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34 results for “compositional data analysis”
Atmospheric clumped O2 isotope composition simulation data and analysis scripts from EMAC/aMC models
<p>This publication contains source code, data and analysis scripts/results of the simulations presented in the following manuscript:</p> <blockquote> <p>Laskar, A.H., G.A. Adnew, S.S. Gromov, R. Peethambaran, B. Steil, J. Lelieveld, T. Blunier and T. Röckmann (2022). "Large variations in atmospheric oxidants and temperature during the Holocene" (in review)</p> </blockquote> <p> </p> <p><strong>EMAC simulations analysis</strong></p> <p>The analysis contains integrals of species burdens and other atmospheric physicochemical parameters obtained with the clumped isotopes of oxygen (CIO)-enabled ECHAM/MESSy Atmospheric Chemistry model (EMAC, see <a href="https://www.messy-interface.org">MESSy consortium website</a> for more information) model in various climate states. Simulations were performed in 2021–2022 at the <a href="https://www.dkrz.de">German Climate Computing Centre</a> (DKRZ) with the support of the <a href="https://www.palmod.de">PalMod project</a>.</p> <p>Analysis data is stored in human/machine-readable file <code>D36-EMAC-analysis.dat</code>, please refer to its header for variables description, etc.</p> <p>Additional (to those presented in the manuscript) analysis plots from EMAC data analysis are available in <code>D36-EMAC-analysis.vsz</code> (see the hardcopy in <code>D36-EMAC-analysis.pdf</code>) prepared using the <a href="https://veusz.github.io">Veusz</a> software.</p> <p> </p> <p><strong>2BM/MC (two-box Monte-Carlo) model code, simulation data and analysis</strong></p> <p>2BM/MC code/simulation setup is implemented within the advanced Monte-Carlo framework (aMC) and is available in the <a href="https://gitlab.com/sergey.gromov/amc/-/tree/vpCIO">respective repository</a>. A copy of the source code used to perform simulations is provided here (see <code>aMC-vpCIO.tar.gz</code> archive).</p> <p>2BM/MC output is stored in the <a href="https://www.unidata.ucar.edu/software/netcdf/">netCDF format</a> (ver. 4) and can be read in by any compatible software. The output contains probe statistics (reference <em>probed</em> distributions of the variables) in <code>vpCIO-probe_stat-*.nc</code> and resulting statistics (distributions <em>matching</em> given criteria, i.e. changes to the Δ36 signature vs. PD conditions) in <code>vpCIO-delta-*.nc</code> files, respectively.</p> <p>We use <a href="https://ferret.pmel.noaa.gov">NOAA Ferret</a> software to derive additional statistics of the third parameter (viz. average STE (<em>S</em>) changes) over the obtained 2D frequency histograms of other parameters (viz. changes to equilibration rate (<em>Req)</em> and temperature (<em>Teq</em>)). The scripts exemplifying this calculation are presented in <code>D36-vpCIO-analysis__proc*</code> files, which output results/overview plots in <code>vpCIO-delta-*__proc.nc</code> and <code>vpCIO-delta-*.gif</code> files.</p> <p>The analysis of the 2BM/MC simulation is available in <code>D36-vpCIO-analysis.vsz</code> script (see the hardcopy in <code>D36-vpCIO-analysis.pdf</code>) prepared using the <a href="https://veusz.github.io">Veusz</a> software. Note that some plots require the abovementioned third-parameter statistics as input.</p> <p><strong>Performing simulations with 2BM/MC</strong></p> <p>In order to perform simulations (e.g. with altered parameters), please follow the <a href="https://gitlab.com/sergey.gromov/amc/-/tree/vpCIO#integrating-your-code-building-executing">respective guide</a> for and build the <code>aMC-vpCIO</code> model. A typical sequence of shell commands to build and run 2BM/MC (which is referred to as <code>vpCIO</code> generic model within the <code>aMC</code>) is:</p> <pre><code># clone the distribution and check-out `vpCIO` branch or particular commit referenced in the repository history [user@pc]/~> git clone https://gitlab.com/sergey.gromov/amc.git [user@pc]/~> cd amc [user@pc]/~/amc> git checkout vpCIO # or unpack the source code available in this publication: [user@pc]/~> tar -xvf `aMC-vpCIO.tar.gz` [user@pc]/~> cd amc # build the aMC/vpCIO model executable # (note that you need at least a GCC or Intel compiler suite and respective netCDF v.4 library Fortran interface available in your environment): [user@pc]/~/amc> make vpCIO # adjust model setup (see the `vpCIO/amc.nml` namelist) ... # perform simulation [user@pc]/~/amc> cd vpCIO [user@pc]/~/amc/vpCIO> ./xamc # calculate additional statistics/produce overview with NOAA Ferret: [user@pc]/~/amc/vpCIO> ferret -gif -script D36-vpCIO-analysis__proc.jnl MH [user@pc]/~/amc/vpCIO> ./D36-vpCIO-analysis__proc</code></pre> <p>Note that output files contain the build timestamp and repository commit hash for the code used in the simulation, e.g.:</p> <pre><code>[user@pc]/~/amc/vpCIO> ncdump -h ./vpCIO-delta-dMH.nc | grep 'build' :build = "vpCIO@https://gitlab.com/sergey.gromov/amc__aMC_v1.9-110-g2566229@2022-12-09T16:43:12+01:00__built@2022-12-09T16:48:03+01:00__<user>@<email.com>" ;</code></pre> <p> </p> <p>Please contact Sergey Gromov ( sergey.gromov (at) mpic.de ) for additional information and access to the original experiment data.</p> <p> </p>
Unique composition and evolution histories of low velocity mantle domains: Data and analysis
<p>Dataset and Jupyter notebook accompanying 'Unique composition and evolution histories of low velocity mantle domains'. </p> <p>Dataset includes:</p> <ul> <li>Present day properties of simulated mantle for simulations RCY, B=0.22, B=0.44, visc2, visc3, CMB2600, CMB2800, COMP, PRM, MER.</li> <li>Present day predicted seismic properties for simulations RCY, B=0.22, B=0.44, visc2, visc3, CMB2600, CMB2800, COMP, MER.</li> <li>Present day predicted seismic properties for simulation PRM assuming 'primordial' material to be i) basaltic oceanic crust ii) chondrite enriched basalt (CEB).</li> <li>Present day delta Vs for simulations RCY, B=0.22, B=0.44, visc2, visc3, CMB2600, CMB2800, COMP, PRM, MER, filtered using the resolution of seismic tomography model S40RTS.</li> <li>Properties of simulated mantle at 100 Myr intervals from 900 Ma - 100 Ma inclusive for simulation RCY. </li> <li>P-T tables with predicted abundance of post-perovskite for different mantle lithologies (harzburgite, lherzolite and basalt - as defined in the paper).</li> </ul> <p>Jupyter notebook `s-llvps.ipynb` contains code for identifying simulated large low-velocity provinces (S-LLVPs), extracting assoicated model properties and plotting results. Python module files terra_utils.py and ppv.py are also included and required by the code in the notebook. </p> <p>There are a number of pre-requisite packages that will need to be installed in order to run the Jupyter notebook, including <a title="terratools" href="https://github.com/mantle-convection-constrained/terratools" target="_blank" rel="noopener">terratools</a>, a software package written specifically for reading and postprocessing outputs from TERRA simulations. Installation instructions can be found on the GitHub repository. </p> <p>Due to the TERRA code pre-dating open source licensing, we do not currently have permission to publicly share all aspects of the code. In code_pieces.F90 we include code snippets which were implemented for this study. </p> <p>Simulations were conducted using ARCHER2, the UK's national super-computing service. </p> <p>RCY.mp4 is a movie produced for simualtion RCY, visualising the evolution of temperature (right panels) and bulk composition (left panels). Hot iso-surface (red) drawn at +500 K and cold iso-surface (blue) drawn at -400 K, composition iso-surface drawn at C=0.6. Red and blue lines indicate overlying ridges / subduction zones taken from the plate motion reconstructions of Müller et al (2022). </p>
Landscape composition and life-history traits influence bat movement and space use: analysis of 30 years of published telemetry data
<p>Using temperate bats, a group of particular conservation concern, we investigated how morphological traits, habitat specialization and environmental variables affect home range sizes and daily foraging movements, using a compilation of 30 years of published bat telemetry data in Northern America and Europe for the period 1988 – 2016.</p> <p>We compiled data on home range size and mean daily distance between roosts and foraging areas at both colony and individual levels from 166 studies of 3,129 radiotracked individuals of 49 bat species. We calculated multi-scale habitat composition and configuration in the surrounding landscapes of all studied roosts. Using mixed models, we examined the effects of habitat availability and spatial arrangement on bat movements, while accounting for body mass, aspect ratio, wing loading and habitat specialization.</p> <p>We found a significant effect of landscape composition on home range size and mean daily distance at both colony and individual levels. On average, home ranges were up to 42% smaller in the most habitat-diversified landscapes while mean daily distances were up to 30% shorter in the most forested landscapes. Bat home range size significantly increased with body mass, wing aspect ratio and wing loading, and decreased with habitat specialization.</p>
