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5,538 results for “population data”
Supporting data for "A simple ATAC-seq protocol for population epigenetics"
<p>This is supporting data for an article in which we describe a protocol for the generation of sequence-ready libraries for population epigenomics studies. The protocol is a streamlined version of the Assay for transposase accessible chromatin with high-throughput sequencing (ATAC-seq) that provides a positive display of accessible, presumably euchromatic regions. The protocol is straightforward and can be used with small individuals such as daphnia and schistosome worms, and probably many other biological samples of comparable size, and it requires little molecular biology handling expertise.</p> <p>In "Agarose picture.Tif" the left lane shows the 100 bp size marker, first 10 bands from down to top: 100bp, 200bp, 300bp, 400bp, 500bp, 600bp, 700bp, 800bp, 900bp and 1kbp.</p> <p>Produced at IHPE (http://ihpe.univ-perp.fr/)</p>
Coordinates and checklists of alien species populations as obtained from the DASCO workflow and the SInAS data set
<p>This data set contains coordinate records of alien (i.e., non-native) species populations worldwide and aggregated checklists of alien species for individual regions. The regions consists of non-overlapping polygons representing countries, sub-national or coastal marine ecoregions. </p><p>The data set was produced by applying the DASCO workflow (https://doi.org/10.5281/zenodo.5841930) using the SInAS database (version 2.5; https://doi.org/10.5281/zenodo.10038256). The workflow imports checklists of alien species such as those stored in SInAS, and extracts coordinates for the alien regions (according to SInAS) from GBIF and OBIS. After cleaning and thinning the coordinates, the workflow exports a list of coordinates of alien populations for all species included in SInAS and with records on GBIF or OBIS.</p><p>These files are part of a manuscript published in the journal Neobiota, where the workflow is described in detail (Seebens & Kaplan 2022, https://doi.org/10.3897/neobiota.74.81082).</p><p>DASCO_AlienCoordinates_SInAS_2.5.gz contains the coordinates of alien populations.</p><p>DASCO_AlienRegions_SInAS_2.5.csv contains the checklists of alien species per region. Note that this only includes species with GBIF and OBIS records. For more comprehensive checklists, other databases such as those listed here (https://doi.org/10.5281/zenodo.10038256) should be consulted.</p><p>OBIS_SpeciesKeys_SInAS_2.5.csv contains the species keys from OBIS.</p><p>GBIF_SpeciesKeys_SInAS_2.5.csv contains the species keys from GBIF.</p><p>DASCO_TaxonHabitats_SInAS_2.5.csv contains habitat information for individual species if available from WoRMS, Fishbase or Sealifebase (used to identify marine species).</p><p>The file DASCO_ListOriginalGBIFData_keys_SInAS_2.5.csv contains the DOIs of the originally downloaded files from GBIF, which provides the basis for the generation of the GBIF part (ie. the DASCO workflow was applied to these data sets from GBIF). Note that OBIS does not provide a DOI for downloads, and thus we cannot provide this.</p>
Data from: "Correlates of mid-winter pregnancy and early reproductive outcomes in a reintroduced elk (Cervus canadensis) population"
<p>Raw and processed datasets used for analysis in "Correlates of mid-winter pregnancy and early reproductive outcomes in a reintroduced elk (<em>Cervus canadensis</em>) population" by Hooven et al., published in <em>Mammalian Biology</em>. Datasets are as follows:</p> <p>Pregnancy.csv - Raw dataset detailing year and date of capture, individual identifier, and measured intrinsic variables, along with confirmed or predicted pregnancy/calf viability status.</p> <p>Pregnancy_final_mass.csv - Raw dataset after body mass estimation for individuals that were not weighed. </p> <p>all_confirmed_preg.csv - Subset of raw data for all individuals with confirmed pregnancy status (via lab PSPB assay).</p> <p>preg_ageclass.csv - Subset of all_confirmed_preg dataset including all individuals with general age classification (e.g., adult or subadult).</p> <p>preg_numeric.csv - Subset of all_confirmed_preg dataset including all individuals with numeric age value (from incisiform canine cementum annuli).</p> <p>all_fns.csv - Subset of dataset including all individuals with confirmed or predicted fetal/early neonatal survival ("offspring viability") status.</p> <p>fns_ageclass.csv - Subset of all_fns.csv including all individuals with general age classification.</p> <p>fns_numeric.csv - Subset of all_fns.csv including all individuals with numeric age values.</p> <p>parameter_est.csv - Parameter estimates from top-performing generalized linear mixed models for both pregnancy and offpsinrg viability, for plotting.</p>
Data from: Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster
<h2>Data from: Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster</h2> <ul> <li>Authors: Matteo Guaita, Alberto Marín-Cebrián, Eduardo Ahedo, Mario Merino, Fabrice Cipriani, Käthe Dannenmayer</li> <li>Contact email: mguaita@pa.uc3m.es</li> <li>Date: 15/11/2024</li> <li>Keywords: Plasma Physics, Plasma Plumes, Gridded Ion Thruster, Cathode, Facility Effects, Particel in Cell</li> <li>Version: 1.0.0</li> <li>Digital Object Identifier (DOI): 10.5281/zenodo.14165272</li> <li>License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0/" target="_blank" rel="noopener">Open Data Commons Attribution License</a></li> </ul> <h2>Abstract</h2> <p>This dataset contains the data from the simulations presented in the article submitted for pubblicaiton in the Journal: Plasma Sources Science and Technology (PSST):</p> <p>"Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster"</p> <p>The data in this repository is the result of several hybrid PIC simulations as described in the reference. For further information on the setup, numerical parameters and physical meaning of the simulations please refer to the article</p> <h2>Dataset description</h2> <p>The simulations that produced the datasets in this repository were run with the full PIC code Picaso. The majority of the data is at steady-state, and has been averaged over the last 7000 simulation time-steps to reduce numerical noise. This averaging has been performed as a first step directly by the code through time-step accumulation techniques, and at a later stage in post-processing by averaging over the last 20 print-outs of the code. The data inside the "time_dependent" folder is instead time-varying.</p> <h2>Data files</h2> <p>Each HDF5 data-group contains the mesh and time coordinates and plasma properties of a specific simulation. In particular, the naming convention is the following:</p> <ul> <li><strong>Ref_planar.hdf5: </strong>Contains the results of the "reference planar simulation" presented in Sections III and IV of the article.</li> <li><strong>2Te_planar.hdf5: </strong>Contains the results of the simulation with a doubled electron temperature at the cathode presented in Section V of the article.</li> <li><strong>2Ie_planar.hdf5: </strong>Contains the results of the simulation with a doubled electron current at the cathode presented in Section V of the article.</li> <li><strong>No_coll_planar.hdf5: </strong>Contains the results of the simulation without inelastic electron collisions presented in Section V of the article.</li> <li><strong>Ref_axisym.hdf5: </strong>Contains the results of the non-accelerated axis-symmetric simulation presented in Section VI of the article</li> <li><strong>fcol_2.5_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 2.5, presented in Section VI of the article</li> <li><strong>fcol_5_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 5, presented in Section VI of the article</li> <li><strong>fcol_7.5_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 7.5, presented in Section VI of the article</li> <li><strong>fcol_10_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 10, presented in Section VI of the article</li> </ul> <p>In each of these files the data is organized in a series of subfolders:</p> <ul> <li><strong>Electrons_prim: </strong>Contains the steady-state properties of primary electrons</li> <li><strong>Electrons_trap: </strong>Contains the steady-state properties of trapped electrons</li> <li><strong>Ions_fast: </strong>Contains the steady-state properties of fast ions (ions injected through the thruster grids)</li> <li><strong>Ions_slow: </strong>Contains the steady-state properties of slow ions (ions produced by collisions in the plume)</li> <li><strong>Time_dependent: </strong>Contains the vector of time-stamps and spatially global data saved at the corresponding time</li> </ul> <p>The data files found in the outer simulation folder are:</p> <ul> <li><strong>xs:</strong> Physical x coordinates [cm]</li> <li><strong>zs:</strong> Physical z coordinates [cm]</li> <li><strong>phi: </strong>electric potential [V]</li> <li><strong>rho_el: </strong>space charge density [C/m³]</li> <li><strong>nn: </strong>Total neutral density [1/m³]</li> </ul> <p>The data files for each particle population are:</p> <ul> <li><strong>n: </strong>Plasma (ion) density [1/m³]</li> <li><strong>f_x: </strong>Particle flux along x [1/(m² s)]</li> <li><strong>f_y: </strong>Particle flux along y [1/(m² s)]</li> <li><strong>f_z: </strong>Particle flux along z [1/(m² s)]</li> <li><strong>p_xx: </strong>xx component of the pressure tensor [J/m³]</li> <li><strong>p_yy: </strong>yy component of the pressure tensor [J/m³]</li> <li><strong>p_zz: </strong>zz component of the pressure tensor [J/m³]</li> </ul> <p>The data files in the time dependent folder are:</p> <ul> <li><strong>t: </strong>Time coordinates [s]</li> <li><strong>phi_W: </strong>Potential of the vacuum chamber walls [V]</li> <li><strong>phi_max:</strong> Maximum value of the potential in the plume [V]</li> <li><strong>nte_frac: </strong>Fraction between the number of trapped electrons and ions in the plume bulk [%]</li> <li><strong>nu_te_ela: </strong>globally averaged trapped electron-neutral elastic collision frequency [Hz]</li> <li><strong>nu_te_ion: </strong>globally averaged trapped electron-neutral ionization collision frequency [Hz]</li> <li><strong>nu_te_exc: </strong>globally averaged trapped electron-neutral excitation collision frequency [Hz]</li> <li><strong>nu_te_cou: </strong>globally averaged trapped electron-neutral Coulomb collision frequency [Hz]</li> <li><strong>nu_pe_ela: </strong>globally averaged primary electron-neutral elastic collision frequency [Hz]</li> <li><strong>nu_pe_ion: </strong>globally averaged primary electron-neutral ionization collision frequency [Hz]</li> <li><strong>nu_pe_exc: </strong>globally averaged primary electron-neutral excitation collision frequency [Hz]</li> <li><strong>nu_pe_cou: </strong>globally averaged primary electron-neutral Coulomb collision frequency [Hz]</li> </ul> <p> </p> <p>Note that all the other quantities shown in the article may be obtained from the ones saved here. We remind here that the gas employed is Xenon and that all ions are considered to be singly charged.