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1,153 results for “conservation data”
Micrometeorological data from Etosha Heights Conservation Centre, Namibia, 2023-ongoing
This data set contains half-hourly micrometeorological data from six weather stations distributed across Etosha Heights Private Reserve in northern Namibia. Data collection began at the end of May 2023, and is ongoing. The data are part of a project funded by Colgate University's Picker Interdisciplinary Science Institute, in collaboration with Giraffe Conservation Foundation and the Namibia University of Science and Technology, aimed at better understanding animal movement. That data are being coupled with gps data from a variety of animals within the reserve and adjacent Etosha National Park.
Conservation and Economic Data for New England Towns 1990-2015
Land protection, whether public or private, is often controversial at the local level because residents worry about lost economic activity. We used panel data and a quasi-experimental impact-evaluation approach to determine how key economic indicators were related to the percentage of land protected. Specifically, we estimated the impacts of public and private land protection based on local area employment and housing permits data from 5 periods spanning 1990-2015 for all major towns and cities in New England. To generate rigorous impact estimates, we modeled economic outcomes as a function of the percentage of land protected in the prior period, conditional on town fixed effects, metro-region trends, and controls for period and neighboring protection. Contrary to narratives that conservation depresses economic growth, land protection was associated with a modest increase in the number of people employed and in the labor force and did not affect new housing permits, population, or median income. Public and private protection led to different patterns of positive employment impacts at distances close to and far from cities, indicating the importance of investing in both types of land protection to increase local opportunities. The greatest magnitude of employment impacts were due to protection in more rural areas, where opportunities for both visitation and amenity-related economic growth may be greatest. Overall, we provide novel evidence that land protection can be compatible with local economic growth and illustrate a method that can be broadly applied to assess the net economic impacts of protection.
On the Moreau–Jean scheme with the Frémond impact law: energy conservation and dissipation properties for elastodynamics with contact, impact and friction — data
<p>This deposit contains the data output of the systems described in <a href="https://hal.science/hal-04230941">On the Moreau–Jean scheme with the Frémond impact law. Energy conservation and dissipation properties for elastodynamics with contact impact and friction.</a> The codes that generated this data are available in another <a href="../records/10953181">deposit</a> archived on Zenodo, as well as in a GitHub repository archived on <a href="https://archive.softwareheritage.org/swh:1:dir:33ff6d960b70505c7939c0ce21c039cabbe1351c;origin=https://github.com/nickcollins-craft/On-the-Moreau-Jean-scheme-with-the-Fremond-impact-law;visit=swh:1:snp:72aede3d3a464732a36ef79c20ef07eebd1f9918;anchor=swh:1:rev:b63b68c25e72d23d7d9ee30225165fa0ebffb3c2">Software Heritage</a>, which is the preferred method of obtaining the codes. Two of the files in this deposit ("deformed_sliding_block_mesh.png" and "sliding_block_mesh.png") are required for one of the codes in the code deposit to run successfully ("block_mesh_plot.py", with the files assumed to be located in the folder specified in the data_folder variable of the file "path_file.py"), but the deposits are otherwise independent.</p>
Data for: Global political responsibility for the conservation of albatrosses and large petrels
<p>Data derivatives from analysis of seabird tracking data. These data allow one to reproduce the results of the paper "Global political responsibility for the conservation of albatrosses and large petrels by Beal et al (in press). </p>
Potential forest conservation value rasters for Denmark from Assmann et al. "LiDAR data fusion and machine learning identify temperate forests of high conservation value"
<p>Potential forest conservation value (high / low) rasters for Denmark based on a remote sensing data fusion approach. Please see manuscript (below) for a detailed description of the methods and data products. </p> <p><br>Jakob J. Assmann, Pil B. M. Pedersen, Jesper E. Moeslund, Cornelius Senf, Urs A. Treier, Derek Corcoran, Zsófia Koma, Thomas Nord-Larsen, Signe Normand. In prep. LiDAR data fusion and machine learning identify temperate forests of high conservation value.</p> <p><br>When using the data, please cite the above manuscript. </p> <p><br>Files description:</p> <ul> <li>Compressed and cloud optimised rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:3857 <ul> <li>forest_quality_ranger_biowide_10m_cog_epsg3857.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m_cog_epsg3857.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m_cog_epsg3857.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m_cog_epsg3857.tif GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul> <p> </p> <ul> <li>Aggregated rasters of potential forest conservation value projections for Denmark (100 m res.) in EPSG:25832 <ul> <li>forest_quality_ranger_biowide_100m.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_100m.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_100m.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_100m.tif GBM model projections based on SustainScapes stratification </li> </ul> </li> </ul> <p> </p> <ul> <li>Uncompressed and tiled rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:25832<br>Please note: the archives contain approx. 42k tiles, each 10 x 10 km, as well as a VRT file for covenient loading. <ul> <li>forest_quality_ranger_biowide_10m.zip RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m.zip RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m.zip GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m.zip GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul>
Data from: An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles
<p><strong> Data from: An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles</strong></p> <p>- Authors: Pedro Jimenez, Luis Chacon, Mario Merino</p> <p>- Contact email: pejimene@ing.uc3m.es</p> <p>- Date: 2024-02-09</p> <p>- Keywords: electric propulsion, plasma simulation, magnetic nozzles, implicit particle-in-cell (PIC)</p> <p>- Version: 1.2</p> <p>- Digital Object Identifier (DOI): 10.5281/zenodo.8081962</p> <p>- License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0">Open Data Commons Attribution License</a></p> <p><strong>Abstract</strong></p> <p>This dataset contains the data found in the plots of the journal article:</p> <p><a href="https://www.sciencedirect.com/science/article/pii/S0021999124000755?via%3Dihub">Pedro Jimenez, Luis Chacon, Mario Merino, "An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles"</a></p> <p>The data in this repository are the results of kinetic plasma simulations as described in the reference. For further information on the setup for the simulation please refer to the article.</p> <p><strong>Data Files</strong></p> <p>The data files are in .csv format. They were produced in Julia using <a href="http://csv.juliadata.org/stable/)">CSV.jl</a> and <a href="https://dataframes.juliadata.org/stable/">DataFrames.jl</a> libraries.