Data archive: Trophic structure of cold-water coral communities revealed from the analysis of tissue isotopes and fatty acid composition
<p>Data belonging to the paper: </p> <p>Dick van Oevelen, Gerard C. A. Duineveld, Marc S. S. Lavaleye, Tina Kutti and Karline Soetaert (2017) Trophic structure of cold-water coral communities revealed from the analysis of 55 tissue isotopes and fatty acid composition. Marine Biology Research, DOI: https://doi.org/10.1080/17451000.2017.1398404</p> <p>Abstract:</p> <p>The trophic structure of cold-water coral reef communities at two contrasting locations, the 800-<br> m deep Belgica Mounds (Irish margin) and 300-m deep Træna reefs (Norwegian Shelf), was<br> investigated using stable isotope (δ13C and δ15N) and fatty-acid composition analysis. A<br> broad range of specimens, with emphasis on (commercial) fish species, and organic matter<br> sources were sampled using a variety of tools. Irrespective of the environmental and<br> geographical setting, the δ15N values indicated that the food web encompasses roughly 1.5<br> to 3 trophic levels. Mobile echinoderms, i.e. sea urchins and sea stars, had highest δ15N<br> values, indicative of a high trophic position in the food web. The fraction of bacterial fatty<br> acids in reef fauna was generally low (<5%), indicating that enhanced bacterial production in<br> the water column through seafloor seepage of nutrients (‘hydraulic theory’) does not form a<br> significant energy pathway into the food web. The high fraction of algal and essential fatty<br> acids in reef fauna and fish at both locations indicates a close coupling with surface<br> productivity, but the transport mechanism depends on the hydrographic setting. At Træna,<br> Calanus copepods and euphausiids form an additional link between primary production and<br> fish, which is largely absent at Belgica Mounds. At Belgica Mounds, the reef community is<br> primarily supported by phytodetritus, as evidenced by the high contribution of algal fatty<br> acids in faunal tissue and seasonal chlorophyll a deposition and marine snow at the reef. The<br> environmental setting of cold-water coral reefs influences the structure of the associated<br> food web.</p>
Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"
<h1><strong>Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"</strong></h1> <p>This repository contains pre-processed data sets and code scripts to reproduce the data processing and analyses that are presented in the corresponding manuscript. Some minor pre-processing was completed before presenting this -- namely, the land cover raster was clipped to the study area of Knox County, Tennessee, USA, prior to placing in the repository to reduce the file size. </p> <h2><strong>How to use this repository to reproduce results </strong></h2> <p>This repository is designed to support the reproduction of analyses in the associated manuscript. The entire project can be downloaded and stored anywhere on your computer, as long as the file structure is not altered. The project contains folders with all data sets and code scripts necessary for analysis.</p> <p><strong>What you will need: </strong><br> - Installed R and RStudio for purely spatial cluster and global model analyses<br> - Basic understanding of how to open R and run code </p> <p><strong> You do NOT need:</strong><br> - To download or install R packages on your own; that is taken care of within this environment<br> - To write any code <br> - To set up any working directories in R </p> <h3><strong>Important: Using `renv`</strong></h3> <p>Short Version: When you open the R project, run `renv::restore()` and follow the prompts to install the necessary R packages. </p> <p>The R package `renv` was used to create a <strong>project library</strong>, which contains all R packages that are used by the project. The packages in the project library are <strong>the versions used during the original analysis</strong>. This means that if any packages are updated by developers in ways that would change the results of the analysis, this project can still produce the original results because of `renv`. When you open this project for the first time, `renv` will automatically download and install itself and ask you to run `renv::restore()`. <strong>You