</p> <h2>Citation</h2> <p>Any works using this dataset or any part of it in any form shall cite it as follows. The BibTeX entry s provided for convenience:</p> <p>@dataset{sim_data_guai25b,<br> author = {Matteo Guaita and Alberto Marín-Cebrián and Mario Merino and Eduardo Ahedo and Fabrice Cipriani and Käthe Dannenmayer},<br> title = {Data from: Electron populations and neutralization process in the plume of a gridded ion thruster},<br> month = November,<br> year = 2024,<br> publisher = {Zenodo},<br> version = {1.0.1},<br> doi = {10.5281/zenodo.14165272},<br> url = {https://doi.org/10.5281/zenodo.12751281}<br>}</p> <p>The journal article associated with this data-set shall also be cited as follows:</p> <p>@article{guai25b,<br> doi = {10.1088/1361-6595/adc482},<br> year = {2025},<br> month = {mar},<br> publisher = {IOP Publishing},<br> author = {Matteo Guaita and Alberto Marín-Cebrián and Mario Merino and Eduardo Ahedo and Fabrice Cipriani and Käthe Dannenmayer},<br> title = {Electron populations and neutralization process in the plume of a gridded ion thruster},<br> journal = {Plasma Sources Science and Technology },<br>}</p> <p> </p> <p><br><br></p> <h2>Acknowledgments</h2> <p>This work, and the corresponding dataset, has been supported by the ECOMODIS project, funded by the European Space Agency, under contract 4000137869/22/NL/RA</p>
CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000
<p><strong>CitiesGOER</strong> is a database that provides environmental data for 52,602 cities and 48 environmental variables, including 38 bioclimatic variables, 8 soil variables and 2 topographic variables. Data were extracted from the same 30 arc-seconds global grid layers that were prepared when making the <strong>TreeGOER (Tree Globally Observed Environmental Ranges)</strong> database that is available from <a href="https://doi.org/10.5281/zenodo.7922927">https://doi.org/10.5281/zenodo.7922927</a>. Details on the preparations of these layers are provided by Kindt, R. (2023). <strong>TreeGOER: A database with globally observed environmental ranges for 48,129 tree species</strong>. Global Change Biology 29: 6303–6318. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>. CitiesGOER was designed to be used together with TreeGOER and possibly also with the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> database (Kindt et al. <a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) to allow users to filter suitable tree species based on environmental conditions of the planting site.</p> <p>The identities and coordinates of cities were sourced from a data set with information for cities with a population size larger than 1000 that was created by <a href="https://public.opendatasoft.com/explore/?sort=modified">Opendatasoft</a> and made available from <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/table/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/table/?disjunctive.cou_name_en&sort=name</a>. The data was downloaded on 22-JULY-2023 and afterwards filtered for cities with a population of 5000 or above. Cities where information on the country was missing were removed. The coordinates of cities were used to extract the environmental data via the <a href="https://cran.r-project.org/web/packages/terra/">terra package</a> (Hijmans et al. 2022, version 1.6-47) in the <a href="https://cran.r-project.org/">R 4.2.1 environment</a>.</p> <p>Version 2023.08 provided median values from 23 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 and from 18 GCMs for SSP 3-7.0, both for the 2050s (2041-2060). Similar methods were used to calculate these median values as in the case studies for the TreeGOER manuscript (calculations were partially done via the <a href="https://rdrr.io/cran/BiodiversityR/man/ensemble.envirem.html">BiodiversityR::ensemble.envirem.run</a> function and with downscaled bioclimatic and monthly climate 2.5 arc-minutes <a href="https://www.worldclim.org/data/cmip6/cmip6_clim2.5m.html">future grid layers available from WorldClim 2.1</a>).</p> <p>Version 2023.09 used similar methods as for previous versions to provide median values from 13 GCMs for the 2090s (2081-2100) for SSP 5-8.5.</p> <p>The locations of the 52,602 cities are mapped in one of the series available from the <strong>TreeGOER Global Zones</strong> atlas that can be obtained from <a href="https://doi.org/10.5281/zenodo.8252756">https://doi.org/10.5281/zenodo.8252756</a>.</p> <p>Version 2024.10 includes a new data set that documents the location of the city locations in <strong>Holdridge Life Zones</strong>. Information is given for historical (1901-1920), contemporary (1979-2013) and future (2061-2080; separately for RCP 4.5 and RCP 8.5) climates inferred from global raster layers that are <a href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.41ns1rnff">available for download from DRYAD</a> and were created for the following article: Elsen et al. 2022. <strong>Accelerated shifts in terrestrial life zones under rapid climate change.</strong> <em>Global Change Biology</em>, 28, 918–935. <a href="https://doi.org/10.1111/gcb.15962">https://doi.org/10.1111/gcb.15962</a>. Version 2024.10 further includes Holdridge Life Zones for the climates that were available from the previous versions, calculating biotemperatures and life zones with similar methods as used by Holdridge (<a href="https://www.jstor.org/stable/1675393?seq=1">1947</a>; <a href="https://app.ingemmet.gob.pe/biblioteca/pdf/Amb-56.pdf">1967</a>) and Elsen et al. (<a href="https://doi.org/10.1111/gcb.15962">2022</a>) (for future climates, median values were determined first for monthly maximum and minimum temperatures across GCMs ). The distributions of the 48,129 species documented in TreeGOER across the Holdridge Life Zones are given in this Zenodo archive: <a href="https://zenodo.org/records/14020914">https://zenodo.org/records/14020914</a>.