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3,023 results for “imports”
CFCs and Radiatively Important Trace Species at Harvard Forest EMS Tower 1996-2005
Measurements of 13 ozone-depleting and/or greenhouse gases are taken above the forest canopy at Harvard Forest, downwind of the New York City - Washington, D. C. corridor, every 25 minutes using a four-channel gas chromatographic system called FACTS (Forest and Atmosphere Chromatograph of Trace Species). The species measured are H2, CO, CH4, methyl chloroform (CH3CCl3), chloroform (CHCl3), carbon tetrachloride (CCl4), CFC-11 (CCl3F), CFC-12 (CCl2F2), CFC-113 (C2Cl3F3), halon-1211 (CBrClF2), perchlorethylene (C2Cl4), nitrous oxide (N2O), and sulfur hexafluoride (SF6). Observations began in January 1996 and are continuing.
Market Power / Import demand elasticity faced by an exporter at 6-digit HS level from Solleder (2020)
<p><strong>Description</strong></p> <p>This dataset contains the market power of exporters at the country level for more than 4000 6-digit HS codes (HS 1992 / H0) from Solleder (2020). Market power is proxied by the inverse of the import demand elasticity faced by the exporting country. Elasticities are estimated following the method developed by Kee et al. (2008). For more information, please refer to Solleder (2020).</p> <p>The <em>dta </em>file can be opened with STATA 14 or above. The <em>csv</em> file is a comma-separated value file. The separator is ',', and the first row is variable names. The content is the same in both files. Variables are:</p> <ul> <li><em>exporter</em>: ISO 3166 3-character country codes, string; </li> <li><em>commoditycode</em>: product 6-digit HS codes in HS revision 1992 (H0), string;</li> <li><em>epsilon</em>: import demand elasticity faced by the exporter, numeric;</li> <li><em>epsilon_se</em>: standard error of <em>epsilon</em>, numeric;</li> <li><em>marketpower</em>: market power, inverse of the absolute value of the import demand elasticity faced by the exporter, numeric.</li> </ul> <p> </p> <p><strong>Reference</strong></p> <div> <div>Kee H.L., A. Nicita, M. Olarreaga 2008 'Import demand elasticities and trade distortions' Rev. Econ. Stat., 90 (4), pp. 666-682</div> <div> </div> <div>Solleder J.M. 2020 'Market power and export taxes' European Economic Review, Volume 125, 103425, ISSN 0014-2921, <a href="https://doi.org/10.1016/j.euroecorev.2020.103425">https://doi.org/10.1016/j.euroecorev.2020.103425</a>.</div> </div> <p> </p>
Growth parameters and resistance to Sphaerulina musiva-induced canker are more important than wood density for increasing genetic gain from selection of Populus spp. hybrids for northern climates
<p>The data was collected from a common garden genetics trial established in 2008 in northern Alberta, Canada. The trial represents 1978 (initial number) hybrid poplar clones from 63 families and includes interspecific crosses between <em>Populus deltoides</em> (D), <em>Populus nigra</em> (N), <em>Populus balsamifera</em> (B), <em>P. maximowiczii</em> (M), and <em>P. × petrowskyana</em> (<em>P. laurifolia</em> × <em>P. nigra</em>). Female clone 24 (‘Walker’ = (<em>Populus deltoides </em>× (<em>P. laurifolia × P. nigra</em>))) and male progeny clone 2403 (‘Okanese’ = (‘Walker’ × (<em>P. laurifolia × P. nigra</em>))) were used as reference clones. The study design was a randomized complete block design, with one ramet per clone in each of four blocks. Measurements were carried out after three, eight, and 10 growing seasons on the genetics trial. Results presented in ‘HybridPoplarsTrial.csv’ file, show is the raw data, while ‘Summary data.csv’ contains the mean values for clones obtained from the four blocks. Measured and calculated traits include: DBH (diameter at breast height; 1.3 m); H (height); canker (canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)); MAI (mean annual increment), V (volume).</p> <p>Description of headings:</p> <p>Trait [unit] - Description</p> <p>DBH_Age_3 [cm] - diameter at breast height at age 3</p> <p>H_Age_3 [m] - height at age 3</p> <p>DBH_Age_8 [cm] - diameter at breast height at age 8</p> <p>H_Age_8 [m] - height at age 8</p> <p>H_Age_10 [m] - height at age 10</p> <p>DBH_Age_10 [cm] - diameter at breast height at age 10</p> <p>Canker_Age_8 - canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)</p> <p>Canker_Age_10 - canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)</p> <p>V_Age_8 [m<sup>3</sup> ha<sup>-1</sup>] - volume at age 8</p> <p>MAI_Age_8 [m<sup>3</sup> ha<sup>-1</sup> yr<sup>-1</sup>] - mean annual increment at age 8</p> <p>V_Age_10 [m<sup>3</sup> ha<sup>-1</sup>] - volume at age 10</p> <p>MAI_Age_10 [m<sup>3</sup> ha<sup>-1</sup> yr<sup>-1</sup>] - mean annual increment at age 10</p> <p>WD_Age_10 [kg m<sup>-3</sup>] - wood density at age 10</p> <p> </p>