</p> <p>The files are organised following the order of the figures in the article. All the plots are 1D series, the first column corresponding to the x-axis data. Y-axis data is presented in the following columns, the total number of additional columns is equal to the number of line series. The title of each series is found in the first row of the .csv files. Please find below some specificalities in certain figures:</p> <p>- The columns for the time evolution in <strong>fig6_left.csv</strong> and<strong> fig6_right.csv </strong>(corresponding to the actual left and right columns in the figure i.e. cases A and B) contain a field tag followed by the corresponding time step (e.g. phi_500).</p> <p>- Due to the different number of nodes, steady state fields for cases A and B are saved in <strong>fig8_a-f.csv</strong> while cases AF and BF are saved in <strong>fig8_a-f_fine.csv</strong>.</p> <p>The rest of the data files should be self descripting</p> <p><strong>Citation</strong></p> <p>Any works using this dataset or any part of it in any form shall cite it as follows:</p> <p>The prefered means of citation is to reference the publication asociated to the jounal article with DOI: <a href="https://doi.org/10.1016/j.jcp.2024.112826">10.1016/j.jcp.2024.112826</a></p> <p>The BibTex is also provided for the sake of convinience:</p> <pre>@article{jimenez2024implicit, title={An implicit, conservative electrostatic particle-in-cell algorithm for paraxial magnetic nozzles}, author={Jim{\'e}nez, Pedro and Chac{\'o}n, Luis and Merino, Mario}, journal={Journal of Computational Physics}, pages={112826}, year={2024}, publisher={Elsevier} }</pre> <p>Optionally the dataset can be cited by referencing the corresponding DOI:</p> <p><a href="https://doi.org/10.5281/zenodo.8081962">https://doi.org/10.5281/zenodo.8081962</a></p> <p><strong>Acknowledgments</strong></p> <p>This dataset was created by the [ERC-ZARATHUSTRA project](https://erc-zarathustra.uc3m.es/).</p> <p>The ERC-ZARATHUSTRA project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 950466).</p>
Data for "Let's not wing it: Effective conservation of subterranean-roosting bats"
<p>Database as both excel (.xls) and tab-delimited (.csv) associated with the publication: </p> <p>Meierhofer M.B., et al. (2023) Let’s not wing it: Effective conservation of subterranean-roosting bats. <em>Conservation biology.</em></p> <p>Please refer to the main publication for a detailed description. An explanation of the database is available in the Metadata file uploaded alongside the database. R code to reproduce the analysis pipeline is available on GitHub:</p> <p>https://github.com/StefanoMammola/Analysis_Cave_bat_conservation.git</p>
Multiple-benefit Conservation in Practice: Metrics Data for Quantifying Multidimensional Impacts of Landscape Change in California's Sacramento–San Joaquin Delta
<p><strong>SUMMARY</strong><br> These data represent estimated mean value, standard error, and units for a range of metrics by land cover class in the Sacramento-San Joaquin Delta. Metrics are grouped into three major categories: Agricultural Livelihoods (including metrics for gross production value, number of agricultural jobs, and annual wages per employee), Water Quality (in terms of the application rates for pesticides identified as critical pesticides, groundwater contaminants, and those posing a high or moderate risk to aquatic organisms), and Climate Change Resilience (qualitative scores representing relative tolerance for heat, drought, and flood).</p> <p><strong>DESCRIPTION</strong><br> These data were developed to facilitate projecting the net impacts of land cover change scenarios on multiple metrics of interest to the Sacramento-San Joaquin Delta, including potential benefits and trade-offs. They were used in initial analyses of scenarios representing habitat restoration and perennial crop expansion, and they are required for using the R package "DeltaMultipleBenefits", which provides the code and work flow for repeating the initial analyses or analyzing new scenarios.</p> <p>For additional details about the development and applications of these data, please see: </p> <ul> <li>Dybala KE, et al. (In review) Multiple-benefit Conservation in Practice: A Framework for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta </li> <li>Dybala KE (2023) <em>DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta.</em> R package version 1.0.0. doi: 10.5281/zenodo.7718620. Available from: https://pointblue.github.io/DeltaMultipleBenefits </li> </ul> <p><strong>FUNDING STATEMENT</strong><br> These data were developed as part of the project "Trade-offs and Co-benefits of Landscape Change on Bird Communities and Ecosystem Services in the Sacramento–San Joaquin River Delta", funded by Proposition 1 Delta Water Quality and Ecosystem Restoration Program, Grant Agreement Number – Q1996022, administered by the California Department of Fish and Wildlife.</p> <p><strong>POINT OF CONTACT</strong><br> Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org</p> <p><strong>SUGGESTED CITATION</strong><br> Dybala KE. 2023. Multiple-benefit Conservation in Practice: Metrics Data for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta. doi:10.5281/zenodo.7504874.</p> <p><strong>DATA DISTRIBUTION</strong><br> Zenodo (https://doi.org/10.5281/zenodo.7504874)</p> <p><strong>PROGRESS</strong><br> Complete, but note that the accompanying manuscript has not yet undergone peer-review, and thus these data may require future revision.</p> <p><strong>UPDATE FREQUENCY</strong><br> As Needed</p> <p><strong>DATE</strong><br> These data were compiled in 2022, based on data from the Quarterly Census of Employment and Wages 2014-2020 (EDD 2022), annual County Agricultural Commissioners Reports 2014-2020 (CDFA 2022), Pesticide Use Report Data 2014-2018 (CDPR 2022), and qualitative assessments of climate change resilience (Peterson et al. 2020, DSC 2021).</p> <p><strong>Literature Cited:</strong></p> <ul> <li>CDFA. 2022. County Ag Commissioners’ Data Listing. California Department of Food & Agriculture. Available from: https://www.nass.usda.gov/Statistics_by_State/California/Publications/AgComm/index.php</li> <li>CDPR. 2022. Pesticide Use Report Data. California Department of Pesticide Regulation. Available from: https://www.cdpr.ca.gov/docs/pur/purmain.htm</li> <li>DSC. 2021. Delta Adapts: Creating a Climate Resilient Future. Public Review Draft. Delta Stewardship Council. Available from https://deltacouncil.ca.gov/delta-plan/climate-change</li> <li>EDD. 2022. Quarterly Census of Employment and Wages (QCEW). California Employment Development Department. Available from: https://data.edd.ca.gov/Industry-Information-/Quarterly-Census-of-Employment-and-Wages-QCEW-/fisq-v939</li> <li>Peterson C, Marvinney E, Dybala K. 2020. Multiple Benefits from Agricultural and Natural Land Covers in the Central Valley, CA. Migratory Bird Conservation Partnership, Sacramento, CA. Dryad Dataset doi:10.25338/B8061X</li> </ul> <p><strong>FIELD DEFINITIONS</strong></p> <ul> <li><strong>METRIC_CATEGORY: </strong>Broad grouping assigned to each METRIC; one of Agricultural Livelihoods, Water Quality, or Climate Change Resilience</li> <li><strong>METRIC: </strong>Specific metric being estimated; one of Agricultural Jobs, Annual Wages, Gross Production Value, Drought, Flood, Heat, Critical Pesticides, Groundwater Contaminant, or Risk to Aquatic Organisms</li> <li><strong>UNIT: </strong>The units in which the <strong>METRIC </strong>is estimated</li> <li><strong>CODE_NAME:</strong> The land cover class or subclass for which the <strong>METRIC </strong>is estimated</li> <li><strong>LABEL: </strong>A more user-friendly version of <strong>CODE_NAME</strong>, useful for creating figures and tables</li> <li><strong>SCORE_MEAN:</strong> The mean value of each METRIC estimated for each land cover class or subclass</li> <li><strong>SCORE_SE: </strong>The standard error of the mean</li> </ul> <p><strong>ABBREVIATION DEFINITIONS</strong></p> <ul> <li><strong>FTE: </strong>full-time equivalents; refers to converting monthly agricultural jobs data to annual estimates by dividing by 12</li> <li><strong>ha:</strong> hectares</li> <li><strong>kg: </strong>kilograms</li> <li><strong>USD: </strong>U.S. dollars</li> <li><strong>yr: </strong>year</li> </ul> <p><strong>ACCESS & USE CONSTRAINTS</strong><br> CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>KEYWORDS</strong></p> <ul> <li><strong>Themes:</strong> agriculture, livelihoods, economy, water quality, pesticides, climate change, resilience, multiple-benefit conservation</li> <li><strong>Place:</strong> Sacramento-San Joaquin River Delta, Central Valley, California<br> </li> </ul>