should run `renv::restore()` to automatically download and install all of the packages within this reproducible environment</strong>. </p> <h2><strong>## Basic step-by-step guide:</strong></h2> <p>- 1. Download the entire repository by clicking "Code -> Download ZIP" on GitHub or by downloading the ZIP file in Zenodo<br>- 2. Extract the ZIP file anywhere on your computer (do not change the structure of the files once extracted)<br>- 3. In RStudio, click *File -> Open Project* and browse to the location where you extracted the repository; in the repository file, open the knoxaedeslandcover R Project file <br>- 4. Open any of the R scripts in the `analysis/` folder<br>- 5. Run the code `renv::restore()` in the script or in the console and follow the prompt to install the packages <br> - Now you can run the R Scripts; start from the top with loading the packages and data, then work your way down line-by-line</p> <h3><strong># `analysis/` Folder</strong></h3> <p>The `analysis/` folder contains scripts for processing data and conducting analyses. Each file is an R script that should be opened in R studio. The first shows how to process and aggregate the various raw data files; if you are only interested in reproducing analyses from the manuscript, you can skip to the second file and work from there. </p> <p><strong><em>## Files within the `analysis/` folder</em></strong></p> <p>The files are numbered in the order that they were run for the original analysis. In this case, none of the analyses are dependent on the others, so they can technically be used in any order. The numbers associated with each file describe the order that the analyses would normally be run. </p> <p> - `(1)dataprep.R` contains the code for cleaning and combining the land cover, climate, and mosquito data -- this includes calculating the land cover percentages at different scales and calculating weekly and timelagged climate values<br> - `(2)summary_analysis.R` contains code for reproducing summary data and creating graphs from the manuscript<br> - `(3)variable_selection.R` contains code for asssessing collinearity and fitting models to identify the best fitting variables for each speceis<br> - `(4)finalmodels.R` contains code for fitting the final models using the selected variables for each species </p> <h3><strong># `data/` Folder</strong></h3> <p>This folder contains several datasets, including one that compiles them all for analyses (`knox_joined`). The raw data are included to show how the data was processed and aggregated, but the individual raw data files are not needed for analyses. See `data dictionary.txt` for a description of all attributes contained within each file. </p> <p><strong><em>## Files within the `data/` folder</em></strong></p> <p> - `knox22_joined.RDS` contains a cleaned and joined version of land cover, climate, and mosquito data in R Data Serialization format, which maintains predefined factor and numeric designations for columns. <br> - `knox22_joined.csv` contains a cleaned and joined version of land cover, climate, and mosquito data in CSV format -- identical to 'knox22_joined.RDS'<br> - `sites22.csv` contains the names, site codes, and coordinates of the study sites<br> - `aedes22_clean.csv` contains the raw mosquito collection data for the study without any climate or land cover information <br> - `NLCD_2019_landcover_clippedtoKnox.tif` contains the NLCD land cover data, already clipped to Knox County, TN, USA<br> - `knox22_temperature.csv` contains raw daily temperatures for the city of Knoxville in 2022<br> - `knox22_rainfall.csv` contains raw daily precipitation for the city of Knoxville watersheds in 2022<br> - `rainfall_stations.csv` contains the descriptions, approximate street addresses, and geographic coordinates for rainfall monitoring sites <br> - `data dictionary.txt` file that defines column names and other data attributes for every dataset </p> <h3><strong># `renv/` Folder</strong></h3> <p>The `renv/` folder contains bits and pieces needed for the `renv` package. Nothing should be altered in this folder. </p> <p> </p> <h2><strong>References for source data </strong></h2> <p> - Some of the data in this repository were originally obtained from open access sources. </p> <p> - Land cover data was obtained from the National Land Cover Database (NLCD) 2019 data product, specifically the "NLCD 2019 Land Cover (CONUS)" product. The original, unclipped raster can be freely downloaded here: https://www.mrlc.gov/data/nlcd-2019-land-cover-conus</p> <p> - Temperature data was downloaded from the United States National Oceanic and Atmospheric Administration (NOAA) weather station for Knoxville, Tennessee. The source data can be downloaded from this site: https://www.weather.gov/mrx/tysclimate</p> <p> - Rainfall data was obtained from the City of Knoxville rainfall data website, located here: https://www.knoxvilletn.gov/government/city_departments_offices/engineering/stormwater_engineering_division/rainfall_data</p> <p> - All mosquito collection data was collected directly by the manuscript authors</p>
