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6,175 results for “Composite”
Data for Predictive Modelling of Laminated Composite Plates
<p>Two different problems, i.e. a low-dimensional (LD) and a high-dimensional (HD) problems are considered. The LD problem has 2 variables for a 4-ply symmetric square composite laminate. Similarly, the HD problem consists of 16 variables for a 32-ply symmetric square composite laminate. The value of <em>h </em>for LD and HD problems is taken as 0.005 and 0.04 respectively.</p> <p>For each problem, three different types of sampling technique, i.e. random sampling (RS), Latin hypercube sampling (LHS) [1] and Hammersley sampling (HS) [2] are adopted. The RS, LHS and HS primarily differ in the uniformity of sample points over the design space such that RS has the least and HS has the maximum uniform distributions of sample points. Based on the recommendations of Jin et al. [3], and Zhao and Xue [4], 72 and 612 sample points are considered in each training dataset of LD and HD problems respectively.</p> <p>Based on the FE formulation, several high-fidelity datasets for the LD and HD problems are generated, as presented in the Supplementary Material file “Predictive modelling of laminated composite plates.xlsx” in nine sheets that are organized as detailed out in Table 1.</p> <p>References:</p> <p>1. McKay, M. D.; Beckman, R. J.; Conover, W. J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. <em>Technometrics</em>, <strong>2000</strong>, <em>42</em>, 55-61.</p> <p>2. Hammersley, J. M. Monte Carlo methods for solving multivariable problems. <em>Annals of the New York Academy of Sciences</em>, <strong>1960</strong>, <em>86</em>, 844-874.</p> <p>3. Jin, R.; Chen, W.; Simpson, T. W. Comparative studies of metamodelling techniques under multiple modelling criteria. <em>Structural and Multidisciplinary Optimization</em>, <strong>2001</strong>, <em>23</em>, 1-13.</p> <p>4. Zhao, D.; Xue, D. A comparative study of metamodeling methods considering sample quality merits. <em>Structural and Multidisciplinary Optimization</em>, <strong>2010</strong>, <em>42</em>, 923-938.</p>
Structure and composition and carbon Stocks of woody plant community in assisted and unassisted ecological succession in a Tamaulipan thornscrub, Mexico
<p>In November of 2017, the structure and composition of woody plant communities were investigated through a floristic composition and diversity evaluation on three areas: a control area, an assisted ecological succession area and an unassisted ecological succession area.</p>
Emission factors and chemical composition of particulate matter from residential biomass combustion
<p>Emission factors and chemical composition of particulate matter from residential biomass combustion.</p>
Data on the sex and age composition of Bramblings Fringilla montifringilla during autumn migration and winter in Europe
<p><strong>Abstract</strong></p> <p>Bramblings <em>Fringilla montifringilla</em> are known to vary in winter distribution according to sex and age (differential migration). This pattern is complicated by the large yearly fluctuation in their preferred winter food, beech seeds. In beechmast areas, large concentrations of Bramblings occur, and their sex and age composition differs from that of winters without a beechmast. Here we present data on the sex and age composition of Bramblings during autumn migration and winters with and without beechmast, mainly from Switzerland, supplemented with data from northern and southern Europe.</p> <p> </p> <p><strong>Literature cited in the Excel-file</strong></p> <p>Arizaga J, Zuberogoitia I, Zabala J, Crespo A, Iraeta A, Belamendia G (2012) Seasonal patterns of age and sex ratios, morphology and body mass of Bramblings <em>Fringilla montifringilla </em>at a large winter roost in southern Europe. Ring. Migr. 27:1–6. https://doi.org/10.1080/03078698.2012.686707</p> <p>Browne SJ, Mead CJ (2003) Age and sex composition, biometrics, site fidelity and origin of Brambling <em>Fringilla montifringilla </em>wintering in Norfolk, England. Ring. Migr. 21:145–153. https://doi.org/10.1080/03078698.2003.9674283</p> <p>Khil L, Samwald O, Tiefenbach A, Tiefenbach M, Pacher H (2011) Der Massenschlafplatz von Bergfinken <em>Fringilla montifringilla </em>in Österreich im Winter 2008/2009. Limicola 25:81–100</p> <p>Kjellén N, Lindström Å (1993) Bergfinkens övervintringsstrategier samt några iakttagelser från en skånsk sovplats i januari-februari 1993. Anser 32:187–199</p> <p>Robson D (1996) Influencia de la temperatura en la masa corporal del Pinzon Real. Ardeola 43:139–144</p> <p>Schierer A (1957) Geschlechts- und Altersverhältnis der Nordfinken. Vögel Heimat 27:68</p> <p>Widemo U (1977) Bergfink <em>Fringilla montifringilla </em>Jan. - Apr. 1977. Sex and age distribution, winglength and change of weight. Fåglar i Sörmland 10:76–81</p>
