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Data from: Genetic analysis of red deer (Cervus elaphus) administrative management units in a human-dominated landscape - patterns of genetic diversity, population structure and gene flow
<p><span><span>Red deer (</span><span><em>Cervus elaphus</em></span><span>) throughout central Europe are</span> impacted by different anthropogenic activities including habitat fragmentation, selective hunting, and translocations<span>. This has substantial influences on genetic diversity and the long-term conservation of local populations of this species. Here we use genetic samples from 480 red deer individuals to assess the genetic diversity and differentiation of the 12 administrative management units located in Schleswig Holstein, the northernmost federal state in Germany. </span></span><span><span>We applied multiple analytical approaches and show that the history of local populations (i.e., translocations, culling of individuals outside of designated red deer zones, and anthropogenic infrastructures) has led to comparably low levels of genetic diversity. The mean expected heterozygosity was below 0.6 and we observed on average 4.2 alleles across 12 microsatellite loci. Effective population sizes below the recommended level of 50 were estimated for multiple local populations. </span></span><span><span>Our estimates of genetic structure and gene flow show that red deer in northern Germany are best described as a complex network of asymmetrically connected subpopulations, with high genetic exchange among some local populations and reduced connectivity of others. Genetic diversity was also correlated with population densities of neighboring management units. </span></span></p> <p><span><span>Based on these findings, we suggest that connectivity among existing management units needs to be considered in the practical management of the species, which means that some administrative management units should be managed together, while the effective isolation of other units needs to be mitigated.</span></span></p>
U-10: United-10 COVID19 CT Dataset
<p>This dataset supports the research detailed in the pre-print "<a href="https://arxiv.org/abs/2308.09730"><strong>Virtual Imaging Trials Improved the Transparency and Reliability of AI Systems in COVID-19 Imaging</strong></a>." The study employs both clinical and simulated CT data to evaluate AI models for COVID-19 diagnosis. By leveraging the Virtual Imaging Trials (VIT) framework, the research addresses reproducibility and generalizability issues prevalent in medical imaging AI models. <br><br><strong>The dataset includes:</strong></p> <ul> <li><strong>Clinical CT Data:</strong> Drawn from 10 publicly available datasets, comprising over 12,000 volumes. These datasets span diverse populations, imaging protocols, and scanner configurations. Each of the 10 zip files contains pre-processed CT TFRecords (Train/Validation/Test) used in the study. Detailed information about the data sources, pre-processing steps, and the inclusion and exclusion criteria can be found in the manuscript (<a href="https://arxiv.org/abs/2308.09730">https://arxiv.org/abs/2308.09730</a>).</li> <li><strong>Simulated CT Data:</strong> Generated using computational anatomical phantoms from the XCAT model and imaged with the DukeSim simulation framework. This synthetic dataset allows controlled experiments that isolate the effects of imaging physics and patient-specific factors. Can be available upon request through Center For virtual Imaging Trial Portal at <a href="https://cvit.duke.edu/">https://cvit.duke.edu/</a></li> </ul> <p>The accompanying study analyzes the performance of lightweight convolutional neural networks on both real and synthetic data, comparing results across multiple internal and external validation scenarios. Insights into factors such as infection severity, imaging dose, and modality type are explored.</p> <p><strong>For further details, visit our Project Page:</strong> <a href="https://fitushar.github.io/ReviCOVID.github.io/">https://fitushar.github.io/ReviCOVID.github.io/</a><br>The full source code is available on gitHub and GitLab<br><strong>GitHub:</strong> <a href="https://github.com/fitushar/CVIT_ReviCOVID19">https://github.com/fitushar/CVIT_ReviCOVID19</a><br><strong>GitLab :</strong> <a href="https://gitlab.oit.duke.edu/cvit-public/cvit_revicovid19">https://gitlab.oit.duke.edu/cvit-public/cvit_revicovid19</a></p> <p><strong>Citation:</strong> When using this dataset, please cite the manuscript () and the original data-source.<br><strong><em>Tushar et al.</em>, "Virtual Imaging Trials Improved the Transparency and Reliability of AI Systems in COVID-19 Imaging", arXiv:2308.09730.</strong></p> <p><strong>Contact:</strong> fakrulislam.tushar@duke.edu</p>
Great Power Dynamics and International Economic Cooperation: Experimental Evidence from Parallel Surveys in China and the United States
Open the record for dataset details and reuse information.