Multiple-benefit Conservation in Practice: Supplemental Spatial Data for Quantifying Multidimensional Impacts of Landscape Change in California's Sacramento–San Joaquin Delta
<p><strong>SUMMARY</strong><br> Spatial data representing climate, proximity to streams, and probability of flooding in the Sacramento-San Joaquin Delta.</p> <p><strong>DESCRIPTION</strong><br> These data were compiled as predictors of the distribution of riparian landbird species and groups of waterbird species, to facilitate projecting the probability of species or group presence across a given landscape. They were used to identify Priority Bird Conservation Areas and in analyses of the impacts of scenarios representing habitat restoration and perennial crop expansion on suitable habitat. These data are required for using the R package "DeltaMultipleBenefits", which provides the code and work flow for repeating the initial analyses or analyzing new scenarios.</p> <p>For additional details about the development and applications of these data, please see: </p> <ul> <li>Dybala KE, et al. (<em>In review</em>) Multiple-benefit Conservation in Practice: A Framework for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta</li> <li>Dybala KE, Sesser K, Reiter M, Shuford WD, Golet GH, Hickey C, Gardali T (<em>In review</em>) Priority Bird Conservation Areas in California’s Sacramento–San Joaquin Delta. </li> <li>Dybala KE (2023) <em>DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta</em>. R package version 1.0.0. doi: 10.5281/zenodo.7718620. Available from: https://pointblue.github.io/DeltaMultipleBenefits.</li> </ul> <p><strong>FUNDING STATEMENT</strong><br> These data were developed as part of the project "Trade-offs and Co-benefits of Landscape Change on Bird Communities and Ecosystem Services in the Sacramento–San Joaquin River Delta", funded by Proposition 1 Delta Water Quality and Ecosystem Restoration Program, Grant Agreement Number – Q1996022, administered by the California Department of Fish and Wildlife.</p> <p><strong>POINT OF CONTACT</strong><br> Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org</p> <p><strong>SUGGESTED CITATION</strong><br> Dybala KE. 2023. Multiple-benefit Conservation in Practice: Supplemental Spatial Data for Quantifying Multidimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta. doi:10.5281/zenodo.7672193.</p> <p><strong>DATA DISTRIBUTION</strong><br> Zenodo (https://doi.org/10.5281/zenodo.7672193)</p> <p><strong>PROGRESS</strong><br> Complete</p> <p><strong>UPDATE FREQUENCY</strong><br> None planned</p> <p><strong>DATE</strong><br> These data were compiled in 2022, based on data from WorldClim (representing 1970-2000), National Hydrography Dataset (published 2020), and Point Blue's Water Tracker (representing 2013-2019).</p> <p><strong>FIELD DEFINITIONS</strong></p> <ul> <li><strong>bio_1: </strong>annual mean temperature (C), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li><strong>bio_12:</strong> total annual precipitation (mm), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li><strong>streamdist: </strong>square root of the distance to the nearest stream (m) (National Hydrography Dataset; USGS 2020)</li> <li><strong>pwater_fall:</strong> mean probability of open surface water during the fall, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> <li><strong>pwater_win:</strong> mean probability of open surface water during the winter, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> </ul> <p><strong>Literature Cited</strong></p> <ul> <li>Fick SE, Hijmans RJ. 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. 37:4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a> </li> <li>Reiter ME, Elliott NK, Barbaree B, Moody D. 2018. An automated open surface water tracking system for California’s Central Valley. Report to the U.S. Fish and Wildlife Service. Petaluma, California: Point Blue Conservation Science. Available from: <a href="https://data.pointblue.org/apps/autowater/ ">https://data.pointblue.org/apps/autowater/ </a></li> <li>[USGS] United States Geological Survey. 2020. National Hydrography Dataset Best Resolution (NHD) for Hydrologic Units (HU) 4 - 1802, 1803, 1804, 1805. Reston (VA): U.S. Geological Survey. Available from: <a href="https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products ">https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products </a></li> </ul> <p><strong>ABBREVIATION DEFINITIONS</strong><br> N/A</p> <p><strong>COORDINATE REFERENCE SYSTEM</strong><br> WGS 84 / UTM zone 10N (EPSG:32610)</p> <p><strong>ACCESS & USE CONSTRAINTS</strong><br> CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>KEYWORDS</strong></p> <ul> <li><strong>Themes:</strong> climate, temperature, precipitation, hydrology, streams, water, flood, remote sensing </li> <li><strong>Place: </strong>Sacramento-San Joaquin River Delta, Central Valley, California</li> </ul>
Influences on charitable giving for conservation: Online survey data of 1,331 respondents across the US, August 2017
This dataset records survey data collected from an online panel of 1,331 anonymous, nationwide U.S respondents. Data collection was both initiated and completed in August 2017. Survey data is of two major types. The first type, information about respondents, includes (1) select background and demographic information; (2) a brief version of a social desirability scale, measured to test and control for potential bias related to socially desirable responding; and (3) a scale developed to measure moral inclusivity, conceptualized as the breadth of an individual’s moral community (i.e., to what extent do different types of entities “count,” in a moral sense). The second type of data records information about an experimental message manipulation featured in the survey. The dataset includes one variable indicating which of seven manipulated textual messages each respondent viewed, along with several variables used as metrics of response to the messages, including (1) attitudes toward the message; (2) hypothetical willingness to donate for the cause promoted in the message; (3) perceived moral salience of the message (i.e., the extent to which it was perceived as a matter of moral concern); (4) manipulation checks, to test whether the manipulated elements of the messages were perceived as intended, and (5) a donation set-up, in which individuals were given the option to donate between $0 and $5 for a conservation organization, from an incentive fee provided by the researchers.