Data and code for: Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats, Suricata suricatta
<p>Data and code to go with our publication "Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats, <em>Suricata suricatta", </em>Nature Communications (2021).</p> <p><strong>FILE DESCRIPTIONS</strong></p> <p><em>****** DATA ******</em></p> <p><strong>meerkat_16S_data.tar.gz</strong> # 16S V4 amplicon sequences sequenced on an Illumina MiSeq platform using primer pair 515F and 806R, including all faecal samples, controls, and sand samples. Sequence identifiers and basic metadata are in <strong>sequence_identifiers.csv.</strong></p> <p><strong>sequence_identifiers.csv </strong># Simple metadata and identifiers for all sequences/samples (what type of sample/sequencing run, etc), required for QIIME2 processing of the raw fasta.gz files contained in meerkat_16S_data.tar.gz. It contains a column for whether the sample was included in the final analysis. Does not include sample biological metadata as generating this data requires access to Kalahari Meerkat Project database. Biological metadata for samples included in the final analysis are instead provided in <strong>processed_data_phyloseq.RDS </strong>and can be accessed via <em>phyloseq::sample_data(processed_data_phyloseq)</em>.</p> <p><strong>processed_data_phyloseq.RDS</strong> # Phyloseq object containing the processed data used in the presented analysis. Contains data for 1109 samples, and includes the ASV table, the taxonomic classification, the phylogenetic tree, and the sample metadata used in the analysis.</p> <p><strong>technical_replicate_data_phyloseq.RDS</strong> # Phyloseq object containing data from the 16 technical replicates.</p> <p><strong>pilot_study_data_phyloseq.RDS</strong> # Phyloseq object containing data from the pilot study on captive meerkats.</p> <p><em>****** CODE ******</em></p> <p><strong>CODE1_QIIME_script.R</strong> # QIIME2 script to generate ASV table, taxonomy, and phylo tree from <strong>meerkat_16S_data.tar.gz. </strong>Requires a reference taxonomy (SILVA) and a reference phylogeny (SEPP) for taxonomic and phylogenetic placements.</p> <p><strong>CODE2_processing_QIIME_output.Rmd</strong> # R markdown script that processes the QIIME2 output generated by <strong>CODE1_QIIME_script.R</strong>. Does not generate meerkat metadata as this requires access to the Kalahari Meerkat Project database. This metadata is provided in <strong>processed_data_phyloseq.RDS.</strong></p> <p><strong>CODE3_data_analysis_script.Rmd </strong># R markdown script that generates data and figures presented in paper, using data from <strong>processed_data_phyloseq.RDS, technical_replicate_data_phyloseq.RDS, </strong>and<strong> pilot_study_data_phyloseq.RDS.</strong></p> <p><em>****** R MARKDOWN REPORTS ******</em></p> <p>The following reports are html files that show the code output for the two RMD files above.</p> <p><strong>RMARKDOWN_data_processing.html </strong># R markdown report for<strong> CODE2_processing_QIIME_output.Rmd</strong></p> <p><strong>RMARKDOWN_data_analysis.html </strong># R markdown report for <strong>CODE3_data_analysis_script.Rmd</strong></p> <p>*****************************</p> <p>For general queries, unexpected errors and/or inconsistencies, please contact riselya@gmail.com.</p> <p> </p>
Landscape composition drives the impacts of artificial light at night on insectivorous bats