SFC-A68: A dataset for benchmarking space function and space access element classification methods in entire floors of multi-unit apartment buildings
<p> </p> <p><strong>SFC-A68</strong>: "A dataset for benchmarking space function and space access element classification methods in entire floors of multi-unit apartment buildings"</p> <p><strong>Authors:</strong> "Amir Ziaee, Georg Suter, Laura Keiblinger"</p> <p><strong>Copyright:</strong> "Design Computing Group TU Wien, 2024"</p> <p><strong>Credits:</strong> "Design Computing Group TU Wien"</p> <p><strong>License:</strong> "GNU GENERAL PUBLIC LICENSE Version 3"</p> <p><strong>Version:</strong> "1.0.2"</p> <p><strong>Maintainer:</strong> "Amir Ziaee"</p> <p><strong>Email:</strong> "amir.ziaee@tuwien.ac.at"</p> <p><strong>Acknowledgments:</strong> "The authors gratefully acknowledge support by Grant Austrian Science Fund (FWF): I 5171-N, Laura Keiblinger, and participants in course '259.428-2021S Architectural Morphology' at TU Wien for data collection. "</p> <p><strong>Description:</strong> "The analysis of building models for usable area, building safety, and energy efficiency requires accurate classification data of spaces and related space elements such as doors. To reduce input model preparation effort and errors, automated classification of spaces and related space elements is desirable. We introduce the SFC-A68 dataset, which models entire floors of 275 multi-unit apartment buildings in 13 countries. The SFC-A68 consists of three data representation formats: tabular, graph, and multi-view image datasets that are derived from the same source data. It covere 22 space function and six space access element classes."</p> <p> </p> <p> </p> <p> </p>
GODEEEP-hydro - Historical and projected power system ready hydropower data for the United States
<p>This dataset contains monthly and weekly hydropower generation and generation constraints (min, max, daily range) for over 1,400 hydropower plants in the conterminous United States. The dataset includes a historical period (1982-2019) and a future period (2020-2099) with 4 future warming scenarios.</p> <p>For more information please refer to Bracken et al. 2024, godeeep_hydro: Historical and projected power system ready hydropower data for the United States, in prep, or refer to the Github repository https://github.com/GODEEEP/tgw-hydro</p> <h3>Data description</h3> <p>The dataset contains 10 data files with the naming convention <code><scenario>_<monthly/weekly>.csv</code> where scenario can be either "historical", "rcp45cooler", "rcp45hotter", "rcp85cooler", or "rcp85hotter". "monthly" or "weekly" refers to the timestep of the data.</p> <ul> <li>datetime - The datetime stamp of the current timestep</li> <li>eia_id - An integer value with the EIA plant code that represents the facility</li> <li>plant - The name of the facility according to the EIA</li> <li>power_predicted_mwh - The total energy gnerated over the period in MWh, aka the energy target</li> <li>n_hours - The number of hours in the period, useful for converting between power and energy</li> <li>p_avg - Average power generation for the period</li> <li>p_max - Maximum allowable power generation for the period</li> <li>p_min - Minimum allowable power generation for the period</li> <li>ador - Average daily operational range for any given day in the period</li> <li>scenario - The name of the scenario, either "historical", "rcp45cooler", "rcp45hotter", "rcp85cooler", or "rcp85hotter"</li> </ul> <p>Also included is the metadata file <code>godeeep_hydro_plants.csv</code> which contains metadata for each hydropower plant that is included in the dataset. Each row in this file refers to one hydropower facility. This file has the following columns:</p> <ul> <li>eia_id - An integer value with the EIA plant code that represents the facility</li> <li>plant - The name of the facility according to the EIA</li> <li>mode - Either "Storage" or "RoR" indicating if the plant is primarily operated as a storage or ron-of-river facility</li> <li>state - Two letter U.S. state name</li> <li>lat - Latitude of the facility</li> <li>lon - Longitude of the facility</li> <li>nameplate_capacity - The total nameplate capacity of the facility according to the EIA</li> <li>nerc_region - Four letter code for the NERC region of the facility</li> <li>ba - Balacing authority of the facility</li> <li>max_param - Value of the a_{max} parameter used to derive p_max</li> <li>min_param - Value of the a_{min} parameter used to derive p_min</li> <li>ador_param - Value of the a_{ador} parameter used to derive ador</li> <li>huc2 - Two digit hydrologic unit code (HUC) which contains the facility</li> </ul> <h3>Funding</h3> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p>Corresponding author:</p> <p>Cameron Bracken, cameron.bracken@pnnl.gov</p> <p>v1.1.0 - Update file naming convention</p>