Data inputs and results for "Mammal niches are not conserved over continental scales" by Goldstein et al.
<p>This data packet provides inputs and results for "Mammal niches are not conserved over continental scales" by Goldstein et al., currently in the submission process. This repository will eventually be updated to link to the published manuscript.</p> <p> </p> <p>===============================================================================<br>===============================================================================<br>Overview<br>===============================================================================<br>===============================================================================</p> <p>Data and model products associated with the manuscript "Mammal niches are not <br>conserved over continental scales" by Goldstein et al. </p> <p>Files are organized into two subdirectories. The first, "model_inputs/", <br>contains 8 data files intended to be used as part of the reproducible code <br>repository at https://github.com/dochvam/Mammal_SVCs_ISDM_reproducible. <br>The second subdirectory, "model_outputs/", contains modeled products giving<br>estimated spatially varying niche relationships and predictions of relative<br>abundance.</p> <p>Below, we describe the contents of each file type. See the main manuscript <br>for full methodology, data sources, and discussions of spatial scales.</p> <p>NOTE: Version 1 of this dataset contained some errors that have been corrected<br>in Version 2. Version 2 was used as the input dataset for the analyses in the<br>associated manuscript. Version 1 should not be used.</p> <p>===============================================================================<br>===============================================================================<br>Subdirectory 1: "model_inputs/"<br>===============================================================================<br>===============================================================================</p> <p>Two versions of each of four files are provided, corresponding to analyses <br>that do or do not consider ancient genetic lineages as potential sources of <br>spatial nonstationarity in mammal niches. Each file type is formatted the same,<br>and the versions are differentiated by either the suffix "nolineage" or <br>"lineage" in the filename. </p> <p>===============================================================================<br>File 1: gridcell_covars_lineage.csv and gridcell_covars_nolineage.csv<br>===============================================================================<br>These files are .csvs giving spatial covariate data for each scale 2 cell<br>in North America, summarized to 5000 m. All percentage values are given in <br>10ths of a percent (scale of 0-1000). The following columns are provided:</p> <p>- grid_cell: Scale 2 cell ID<br>- Arable: pct arable land (Jung et al. 2020)<br>- EVI_mean: mean enhanced vegetation index (Didan 2021)<br>- EVI_Q95: 95th quantile of EVI (Didan 2021)<br>- Forest: Pct forest cover (Jung et al. 2020)<br>- Grassland: pct grassland (Jung et al. 2020)<br>- Pastureland: pct pastureland (Jung et al. 2020)<br>- Pop_den: Human population density, from Gridded Population of the World (CIESIN 2018)<br>- Precipitation: avg annual precip. (Vega et al. 2017)<br>- Shrubland: pct shrubland (Jung et al. 2020)<br>- Temp_max: Average maximum daily temperature (Vega et al. 2017)<br>- Terrain_roughness (Amatulli et al. 2018)<br>- Wetlands: pct wetlands (Jung et al. 2020)<br>- is_land: Whether or not the cell is on land vs. ocean, used for filtering<br>- Agriculture: Pct. agricultural land (Jung et al. 2020)<br>- Pop_den_sqrt: Square root of human population density (CIESIN 2018)<br>- EVI_variability: Distance btw the 95% inner quantiles of EVI (Didan 2021)</p> <p> </p> <p>===============================================================================<br>File 2: inat_cts_lineage.csv and inat_cts_nolineage.csv<br>===============================================================================</p> <p>These files give summaries of iNaturalist sampling effort and detections<br>for target species. The following columns are provided:</p> <p>- grid_cell: Scale 3 cell ID<br>- n: Total iNaturalist effort in the cell (number of obs. of all mammals)<br>- The remaining columns are named for species. Each column gives the count<br> of observations of the species in the cell.</p> <p>===============================================================================<br>File 3: ct_datlist_lineage.RDS and ct_datlist_nolineage.RDS<br>===============================================================================</p> <p>The ct_datlist files contain R objects that are lists of lists. These objects ultimately<br>contain all of the camera detection histories and camera-level covariate data used<br>in modeling. We use the nice data type "unmarkedFrameOccu" from the unmarked R package<br>to organize these detection data.</p> <p>Each outer list is of length equal to the number of species. The ith element of each<br>list contains the following named slots:</p> <p>- species: a string giving the name of the ith species<br>- umf: an unmarkedFrameOccu object. This object has three important slots:<br> - y: a (# deployments) x (max # replicates) matrix giving 1s, 0s, or NAs indicating<br> whether the target species was observed in that 10-day window;<br> - siteCovs: a (# deployments) x 2 data frame with the following columns:<br> - site_ID: A unique ID of the exact location, shared by deployments with the same<br> coordinates<br> - subproject_ID: A unique ID indicating which camera array is associated <br> with this deployment<br> - obsCovs: a (# deployments * max # replicates) x 6 data frame with the following columns:<br> - year: the year of survey, relative to 2020 (zero-year is 2020)<br> - yday_scaled: the (scaled) Julian date of the beginning of the window<br> - yday_scaled_sq: yday_scaled^2, for use in estimating a quadratic effect<br> - log_roaddist_scaled: Scaled distance to nearest road (Meijer et al. 2018)<br> - Canopy_height_scaled: Scaled canopy height (Potapov et al. 2021)<br> - obs_len_scaled: Scaled duration of window, to account for some windows <br> being cut off at < 10 days<br>- coords: a data frame. Originally, this file gave the exact position for each camera,<br> but these exact locations have been scrubbed for privacy. See the original sources<br> cited in the manuscript for full details. This data frame contains the following column:<br> - scale2_grid_ID: the ID of the Scale-2 5000 m grid cell containing the camera</p> <p>===============================================================================<br>File 4: grid_translator_wspecs_nolineage.csv and grid_translator_wspecs_lineage.csv<br>===============================================================================</p> <p>These files are used for bookkeeping to track the relationships between the <br>three spatial scales in this study. Each row corresponds to a single "scale 2"<br>cell, giving the ID of the corresponding S3 and S4 grid and also an ID for each<br>species indicating whether and where it is in the species' range. </p> <p>Note that the scale names in the code don't match the manuscript. In the code,<br>"scale 1" is the level of an individual camera, "scale 2" is the 5 km intensity<br>grid, "scale 3" is the 50 km iNaturalist grid, and "scale 4" is the 100 km<br>SVC grid.