Genomic Epidemiology Dataset for Important Nosocomial Pathogenic Bacteria Acinetobacter baumannii
<p>The<strong> </strong>infections caused by various bacterial pathogens both in clinical and community settings represent a significant threat to public healthcare worldwide. The growing resistance to antimicrobial drugs acquired by bacterial species causing healthcare-associated infections has already become a life-threatening danger noticed by the World Health Organization. Several groups or lineages of bacterial isolates usually called 'the clones of high risk' often drive the spread of resistance within particular species. </p><p>Thus, it is vitally important to reveal and track the spread of such clones and the mechanisms by which they acquire antibiotic resistance and enhance their survival skills. Currently, the analysis of whole genome sequences for bacterial isolates of interest is increasingly used for these purposes, including epidemiological surveillance and developing of spread prevention measures. However, the availability and uniformity of the data derived from the genomic sequences often represents a bottleneck for such investigations. </p><p>In this dataset, we present the results of a comprehensive genomic epidemiology analysis of 17,546 genomes of a dangerous bacterial pathogen <i>Acinetobacter baumannii</i>. Important typing information including multilocus sequence typing (MLST)-based sequence types (STs), intrinsic<i> blaOXA-51-like</i> gene variants, capsular (KL) and oligosaccharide (OCL) types, CRISPR-Cas systems, and cgMLST profiles are presented, as well as the assignment of particular isolates to nine known international clones of high risk. The presence of antimicrobial resistance genes within the genomes is also reported. </p><p>These data will be useful for researchers in the field of <i>A. baumannii</i> genomic epidemiology, resistance analysis and prevention measure development.</p>
Summary of the most important features for selected ABs
<p>These data summarizes the relevant findings and the identified limitations (in terms of "Category", "Technology", "Properties", "Limitation", and "Applicability to railway"), coming from the overview of different Alternative Bearers (ABs), carried out in deliverable D21 (AB4Rail project, www.ab4rail.eu).<br> The results have provided an overview of several technologies, each of them showing specific characteristics. The heterogeneous nature of different ABs allows to provide a plethora of available communication technologies to be potentially used by the Adaptable Communication System (ACS) for different railway scenarios. All the selected ABs provide the IP interconnection feature since they are Integrated within OSI reference model.<br> In this way, it collects the planned objectives of deliverable D2.1, expressed as a technological overview of selected ABs, as possible candidates coexisting with Traditional Bearers (TBs) for supporting railway applications.</p>
Data for: Impact of SO2 injection profiles on simulated volcanic forcing for the Sarychev 2009 eruptions - investigating the importance of using high vertical resolution methods when compiling SO2 data
<p>The files are data assosicated with the study High-resolution stratospheric volcanic SO2 injections in WACCM. The files are associated with four differnt simulaions described in the paper: M16, S21-1D, S21-3D and No-Volc. The files with "input" in the name are the SO2 input files used in the WACCM (Whole Atmosphere Community Climate Model) simulations in the paper. The files with "monthly_averages" in the filenames are monthly averages of model output data the variables used in the paper. </p> <p>The CALIOP_monthly_averages.nc file is monthly average of the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) satellite data used in the study to evaluate the WACCM simulations. </p> <p> </p>