A Unified Ensemble Soil Moisture Dataset Across the Continental United States
<p>A unified ensemble soil moisture (SM) package has been developed over the Continental United States (CONUS). The data package includes 19 products from land surface models, remote sensing, reanalysis, and machine learning models. All datasets are unified to a 0.25-degree and monthly spatiotemporal resolution, covering various temporal spans and providing a comprehensive view of surface SM dynamics. The statistical analysis of the datasets leverages the Koppen-Geiger Climate Classification to explore surface SM’s spatiotemporal variabilities. The extracted SM characteristics highlight distinct patterns, with the western CONUS showing larger coefficient of variation values and the eastern CONUS exhibiting higher SM values. Remote sensing datasets tend to be drier, while reanalysis products present wetter conditions. In-situ SM observations serve as the basis for wavelet power spectrum analyses to explain discrepancies with respect to temporal scales across the 16 datasets facilitating daily SM records. This study provides a comprehensive soil moisture data package and an analysis framework that can be used for Earth system model evaluations and uncertainty quantification, quantifying drought impacts and land–atmosphere interactions, and making recommendations for drought response planning.</p> <p>Data details: 1. scripts: 1) process data from original spatial resolution to 0.25 degree; 2) process data from original temporal resolution to monthly; 3) process the monthly data to seasonal mean analysis; 4) wavelet analysis. 2. data: 1) monthly 0.25deg data processed from raw datasets; 2) monthly and seasonal climatology data for comparison; 3) site data for wavelet analysis.</p> <p>Reference: The data manuscript is under review now and will add it here later.</p> <p>Contact: Mingjie Shi <mingjie.shi@pnnl.gov>; Lingcheng Li <lingcheng.li@pnnl.gov></p>
Effects of COVID-19 lockdown restrictions on parents' attitudes towards green space and time spent outside by children in Cambridgeshire and North London, United Kingdom
<p>1. In the United Kingdom, children are spending less time outdoors and are more disconnected from nature than previous generations. However, interaction with nature at a young age can benefit wellbeing and long-term support for conservation. Green space accessibility in the UK varies between rural and urban areas and is lower for children than for adults. It is possible that COVID-19 lockdown restrictions may have influenced these differences.</p> <p>2. In this study, we assessed parents' attitudes towards green space, as well as whether the COVID-19 lockdown restrictions had affected their attitudes or the amount of time spent outside by their children, via an online survey for parents of primary school-aged children in Cambridgeshire and North London, UK (n = 171). We assessed whether responses were affected by local environment (rural, suburban or urban), school type (state-funded or fee-paying) or garden access (with or without private garden access).</p> <p>3. Parents' attitudes towards green space were significantly different between local environments: 76.9% of rural parents reported being happy with the amount of green space to which their children had access, in contrast with only 40.5% of urban parents.</p> <p>4. COVID-19 lockdown restrictions also affected parents' attitudes to the importance of green space, and this differed between local environments: 75.7% of urban parents said their views had changed during lockdown, in contrast with 35.9% of rural parents. The change in amount of time spent outside by children during lockdown was also significantly different between local environments: most urban children spent more time inside during lockdown, whilst most rural children spent more time outside.</p> <p>5. Neither parents' attitudes towards green space nor the amount of time spent outside by their children varied with school type or garden access.</p> <p>6. Our results suggest that lockdown restrictions exacerbated pre-existing differences in access to nature between urban and rural children in our sampled population. We suggest that the current increased public and political awareness of the value of green space should be capitalised on to increase provision and access to green space and to reduce inequalities in accessibility and awareness of nature between children from different backgrounds.</p>