</p> <p>The following columns are provided:<br>- scale2_grid_ID: unique ID for each cell in the 5 km intensity grid<br>- scale3_grid_ID: unique ID for each cell in the 50 km iNaturalist aggregation<br>- scale4_grid_ID: unique ID for each cell in the 100 km SVC grid<br>- GRID_ID_[species]: for each species, a column is provided on the S4 scale<br> counting each cell in the species' modeled range. NAs<br> indicate that the S2 cell defined in the row is not<br> included in the species' modeled range.</p> <p>===============================================================================<br>===============================================================================<br>Subdirectory 2: "model_outputs/"<br>===============================================================================<br>===============================================================================</p> <p>===============================================================================<br>File 1: svc_estimates.csv<br>===============================================================================</p> <p>This file gives an estimate of the effect of each covariate on each species'<br>intensity, and the uncertainty in that estimate, for each species/covariate<br>pair. Results correspond to lineage models for species with phylogeographies<br>and non-lineage species otherwise. Each row represents the effect of one <br>covariate on one species' relative intensity process within one 100 km cell g. <br>Note that many estimates of beta_g are uncertain even for strong spatial <br>effects---the model is often confident that a spatial process is supported <br>while estimates of the realized process are uncertain.</p> <p>The following columns are provided:<br>- x: the x-coordinate of the 100 km cell<br>- y: the y-coordinate of the 100 km cell<br>- species<br>- parname: the name of the covariate<br>- mean: the mean of the posterior samples of beta_g<br>- 2.5%: the 2.5th quantile of the posterior samples of beta_g<br>- 50%: the 50th quantile of the posterior samples of beta_g<br>- 97.5%: the 97.5th quantile of the posterior samples of beta_g</p> <p>The following spatial projection is used to define X/Y coordinates:<br>"+proj=aea +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83"</p> <p>===============================================================================<br>File 2: predicted_intensity.tif<br>===============================================================================</p> <p>This file contains a raster "brick" giving the predicted intensity surface <br>and uncertainty in this surface for each species. All predictions are generated<br>using models that do *not* account for lineage information---this means that <br>predictions for species with lineages are not from the models reported in the<br>main manuscript. The reason for this is that we found that lineages were <br>overall unsupported, so better predictions can be arrived at by excluding this<br>source of uncertainty in the underlying intensity process.</p> <p>The raster brick has 66 layers. Each layer provides either the mean predicted<br>log intensity in each grid cell across the species range or else provides<br>the standard error of that predicted log intensity. Layer names indicate output<br>type and species associated with each layer.</p> <p><br>===============================================================================<br>===============================================================================<br>References<br>===============================================================================<br>===============================================================================</p> <p>Camera data are obtained from the following sources, which can be consulted to<br>obtain the original raw camera data</p> <p>- Cove, Michael V., et al. "SNAPSHOT USA 2019: a coordinated national camera trap survey of the United States." (2021): e03353.<br>- Kays, Roland, et al. "SNAPSHOT USA 2020: A second coordinated national camera trap survey of the United States during the COVID‐19 pandemic." (2022): e3775.<br>- Shamon, H., et al. “SNAPSHOT USA 2021: A third coordinated national camera trap survey of the United States.” Ecology, 105.6 (2024): e4318.<br>- Rooney, B., et al. “SNAPSHOT USA 2019–2023: The first five years of data from a coordinated camera trap survey of the United States.” In Press (2024).<br>- Kays, Roland, et al. "Does hunting or hiking affect wildlife communities in protected areas?." Journal of Applied Ecology 54.1 (2017): 242-252.<br>- Roberts, R. California Department of Fish and Wildlife, Bobcat Program Initiative. wildlifeinsights.org (2023).<br>- Lasky, Monica, et al. "CAROLINA CRITTERS: a collection of camera trap data from wildlife surveys across North Carolina." Ecology 102.7 (2021): e03372.<br>- Forrester, T. (2000). Urban to Wild Project. http://n2t.net/ark:/63614/w12004302. Accessed via wildlifeinsights.org on 2024-08-29.<br>- McMurry, S. et al. In review (2024).<br>- Forrester, T. (2011) Okaloosa S.C.I.E.N.C.E. Project. http://n2t.net/ark:/63614/w12004287. <br>- Myers, J. (2014) Tyson Research Center ForestGEO Project. http://n2t.net/ark:/63614/w12004295.<br>- McMurry, S., and Kays, R.(2023). Calloway Forest Preserve. http://n2t.net/ark:/63614/w12006449. Accessed via Wildlife Insights on 2024-08-29.<br>- McMurry, S., Parsons, A., Lasky, M., Luongo, K., Clark, J., McShea, W., Scher, L., Kays, R., Spurlin, J., Martin, G., Frech, G., Barajas-Salazar, K., Snider, M. (2022). Last updated October 2023. Calloway Forest Preserve. http://n2t.net/ark:/63614/w12004251. Accessed via wildlifeinsights.org on 2024-08-29.<br>- Kays, R.. (2008). Last updated March 2024. Albany Area Camera Trapping Project. http://n2t.net/ark:/63614/w12003860. Accessed via wildlifeinsights.org on 2024-08-29.<br>- Kays, R., Snider, M., McMurry, S., Alyetama, M. (2024). Last updated April 2024. Pilot Mountain Density 2024. http://n2t.net/ark:/63614/w12007160. Accessed via wildlifeinsights.org on 2024-08-29.<br>- Malleshappa, V., Smithsonian, E., Kays, R., Schuttler, S. (2015). Last updated December 2022. Museums Connect Mexico. http://n2t.net/ark:/63614/w12004298. Accessed via wildlifeinsights.org on 2024-08-29.