<p>Abstract of the related publication :</p> <p>Among the most prevalent sources of biodiversity declines, Artificial Light At Night (ALAN) is an emerging threat<br> to global biodiversity. Much knowledge has already been gained to reduce impacts. However, the spatial variation<br> of ALAN effects on biodiversity in interaction with landscape composition remains little studied, though it is<br> of the utmost importance to identify lightscapes most in need of action. Several studies have shown that, at local<br> scale, tree cover can intensify positive or negative effects of ALAN on biodiversity, but none have – at landscape<br> scale – studied a wider range of landscape compositions around lit sites. We hypothesized that the magnitude of<br> ALAN effects will depend on landscape composition and species’ tolerance to light. Taking the case of insectivorous<br> bats because of their varying sensitivity to ALAN, we investigated the species-specific activity response to<br> ALAN. Bat activity was recorded along a gradient of light radiance. We ensured a large variability in landscape<br> composition around 253 sampling sites. Among the 13 bat taxa studied, radiance decreased the activity of two<br> groups of the slow-flying gleaner guild (Myotis and Plecotus spp.) and one species of the aerial-hawking guild<br> (Pipistrellus pipistrellus), and increased the activity of two species of the aerial-hawking guild (Pipistrellus kuhlii<br> and Pipistrellus pygmaeus). Among these five effects, the magnitude of four of them was driven by landscape composition.<br> For five other species, ALAN effects were only detectable in particular landscape compositions, making<br> the main effect of radiance undetectable without account for interactions with landscape. Specifically, effects<br> were strongest in non-urban habitats, for both guilds. Results highlight the importance to prioritize ALAN reduction<br> efforts in non-urban habitats, and how important is to account for landscape composition when studying<br> ALAN effects on bats to avoid missing effects.</p>
A compositional model of the Earth's mantle transition zone from SS and PP data
<p>This dataset provides the thermochemical model of the Earth's mantle transition zone obtained from the inversion of travel-times and amplitudes of seismic waves reflected at mineralogical phase transitions (PP and SS precursors).</p> <p>3 columns : latitude (degree), longitude (degree), potential temperature Tpot (Kelvin), basalt fraction (min 0.0, max 1.0)</p>
Calculation of Ferrite Core Losses with Arbitrary Waveforms using the Composite Waveform Hypothesis: Reproducibility Dataset
<p><strong>Paper</strong></p> <p>This package contains the datasets used in the following paper:</p> <ul> <li><strong>Calculation of Ferrite Core Losses with Arbitrary Waveforms using the Composite Waveform Hypothesis</strong></li> <li><strong>Thomas Guillod, Jenna S. Lee, Haoran Li, Shukai Wang, Minjie Chen, and Charles R. Sullivan</strong></li> <li><strong><a href="https://doi.org/10.1109/APEC43580.2023.10131348">https://doi.org/10.1109/APEC43580.2023.10131348</a></strong></li> <li><strong>IEEE APEC 2023, Orlando, Florida, USA</strong></li> </ul> <p><strong>Datasets</strong></p> <p>The EPCOS TDK N87 datasets used in this paper are part of the MagNet initiative (<a href="http://mag-net.princeton.edu">https://mag-net.princeton.edu</a>). MagNet is an openly available large-scale dataset including measurements of several core materials under various operating conditions. MagNet is a joint project between Princeton University, Dartmouth College, and Plexim GmbH.</p> <p>This package includes the two datasets used in the paper:</p> <ul> <li>"N87_ambient_temperature" - Loss dataset for EPCOS TDK N87 at ambient temperature (measured on a R22.1X13.7X7.9, 2022-02-01).</li> <li>"N87_variable_temperature" - Loss dataset for EPCOS TDK N87 at different temperatures (measured on a R34.0X20.5X12.5, 2022-07-14).</li> </ul> <p>It should be noted that the datasets contains more measurements than used in the paper:</p> <ul> <li>The measurements where the iGCC can be evaluated without extrapolation are used in the paper.</li> <li>The measurements where the iGCC require an extrapolation of the loss data are not used in the paper.</li> <li>A flag in the dataset indicates in which category a measurement belongs.</li> </ul> <p>More details about the measurement setup can be found on the MagNet website (<a href="http://mag-net.princeton.edu">https://mag-net.princeton.edu</a>).