Supporting data for: Post-fire early successional vegetation buffers surface microclimate and increases survival of planted conifer seedlings in the southwestern United States
<p>Climate change and fire-exclusion have increased the flammability of western US forests, leading to forest cover loss when wildfires occur under severe weather conditions. Increasingly large high-severity burn patches are a limitation to natural regeneration because of dispersal distance, increasing the chance that these areas are converted to non-forest. Post-fire planting can overcome dispersal limitations, yet warmer and drier post-fire conditions can still limit survival. Early successional vegetation can alter surface microclimate; however, it is unclear whether this is enough to increase planted seedling survival in southwestern US forests. Here we examined how two shrub species of different canopy density would affect survival rates of planted tree seedlings following a high-severity fire in northern New Mexico. We expected that shrubs with a higher density canopy (Gambel oak) would have a greater effect on buffering below-shrub climate than shrubs with a lower density canopy (New Mexico locust) and seedlings planted under Gambel oak would have higher survival rates. We found that seedlings planted under Gambel oak had survival rates approximately 10% to 35% greater than those planted under New Mexico locust. The higher light availability beneath New Mexico locust corresponded to higher temperatures, lower humidity, and higher VPD, which impacted the mortality of planted tree seedlings. These results suggest that by waiting for post-fire shrub establishment, shrubs can be leveraged to buffer microclimate and increase post-fire planting success in the southwestern US.</p>
Dataset of "Hybrid modeling on 3D hydraulic features of a step-pool unit"
<p>In this repository you can find the data for the submission "Hybrid modeling on 3D hydraulic features of a step-pool unit" by Zhang et al. to Earth Surface Dynamics.</p> <p>The topographic models of the step-pool unit made of natural stones after FAVORization in FLOW3D for the six flow rates are kept in .stl files which were named after the flow rates (L/s). The mesh size for the step-pool feature is 2.5 mm for X, Y and Z directions. The locations, water level and flow velocity for the inlet boundary in all the numerical models are presented in the excel file. </p>
Wikidata Dump economic_unit_and_person
<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> p31 instance of - Q783794 company - Q6881511 enterprise - Q4830453 business - Q3563237 economic unit P106 occupation - Q43845 businessperson - Q131524 entrepreneur - Q6606110 industrialist - Q484876 chief executive officer - Q978044 executive - Q2961975 business executive<br> <a href="https://tools.wmflabs.org/wdumps/dump/2071">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>
Wikidata Dump economic_unit_and_businessmen
<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> p31 instance of - Q783794 company - Q6881511 enterprise - Q4830453 business - Q3563237 economic unit P106 occupation - Q43845 businessperson<br> <a href="https://tools.wmflabs.org/wdumps/dump/2070">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>
Wikidata Dump economic_unit
<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> p31 instance of - Q783794 company - Q6881511 enterprise - Q4830453 business - Q3563237 economic unit - Q43845 businessperson<br> <a href="https://tools.wmflabs.org/wdumps/dump/2069">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>
United-atom P3HT workspace
<p><a href="https://docs.signac.io/en/latest/index.html">Signac</a> workspace of united-atom P3HT <a href="https://hoomd-blue.readthedocs.io/en/latest/">HOOMD</a> molecular dynamics simulation data created using <a href="https://github.com/cmelab/planckton-flow">PlanckTon-flow</a> and <a href="https://github.com/cmelab/planckton/releases/tag/v0.6.1">PlanckTon v0.6.1</a>.</p>
Model-based analysis of tuberculosis genotype clusters in the United States reveals high degree of heterogeneity in transmission, and state-level differences across California, Florida, New York, and Texas.