Standardized map of habitat types and regionally important biotopes in Flanders
<p>The <code>habitatmap_stdized.gpkg</code> file is a processed version of the <a href="https://www.vlaanderen.be/datavindplaats/catalogus/biologische-waarderingskaart-en-natura-2000-habitatkaart-toestand-2023">Natura 2000 habitat map of Flanders</a> (De Saeger et al., 2023; see also De Saeger et al. 2017). It contains all polygons with Natura 2000 habitat types or regional important biotopes (RIB). This file is used as a basis for designing monitoring schemes in Flanders. </p> <p>In the original habitat map, every polygon can consist of maximum 5 different types (habitat (sub)types and regionally important biotopes). This information is stored in the columns <code>HAB1</code>, <code>HAB2</code>,..., <code>HAB5</code> of the attribute table. The fraction of each type within the polygons is stored in the columns <code>PHAB1</code>, <code>PHAB2</code>, ..., <code>PHAB5</code>.</p> <p>The <code>habitatmap_stdized.gpkg</code> file is a GeoPackage that contains:</p> <ul> <li><code>habitatmap_polygons</code>: a spatial layer with every habitat map polygon that contains a Natura 2000 habitat or RIB type.</li> <li><code>habitatmap_types</code>: a table with information on the habitat and RIB types (HAB1, HAB2,..., HAB5) that occur within each polygon of <code>habitatmap_polygons.</code></li> </ul> <p>The processing of the habitatmap_types table included following adjustments:</p> <ul> <li>For some polygons the type is uncertain, and the type code in the raw habitatmap data source consists of 2 or 3 possible types, separated with a ','. The different possible types are split up and one row is created for each of them, with <code>phab</code> for each new row simply set to the original value of <code>phab</code>. The variable <code>certain</code> will be <code>FALSE</code> if the original type code consists of 2 or 3 possible types, and <code>TRUE</code> if only one type is provided.</li> <li>Some polygons contain both a standing water habitat type and <code>rbbmr</code>: <ul> <li><code>3130_rbbmr</code>,</li> <li><code>3140_rbbmr</code>,</li> <li><code>3150_rbbmr</code>, and</li> <li><code>3160_rbbmr</code>.</li> </ul> </li> <li>Since <code>habitatmap_stdized_2020_v1</code>, the two types <code>31xx</code> and <code>rbbmr</code> are split up and one row is created for each of them, with <code>phab</code> for each new row simply set to the original value of <code>phab</code>. The variable certain in this case will be <code>TRUE</code> for both types.</li> <li>After those steps, a given polygon could contain the same type with the same value for <code>certain</code> repeated several times, e.g. when <code>31xx_rbbmr</code> is present with <code>phab</code> = yy% and <code>31xx</code> is present with <code>phab</code> = zz%. In that case the rows with the same <code>polygon_id</code>, <code>type</code> and <code>certain</code> were gathered into one row and the respective phab values were added up.</li> </ul> <p>The R-code for creating the <code>habitatmap_stdized</code> data source can be found in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/abf596e/src/generate_habitatmap_stdized">'n2khab-preprocessing' at commit abf596e</a>.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://github.com/inbo/n2khab">n2khab</a>.</p> <p>Attributes of <code>habitatmap_polygons</code>:</p> <ul> <li><code>polygon_id</code></li> <li><code>description_orig</code>: polygon description based on the original type codes in the raw habitatmap </li> </ul> <p>Attributes of <code>habitatmap_types</code>:</p> <ul> <li><code>polygon_id</code></li> <li><code>type</code>: the interpreted habitat or RIB type</li> <li><code>certain</code>: <code>TRUE</code> when type is certain and <code>FALSE</code> when type is uncertain</li> <li><code>code_orig</code>: original type code in raw habitatmap</li> <li><code>phab</code>: proportion of polygon covered by type, as a percentage.</li> </ul> <p>Since version <code>habitatmap_stdized_2020_v1</code>, rows are unique only by the combination of the <code>polygon_id</code>, <code>type</code> and <code>certain</code> columns.</p>
Dataset for: Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning
<p>Dataset for: Importance of satellite observations for high-resolution mapping of near-surface NO<sub>2 </sub>by machine learning</p> <p>This dataset is uploaded as a part of the article by Kim et al. (2021). The dataset is the hourly maps of near-surface nitrogen dioxide (NO<sub>2</sub>) concentrations at 100 m resolution for an Alpine domain (Switzerland and northern Italy, 6-12 °E, 42-48 °N). The dataset is provided per day (24 hours) in a netcdf (*.nc ~550MB). In this work, we have generated NO<sub>2 </sub>hourly maps for Feb. 2019 to May 2020 and, here, we upload for March 2019 only (~16 GB). If you need data for another period of time, please contact Gerrit Kuhlmann (gerrit.kuhlmann@empa.ch) or Minsu Kim (minsu.kim@empa.ch). </p>
Data for figures in the Publication "The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2"
<p>This repository contains the data to produce figures for the paper:</p> <p>"Lohmann, U. and Neubauer, D.: The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2, Atmos. Chem. Phys., 18, 8807–8828, https://doi.org/10.5194/acp-18-8807-2018, 2018."</p> <p>Note that the scripts are to be found in the accompanying package (https://doi.org/10.5281/zenodo.8183412)</p>
PWV02 Importance values of gallery forest vegetation at Konza Prairie, 1983
Eighteen gallery forest stands, representing nearly all of the nondisturbed forests on Konza Prairie, were sampled during the 1983 growing season. The point-quarter method at 20 sample points was used to sample overstory vegetation. Species names and diameter were recorded for the four sampled trees in each plot. From these data frequency, density and dominance were calculated to derive importance values for each species in a stand.
Supplementary data to: Importance and vulnerability of the world's water towers
<p>This archive contains data produced for a study assessing the importance and vulnerability of the world’s water towers. Code (R-scripts) used to process these files is available on the <a href="https://github.com/mountainhydrology/pub_ngs-watertowers">MountainHydrology Github page</a></p> <p>The archive is organized in directories with specific topics. Each directory contains input files (optional) and output/processed files. The input files can be used in combination with the R-scripts published on <a href="https://github.com/mountainhydrology/pub_ngs-watertowers">Github</a> to generate the processed files included in this archive. In many cases external published data is used as input data for the calculations. In that case the data is not included in this archive but literature references and links to the specific files are provided in the description below. Files which have been preprocessed before use in the R-scripts are included in this archive. For calculation details please see the publication, in particular Extended Data Tables 3 and 4.</p> <p><strong>Archive contents</strong></p> <p>The archives contents are organized in eight separate directories, which are listed here, along with their contents:</p> <ul> <li><strong>ERA5</strong></li> </ul> <p>Precipitation and evaporation data are extracted from ERA5 reanalysis available online in the Copernicus Climate Data Store at https://cds.climate.copernicus.eu</p> <p>This directory includes:</p> <p><em>Input</em></p> <pre><code>ERA5_evaporation_avgannual_2001_2017.nc - Average annual evaporation (mm) for 2001-2017 ERA5_evaporation_ymonmean_2001_2017.nc - Multi-year mean monthly evaporation (mm) for 2001-2017 era5_total-precipitation_ymonmean_2001-2017_global.tif - Multi-year mean monthly precipitation (mm) for 2001-2017 era5_total-precipitation_yearsum_2001-2017.tif - Average annual precipitation (mm) for 2001-2017</code></pre> <p><em>Output</em></p> <pre><code>P_avg_annual_basin_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to basins P_avg_annual_DS_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to downstream basins P_avg_annual_mm.tif - Average annual precipitation 2001-2017 (mm) P_avg_annual_WT_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to Water Tower Units P_var_interannual.tif - Interannual variablity in precipitation 2001-2017 P_var_interannual_basin.tif - Interannual variablity in precipitation 2001-2017 aggregated to basins P_var_interannual_DS.tif - Interannual variablity in precipitation 2001-2017 aggregated to downstream basins P_var_interannual_WT.tif - Interannual variablity in precipitation 2001-2017 aggregated to Water Tower Units P_var_intraannual.tif - Intra-annual variablity in precipitation 2001-2017 P_var_intraannual_basin.