</p> <p>More details about the iGCC method can be found on GitHub (<a href="https://github.com/otvam/magnet_webinar_eqn_models">https://github.com/otvam/magnet_webinar_eqn_models</a>).</p> <p><strong>File Formats</strong></p> <p>The datasets are available in three different formats:</p> <ul> <li>CSV (text files).</li> <li>MATLAB tables (MAT v7.3 binary files, exported with MATLAB 2021a).</li> <li>Pandas dataframes (HDF5 binary files, exported with Python 3.10.6 and Pandas 1.3.5).</li> </ul> <p>The file "dataset_metadata.csv" contains the description of the different variables.<br>The file "test_matlab.m" is a MATLAB test file for loading the MATLAB tables.<br>The file "test_python.py" is a Python test file for loading the Pandas dataframes.</p>
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>
Surface sediment Nd isotope compositions from the Ross Sea, Antarctica
<p>The dataset contains seafloor surface sediment neodymium isotope compositions for several sites in the Ross Sea, Antarctica. The sediments were supplied by Helen Bostock from NIWA, Wellington, New Zealand, plus two samples from International Ocean Discovery Program (IODP) Expedition 374.</p>
The Effect of Soundscape Composition on Bird Vocalization Classification in a Citizen Science Biodiversity Monitoring Project
<p>This archive includes sound clips (.wav files) and associated mel-scale spectrograms of bird vocalizations for 54 species in Sonoma County, California, USA. These data were used for training and validating convolutional neural network (CNN) models for bird species detection. We also include xeno-canto training and validation mel spectrograms used to pretrain CNNs. Details on these data are explained in the paper by Clark et al. (2023) titled "The effect of soundscape composition on bird vocalization classification in a citizen science biodiversity monitoring project". These data are available for use without restrictions, with no warranty on data quality or utility for a given application. We request that any work that does use these data cite the Clark et al. (2023) paper.<br> <br> Clark, M.L., Salas, L., Baligar, S., Quinn, C., Snyder, R.L., Leland, D., Schackwitz, W., Goetz, S.J., Newsam, S. (2023). The effect of soundscape composition on bird vocalization classification in a citizen science biodiversity monitoring project. <em>Ecological Informatics</em>. <a href="https://doi.org/10.1016/j.ecoinf.2023.102065">https://doi.org/10.1016/j.ecoinf.2023.102065</a></p> <p>Associated code for training CNN models, performing inference, and applying post-classification corrections can be found in the GitHub archive <a href="https://github.com/pointblue/Soundscapes2Landscapes/tree/master/CNN_Bird_Species">https://github.com/pointblue/Soundscapes2Landscapes/tree/master/CNN_Bird_Species</a></p> <p>Raw sound data from the Soundscapes to Landscapes project are available upon request: Dr. Matthew Clark, matthew.clark@sonoma.edu</p> <p>These data were collected as part of the Soundscapes to Landscapes project (<a href="https://soundscapes2landscapes.org/">soundscapes2landscapes.org</a>), funded by NASA’s Citizen Science for Earth Systems Program (CSESP) 16-CSESP 2016-0009 under cooperative agreement 80NSSC18M0107.<br> <br> ----------------------------<br> This depository includes the following archives:</p> <ul> <li> <p>mel_specs.zip: contains 2-sec mel spectrograms split into training (“tr”), validation (“val”), testing (“test”) data for each target bird species (n = 54) used to fine-tune the CNNs. Select spectrogram files are appended with “aug” if they are augmented versions for the training data.</p> </li> <li> <p>wav.zip: contains the associated wav-format sound recordings used to generate the training, validation, testing mel spectrograms found in mel_specs.zip.