<p>Data and codes for the publication</p>
A dataset of modeled hydrologic alteration and ecological consequences in stream reaches of the conterminous United States
<p>A dataset of modeled anthropogenically induced hydrologic alteration values for 43 hydrologic indices and modeled losses in native fish biodiversity as a result of streamflow modification, both mapped at the NHDPlus V1 and V2 stream-reach resolution.</p>
Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines
<p><strong>Code and data for Section 2 of the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines</strong></p> <p><strong>Versions:</strong></p> <p>Version 1.1 This one:</p> <ul> <li>updated region names</li> </ul> <p>Version 1.0 <a href="https://doi.org/10.5281/zenodo.5951626">https://doi.org/10.5281/zenodo.5951626</a></p> <p>This repository contains the code and data needed to produce the trajectories, projections, and observations for the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines.</p> <p>The report can be found on <a href="https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html">https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html</a></p> <p>An interactive tool to study the observations, trajectories, and scenarios can be accessed from <a href="https://sealevel.nasa.gov/task-force-scenario-tool">https://sealevel.nasa.gov/task-force-scenario-tool</a></p> <p>Frequently-asked questions: <a href="https://sealevel.nasa.gov/faq/16/">https://sealevel.nasa.gov/faq/16/</a></p> <p><strong>Authors</strong></p> <ul> <li>William V. Sweet, NOAA National Ocean Service</li> <li>Benjamin D. Hamlington, NASA Jet Propulsion Laboratory</li> <li>Robert E. Kopp, Rutgers University</li> <li>Christopher P. Weaver, U.S. Environmental Protection Agency</li> <li>Patrick L. Barnard, U.S. Geological Survey</li> <li>Michael Craghan, U.S. Environmental Protection Agency</li> <li>Gregory Dusek, NOAA National Ocean Service</li> <li>Thomas Frederikse, NASA Jet Propulsion Laboratory</li> <li>Gregory Garner, Rutgers University</li> <li>Ayesha S. Genz, University of Hawai‘i at Mānoa, Cooperative Institute for Marine and Atmospheric Research</li> <li>John P. Krasting, NOAA Geophysical Fluid Dynamics Laboratory</li> <li>Eric Larour, NASA Jet Propulsion Laboratory</li> <li>Doug Marcy, NOAA National Ocean Service</li> <li>John J. Marra, NOAA National Centers for Environmental Information</li> <li>Jayantha Obeysekera, Florida International University</li> <li>Mark Osler, NOAA National Ocean Service</li> <li>Matthew Pendleton, Lynker</li> <li>Daniel Roman, NOAA National Ocean Service</li> <li>Lauren Schmied, FEMA Risk Management Directorate</li> <li>William C. Veatch, U.S. Army Corps of Engineers</li> <li>Kathleen D. White, U.S. Department of Defense</li> <li>Casey Zuzak, FEMA Risk Management Directorate</li> </ul> <p><strong>Contents</strong></p> <p>This data and code set contains the following directories:</p> <p><em>Results</em></p> <p>The <code>Results</code> folder contains the resulting projections, trajectories and observations from the report.</p> <ul> <li><code>TR_global_projections.nc</code>: GMSL projections, trajectory, and observations</li> <li><code>TR_regional_projections.nc</code>: Regional observations, projections and trajectories</li> <li><code>TR_local_projections.nc</code>: Local observations, projections and trajectories</li> <li><code>TR_gridded_projections.nc</code>: Gridded projections</li> </ul> <p>These files are in the NetCDF forrmat. To read the NetCDF files, many free software packages are available, including <a href="http://meteora.ucsd.edu/~pierce/ncview_home_page.html">ncview</a> and <a href="https://www.giss.nasa.gov/tools/panoply/">Panoply</a>. Free NetCDF packages are available to directly import the data into <a href="https://github.com/Alexander-Barth/NCDatasets.jl">Julia</a> and <a href="https://unidata.github.io/netcdf4-python/">Python</a> code.</p> <p><em>Code</em></p> <p>The <code>Code</code> folder contains all the computer code used to read and analyze the observations and the projections, and to generate the trajectories.</p> <p>To run this code, you need <a href="https://julialang.org/">Julia</a>. The code requires the Julia packages <code>CSV</code>, <code>Interpolations</code>, <code>JSON</code>, <code>LoopVectorization</code>, <code>MAT</code>, <code>NCDatasets</code>, <code>NetCDF</code>, <code>Plots</code>, <code>XLSX</code>, <code>LinearAlgebra</code>, and <code>Statistics</code>. They can be installed by pressing <code>]</code> at the Julia REPL and typing:</p> <pre><code>add CSV Interpolations JSON LoopVectorization MAT NCDatasets NetCDF Plots XLSX LinearAlgebra Statistics </code></pre> <p>This program also requires <a href="http://segal.ubi.pt/hector/">Hector</a>. Hector needs to be installed or compiled. In the file <code>Hector.jl</code> update the path to the Hector executable on lines 30 and 104.