Data from: Analysis of leaf microbiome composition of near-isogenic maize lines differing in broad-spectrum disease resistance
<p>Data and code associated with the submitted manuscript "Analysis of leaf microbiome composition of near-isogenic maize lines differing in broad-spectrum disease resistance". Detailed descriptions of each file can be found in the README.txt . The raw sequence data associated with this work can be downloaded from the NCBI Sequence Read Archive, listed under BioProject #PRJNA565009<strong>.</strong></p>
Raw data used in the manuscript titled "Metabolomic Analysis of Histological Composition Variability of High-Grade Serous Ovarian Cancer Using 1H HR MAS NMR Spectroscopy "
<p>The folder contains raw data used in the manuscript titled "Metabolomic Analysis of Histological Composition Variability of High-Grade Serous Ovarian Cancer Using <sup>1</sup>H HR MAS NMR Spectroscopy ".</p> <p> </p> <p> Raw data measured on Bruker Avance III 400 MHz NMR spectrometer:</p> <p>- 1D <sup>1</sup>H HR MAS NMR spectra (path: <em>Patient_code – Sample_code/500/fid</em>)</p> <p>- 2D <sup>1</sup>H-<sup>1</sup>H J-resolved HR MAS NMR spectra (path: <em>Patient_code – Sample_code/600/ser</em>).</p> <p> </p> <p>Metadata is included in <em>Metadata.xlsx</em> file.</p> <p>Each sample is described with the following parameters:</p> <p>- patient code (after anonymization),</p> <p>- sample code (the label <em>l</em> or <em>r</em> denotes the <em>left</em> or <em>right</em> ovary in patients from whom samples were obtained bilaterally),</p> <p>- sample weight,</p> <p>- clinic-pathological parameters (such as: age, BMI, menopausal status, diagnosis, FIGO stage),</p> <p>- percentage tissue content obtained from histopathological analysis after HR MAS NMR studies (cancer cells, epithelial compartment within benign tumors, necrosis, inflammation, fibrosis, calcification, normal ovary, vessels, fatty tissue).</p> <p> </p> <p>Some samples were considered representative of particular tissue components:</p> <p>- cancer (HGSOC) compartment,</p> <p>- fibrotic stroma within malignant (HGSOC) tumors,</p> <p>- fibrotic stroma within benign tumors,</p> <p>- normal ovary tissue (the samples collected from the control group),</p> <p>- normal ovary tissue (the samples collected from the cancer patients),</p> <p>- necrosis,</p> <p>- non-tumoral fibrous tissue / fibrous tumor capsule (obtained from the patients with benign non-neoplastic lesions)</p> <p>- corpus albicans</p> <p>The assignment of the samples to these categories is indicated in the column <em>Tissue components.</em></p> <p><em> </em></p> <p>The samples classified as outliers in PCA model 1 are indicated in the column <em>Outliers</em>.</p> <p>The samples included in multivariate models are indicted in the columns: <em>PCA 2, PCA 3, PCA 4, PCA 5, PCA 5a, PCA 6, OPLS-DA 1, OPLS-DA 2, OPLS-DA 3, OPLS-DA 4, OPLS-DA 5, OPLS-DA 6 and OPLSR.</em></p> <p><em> </em></p>
CS#1H Monitoring Data of Subsurface Passage in Managed Aquifer Recharge: Microbial and Organic Composition Analysis
<div>This dataset contains flow cytometry and cultivation-based microbial data, along with measurements of natural organic matter (NOM) characterized by fluorescence, absorbance, and liquid chromatography-organic carbon detection (LC-OCD) from CS#1H. Data were collected over a one-year monitoring period at two sampling locations: the infiltration ditch and the abstraction well. </div>
Data for "Training data composition affects performance of protein structure analysis algorithms" by A. Derry, K. A. Carpenter, & R. B. Altman