</p> </li> <li> <p>Xeno-canto_pretrain.tar: contains 2-sec mel spectrograms split into training and validation data for 40 bird species used for CNN pre-training that were generated using a warbleR segmentation methodology described in the paper. The sound files used to generate these mel spectrograms came from the Kaggle competition, <a href="https://www.kaggle.com/datasets/imoore/xenocanto-bird-recordings-dataset">https://www.kaggle.com/datasets/imoore/xenocanto-bird-recordings-dataset</a><br> Mel spectrogram naming reflects the XC number used for cataloging on Xeno-canto in the format XC123456_2.png. The six numbers following the XC characters can be used to search for unique recordings on Xeno-canto (<a href="https://xeno-canto.org/">https://xeno-canto.org/</a>) using the search query “nr:123456” in the search tool or queried using the Xeno-canto API (<a href="https://xeno-canto.org/explore/api">https://xeno-canto.org/explore/api</a>). Unique recording names can be extracted from the mel spectrogram filenames.</p> </li> <li> <p>soundscape_test_wavs.zip: the wav-format sound recordings used to perform soundscape testing.</p> </li> </ul>
Fruit-feeding butterfly community data analysed in "Recovery patterns in community composition of fruit-feeding butterflies following 26 years of active forest restoration"
<p>Community data of fruit-feeding butterflies collected from Kibale National Park, Uganda, in the periods 2011-2012 and 2020-2021 analysed in our paper Korkiatupa et al. 2023: "Recovery patterns in community composition of fruit-feeding butterflies following 26 years of active forest restoration" (<em>Ecosphere</em> <span>14</span>(<span>5</span>): e4514. <a href="https://doi.org/10.1002/ecs2.4514">https://doi.org/10.1002/ecs2.4514</a>).</p> <p>The table consists of two parts. First part shows counts of individuals of butterfly species in each study site. Second part shows the metadata: code of studysite, census (2011-2012/2020-2021), planting year (planting year or "Primary forest"), and coordinates (WGS 84 coordinate system).</p>
Unified Structures of Temperature and Composition of Lower Mantle (USTCLM)
<p>Compositional and thermal state of the lower mantle from joint 3D inversion with seismic tomography and mineral elasticity</p> <p>Data including Vp&Vs structure, chemical proportion, and thermal and density structure.</p> <p><br> </p>
Unified Structures of Temperature and Composition of Lower Mantle (USTCLM)
<p>Unified Structures of Temperature and Composition of Lower Mantle (USTCLM)</p> <p>Author: Xin Deng <br> 769615090@qq.com</p> <p>Compositional and thermal state of the lower mantle from joint 3D inversion with seismic tomography and mineral elasticity</p> <p>Data including Vp&Vs structure, chemical proportion, and thermal and density structure.</p> <p><br> Related: https://doi.org/10.1073/pnas.2220178120</p>
Seasonal controls override forest harvesting effects on the composition of dissolved organic matter mobilized from boreal forest soil organic horizons
<p>Dataset comprised of nutrient fluxes (DOC, TDN, NH4, TDN and SRP), optical parameters related to DOM composition (SUVA, spectral slopes and slope ratio), pH, and other nutrient and elemental ratios for passive pan lysimeters installed across terrestrial sites in Pynn's Brook, Newfoundland.</p>
European river typologies fail to capture trends in diatom, fish, and macrophyte community composition
<p>This repository contains files related to the publication: "European river typologies fail to capture trends in diatom, fish, and macrophyte community composition".</p> <p> </p> <p> </p>
Data from: Pathogen community composition and co-infection patterns in a wild community of rodents
<p><strong>ABSTRACT</strong></p> <p>Rodents are major reservoirs of pathogens that can cause disease in humans and livestock. It is therefore important to know what pathogens naturally circulate in rodent populations, and to understand the factors that may influence their distribution in the wild. Here, we describe the incidence and distribution patterns of a range of endemic and zoonotic pathogens circulating among rodent communities in northern France. The community sample consisted of 713 rodents, including 11 host species from diverse habitats. Rodents were screened for virus exposure (hantaviruses, cowpox virus, Lymphocytic choriomeningitis virus, Tick-borne encephalitis virus) using antibody assays. Bacterial communities were characterized using 16S rRNA amplicon sequencing of splenic samples. Multiple correspondence (MCA), regression and association screening (SCN) analyses were used to determine the degree to which extrinsic factors contributed to