</p> <p>Version 2024.11 includes a new data set that documents the location of the city locations in <strong>Köppen-Geiger climate zones</strong>. Information is given for historical (1901-1930, 1931-1960, 1961-1990) and future (2041-2070 and 2071-2099) climates, with for the future climates seven scenarios each (SSP 1-1.9, SSP 1-2.6, SSP 2-4.5, SSP 3-7.0, SSP 4-3.4, SSP 4-6.0 and SSP 5-8.5). This data set was created from 30 arc-second raster layers available via: Beck, H.E., McVicar, T.R., Vergopolan, N. et al. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724 (2023). <a href="https://doi.org/10.1038/s41597-023-02549-6">https://doi.org/10.1038/s41597-023-02549-6</a> </p> <p>Version 2025.03 includes extra columns for the baseline, 2050s and 2090s datasets that partially correspond to climate zones used in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> database. One of these zones are the <a href="https://rawgit.com/valentinitnelav/plotbiomes/master/html/Whittaker_biomes_dataset.html">Whittaker biome types</a>, available as a polygon from the <a href="https://rawgit.com/valentinitnelav/plotbiomes/master/html/Whittaker_biomes_dataset.html">plotbiomes</a> package (see also <a href="https://www.davidzeleny.net/wiki/lib/exe/fetch.php/vegecol:materials:ricklefs_bioms_chapter_5.pdf">here</a>). Whittaker biome types were extracted with similar R scripts as described by <a href="https://rpubs.com/Roeland-KINDT/1275232">Kindt 2025</a> (these were also used to calculate environmental ranges of TreeGOER species, as archived <a href="https://zenodo.org/records/14908944">here</a>).</p> <p>Version 2025.03 further includes information for the baseline climate on the steady state water table depth, obtained from a 30 arc-seconds raster layer calculated by the GLOBGM v1.0 model (Verkaik et al. <a href="https://gmd.copernicus.org/articles/17/275/2024/">2024</a>). Also included was the elevation, obtained from the same WorldClim 2.1 raster layer used to prepare TreeGOER.</p> <p> </p> <p>As an alternative to CitiesGOER, the <strong>ClimateForecasts</strong> database (<a href="https://zenodo.org/records/10776414">https://zenodo.org/records/10776414</a>) documents the environmental conditions at the locations of 15,504 weather stations. ClimateForecasts was integrated in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees"><strong>GlobalUsefulNativeTrees</strong> database</a> (see <a href="https://doi.org/10.1038/s41598-023-39552-1">Kindt et al. 2023</a>).</p> <p> </p> <p>When using CitiesGOER in your work, cite this depository and the following:</p> <ul> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. <em>Ecography</em>, <em>41</em>(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. <a href="https://doi.org/10.5194/soil-7-217-2021">https://doi.org/10.5194/soil-7-217-2021</a></li> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology 29: 6303–6318. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> <li>Opendatasoft (2023) Geonames - All Cities with a population > 1000. <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name</a> (accessed 22-JULY-2023)</li> </ul> <p>When using information from the Holdridge Life Zones, also cite:</p> <ul> <li>Elsen, P. R., Saxon, E. C., Simmons, B. A., Ward, M., Williams, B. A., Grantham, H. S., Kark, S., Levin, N., Perez-Hammerle, K.-V., Reside, A. E., & Watson, J. E. M. (2022). Accelerated shifts in terrestrial life zones under rapid climate change. <em>Global Change Biology</em>, 28, 918–935. <a href="https://doi.org/10.1111/gcb.15962">https://doi.org/10.1111/gcb.15962</a></li> </ul> <p>When using information from Köppen-Geiger climate zones, also cite:</p> <ul> <li>Beck, H.E., McVicar, T.R., Vergopolan, N., Berg, A., Lutsko, N.J., Dufour, A., Zeng, Z., Jiang, X., van Dijk, A.I. and Miralles, D.G. 2023. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724. <a href="https://doi.org/10.1038/s41597-023-02549-6">https://doi.org/10.1038/s41597-023-02549-6</a></li> </ul> <p>When using information on the Whittaker biome types, also cite:</p> <ul> <li>Ricklefs, R. E., Relyea, R. (2018). Ecology: The Economy of Nature. United States: W.H. Freeman.</li> <li>Whittaker, R. H. (1970). Communities and ecosystems.</li> <li>Valentin Ștefan, & Sam Levin. (2018). plotbiomes: R package for plotting Whittaker biomes with ggplot2 (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.7145245">https://doi.org/10.5281/zenodo.7145245</a></li> </ul> <p>When using information on the steady state water table depth, also cite:</p> <ul> <li>Verkaik, J., Sutanudjaja, E. H., Oude Essink, G. H., Lin, H. X., & Bierkens, M. F. (2024). GLOBGM v1. 0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model. Geoscientific Model Development, 17(1), 275-300. <a href="https://gmd.copernicus.org/articles/17/275/2024/">https://gmd.copernicus.org/articles/17/275/2024/</a></li> </ul> <p> </p> <p>The development of <strong>CitiesGOER</strong> was supported by the <strong>Darwin Initiative</strong> to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by <strong>Norway’s International Climate and Forest Initiative</strong> through the Royal Norwegian Embassy in Ethiopia to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, and by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project. Development of version 2024.10 was further supported by the <strong>Green Climate Fund</strong> through the <em>Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso</em> project, by the <strong>Bezos Earth Fund</strong> to the <em>Quality Tree Seed for Africa in Kenya and Rwanda</em> project and by the <strong>German International Climate Initiative (IKI)</strong> to the regional tree seed programme on <em>The Right Tree for the Right Place for the Right Purpose in Africa</em>.</p>