</p> <p>Run <code>Run_TR.jl</code> in the REPL or run <code>julia Run_TR.jl</code> from the command line to run the projections. The projections are then written to the <code>.\Data</code> directory.</p> <p>The folder contains the following files:</p> <ul> <li><code>Run_TR.jl</code>: This is the main routine that (eventually) calls all the functions to compute the projections.</li> <li><code>ConvertNCA5ToGrid.jl</code>: Converts the original NCA5 projections to a set of netCDF files that's used throughout this code</li> <li><code>ProcessObservations.jl</code>: Reads and processes the tide-gauge and altimetry observations</li> <li><code>GlobalProjections.jl</code>: Reads and processes the GMSL observations and projections, and computes the trajectory</li> <li><code>RegionalProjections.jl</code>: Reads and processes the regional projections and computes the trajectories</li> <li><code>LocalProjections.jl</code>: Reads and processes the local projections at the tide-gauge locations and computes the trajectories</li> <li><code>GriddedProjections.jl</code>: Reads the gridded NCA5 projections and add a GMSL baseline correction for the 2005 vs 2000 baseline</li> <li><code>SaveFigureData.jl</code>: Reads the results and writes text files for GMT</li> <li><code>Hector.jl</code>: Wrapper for <a href="http://segal.ubi.pt/hector/">Hector</a>, used to compute trends and uncertainties.</li> <li><code>Masks.jl</code>: Defines the region masks for each region.</li> </ul> <p><em>Data</em></p> <p>The <code>Data</code> directory contains the input data sets used during the computations. Please appropriately cite the input data if you use it. It contains the following:</p> <p>Directories:</p> <ul> <li><code>ClimIdx</code>: Map with climate indices (NAO, PDO, MEI) used to remove internal variability. All the indices come from NOAA <a href="https://psl.noaa.gov/data/climateindices/">Physical Sciences Laboratory (PSL)</a> and <a href="https://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml">NOAA Climate Prediction Centre (CPC)</a></li> <li><code>NCA5_projections</code> Contains the NCA5 projections for each scenario (Low, IntLow, Int, IntHigh, and High). For each scenario, the GMSL projections, projections at tide-gauge locations and on a 1-degree grid are provided.</li> </ul> <p>Files:</p> <ul> <li><code>basin_codes.nc</code>: Map with basin codes. from Eric Leuliette/NOAA. Data provided by the NOAA Laboratory for Satellite Altimetry.</li> <li><code>CDS_monthly_1993_2020.nc</code>: Monthly-mean sea level (1993-2020) from gridded altimetry. Obtained from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global">Copernicus Climate Data Store</a>. This dataset contains modified Copernicus Climate Change Service information [2020]</li> <li><code>enso_correction.mat</code>: GMSL correction for ENSO/PDO from Hamlington, B. D., Frederikse, T., Nerem, R. S., Fasullo, J. T., & Adhikari, S. (2020). Investigating the Acceleration of Regional Sea‐level Rise During the Satellite Altimeter Era. Geophysical Research Letters. <a href="https://doi.org/10.1029/2019GL086528">https://doi.org/10.1029/2019GL086528</a></li> <li><code>filelist_psmsl.txt</code>: List with PSMSL file names and PSMSL IDs. Obtained from the Permanent Service for Mean Sea Level (<a href="http://www.psmsl.org/">PSMSL</a>), 2021, Retrieved 29 Nov 2021. Simon J. Holgate, Andrew Matthews, Philip L. Woodworth, Lesley J. Rickards, Mark E. Tamisiea, Elizabeth Bradshaw, Peter R. Foden, Kathleen M. Gordon, Svetlana Jevrejeva, and Jeff Pugh (2013) New Data Systems and Products at the Permanent Service for Mean Sea Level. Journal of Coastal Research: Volume 29, Issue 3: pp. 493 – 504. <a href="https://doi.org/:10.2112/JCOASTRES-D-12-00175.1">https://doi.org/:10.2112/JCOASTRES-D-12-00175.1</a>.</li> <li><code>GEBCO_bathymetry_05.nc</code>: Bathymetry map of the global oceans from the General Bathymetric Chart of the Oceans (<a href="https://www.gebco.net/">GEBCO</a>). Source: GEBCO Compilation Group (2021) GEBCO 2021 Grid (<code>doi:10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f</code>) The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>GIA_Caron_stats_05.nc</code>: Glacial Isostatic Adjustment estimates from Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., & Blewitt, G. (2018). GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science. Geophysical Research Letters, 45(5), 2203–2212. <a href="https://doi.org/10.1002/2017GL076644">https://doi.org/10.1002/2017GL076644</a>. The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>global_timeseries_measures.nc</code>: Time series of estimated 20th-century GMSL and its components, based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_ensembles.nc</code>: Ensemble GMSL reconstruction from tide-gauges based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_TPJAOS_5.0_199209_202106.txt</code>: Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1 [Data set]. NASA Physical Oceanography DAAC. <a href="https://doi.org/10.5067/GMSLM-TJ151">https://doi.org/10.5067/GMSLM-TJ151</a>. This altimetry dataset uses the methods as described in Beckley, B. D., Callahan, P. S., Hancock, D. W., Mitchum, G. T., & Ray, R. D. (2017). On the “Cal-Mode” Correction to TOPEX Satellite Altimetry and Its Effect on the Global Mean Sea Level Time Series. Journal of Geophysical Research: Oceans, 122(11), 8371–8384. <a href="https://doi.org/10.1002/2017JC013090">https://doi.org/10.1002/2017JC013090</a></li> <li><code>grd_1992_2020.nc</code>: Seafloor deformation due to contemporary GRD effects based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>region_mask.nc</code>: Mask with the definition of all regions.