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to basins P_var_intraannual_DS.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to downstream basins P_var_intraannual_WT.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to Water Tower Units WTU_P_indicators.csv - Table listing all calculated precipition indicators per Water Tower Unit</code></pre> <ul> <li><strong>Glaciers</strong></li> </ul> <p>Glacier volume and mass balance are derived from published datasets. This directory includes:</p> <p><em>Output</em></p> <pre><code>Glac_area_WT_km2.tif - Glacier area (km2) aggregated for Water Tower Units Glac_volume_WT_km3.tif - Glacier volume (km3) aggregated for Water Tower Units WTU_Glacier_indicators.csv - Table listing all derived glacier indicators per Water Tower Unit WTU_MB.shp - shapefile of Water Tower Units including the glacier mass balance per Water Tower Units as attribute</code></pre> <p><em>External data</em></p> <p>Glacier volume data published in<em> Farinotti et al., 2019, Nature Geoscience</em>, were used.<br> Reference: Farinotti, D. et al. A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nat. Geosci. 12, 168–173 (2019).<br> Glacier volume (km3) and glacier area (km2) at 0.05 degrees spatial resolution were used, which are available <a href="https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/315707/global_fraction-of-degree_grids.zip?sequence=60&isAllowed=y">here</a>.<br> The used files are <em>p05_degree_glacier_area_km2.tif</em> and <em>p05_degree_glacier_volume_km3.tif</em></p> <p>Glacier mass balance data published by the World Glacier Monitoring Service were used to derive an average glacier mass balance per Water Tower Unit.<br> References:<br> Zemp, M. et al. Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. Nature 568, 382–386 (2019).<br> World Glacier Monitoring Service. Fluctuations of Glaciers (FoG) Database. (2018). doi:10.5904/wgms-fog-2018-06</p> <ul> <li><strong>HydroLAKES</strong></li> </ul> <p>Surface lake and water storage per Water Tower Unit was calculated. This directory includes:</p> <p><em>Output</em></p> <pre><code>WTU_lake_storage_volume.csv - Table listing lake and reservoir volume (km3) per Water Tower Unit WTU_surface_water_storage_km3.tif - Lake and reservoir storage volume (km3) aggregated to Water Tower Units</code></pre> <p><em>External data</em></p> <p>For surface water lakes and reservoirs the HydroLAKES dataset is used. The shapefile <em>HydroLAKES_polys_v10.shp</em> can be downloaded from <a href="http://https://97dc600d3ccc765f840c-d5a4231de41cd7a15e06ac00b0bcc552.ssl.cf5.rackcdn.com/HydroLAKES_polys_v10_shp.zip">HydroSheds</a></p> <p>Reference: Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 1–11 (2016).</p> <ul> <li><strong>Indicators</strong></li> </ul> <p>All indicators and subindicators calculated for the Water Tower Index calculation are stored per Water Tower Unit.</p> <p>This directory includes:</p> <pre><code>indicators.csv - Table with all indicators and subindicators per Water Tower Unit</code></pre> <ul> <li><strong>Snow</strong></li> </ul> <p>The MODIS MOD10CM006 snow cover product was used to derive snow persistence.<br> Reference: Hall, D. K. & Riggs, G. A. MODIS/Terra Snow Cover Monthly L3 Global 0.05Deg CMG, Version 6. (2015). doi:10.5067/MODIS/MOD10CM.006</p> <p>This archive includes:<br> <em>Input</em></p> <pre><code>MOD10CM006_yearmean_2001-2017.tif - Annual mean snow cover 2001-2017 MOD10CM006_ymonmean_2001-2017.tif - Multi-year mean monthly snow cover 2001-2017</code></pre> <p><em>Output</em></p> <pre><code>Snow_persistence_avg_annual.tif - Average annual snow persistence 2001-2017 Snow_persistence_avg_annual_WT.tif - Average annual snow persistence 2001-2017 aggregated to Water Tower Units Snow_persistence_var_interannual.tif - Interannaul variability in snow persistence 2001-2017 Snow_persistence_var_interannual_WT.tif - Interannaul variability in snow persistence 2001-2017 aggregated to Water Tower Units Snow_persistence_var_intraannual.tif - Intra-annaul variability in snow persistence 2001-2017 