Data & R-Code for "Weather and food availability additively affect reproductive output in an expanding raptor population"
<p><strong>Abstract</strong></p> <p>The joint effects of interacting environmental factors on key demographic parameters can exacerbate or mitigate the separate factors’ effects on population dynamics. Given ongoing changes in climate and land use, assessing interactions between weather and food availability on reproductive performance is crucial to understand and forecast population dynamics. By conducting a feeding experiment in 4 years with different weather conditions, we were able to disentangle the effects of weather, food availability and their interactions on reproductive parameters in an expanding population of the red kite (<em>Milvus milvus</em>), a conservation-relevant raptor known to be supported by anthropogenic feeding. Brood loss occurred mainly during the incubation phase, and was associated with rainfall and low food availability. In contrast, brood loss during the nestling phase occurred mostly due to low temperatures. Survival of last-hatched nestlings and nestling development was enhanced by food supplementation and reduced by adverse weather conditions. However, we found no support for interactive effects of weather and food availability, suggesting that these factors affect reproduction of red kites additively. The results not only suggest that food-weather interactions are prevented by parental life-history trade-offs, but that food availability and weather conditions are crucial separate determinants of reproductive output, and thus population productivity. Overall, our results suggest that the observed increase in spring temperatures and enhanced anthropogenic food resources have contributed to the elevational expansion and the growth of the study population during the last decades.</p>
Data and code for 'Age structure of amphibian populations with endemic chytridiomycosis, across climatic regions with markedly different infection risk'
<p>This repository provides all data and R code from the analysis presented in the following paper:</p> <p>Turner, A., Heard, G., Hall, A., Wassens, S. (in review). Age structure of amphibian populations with endemic chytridiomycosis, across climatic regions with markedly different infection risk.</p> <p>The data are provided as a series of .csv files, R script and two zip folders of R packages (Surv_mod and VB_mod)</p> <p>1. <strong>Skeleto_dat_ready_Jan2021.csv</strong> Data from frog surveys conducted by Anna Turner</p> <p>2. <strong>Geoffs_data.csv</strong> Data from frog surveys conducted by Geoff Heard</p> <p>3. <strong>Environmental_variables_skeleto.csv</strong> Environmental data collected during surveys </p> <p>4. <strong>sk.dat_July21.csv</strong> Collated data from Anna and Geoff - created by 'Data_collation_for_analysis_2.R' ready for analysis</p> <p>5. <strong>Variables_that_are_highly_correlated_with_each_other_season_wide.csv</strong> Testing for correlation</p> <p>6. <strong>Model_structure_skeleto_2.csv </strong>creates model structure for analysis</p> <p>7. <strong>Model_selection_statistics_June_21.csv </strong>Output from model</p> <p>R code is provided seperately for each of the following components:</p> <p>1. <strong>Data_collation_for_analysis_2.R</strong> Collating data from Anna and Geoffs datasets</p> <p>2. <strong>Skeleto_analysis_5.R - </strong>First uses regression modelling to explore factors correlated with variation in age</p> <p> - Following Scheele et al. (2016) regression models with a poisson distribution</p> <p> - Use bayesian non-linear regression to fit the Von Bertalanffy growth model to size-at-age data</p> <p> - Plots male and female growth curves</p> <p> - Uses catch curve approach to estimate survival from best fitting regression model following Scroggie (2012) but with bayesian implementation</p>
Pacific salmon population time-series dataset to support Appendix S1: Data and additional information on declines of Pacific Salmon
<p>Dataset used to support the main paper 'Protecting our coast for everyone’s future: Indigenous and scientific knowledge support marine spatial protections proposed by Central Coast First Nations in Pacific Canada' by Reid et al. 2022. Dataset cited in Appendix S1 regarding trends in adult salmon abundances in the Central Coast. The data were as compiled by Will Atlas from the <a href="https://wildsalmoncenter.org/">Wild Salmon Center</a> to describe trends in the abundance of adult salmon returning to the Central Coast, which is the sum of escapement and harvest, as derived from the following sources:</p> <ol> <li>Escapement data from DFO: <a href="https://open.canada.ca/data/en/dataset/c48669a3-045b-400d-b730-48aafe8c5ee6">NuSEDS-New Salmon Escapement Database System - Open Government Portal (canada.ca)</a></li> <li>Harvest rates estimated by Karl English and colleagues and available at: <a href="https://data.salmonwatersheds.ca/data-library/">Salmon Watersheds Program - Data Library</a>.</li> <li>Information on total harvest that is reported in the DFO post season review (DFO 2020).</li> </ol>
Data for: From pattern to process? Dual travelling waves, with contrasting propagation speeds, best describe a self-organised spatio-temporal pattern in population growth of a cyclic rodent