</p> <p>Covariate data are obtained from the following sources:<br>- Vega, G. C., Pertierra, L. R. & Olalla-Tárraga, M. Á. MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling. Sci. Data 4, 170078 (2017).<br>- Jung, M. et al. A global map of terrestrial habitat types. Sci. Data 7, 256 (2020).<br>- Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018).<br>- Center For International Earth Science Information Network-CIESIN-Columbia University. Documentation for the Gridded Population of the World, Version 4 (GPWv4), Revision 11 Data Sets. (2018) doi:10.7927/H45Q4T5F.<br>- Didan, K. MODIS/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid V061. NASA EOSDIS Land Processes Distributed Active Archive Center https://doi.org/10.5067/MODIS/MOD13A2.061 (2021).<br>- Meijer, J. R., Huijbregts, M. A. J., Schotten, K. C. G. J. & Schipper, A. M. Global patterns of current and future road infrastructure. Environ. Res. Lett. 13, 064006 (2018).<br>- Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021).<br>- Jensen, A. J. et al. Geographic barriers but not life history traits shape the phylogeography of North American mammals. Glob. Ecol. Biogeogr. e13875 (2024).</p> <p>iNaturalist data are obtained from inaturalist.org via the data exporter (see manuscript for details).</p>
Numerical Data Set Belonging to: 'Numerical Study of Phase-Change Phenomena: A Conservative Linearized Enthalpy Approach'
<p>This is the numerical data set belonging to the Nureth conference paper entitled: 'Numerical Study of Phase-Change Phenomena: A Conservative Linearized Enthalpy Approach'. </p> <p>The files 'Stefan_singlePhase_Tfield.dat' and 'Stefan_singlePhase_interface.dat' represent the raw data belonging to figure 1 in the paper and contain the solution to the one-phase Stefan problem for the temperature field and interface position (section 3.1 in the paper). The files 'Stefan_singlePhase_error.dat' and 'Stefan_twoPhase_error.dat' represent the raw data belonging to figure 2 in the paper and contain the L2 norm of the relative difference between the numerical and analytical solution for the single and two phase Stefan problem respectively. </p> <p>The files 'Gau_1140s_lf_50x50_3D', 'Gau_1140s_lf_100x100_3D', 'Gau_1140s_lf_200x200_3D' feature the raw OpenFOAM(v7) data containing the numerical solution to the liquid fraction of the Gallium melting in a rectangular enclosure problem (Gau, 1986) at 1140s of simulation time. These data were used for the mesh convergence study (figure 3, section 3.2). </p> <p>The files 'Gau_120s_U_200x200_3D', 'Gau_360s_U_200x200_3D', 'Gau_750s_U_200x200_3D', 'Gau_1140s_U_200x200_3D' feature the raw OpenFOAM(v7) data containing the 3-dimensional numerical solution to the velocity of the Gallium melting in a rectangular enclosure problem (Gau, 1986) at respectively 120s, 360s, 750s and 1140s of simulation time. These data underly the velocity colours shown in figure 4 and figure 6 (section 3.2).</p> <p>Likewise, the files 'Gau_120s_U_200x200_2D' and 'Gau_360s_U_200x200_2D' feature the raw OpenFOAM(v7) data containing the 2-dimensional numerical solution to the velocity of the Gallium melting in a rectangular enclosure problem. These data underly the velocity colours shown in figure 5 (section 3.2).</p> <p> </p> <p> </p>
Assessing ambitious nature conservation strategies in a below 2-degree and food-secure world – supplementary spatial data
<p><strong>Assessing ambitious nature conservation strategies in a below 2-degree and food-secure world – supplementary spatial data</strong></p><p><strong>Authors: </strong>Marcel Kok, Johan Meijer, Willem-Jan van Zeist, Jelle Hilbers, Marco Immovilli, Jan Janse, Elke Stehfest, Michel Bakkenes, Andrzej Tabeau, Aafke Schipper, Rob Alkemade</p><p><strong>Point of contact:</strong> <a href="mailto:Marcel.Kok@pbl.nl">Marcel.Kok@pbl.nl</a></p><p><strong>Research paper summary:</strong> Global biodiversity is projected to further decline under a wide range of future socio-economic development pathways, even in sustainability-oriented scenarios. This raises the question how biodiversity can be put on a path to recovery, the core challenge for the implementation of the CBD Kunming-Montreal Global Biodiversity Framework. We designed two ambitious global conservation strategies, 'Half Earth' (HE) and 'Sharing the Planet' (SP), and evaluated their ability to restore terrestrial and freshwater biodiversity and to provide nature's contributions to people (NCP), while also limiting global warming below 2 degrees and ensuring food security. We applied the integrated assessment framework IMAGE with the GLOBIO biodiversity model, using the 'Middle of the Road' Shared Socio-economic Pathway (SSP2) with its projected human population growth as baseline. We found that the HE strategy performs generally better for terrestrial biodiversity (biodiversity intactness (MSA), Area of Habitat, Living Planet Index, Red List Index) in currently still natural regions. The SP strategy yields more improvements for biodiversity in human-used areas, for freshwater biodiversity and for regulating NCP (pest control, pollination, erosion control, water quality). However, both strategies were insufficient to restore biodiversity and corresponded with considerable increases in food security risks and global temperature. Only when we combined the conservation strategies with a portfolio of 'integrated sustainability measures', including climate change mitigation and reductions of food waste and animal product consumption, our scenarios resulted in a restoration of biodiversity and NCP while keeping global warming below two degrees and food security risks below the baseline projection.</p><p><strong>Contents:</strong> This repository contains the supplementary spatial data describing the specific prioritization of conservation areas under the Half Earth (HE) and Sharing the Planet (SP) scenarios, and the resulting scenario land use and MSA data sets for the year 2050, including also a baseline (BL) scenario. All spatial data is in geotiff format at a 10 arcsecond resolution in WGS84 coordinate system. Detailed description of the methodology is provided in the paper listed under "related identifiers".</p><p><strong>Keywords:</strong> Nature conservation, Half Earth, Sharing the Planet, Climate Change, Food Security, Solution-oriented scenarios, Biodiversity, Nature's Contribution to People, NCP</p>
Raw data of the study: Categorizing urban avoiders, utilizers, and dwellers for identifying bird conservation priorities in a northern Andean city