pathogen community structure, and to identify patterns of associations between pathogens within hosts. We found a rich diversity of bacterial genera, with 36 known or suspected to be pathogenic. We revealed that host species is the most important determinant of pathogen community composition, and that hosts that share habitats can have very different pathogen communities. Pathogen diversity and co-infection rates also vary among host species. Aggregation of pathogens responsible for zoonotic diseases suggests that some rodent species may be more important for transmission risk than others. Moreover we detected positive associations between several pathogens, including <em>Bartonella</em>, <em>Mycoplasma</em> species, Cowpox virus (CPXV) and hantaviruses, and these patterns were generally specific to particular host species. Altogether, our results suggest that host and pathogen specificity is the most important driver of pathogen community structure, and that interspecific pathogen-pathogen associations also depend on host species.</p> <p><strong>FILE DESCRIPTION:</strong></p> <p><strong>MiSeq raw sequences of the 16Sv4 rRNA gene from spleen rodent samples</strong></p> <p>This ZIP file contains the FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each spleen rodent sample using the MiSeq platform. The 749 multiplexed PCR products were indexed using both forward and reverse indices. Information of the multiplexed samples (<em>n</em>=363 in replicate) and positive (<em>n</em>= 6) & negative controls (<em>n</em>= 17) is provided in the following XLSX file titled: 16S_raw_abundance_data.xlsx</p> <p>File name: <strong>MiSeq raw sequences of the V4 region 16S rRNA gene.zip</strong></p> <p><strong>Raw input and output files generated by the mothur program</strong></p> <p>This ZIP file contains all the input and output files generated during the MiSeq sequence analysis with the mothur program.</p> <p>File name: <strong>Raw input and output files generated by the mothur program.zip</strong></p> <p><strong>Log file generated by the mothur program</strong></p> <p>This TXT file contains is the history of all the command lines and parameters used during the MiSeq sequence analysis with the mothur program.</p> <p>File name: <strong>mothur.1428506786.logfile</strong></p> <p><strong>Raw abundance table of the 16v4 rRNA gene from spleen rodent samples before data filtering</strong></p> <p>This XLSX file contains the number of reads for each distinct Operational Taxonomic Unit (OTU) and each of the PCR products, including the 332 spleen rodent samples analyzed in the study and the negative & positive controls, sequenced in the MiSeq run before the data filtering. This file contains also the following information: Study_site, Study_year, Sample_habitat, Host_species, Host_age, Host_sex, PCR_ID and the taxonomic classification (Kingdom to Genus) of each OTU.</p> <p>File name: <strong>16S_raw_abundance_data.xlsx</strong></p> <p><strong>Occurrence table of the 16v4 rRNA gene from spleen rodent samples after data filtering</strong></p> <p>This XLSX file contains the occurrences (presence: 1 ; absence: 0) after data filtering of each putative pathogenic Operational Taxonomic Unit (OTU) for each of the 332 spleen rodent samples analyzed in the study.</p> <p>File name: <strong>16S_presence_absence_data.xlsx</strong></p> <p><strong>Statistical Analysis Scripts and Data File</strong></p> <p>This ZIP file contains the R scripts for performing statistical analyses reported in the main text and supplemental materials. There is one main file (Analyses.R), as well as two source scripts required for association screening analyses (SCN.txt and FctTestScreenENV.txt). It also includes an R-legible data file containing occurrences (presence: 1 ; absence: 0) for all pathogen exposure variables on which statistical analyses were conducted (PA_DATA.csv) for each of the 332 spleen rodent samples analyzed in the study. The column names for bacterial exposures correspond to the “Pathogen Code” given in the 16S_presence_absence_data.xlsx file.</p> <p>File name: <strong>Statistical Analysis Scripts and Data File.zip</strong></p>
Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts. Soil and airborne mineral fraction datasets.