<p>Centroid data used for the analysis in Roos et al. Eco Lett.</p> <p>Transects, up to 99 m in length (dependent on the field's length), were surveyed in linear stable landscape features (field, track or ditch margins) to estimate vole abundance from November 2011 until September 2017. Each transect was divided into 3 m sections (33 in total) and the presence or absence of one or more signs of vole activity (i.e., latrines by burrows, fresh vegetation clippings, and recent burrow excavations) in each section was noted. The proportion of sections with signs of vole presence per transect was then used as the abundance index. The number of surveys carried out at any time varied adaptively with the perceived risk of an outbreak (according to changes in estimated abundance in previous monitoring surveys).</p> <p>The response variable typically used in all models is proportional growth rate (r_{t,i}, where is the abundance index for site at time (Royama 1992; Berryman 2002). A benefit of using r_{t,i}, rather than ln(N_{t,i}), is that any multiplicative effects of site quality are cancelled out, provided they are constant over time. To calculate r_{t,i}, vole abundance indices are required at the same location in successive time periods (i.e., N_{t,i} and N_{t+1,i}). Given that exact transect locations were rarely reused in successive months, and all transect measurements took place throughout the year rather than discrete seasons, the data had to be aggregated to consistent locations and times to allow growth rate to be calculated. As such, transects were temporally aggregated into a respective yearly quarter (e.g., January to March 2014). Transects were spatially aggregated by sequentially selecting an unassigned transect as a reference point for the ith centroid and assigning all unassigned transects within a 5 km radius to the ith centroid, and repeating until all transects had been allocated (see Figure 2 for a summary of the number of transects assigned to each centroid, centroid locations, and time series of growth rate of each centroid). Once complete, the mean Julian day, X and Y UTM (Universal Transverse Mercator) and the mean index was calculated for all transects assigned to each centroid for each time period. Where a centroid had successive values of N_{t,i} and N_{t+1,i} available, the corresponding proportional growth rate was calculated.</p> <p>A constant of 3.03 was added to N_{t,i} to avoid zero entries (3.03 was the lowest non-zero value of <em>N</em> observed). The final dataset consisted of 3,751 observations.</p>
Supplementary data release for "Cosmology and modified gravitational wave propagation from binary black hole population models"
<p>We release the data products associated to the paper <a href="https://arxiv.org/abs/2112.05728">"Cosmology and modified gravitational wave propagation from binary black hole population models", </a><a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.064030"><em>Phys.Rev.D</em> 105 (2022) 6 </a>.</p> <p>The data can be used in conjunction with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> to reproduce the results of the paper. </p> <p>The data product contains the following folders:</p> <p>* injections_GWTC3: injections used to analyze the GWTC3 catalog, generated with the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> . Injections are available separately for O1-O2, O3a, O3b for minimum SNR of 10, 11, 12 (folder names are self-explicative). Each folder contains a file named selected.h5 with the injections. For loading them, refer to the tutorial of the code <a href="https://github.com/CosmoStatGW/MGCosmoPop">MGCosmoPop</a> .</p> <p>* mock_BPL_5yr_GR : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to General Relativity (see the paper for details)</p> <p>* mock_BPL_5yr_MG : mock data for 5 years of aLIGO observations, with fiducial cosmological model set to a modified gravity model with modified gravitational-wave propagation (see the paper for details)</p> <p>* injections_mock : injections for analyzing the mock datasets above</p>
Fine-scale population spatialization data of China in 2018 based on real location-based big data
<p><strong>This data contains a geospatial population raster layer in GeoTIFF format with 1*1 km resolution for 31 provincial regions (2851 counties) of China in 2018 (pop2018.tif). It also provides the Tencent positioning data in 2018 (TN_hSum2018.tif), the table of statistical population of 2851 counties (statistical_population_2018_china_county.xls) and its vector map (statisitcal_pop.shp) and codes (code.docx).</strong></p>
Data for: Seasonal dynamics of faunal diversity and population ecology in an estuarine seagrass bed
<p>These are the data used in the analyses described in the paper titled "Seasonal dynamics of faunal diversity and population ecology in an estuarine seagrass bed", accepted at Estuaries and Coasts. We acknowledge the tangata whenua for the rohe in which these data were collected, Ngāi Tārewa and Ngāti Īrakehu. We thank the Akaroa Taiāpure for their support of this research.</p> <p>The data included are:</p> <p>Raw count data of taxa for each tow, associated with additional metadata including the date of collection, tow coordinates, and estimated seagrass cover (MonthlyRawSampling_Duvauchelle_2020.csv). This data was put through cleaning steps outlined in the file docs/dataCleaning.Rmd prior to being used in any analyses.</p> <p>The cleaned community composition data (cleanedCommunity.csv), output from <a href="https://github.com/spflanagan/ecology-duvauchelle/blob/main/docs/dataCleaning.Rmd">docs/dataCleaning.Rmd</a> and used in the downstream community and population analyses.</p> <p>The GPS coordinates for the tows (gpsdat.csv). These were extracted from the raw data in the data cleaning process.</p> <p>NZsyngnathids_measurements.csv contains the measurements of the pipefish from images. These data also underwent a cleaning process documented in <a href="https://github.com/spflanagan/ecology-duvauchelle/blob/main/docs/dataCleaning.Rmd">docs/dataCleaning.Rmd</a>.</p> <p>The cleaned pipefish trait data (pipefishTraits.csv), output from docs/dataCleaning.Rmd and used in the downstream population analysis documented in <a href="https://github.com/spflanagan/ecology-duvauchelle/blob/main/docs/populationAnalyses.Rmd">docs/populationAnalyses.Rmd</a>.<br> </p>
