regime, where superdiffusive and diffusive scaling is observed,<br> respectively.</p>
HawaiiCoast_GT: Curated AIS for Hawaii's coast correlated with ground truth incidents
<p>Because of the high-risk nature of emergencies and illegal activities at sea, it is critical that algorithms designed to detect anomalies from maritime traffic data be robust. However, there exist no publicly available maritime traffic datasets with real-world labelled anomalies. As a result, most anomaly detection algorithms for maritime traffic are validated without ground truth. We introduce the HawaiiCoast_GT dataset, the first ever publicly available automatic identification system dataset with a large corresponding set of true anomalous incidents. This dataset—cleaned and curated from Bureau of Ocean Energy Management (BOEM) and National Oceanic and Atmospheric Administration (NOAA) automatic identification system (AIS) data--covers Hawaii’s coastal waters for four years (2017-2020) and contains 88,749,176 AIS points for a total of 2,622 unique vessels. 208 tracks are labelled corresponding to 154 labelled real-world incidents. The codebase used to curate the original AIS data is being made openly available on GitHub.</p>
Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization"
<p>Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization".<br>Preprint of the paper available at: <a href="https://arxiv.org/abs/2309.03308">https://arxiv.org/abs/2309.03308</a></p>
Stachewicz et al. 2021: Trait correlation, phylogenetic signal in Carabidae morphology (repackaging of occurrences published by the NEON Biorepository Data Portal)
Stachewicz JD, Fountain-Jones NM, Koontz A, Woolf H, Pearse WD, Gallinat AS. 2021. Strong trait correlation and phylogenetic signal in North American ground beetle (Carabidae) morphology bioRxiv 02.12.431029; doi: https://doi.org/10.1101/2021.02.12.431029 Many NEON samples and specimens used in this work resulted from NEON prototype data and will not be archived in the Biorepository. See the appendices in the above linked article for a full list of NEON samples and specimens and their associated collection data. Additionally, see appendices of above linked article for specimen-level morphological trait measurements and genetic sequence data.
Fig. 3 in Histopathological alterations in Astyanax bifasciatus (Teleostei: Characidae) correlated with land uses of surroundings of streams
Fig. 3. Photomicrograph of liver of Astyanax bifasciatus in streams of the basin of the lower Iguaçu River. a. Normal liver, central vein (CV), and sinusoids capillary (S) in forest stream (F1). b. Tubular arrangement of hepatocytes (H), nucleus (arrow), nucleolus (head of arrow), and centrilobular vein (*) in forest stream (F1); c. Pancreatic tissue (*), presence of eosinophils around the hepatopancreas tissue (arrow) in forest stream (F2). d. Presence of picnotic nucleus (head of arrow) and nuclear hypertrophy (arrow) in rural stream (R2). e. Cytoplasmic degeneration (*), the picnotic nucleus (arrow), the nucleus in a lateral position (head of arrow) in urban stream (U2). f. Picnotic nucleus (head of arrow) and cytoplasmic vacuolization (*) in rural stream (R2). g. Leukocyte infiltration (arrow) in urban stream (U1). h. Melanomacrophage aggregates (arrows) around the central vein (CV) in urban stream (U2). Stained Hematoxylin Harris and eosin.
Fig. 2 in Histopathological alterations in Astyanax bifasciatus (Teleostei: Characidae) correlated with land uses of surroundings of streams
Fig. 2. Photomicrograph of gills of Astyanax bifasciatus from streams of the basin of the lower Iguaçu River. a. Normal aspect of gill in forest stream (F1), F - filament, L - lamellae. b. Lamellar oedema (arrows) in forest stream (F2). c. Lamellar aneurysm (*) in urban stream (U1). d. Lamellar hyperplasia (arrows) in rural stream (R2). e. Lamellar hyperplasia (arrows) in highest magnification in rural stream (R2). f. Partial fusion of lamellae (*) in rural stream (R1). g. Epithelium rupture and hemorrhage (arrow) in urban stream (U2). h. Mitochondria-rich cells hyperplasia (arrow) and mucous cells hyperplasia (head of arrow) in urban stream (U2). a-g. Stained Hematoxylin Harris and eosin. h. Stained toluidine blue.
Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants
<p>original daily data for 'Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants'</p>
Fig. 4 in Silk Spinning Behavior Varies from Species-Specific to Individualistic in Embioptera: Do Environmental Correlates Account for this Diversity?
Fig. 4. Computation of similarity between two sequences. For two individuals of Aposthonia borneensis (Hagen) (Oligotomidae), the first 500 steps of their spin sequences are shown (A and B).The red bar underlining a short sequence indicates one 15-step subsequence that is highly similar between these two individuals. For all possible pairs of 15 step subsequences, the heatmap displays the sequence similarity (C), with red areas indicating regions of the spin sequence that are highly similar.The profiles on the margins of the heat map indicate the marginal maxima—that is, for each 15-step subsequence, what is the similarity to the most similar subsequence in the other individual. Portions of the sequence with similarities about 12 were deemed sufficiently similar to the other sequence (vertical or horizontal lines), amounting to about 5% of individual 1's sequence and 10% of individual 2's sequence.
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