</li> <li><code>US_tg_monthly.xlsx</code>: Tide gauge observations from the NOAA tide gauge network</li> </ul> <p><em>GMT</em></p> <p>This directory contains the <a href="https://www.generic-mapping-tools.org/">GMT</a> scripts to make Figures 1.2, 2.1, 2.2, 2.6, and A.1.2 from the report. To generate the figures, make sure GMT is installed and run the Shell script in each directory.</p>
The impact of the SARS-COV2 infection on the disorder of consciousness rehabilitation unit
<p>Disorders of consciousness include coma (cannot be aroused, eye remain closed), vegetative state—VS (can appear to be awake, but unable to purposefully interact) and minimally conscious state—MCS (minimal but definite awareness). The objective of this study is to assess the impact of the SARS-CoV-2 infection on the Disorder of Consciousness (DOC) Rehabilitation Unit.This is a retrospective, longitudinal, descriptive, observational, pilot study. We consecutively enrolled 18 patients (age range: 40–72 years, 9 females and 9 males), from three to five months after a brain injury. They were grouped into VS (n = 8) and MCS (n = 10). A confirmed case of COVID-19 was defined as a positive result on high-throughput sequencing or real-time reverse-transcription polymerase chain reaction analysis of throat swab specimens. We collected data of lung Computed Tomography (CT) and laboratory exams. DOC patients who were positive for SARS-CoV-2 were classified into severe and no severe infected group, according to the American Thoracic Society guidelines.A total of 18 hospitalized patients with (16) and without confirmed (2) SARS-CoV-2 infection were included in the analysis. After one month, a follow-up clinical evaluation reported that one patient died, one patient was transferred from Covid Unit to Emergency Unit and 3 patients were resulted negative to double swab and they returned to Rehabilitative Unit. Significant differences were reported about hypertension, cardiac disease and respiratory problems between the patients with severe infection and patients without severe infection (P< 0.001). The laboratory findings, such as blood cell counts (<em>P</em> < 0.001), C-reactive protein, D-dimer, potassium and vitamin D levels, seemed to be considered as useful prognostic predictors.To our knowledge, this is the first longitudinal study on a sample of chronic DOC patients affected by SARS-CoV-2. This study may offer important new clinical information on COVID-19 for management of DOC patients. Our findings showed that for the subjects with severe infection due to COVID-19, rapid clinical deterioration or worsening could be associated with clinical and laboratory findings, which could contribute to high mortality rate. During the COVID-19 epidemic period, the clinicians should consider all the reported risk factors to avoid delayed diagnosis or misdiagnosis and to prevent the infection transmission in DOC Rehabilitation Unit.</p>
Seismic acquisition parameters to improve imaging beneath mafic igneous units: Case study from Australia's Northwest Shelf; supplementary material
<p>This dataset comprises two supplementary materials. Supplementary Materials A includes seismic processing workflows conducted by industry on the seismic lines used in this study. The seismic processing workflows are not the property of the author but are publicly available on the NOPIMS and WAPIMS databases. Collating these workflows into supplementary materials provides a simple method for readers to access material important for this research paper. Supplementary Materials B is a collection of 2D seismic lines the author conducted stratigraphic horizon mapping on for this study as viewed in 3D. More details on this dataset can be found throughout the research paper "Seismic acquisition parameters to improve imaging beneath mafic igneous units: Case study from Australia’s Northwest Shelf".</p>
Data: Survey of Implemented Mitigation Strategies and Further Needs of the United States Food Industry to Control COVID-19 in the Work Environment in Early 2021
<p>Data set related with the COVID-19 needs assessment study</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2019/2020 – The United Kingdom
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2020 AMR data collection: Animal and Plant Health Agency</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.