<p>These datasets correspond to soil and airbone mass mineral fractions as described and generated for "Modeling dust mineralogical composition: sensitivity to soil mineralogy" by Gonçalves Ageitos, M., Obiso, V., Miller, R.L., Jorba, O., Klose, M., Dawson, M., Balkanski, Y., Perlwitz, J., Basart, S., Di Tomaso, E., Escribano, J., Macchia, F., Montané, G., Mahowald, M.M., Green, R.O., Thompson, D.R. and Pérez García-Pando, C., ACP, 2023. </p> <p>There are 4 netCDF files that include the soil mass mineralogical fractions (0-1) in the clay (0-2 <span class="math-tex">\(\mu\)</span>m in diameter) and silt (2-63 <span class="math-tex">\(\mu\)</span>m in diameter) size classes as derived from the works of Claquin et al., (1999), and updated by Nickovic et al. (2012): <strong>C1999-SMA</strong>, and Journet et al. (2014): <strong>J2014-SMA</strong>. The data is mapped in a regular global grid with a horizontal resolution of 0.083º. Additional information on the FAO soil units, and soil texture data from HWSDv1.2 is provided in the J2014-SMA files. </p> <p>File details: </p> <ul> <li>C1999-SMA_CLAY_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>C1999-SMA_SILT_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_CLAY_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_SILT_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> </ul> <p>There are 2 additional files that report the multiannual (2006-2010 period) monthly mean of the <strong>aerosol mass mineral fractions</strong> as obtained from the <strong>MONARCH model</strong> simulations described in Gonçalves Ageitos et al. (2023). The mass fractions are provided in each of the 8 size bins used in the model (ranging from 0.2 to 20 <span class="math-tex">\(\mu\)</span>m in diameter), and normalized so as to sum 1 (i.e., the sum of all minerals in all bins equals 1). Note that in order to reduce the size of these files, the variables have been compressed to short format and include an offset and scale factor as attributes. </p> <p>File details: </p> <ul> <li>20062010_monarch_minfrac_C1999.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH C1999 experiment. </li> <li>20062010_monarch_minfrac_J2014.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH J2014 experiment. </li> </ul> <p> </p> <p><em>Legend for the minerals:</em></p> <p>quar: quartz, feld: feldspars, calc: calcite, gyps: gypsum, illi: illite, mont: montmorillonite/smectite, kaol: kaolinite, verm:vermiculite, chlo: chlorite, mica: mica, hema: hematite, goet: goethite, irox:iron oxides (hematite and goethite). </p> <p>References:</p> <p>Claquin, T., Schulz, M., and Balkanski, Y. J.: Modeling the mineralogy of atmospheric dust sources, Journal of Geophysical Research<br> Atmospheres, https://doi.org/10.1029/1999JD900416, 1999.</p> <p>FAO-UNESCO: Soil Map of the World- Volume I Legend, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Paris, http://www.fao.org/3/as360e/as360e.pdf, 1974.</p> <p>FAO-UNESCO: Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization. Digital Soil Map of the World and Derived Soil Properties, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Rome, 1995.</p> <p>FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database (version 1.2), Food and Agriculture Organization, FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2012.</p> <p>Journet, E., Balkanski, Y., and Harrison, S. P.: A new data set of soil mineralogy for dust-cycle modeling, Atmospheric Chemistry and<br> Physics, 14, 3801–3816, https://doi.org/10.5194/acp-14-3801-2014, 2014.</p> <p>Nickovic, S., Vukovic, A., Vujadinovic, M., Djurdjevic, V., and Pejanovic, G.: Technical Note: High-resolution mineralogical database of dust-productive soils for atmospheric dust modeling, Atmospheric Chemistry and Physics, 12, 845–855, https://doi.org/10.5194/acp-12-845-2012, 2012.</p> <p> </p>
Supplementary file 1 from: Moliner Cachazo L, Makati K, Chadwick MA, Catford JA, Price BW, Mackay AW, Guiry MD, Murray-Hudson M, Murray-Hudson F (2023) A review of the freshwater diversity in the Okavango Delta and Lake Ngami (Botswana): taxonomic composition, ecology, comparison with similar systems and conservation status. Aquatic Sciences
<p>Dataset with 2,204 freshwater species from the Okavango Delta and Lake Ngami (Botswana), with additional 355 species found in other areas of Botswana that are likely to be present in the study region. The dataset covers the following groups: amphibians, birds, fishes, macroinvertebrates, macrophytes, mammals, reptiles, phytoplankton, and zooplankton. The following information is given for each species: status in the Okavango Delta and Lake Ngami (present/potentially present); conservation status globally, Phylum, Class, Order, Family, Genus, species name, cited synonyms, common name, habitat, presence in high water, presence in low water, ecology, distribution in continental Africa, confirmed locations in the Okavango Delta, site coordinates, references, notes.</p>