Snow_persistence_var_intraannual_WT.tif - Intra-annaul variability in snow persistence 2001-2017 aggregated to Water Tower Units WTU_Snow_indicators.csv - Table listing all derived snow indicators per Water Tower Unit</code></pre> <ul> <li><strong>Uncertainty</strong></li> </ul> <p>The directory contains the uncertainty ranges used in the uncertainty analysis<br> The directory includes:</p> <pre><code>ET_uncertainty_per_downstream.csv - Table listing SD in evaporation per downstream basin ET_uncertainty_per_WTU.csv - Table listing SD in evaporation per Water Tower Unit P_uncertainty_per_downstream.csv - Table listing SD in precipitation per downstream basin P_uncertainty_per_WTU.csv - Table listing SD in precipitation per Water Tower Unit WTU_IceVol_uncertainty.csv - Table listing uncertainty in ice volume per Water Tower Unit</code></pre> <ul> <li><strong>Water demands</strong></li> </ul> <p>Net water demands for irrigation, industrial and domestic water use, as well as the environmental flow requirement are extracted from PCR-GLOBWB hydrological model output.<br> Reference: Wada, Y., De Graaf, I. E. M. & van Beek, L. P. H. High-resolution modeling of human and climate impacts on global water resources. J. Adv. Model. Earth Syst. 8, 735–763 (2016).</p> <p>The directory includes:<br> <em>Input</em></p> <pre><code>Dom_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net domestic water demand 2001-2014 at 0.05 degrees resolution (km3) Ind_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net industrial water demand 2001-2014 at 0.05 degrees resolution (km3) Irr_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net irrigation water demand 2001-2014 at 0.05 degrees resolution (km3) Tot_use_ymonmean_2001_2014_005.tif - Sum of the three above global_historical_riverdischarge_ymonmean_m3second_5min_2001_2014.nc4 - Multi-year mean monthly natural discharge (m3/s) 2001-2014</code></pre> <p><em>Output</em></p> <pre><code>Domestic_use_avg_annual_basin_km3.tif - Average annual net domestic water demand 2001-2014 aggregated to basins Domestic_use_avg_annual_km3.tif - Average annual net domestic water demand 2001-2014 Industrial_use_avg_annual_basin_km3.tif - Average annual net industrial water demand 2001-2014 aggregated to basins Industrial_use_avg_annual_km3.tif - Average annual net industrial water demand 2001-2014 Irrigation_use_avg_annual_basin_km3.tif - Average annual net irrigation water demand 2001-2014 aggregated to basins Irrigation_use_avg_annual_km3.tif - Average annual net irrigation water demand 2001-2014 Natural_demand_avg_annual_basin_km3.tif - Average annual natural water demand 2001-2014 aggregated to basins Total_human_demand_avg_annual_basin_km3.tif - Average annual net human (sum of domestic, industrial and irrigation) water demand 2001-2014 aggregated to basins Water_gap_average_annual_basin.tif - Average annual water gap 2001-2014 aggregated to basins WTU_Demand_DS_P_available.csv - Table listing dowstream water availability per sector per basin WTU_Demand_indicators.csv - Table listing demand per sector per basin WTU_Domestic_Water_Gap_monthly.csv - Table listing multi-year average monthly domestic water gap per basin WTU_Industrial_Water_Gap_monthly.csv - Table listing multi-year average monthly industrial water gap per basin WTU_Irrigation_Water_Gap_monthly.csv - Table listing multi-year average monthly irrigation water gap per basin WTU_Natural_Water_Gap_monthly.csv - Table listing multi-year average monthly natural water gap per basin WTU_Total_Water_Gap_monthly.csv - Table listing multi-year average monthly water gap per basin</code></pre> <ul> <li><strong>WTU units</strong></li> </ul> <p>The spatial units for all calculations are the Water Tower Units, their downstream basins, and the entire basins (Water Tower Unit + downstream basin). They are extracted using definitions of basins and mountain ranges. This directory includes:</p> <p><em>Output</em></p> <pre><code>basins.tif - Definition of basins with Water Tower Units at 0.05 degrees spatial resolution basins_downstream.tif - Definition of downstream basins at 0.05 degrees spatial resolution basins_vector.shp - Definition of basins with Water Tower Units as vector data downstream_vector.shp - Definition of downstream basins as vector data gmba_all.shp - All GMBA