<p><strong>Description</strong></p> <p>This repository contains all data used in "Training data composition affects performance of protein structure analysis algorithms", published in the Pacific Symposium on Biocomputing 2022 by A. Derry, K. A. Carpenter, & R. B. Altman. </p> <p>The data consists of the following files:</p> <ul> <li>ema_zenodo_data.tar.gz: train, validation, and test splits for Estimation of Model Accuracy task, in LMDB format</li> <li>design_zenodo_data.tar.gz: train, validation, and test splits for Protein Sequence Design task, in JSON format</li> <li>enz_cat_res_zenodo_data.tar.gz: train, validation, and test splits for Catalytic Residue and Enzyme Prediction task, in TF record format</li> </ul> <p>Details on dataset construction can be found in our paper and dataloaders can be found in our <a href="https://github.com/awfderry/ml-structure-bias">Github repo</a>.</p> <p><strong>Reference</strong></p> <p>A. Derry*, K. A. Carpenter*, & R. B. Altman, "Training data composition affects performance of protein structure analysis algorithms", 2021.</p> <p><strong>Dataset References</strong></p> <p>Datasets used were derived from the following works:</p> <p>Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2019). Critical assessment of methods of protein structure prediction (CASP)—Round XIII. In <em>Proteins: Structure, Function and Bioinformatics</em> (Vol. 87, Issue 12, pp. 1011–1020). https://doi.org/10.1002/prot.25823</p> <p>Ingraham, J., Garg, V. K., Barzilay, R., & Jaakkola, T. (2019). <em>Generative Models for Graph-Based Protein Design</em>. https://openreview.net/pdf?id=SJgxrLLKOE</p> <p>Furnham, N., Holliday, G. L., de Beer, T. A. P., Jacobsen, J. O. B., Pearson, W. R., & Thornton, J. M. (2014). The Catalytic Site Atlas 2.0: cataloging catalytic sites and residues identified in enzymes. <em>Nucleic Acids Research</em>, <em>42 </em>(Database issue), D485–D489.</p>
The data sheet of "Phytochemicals, Proximate Composition, Minerals and Volatile Oil Analysis of Zanthoxylum acanthopodium DC. Fruits"
<p>The dataset of ‘Phytochemicals, Proximate Composition, Minerals and Volatile Oil Analysis of <em>Zanthoxylum acanthopodium</em> DC. Fruits’</p>
Landscape composition and life‐history traits influence bat movement and space use: Analysis of 30 years of published telemetry data
Open the record for dataset details and reuse information.
Data from: Landscape composition and life-history traits influence bat movement and space use: analysis of 30 years of published telemetry data
<p><span><b>Aim: </b>Animal movement determines home range patterns, which in turn affect individual fitness, population dynamics and ecosystem functioning. Using temperate bats, a group of particular conservation concern, we investigated how morphological traits, habitat specialization and environmental variables affect home range sizes and daily foraging movements, using a compilation of 30 years of published bat telemetry data.</span></p> <p><span><b>Location</b>: Northern America and Europe.</span></p> <p><span><b>Time period</b>: 1988 – 2016.</span></p> <p><span><b>Major taxa studied</b>: Bats.</span></p> <p><span><b>Methods</b>: We compiled data on home range size and mean daily distance between roosts and foraging areas at both colony and individual levels from 166 studies of 3,129 radiotracked individuals of 49 bat species. We calculated multi-scale habitat composition and configuration in the surrounding landscapes of the 165 studied roosts. Using mixed models, we examined the effects of habitat availability and spatial arrangement on bat movements, while accounting for body mass, aspect ratio, wing loading and habitat specialization.</span></p> <p><span><b>Results:</b><i> </i>We found a significant effect of landscape composition on home range size and mean daily distance at both colony and individual levels. On average, home ranges were up to 42% smaller in the most habitat-diversified landscapes while mean daily distances were up to 30% shorter in the most forested landscapes. Bat home range size significantly increased with body mass, wing aspect ratio and wing loading, and decreased with habitat specialization.</span></p> <p><span><b>Main conclusions: </b>Promoting bat movements through the landscape surrounding roosts at large spatial scales is crucial for bat conservation. Forest loss and overall landscape homogenization lead temperate bats to fly farther to meet their ecological requirements, by increasing home range sizes and daily foraging distances. Both processes might be more detrimental for smaller, habitat-specialized bats, less able to travel increasingly longer distances to meet their diverse needs.</span></p>
Compositional Data Analysis (CoDA) of Clinopyroxene from Abyssal Peridotites