Tracking Selection using Temporal Population Genomics Data
<p>This repository contains the implementation of a pipeline to run the simulations and to produce a reference table for the ABC-RF inference of demography and selection. In its new release, this repository contains the whole-genome polymorphism of contemporary and museum specimens of <em>Apis mellifera</em> feral populations analyzed by Cridland et al. (2018).</p>
Population size, HIV prevalence, and antiretroviral therapy coverage among key populations in sub-Saharan Africa: collation and synthesis of survey data 2010-2023
<p>This dataset contains surveillance study estimates for population size, HIV prevalence, and ART coverage among female sex workers (FSW), men who have sex with men (MSM), people who inject drugs (PWID), and transgender men and women (TGM/W) from 2010-2023. It was created to support the UNAIDS Estimates Key Population Workbook for use by HIV estimates teams in sub-Saharan Africa. Key population surveillance reports, including Ministry of Health-led biobehavioural surveys, mapping studies, and academic studies were used to populate the database.</p> <p>The dataset was populated using existing key population size estimate databases including:</p> <ul> <li>UNAIDS Key Population Atlas</li> <li>US Centers for Disease Control and Prevention surveillance database</li> <li>Global Fund against HIV/AIDS, TB, and Malaria surveillance database</li> <li>Global.HIV database</li> <li>Systematic review databases among MSM (<a href="https://pubmed.ncbi.nlm.nih.gov/31601542/" target="_blank" rel="noopener">Stannah et al, 2019</a> and <a href="https://pubmed.ncbi.nlm.nih.gov/37453439/" target="_blank" rel="noopener">Stannah et al., 2023</a>) and PWID (<a href="https://pubmed.ncbi.nlm.nih.gov/36996857/" target="_blank" rel="noopener">Degenhardt et al., 2023</a>)</li> </ul> <p><br>and was additionally supplemented by a literature review of peer-reviewed and grey literature sources.</p> <p>The data can be <a href="https://shiny.dide.ic.ac.uk/kp-data/" target="_blank" rel="noopener">explored in this web application</a> and the <a href="https://www.medrxiv.org/content/10.1101/2022.07.27.22278071v3" target="_blank" rel="noopener">accompanying manuscript can be found here</a></p>
CellSIUS provides sensitive and specific detection of rare cell populations from complex single cell RNA-seq data: Codes and processed data
<p>Codes and processed data to reproduce the analysis discussed in: </p> <p>Wegmann <em>et Al.</em>,<strong> CellSIUS provides sensitive and specific detection of rare cell<br> populations from complex single cell RNA-seq data</strong>, Genome Biology 2019 (Accepted)<br> </p>
Example Dataset for npstat: Population genetics from Pooled NGS data NPStat v1: User guide
<p>Example Dataset for npstat to test the program and the different options.</p> <p>The example dataset contains a pileup file with sequences of of the 2L chromosome from fifteen pooled inbreed individuals of <em>Drosophila melanogaster </em>(<span>doi: 10.1038/nature10811</span>). The dataset also contains the sequence reference of the 2L chromosome in fasta format, an outgroup sequence in fasta format of <em>D. yakuba</em> (SRR26246471), a GFF3 annotation file and a file with a brief list of selected SNPs to be analyzed.</p>
The Brief Symptom Inventory in the Swiss general population: Presentation of norm scores and predictors of psychological distress: Data supporting the publication
This is the dataset on which the following publication is based: • Michel G, Baenziger J, Brodbeck J, Mader L, Kuehni CE, Roser K (2024). The Brief Symptom Inventory in the Swiss general population: Presentation of norm scores and predictors of psychological distress. PLOS One. 19(7), e0305192. Doi: 10.1371/journal.pone.0305192, https://doi.org/10.1371/journal.pone.0305192 A description of the sample and the data collection procedure is available in the publication. The dataset contains the following variables: • Socio-demographic characteristics of the sample - Weights according to representative general population sample - Sex from Swiss Federal Statistical Office (SFSO) - Age at study (rounded to integer) - Age categories (10-year age groups) - Language questionnaire (German/Rumantsch, French, Italian) - Nationality from SFSO - Migration background - Education - Employment status • Original and prepared data on the Brief Symptom Inventory A detailed data dictionary is available in a separate excel file. Version • 1.0 (15 August 2024)
Data and code from: "Building multidimensional tolerance landscapes to predict the population dynamics of bacteria exposed to antibiotics in urban sewers"