Compositionality of Nominal Compounds
<p>Originally published at <a href="https://pageperso.lis-lab.fr/carlos.ramisch/?page=downloads/compounds">https://pageperso.lis-lab.fr/carlos.ramisch/?page=downloads/compounds</a></p> <p><strong>=> comp-datasets-release-v2.tar.gz</strong></p> <p>This package contains numerical judgements by native speakers on the compositionality of 190 nominal compound in English (EN), 180 nominal compounds in French (FR), and 180 nominal compounds in Brazilian Portuguese (PT). The English data is split into two parts. The original 90 English compounds were annotated to complement the 90 compounds in the Reddy dataset (see below). The "extra" 100 English compounds were annotated to perform generalisation experiments in the <a href="https://aclanthology.org/J19-1001/">Computational Linguistics</a> paper (Section 6.3).</p> <p>Judgements were obtained using Amazon Mechanical Turk (EN and FR) and a web interface for volunteers (PT). Every compound has 3 scores: compositionality of head word, compositionality of modifier word and compositionality of the whole. Scores range from 1 (fully idiomatic) to 5 (fully compositonal) and are averaged over several annotators (around 10 to 20 depending on the language). All compounds also have synonyms and similar expressions given by annotators.</p> <p>The datasets are described in detail and used in the experiments of papers below. Please cite one of them if you use this material in your research.</p> <ul> <li><a href="https://aclanthology.org/J19-1001/">Unsupervised Compositionality Prediction of Nominal Compounds</a> <a href="https://aclanthology.org/J19-1001/.bib">[bib]</a></li> <li><a href="http://aclweb.org/anthology/P16-2026">How Naked is the Naked Truth? A Multilingual Lexicon of Nominal Compound Compositionality</a> <a href="http://aclweb.org/anthology/P16-2026.bib">[bib]</a></li> <li><a href="http://aclweb.org/anthology/P16-1187">Predicting the Compositionality of Nominal Compounds: Giving Word Embeddings a Hard Time</a> <a href="http://aclweb.org/anthology/P16-1187.bib">[bib]</a></li> <li><a href="http://aclweb.org/anthology/W16-1804">Filtering and Measuring the Intrinsic Quality of Human Compositionality Judgments</a> <a href="http://aclweb.org/anthology/W16-1804.bib">[bib]</a></li> </ul> <p>Our methodology is inspired from <a href="http://www.aclweb.org/anthology/I/I11/I11-1024.pdf">Reddy, McCarthy and Manandhar (2011)</a>. We include their set of 90 compounds and judgments in our dataset for the analyses and experiments on English in our papers above. However, we do not include their dataset here, though. Please also <a href="http://www.dianamccarthy.co.uk/downloads.html">download their data</a> and cite their paper to obtain a fully comparable English dataset to the one used in our experiments.</p> <p><strong>=> lexsub-nc.tar.gz</strong></p> <p>This package is an extension of the original compositionality datasets and includes more detailed annotation for Portuguese lexical substitution candidates in the original dataset. It contains the same 180 nominal compounds in Portuguese as the compositionality dataset. It additionally contains frequency and PMI from a large Brazilian Portuguese corpos (around 1.2 billion words), as well as lexical substitutes annotated according to the following categories:</p> <ul> <li>Invalid: the substitution candidate is not fit for substitution, either for being too specific for a given context or for simply not being valid for the target MWE.</li> <li>Syn-SW: the substitution candidate is a single-word matching synonym in relation to the target MWE.</li> <li>NearSyn-SW: the substitution candidate is a single-word quasi-synonym in relation to the target MWE.</li> <li>Syn-MWE: the substitution candidate is a multiword matching synonym in relation to the target MWE.</li> <li>NearSyn-MWE: the substitution candidate is a multiword quasi-synonym in relation to the target MWE.</li> <li>Paraphrase: the substitution candidate is a paraphrasis of the target MWE.</li> <li>Definition: the substitution candidate is a definition of the target MWE.</li> <li>Head</li> <li>Modifier</li> </ul> <p>The lexical substitutes were provided by volunteer native speaker annotators, who were requested to provide suggestions of substitution candidates for the compounds in context. The suggestions from all annotators were pooled together and sorted according to their frequency. This pool was then manually categorized by a linguist, who attributed categories to each different substitution candidate.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.