<p>This datasheet contains raw data on bird count records made from 2016 and 2019. Data were taken in urban and adjacent non-urban areas of Medellín, Colombia. It was part of a collaborative sampling effort during environmental assessments and personal research, summarizing systematic information on 139 sampling points (124 within the city and 15 in adjacent non-urban areas). All points were sampled under the same protocol in order to facilited data for research; in all cases, sampling was in charge of ornithologist with at least 4 years of previous experience in bird surveys. This protocol consisted in sampling during 10 minutes, four times per point (i.e., repetitions), using a fixed radius of 25 m. </p> <p>Information on bird surveys (Count_Data within the corresponding datasheet tab) contains the ID of each site; whether corresponded to a urban or non-urban site; in what category of urban development the site was located, based on 1000, 500 and 200 m buffers (from the observer during bird counts: moderate, low or high); the taxonomic information of each species (order, family, scientific name); the number of recorded individuals; the repetition or number of the visit (1, 2, 3, or 4); the name of the project; the name of the observer, and the date of sampling. </p> <p>Information on categorization of bird species (Categorization within the corresponding datasheet tab) represents additional information on altitudinal ranges, trophic guilds, distribution, and others. In addition, information on frequency for each bird species is given, according to the location of each sampling site and the way it was grouped. This information was the base for categorizing bird species as urban avoider, utilizer, or dweller, under the calculations and decision rules that are also given within the corresponding cells of the datasheet.</p> <p>Any further information or questions about this data could be ask directly, writing to the e-mails: jgarizabal@unal.edu.co or njmacer@unal.edu.co.</p> <p> </p>
Data from: Species delimitation in endangered groundwater salamanders: implications for aquifer management and biodiversity conservation
Groundwater-dependent species are among the least-known components of global biodiversity, as well as some of the most vulnerable because of rapid groundwater depletion at regional and global scales. The karstic Edwards–Trinity aquifer system of west-central Texas is one of the most species-rich groundwater systems in the world, represented by dozens of endemic groundwater-obligate species with narrow, naturally fragmented distributions. Here, we examine how geomorphological and hydrogeological processes have driven population divergence and speciation in a radiation of salamanders (Eurycea) endemic to the Edwards–Trinity system using phylogenetic and population genetic analysis of genome-wide DNA sequence data. Results revealed complex patterns of isolation and reconnection driven by surface and subsurface hydrology, resulting in both adaptive and non-adaptive population divergence and speciation. Our results uncover new cryptic species diversity and refine the borders of several threatened and endangered species. The U.S. Endangered Species Act has been used to bring state regulation to unrestricted groundwater withdrawals in the Edwards (Balcones Fault Zone) Aquifer, where listed species are found. However, the Trinity and Edwards–Trinity (Plateau) aquifers harbor additional species with similarly small ranges that currently receive no protection from regulatory programs designed to prevent groundwater depletion. Based on regional climate models that predict increased air temperature, together with hydrologic models that project decreased springflow, we conclude that Edwards–Trinity salamanders and other co-distributed groundwater-dependent organisms are highly vulnerable to extinction within the next century.
Carbon data for: Evidence for the Multiple Benefits of Wetland Conservation in North America
<p>These data were synthesized as part of a rapid evidence assessment of the scientific literature on a wide range of benefits associated with wetland conservation and restoration. Our synthesis emphasized data from North America and especially the United States, although many of the high priority meta-analyses and reviews we incorporated were global in scope. The data in these files represent a range of metrics related to carbon sequestration, storage, or flux compiled from multiple sources for the purposes of summarizing the range of observed values and how they vary across wetland classes or by restoration status. For more detail on the synthesis methods and each set of metrics, please see the full report: </p> <p>Conlisk E, Chamberlin L, Vernon M, Dybala KE. 2022. Evidence for the Multiple Benefits of Wetland Conservation in North America: Carbon, Biodiversity, and Beyond. Point Blue Conservation Science, Petaluma, CA.</p>
Data from: Maximizing the potential for living cell banks to contribute to global conservation priorities
<p><strong>Summary</strong></p> <p>This dataset accompanies the publication "<strong>Maximizing the potential for living cell banks to contribute to global conservation priorities</strong>" published in Zoo Biology. This study analyzed the representation of amphibian, bird, mammal and reptile species within the San Diego Zoo Wildlife Alliance (SDZWA) Frozen Zoo® living cell collection (as of April 2019) and implemented a qualitative framework for the prioritization of species for future sampling. We used global conservation assessment schemes (including the IUCN Red List of Threatened Species<sup>TM</sup>, CITES, the Alliance for Zero Extinction, the EDGE of Existence, and Climate Change Vulnerability), and opportunities for sample acquisition from the global zoo and aquarium community, to identify priority species for future cryobanking efforts.</p> <p>Two datasets accompany this publication. The first provides a complete list of all “<em>Threatened</em>” species not currently represented within the San Diego Zoo Wildlife Alliance (SDZWA) Frozen Zoo®, their representation under various conservation assessment schemes, and their presence in the global zoo and aquarium community, as represented by Species360 members (<strong>Supporting Information Data S1</strong>). The second provides a list of the 2,351 amphibian and bird species not currently represented in the SDZWA Frozen Zoo® or listed as “<em>Threatened</em>” under the IUCN Red List, but assessed by Foden <em>et al.</em> (2013) as highly vulnerable to climate change. This dataset also shows their presence in the global zoo and aquarium community, as represented by Species360 members (<strong>Supporting Information Data S2</strong>).