<p>City sewers harbor diverse bacterial communities exposed to various antibiotic residues resulting from human consumption and excretion. Although these residues typically occur at sub-inhibitory concentrations, they can still impact the growth rate and yield of susceptible wastewater bacteria. Many bacteria exhibit antibiotic tolerance through transient phenotypic changes. Antibiotic residues, combined with complex environmental factors like temperature and salinity, especially in coastal cities, contribute to non-additive interactions that modulate antibiotic tolerance and affect population dynamics.</p> <p>To better understand these interactions, we developed continuous multivariate tolerance landscapes for three bacterial species: <strong><em><span>Escherichia coli</span></em></strong>, the emerging pathogen <strong><em><span>Streptococcus suis</span></em></strong>, and the sewer-inhabiting <strong><em><span>Arcobacter cryaerophilus</span></em></strong>. We modeled their intrinsic growth rates and carrying capacities across complex environments, incorporating temperature, salinity, and concentrations of two antibiotics (ciprofloxacin and azithromycin).<span> Using</span> these multivariate tolerance curves, we predicted microbial population dynamics in two sewers of Barcelona, highlighting the importance of environmental complexity in shaping microbial responses to antibiotic stressors.</p> <p> </p> <p><strong>Usage</strong></p> <p>Users can perform the analysis by running the R script (TC3D.R) after the installation of all</p> <p>package mentioned in the preamble,<span> </span></p> <p>This folder contains:</p> <p>- 3 datasets with OD measures for the 3 species:</p> <p><span> </span>* data_acrya.xlsx</p> <p><span> </span>* data_ecoli.xlsx</p> <p><span> </span>* data_ssuis.xlsx</p> <p>- 1 excel files with metadata (plate, well, species, environmental conditions)</p> <p><span> </span>* map_plate_all.xlsx</p> <p>- 4 datasets giving time series of the flow and several measures including <span> </span>conductivity and <span> </span>temperaturefor 2 sewers of Barcelona obtained from sample cabines <span> </span>set during the implementation of SCOREWATER (ID:820751)</p> <p><span> </span>* carmel_flow.csv</p> <p><span> </span>* carmel_quality.csv</p> <p><span> </span>* poblenou_flow.csv</p> <p><span> </span>* poblenou_quality.csv</p> <p>- 1 C++ script compiled and run with the R TMB package:</p> <p><span> </span>* fit_growth_r_K_SS_treatment.cpp : computes the negative loglikelihood for r and K, and state DOs, given the observed DO, for the populations under one same environmental treatment (salinity * temperature * antibiotic), and computes the density-dependence parameter alpha from r and K using the Delta Method.</p> <p><br><br></p>
Genome data and resources on the recombination landscape and population history of the harlequin fly
<p>This dataset contains phased vcf files of <em>Chironomus riparius, </em>ouput files of RepeatMasker, MELT, RepeatOBserver, MSMC2, iSMC and bedtools, such as supporting files. </p> <p>For further details also check the GitHub page: <a href="https://github.com/lpettrich/Crip_Recombination_PopHistory_Cla_2024" target="_blank" rel="noopener">https://github.com/lpettrich/Crip_Recombination_PopHistory_Cla_2024</a></p> <ul> <li><strong>phased-vcfs: </strong>Artificially phased vcf-files of five populations with four individuals each. Needed to generate multihetsep files. Input files for iSMC.<br> <ul> <li>Hesse in Germany = MG</li> <li>Rhône-Alpes in France = MF</li> <li>Lorraine in France = NMF</li> <li>Piemont in Italy = SI</li> <li>Andalusia in Spain = SS</li> </ul> </li> <li><strong>multihetsep-files: </strong>Created with msmc-tools. Input files for MSMC2. </li> <li><strong>RepeatMasker: </strong>Raw output of RepeatMasker run. Summary file and file with filtered <em>Cla</em>-element (a transposable element) included.<strong><br></strong></li> <li><strong>MELT: </strong>MELT ouput with added info on population and numbered insertions reflecting all 441 detected <em>Cla </em>insertions.<strong><br></strong></li> <li><strong>RepeatOBserver: </strong>Summary files on centromere predictions based on histograms and Shannon Diversity from RepeatOBserver. Genome-wise Shannon Diversity per chromosome included. <strong><br></strong></li> <li><strong>MSMC2: </strong>Raw ouput of combined cross-coalescence and mean values if MSMC2 per populations. <strong><br></strong></li> <li><strong>iSMC: </strong>Recombination rate rho in 10 kb windows and 100 kb windows along the genome. <strong><br></strong></li> <li><strong>bedtools closest ismc 10 kb: </strong>Bedtools closest analysis of the distance of the next <em>Cla</em>-element to the recombination rate rho in 10 kb windows.<strong><br></strong></li> <li><strong>bedtools closest ismc 100 kb: </strong>Bedtools closest analysis of the distance of the next <em>Cla</em>-element to the recombination rate rho in 100 kb windows.</li> <li><strong>input-files figures: </strong>Supporting files needed to create figures.<strong><br></strong></li> </ul>
Van Dijk et al. (2021), A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050, data and scripts
<p>This repository contains all data and R scripts to reproduce the figures in Van Dijk et al. (2021), A meta-analysis of global food demand and population at risk of hunger projections for the period 2010-2050, Nature Food. More specifically, it includes two databases: (1) A database with standardized information to describe the characteristics of 57 studies that were identified by the systematic literature review and (2) The Global Food Security Projections Database v1.0.1 with harmonized projections for three global food security indicators: food consumption in kcal per capita and total kcal, and population at risk of hunger. The database also includes projections for total global population that are required to derive the global food security indicators.</p> <p>The two scripts (nf_figures.r and nf_meta_regression.r) can be used to reproduce the figures and tables in the main paper and the supplementary information. Please start with the first script, which sources the second script. </p> <p>This is the first version of the Global Food Projections Database. We expect to update the data, including additional studies and variables in the future. For issues and suggestions, please contact michiel.vandijk@wur.nl.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.