<ol> <li> <p>Additional supporting information includes data, R script, and QGIS file supporting the main text:</p> <p><strong>CSV (Data Set)</strong></p> <ul> <li> <p>residual_abyssal_peridotites.csv: Compilations of residual abyssal peridotites (n = 1162) and depleted MORB-mantle (n = 1)</p> </li> <li> <p>residual_abyssal_peridotites_coda_results.csv: Filtered data and results of PCA and k-means clustering (n = 267)</p> </li> <li> <p>model_cpx.csv: Clinopyroxene compositions obtained by open-system melting model</p> </li> <li> <p>test.csv: csv file for testing new data<br> <br> <strong>R</strong></p> </li> <li> <p>abyssal_cpx_pca.Rproj</p> </li> <li> <p>coda.R: R script implemented in this study</p> </li> <li> <p>test_your_data.R: R script to test new data comparing to abyssal clinopyroxenes</p> <p>and modeled clinopyroxenes</p> <p><strong>QGIS</strong></p> </li> <li> <p>residual_abyssal_peridotites.qgz: QGIS using residual_abyssal_peridotites.csv and residual_abyssal_peridotites_coda_results.csv for Figure 1 and Figure S7</p> </li> <li> <p>color_etopo1_ice_low_modified.tiff: ETOPO1 is a 1 arc-minute global relief model of Earth's surface that integrates land topography and ocean bathymetry from NOAA</p> </li> </ul> </li> </ol> <p> </p> <p>2. Instruction</p> <p>We prepared an R script to compare new (your) clinopyroxene data with clinopyroxene from abyssal peridotites. New data will be plotted using the principal components derived from the natural clinopyroxene database presented in this paper.</p> <p>The procedure is as follows:</p> <p>1. add data below the second row in test.csv<br> * Do not change the file name<br> * Do not change the first row<br> * Add clinopyroxene data (10 elements) and its label replacing under 2nd row * Label of data can be sample name, lithology, locality etc.</p> <p>2. Open abyssal_cpx_pca.Rproj by R studio (double click) 3. Open test_your_data.R (double click)<br> 4. Implement test_your_data.R.</p> <p>To use test_your_data.R, first press cmd+A (ctrl+A) and press Run/cmd+enter (ctrl+enter).</p> <p>5. Results files<br> 5-1. abyssalcpx_vs_test.csv: PC1&PC2 values using abyssal clinopyroxene PC coordinates<br> 5-2. plot1.pdf: abyssal clinopyroxene (cluster) vs. test data plot<br> 5-3. plot2.pdf: modeled clinopyroxene vs. test data plot<br> 5-4. spider_cl1.pdf: PM-normalized trace elements patterns of cluster 1 from abyssal peridotites 5-5. spider_cl2.pdf: PM-normalized trace elements patterns of cluster 2 from abyssal peridotites 5-6. spider_cl3.pdf: PM-normalized trace elements patterns of cluster 3 from abyssal peridotites 5-7. spider_cl4.pdf: PM-normalized trace elements patterns of cluster 4 from abyssal peridotites 5-8. spider_test.pdf: PM-normalized trace elements patterns of new data (your data)<br> 5-9. plot3.pdf: Discrimination diagram for clinopyroxene trace elements compositions. PM normalized Sr/Nd ratio vs. Ce/Yb ratio of clinopyroxenes from abyssal peridotites vs. test data</p>
Research Data Supporting "Coupling Lipid Nanoparticle Structure and Automated Single Particle Composition Analysis to Design Phospholipase Responsive Nanocarriers"
<p>Raw research data supporting Barriga, Pence, et al. 2022, Advanced Materials. <a href="https://doi.org/10.1002/adma.202200839">https://doi.org/10.1002/adma.202200839</a></p>
Changes in holopelagic Sargassum spp. biomass composition across an unusual year - supporting particle data and analysis source code
<p>As specified in 'Data, Materials, and Software Availability' of Tonon et al. (2024), PNAS, Vol. 121, e2312173121, https://doi.org/10.1073/pnas.2312173121, the following data and software are provided:</p> <p>Particle forward tracking (statistical data – for Fig. 2)</p> <p>Particle backward tracking (primary data – for Fig. 3)</p> <p>Analysis of primary data for gridded particle fractional coverage and mean age (Fortran source code)</p>
Data of "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"
<p><strong>Id</strong><br>title = "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"<br>journal = International Journal for Numerical Methods in Engineering<br>year = 2023<br>volume = 124<br>pages = 5200-5262<br>doi = 10.1002/nme.7344<br>authors = "Calleja, Juan Manuel and Wu Ling, and Nguyen, Van-Dung and Noels, Ludovic"</p> <p>If you use these data or model, we would be grateful if you could cite this above paper</p> <p><strong>Software</strong><br>Requires GMSH and Python 3 with packages numpy, matplotlib, sklearn (scikit-learn), os, pickle, scipy, pandas, cvs, math, seaborn.<br>Each folder contains readme that will help the user to navigate through the data.