mountain ranges including glacier volume and snow persistence gmba_ss.shp - GMBA mountain ranges included in Water Tower Units WTU.tif - Definition of Water Tower Units as 0.05 degrees spatial resolution WTU_specs.csv - Table with set of specifications of Water Tower Units WTU_vector.shp - Definition of Water Tower Units as vector data</code></pre> <p><em>External data</em></p> <p>FAO's classification of major hydrological basins and FAO's classification of subbasins per continent are used. These are based on HydroSheds and are available as shapefiles at <a href="http://www.fao.org/nr/water/aquamaps/">FAO Aquamaps</a></p> <p>The specific shapefiles used are:</p> <p><a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=38047&fname=Major_hydrological_basins.zip&access=private">major_hydrobasins.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37039&fname=hydrobasins_asia.zip&access=private">hydrobasins_asia.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37174&fname=hydrobasins_southam.zip&access=private">hydrobasins_southam.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=38044&fname=hydrobasins_northam.zip&access=private">hydrobasins_northam.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37299&fname=hydrobasins_neareast.zip&access=private">hydrobasins_neareast.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37250&fname=hydrobasins_europe.zip&access=private">hydrobasins_europe.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37173&fname=hydrobasins_centralam.zip&access=private">hydrobasins_centralam.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37251&fname=hydrobasins_austpacific.zip&access=private">hydrobasins_austpacific.shp</a>:</p>
Ogham Stones Wikidata Import
<p><strong>Ogham Stones Wikidata Import</strong></p> <p>more at: <a href="https://github.com/ogi-ogham/ogham-wikidata/tree/master/OgamStones">https://github.com/ogi-ogham/ogham-wikidata/tree/master/OgamStones</a></p>
Ogham Townlands Wikidata Import
<p><strong>Ogham Townlands Wikidata Import</strong></p> <p>more at: <a href="https://github.com/ogi-ogham/ogham-wikidata/tree/master/Townlands">https://github.com/ogi-ogham/ogham-wikidata/tree/master/Townlands</a></p>
Data from: Behavioural responses to potential dispersal cues in two economically important cereal-feeding eriophyoid mite species
<p>Variables:</p> <ol> <li>species (ABH = <em>Abacarus hystrix</em>, WCM = <em>Aceria tosichella</em> MT1 genetic lineage)</li> <li>variant - experimental treatment (type of dispersal cue): wind, an insect vector, presence of a fresh plant</li> <li>feeding - no. of feeding specimens</li> <li>walking - no. of walking specimens</li> <li>standing - no. of specimens standing vertically</li> <li>cha - no. of specimens forming chains</li> <li>mob - no. of specimens capable to move</li> <li>pop - no. of all specimens (including quiescent stages)</li> </ol>
Dataset for publication "Importance of Substrate Pore Size and Wetting Behavior in Gas Diffusion Electrodes for CO2 Reduction"
<p>Dataset for the publication "Importance of Substrate Pore Size and Wetting Behavior in Gas Diffusion Electrodes for CO2 Reduction" containing war and processed data used to compose the various figures. </p> <p>DOI Publication: <a href="https://doi.org/10.1021/acsaem.2c03054">https://doi.org/10.1021/acsaem.2c03054</a> </p>
Carbon storage in old hedgerows: The importance of below-ground biomass
<p>Dataset to the manuscript: Drexler, S., Thiessen, E., & Don, A. (2023). Carbon storage in old hedgerows: The importance of below-ground biomass. GCB Bioenergy. https://doi.org/10.1111/gcbb.13112</p><ul><li>Drexler_et_al_2023-cn_biomass: contains the data on the biomass C/N measurements</li><li>Drexler_et_al_2023-overallstocks: contains the calculated carbon stocks per subplot for all carbon pools</li><li>Drexler_et_al_2023-soc_cropland: contains the calculated soil organic carbon stocks (0-100cm soil depth) of the reference cropland</li><li>Drexler_et_al_2023-soc_weight_fine_roots: contains the raw data on the dry weight of the fine roots and the raw data on the soil samples (C/N data, dry weight, stone/root fraction) per subplot and sampling depth</li><li>Drexler_et_al_2023-weight_above_ground_biomass: contains the raw data on the dry weight of the harvestable biomass and biomass of the mature trees per subplot</li><li>Drexler_et_al_2023-weight_coarse_roots_litter: contains the raw data on the dry weight of the coarse roots, litter and stumps per subplot</li></ul>