</p> <p>Data provided by SDZWA are only available upon request. Data for each conservation assessment scheme were collected from each source individually (all freely available), and are accurate to April 2019. Zoo species holdings data were provided upon request by Species360 (https://www.species360.org/), which operates the real-time database ZIMS (Zoological Information Management System). ZIMS is the largest real-time database of comprehensive and standardized information spanning more than 1,200 zoological collections globally, and provides the number of institutions currently managing each species and their current population sizes. </p> <p> </p> <p><strong>Description of the Datasets</strong></p> <p> </p> <p><strong>Supporting Information Data S1 (accurate to April 2019):</strong> A complete list of all “<em>Threatened</em>” species not currently represented within the San Diego Zoo Wildlife Alliance (SDZWA) Frozen Zoo®, their representation under various conservation assessment schemes, and their presence in the global zoo and aquarium community, as represented by Species360 members. Provided in .csv format, variables include:</p> <ul> <li><strong>Class: </strong>The taxonomic class of the species</li> <li><strong>IUCN Name: </strong>The taxonomic name of the species (genus and epithet) according to the IUCN Red List</li> <li><strong>IUCN Status: </strong>The IUCN Red List status of the species (VU, EN, or CR)</li> <li><strong>ZIMS:</strong> The presence (1) or absence (0) of the species from Species360 member zoos and aquariums</li> <li><strong>AZE:</strong> The listing of the species on the Alliance for Zero Extinction assessment (listed = 1, not listed = 0)</li> <li><strong>AZA:</strong> The presence of an active <em>ex situ</em> management programme for the species in the Association of Zoos and Aquariums (active programme = 1, no programme = 0)</li> <li><strong>EAZA: </strong>The presence of an active <em>ex situ</em> management programme for the species in the European Association of Zoos and Aquaria (active programme = 1, no programme = 0)</li> <li><strong>CITES:</strong> The listing of the species on of the three CITES appendices (listed = 1, not listed = 0)</li> <li><strong>CC Vulnerability: </strong>The listing of the species as being highly vulnerable to climate change by Foden <em>et al</em>. 2013 (vulnerable = 1, not vulnerable = 0)</li> <li><strong>EDGE: </strong>The listing of the species on the Evolutionary Distinctiveness and Global Endangerment assessment (listed = 1, not listed = 0)</li> </ul> <p> </p> <p><strong>Supporting Information Data S2 (accurate to April 2019):</strong> A list of the 2,351 amphibian and bird species not currently represented in the SDZWA Frozen Zoo® or listed as “<em>Threatened</em>” under the IUCN Red List, but assessed by Foden <em>et al.</em> (2013) as highly vulnerable to climate change. This dataset also shows their presence in the global zoo and aquarium community, as represented by Species360 members. Provided in .csv format, variables include:</p> <ul> <li><strong>Class: </strong>The taxonomic class of the species</li> <li><strong>IUCN Name: </strong>The taxonomic name of the species (genus and epithet) according to the IUCN Red List</li> <li><strong>CC Vulnerability:</strong> The listing of the species as being highly vulnerable to climate change by Foden <em>et al</em>. 2013 (all species are vulnerable = H)</li> <li><strong>IUCN Status: </strong>The IUCN Red List status of the species (excluding VU, EN, or CR)</li> <li><strong>ZIMS:</strong> The presence (1) or absence (0) of the species from Species360 member zoos and aquariums</li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We sincerely thank the San Diego Zoo Wildlife Alliance and Species360 member institutions for their support and data input. This research was funded by the Irish Fulbright Commission 2018/2019 to A.M., the Irish Research Council Laureate Awards 2017/2018 IRCLA/2017/60 to Y.M.B. D.A.C. and J.S. were funded by Species360, the University of Southern Denmark and the Species360 Conservation Science Alliance sponsors: the World Association of Zoos and Aquariums (WAZA), Mandai Wildlife Reserve, and Copenhagen Zoo. </p> <p> </p> <p><strong>Disclaimer</strong></p> <p>Despite our best efforts at screening the data for errors and inconsistencies, some information could be erroneous. Similarly, data collected from the various conservation assessment schemes, and those contained within ZIMS, could contain errors or reporting failures. For example, ZIMS data are are based on submitted records from individual institutions, and are not subject to editorial verification, potentially permitting errors or failure to update species holdings etc. Despite this, ZIMS represents the only global database of zoo collection composition records, and as a result, is used by the IUCN, Convention on International Trade in Endangered Species (CITES), the Wildlife Trade Monitoring Network (TRAFFIC), United States Fish and Wildlife Service (USFWS) and Department for Environment, Food and Rural Affairs (DEFRA). </p> <p> </p> <p><strong>Credit</strong></p> <p>If you use this dataset, please cite the corresponding publication:</p> <p>Mooney, A., Ryder, O. A., Houck, M. L., Staerk, J., Conde, D. A., & Buckley, Y. M. (2023). Maximizing the potential for living cell banks to contribute to global conservation priorities. <em>Zoo Biology</em>, 1– 12. <a href="https://doi.org/10.1002/zoo.21787">https://doi.org/10.1002/zoo.21787</a></p>
Data of the publication "Hydrodynamics in long-range interacting systems with center-of-mass conservation"
<p>In systems with a conserved density, the additional conservation of the center of mass (dipole moment) has been shown to slow down the associated hydrodynamics. At the same time, long-range interactions generally lead to faster transport and information propagation. Here, we explore the competition of these two effects and develop a hydrodynamic theory for long-range center-of-mass-conserving systems. We demonstrate that these systems can exhibit a rich dynamical phase diagram containing subdiffusive, diffusive, and superdiffusive behaviors, with continuously varying dynamical exponents. We corroborate our theory by studying quantum lattice models whose emergent hydrodynamics exhibit these phenomena.</p>
Data from: Species delimitation in endangered groundwater salamanders: implications for aquifer management and biodiversity conservation
Open the record for dataset details and reuse information.
Data for "Pollinator Conservation Paradox: Exotic Forbs Support Native Pollinators Under Global Changes" by Nelson, Seabloom and Borer 2025, California grasslands, 2023-2024
Data for analysis on how plant provenance mediates plant-pollinator interaction responses to fertilization and herbivore exclusion, associated with Nelson, Seabloom, and Borer 2025. Data on pollinator visitation and floral abundance were collected in plots that received factorial experimental treatments of combined nitrogen, phosphorus and potassium with micronutrients by herbivore exclusion fencing in three California grasslands in 2023-2024.
ScienceDex guides
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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.