</p> <p>To run the model you need the open source code <a href="http://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries </a>but you need to request access to cm3MFH as well</p> <p><strong>Directories</strong></p> <ol> <li>Main: Contains fast and easy access to the plots presented in the paper. The readme contained in this plot specifies the plots that are run with each code.</li> <li>1_SVE_Generator:Contains the files needed for the generation of the SVE, the statistical properties of the microstructure, and PLY samples for the full-field simulations, as well as the used samples</li> <li>2_Full_Field: contains the extracted data from the FF composite realizations, as well as the used random SVE geometries.</li> <li>3_Identification: Contains the identification code to find the effective parameters for each SVE realization as well as the obtained identification results.</li> <li>4_Generator: Contains the generated set of parameters for the 25 and 45 micrometer squared SVEs as well as the codes for the new data generation, the file with the generated data and the plots related with the MF-ROM random parameters and their cross-relations shown in Sections 2.5.2, 3.2.3 and 4.</li> <li>5_Tests: Contains all the information concerning the tests used for the verification of the MF-ROM and the ply and experimental compression results.</li> <li>MFH_vs_FF: Allows to easily test the inverse identification process through the use of random SVEs and verify the performance of the identified MFH parameters against its full-field counterpart.</li> </ol> <p><strong>Plot of figures</strong></p> <p>Figure 9 : Run "python plot_Gc.py" which can be found in folder Main/Full_Field_Energy<br>Figure 10: Run "python3 PDF_HIST_Gc.py", which can be found in folder Main/Histograms<br>Figure 23: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 24: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 27: Run "python3 Correlation_Graphs_25.py contained in folder Main/Distributions_25_Micrometer_SVE<br>Figure 29: Run "python3 PDF_HIST.py" which can be found in folder Main/Histograms<br>Figure 30: To obtain the data used in this figure, run "python3 DistanceCorrelation_25.py" which can be found in folder /4_Generator<br>Figure 31: To obtain the data used in this figure, run "python3 DistanceCorrelation_45.py" which can be found in folder /4_Generator<br>Figure 32: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 33: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 34: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 36: Run "python3 plot_New.py" which can be found in folder Main/PlyTests<br>Figure 46: Run "python3 plot_Test.py" which can be found in folder Main/CompressionExperiment<br>Figure B3: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure B4: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure D5: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D6: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D7: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D8: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D9: Run "python3 PDF_HIST_B.py" which can be found n folder Main/Histograms<br>Figure D10: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D11: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D12: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D13: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D14: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure E15: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E16: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E17: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure F18: Run "python3 plot_Convergence_25.py" which can be found in folder Main/Convergence<br>Figure F19: Run "python3 plot_Convergence_45.py" which can be found in folder Main/Convergence<br> </p> <p> </p> <p> </p> <p> </p>
Data from: Detecting damage in composites using volume decomposition analysis of tomographic data
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Data from: Landscape composition and life-history traits influence bat movement and space use: analysis of 30 years of published telemetry data
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Composite analysis data
<p>Composite analysis data</p>
ASV output data for all in the details: A first assessment for the viability of metabarcoding in diet composition analysis of African wild dogs (Lycaon pictus)
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Allen Brain Atlas
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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.
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