Genomic Typing, Antimicrobial Resistance Gene, Virulence Factor and Plasmid Replicon Dataset for the Important Pathogenic Bacteria Klebsiella pneumoniae
<p>The infections caused by various bacterial pathogens both in clinical and community settings represent a significant threat to public healthcare worldwide. The growing resistance to antimicrobial drugs acquired by bacterial species causing healthcare-associated infections has already become a life-threatening danger noticed by the World Health Organization. Several groups or lineages of bacterial isolates usually called 'the clones of high risk' often drive the spread of resistance within particular species. </p> <p>Thus, it is vitally important to reveal and track the spread of such clones and the mechanisms by which they acquire antibiotic resistance and enhance their survival skills. Currently, the analysis of whole genome sequences for bacterial isolates of interest is increasingly used for these purposes, including epidemiological surveillance and developing of spread prevention measures. However, the availability and uniformity of the data derived from the genomic sequences often represents a bottleneck for such investigations. </p> <p>In this dataset, we present the results of a genomic epidemiology analysis of 61,857 genomes of a dangerous bacterial pathogen <em>Klebsiella pneumoniae</em> obtained from NCBI Genbank database. Important typing information including multilocus sequence typing (MLST)-based sequence types (STs), capsular (KL) and oligosaccharide (OL) types, CRISPR-Cas systems, and cgMLST profiles are presented, as well as the assignment of particular isolates to clonal groups (CG). The presence of antimicrobial resistance and virulence genes, as well as plasmid replicons, within the genomes is also reported. </p> <p>These data will be useful for researchers in the field of <em>K. pneumoniae</em> genomic epidemiology, resistance analysis and prevention measure development.</p>
Map of standing water habitat types and regionally important biotopes in Flanders
<p>This map is a combination of the <a href="https://zenodo.org/records/13865531">standardized habitat map of Flanders</a> (version habitatmap_stdized_2023_v1) and <a href="https://zenodo.org/records/14203168">the watersurface map of Flanders</a> (version watersurfaces_2024). It contains standing water Natura 2000 habitat types (2190_a and 31xx) and regionally important biotopes (rbbah) in Flanders.</p> <p>The polygons with 2190_a habitat (dune slack ponds) are generated by selecting all watersurface polygons that overlap with dune habitat polygons (21xx) of the standardized habitat map.</p> <p>For each of the other aquatic habitat types (31xx and rbbah) we select the watersurface polygons that overlap with the selected habitat type polygons of the standardized habitat map. We also select polygons of the standardized habitat map containing standing water types but that do not overlap with polygons of the watersurface map.</p> <p>The <code>watersurfaces_hab.gpkg</code> file is a GeoPackage that contains:</p> <ul> <li><code>watersurfaces_hab_polygons</code>: a spatial layer with the selected polygons that contain standing water habitat types or regionally important biotopes. </li> <li><code>watersurfaces_hab_types</code>: a table with information on standing water habitat types and regionally important biotopes in each watersurface polygon.</li> </ul> <p>The R-code for creating the <code>watersurfaces_hab</code> data source can be found in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/58138a8/src/generate_watersurfaces_hab">'n2khab-preprocessing' at commit 58138a8</a>.</p> <p>A reading function to return the data source in a standardized way into the R environment is provided by the R-package <a href="https://github.com/inbo/n2khab">n2khab</a>.</p>
The Bomber's Baedeker. A Guide to the Economic Importance of German Towns and Cities
<p>The Bomber's Baedeker</p> <p>The two-volume printed work “The Bomber's Baedeker. A Guide to the Economic Importance of German Towns and Cities” was produced during the Second World War by the British Foreign Office and the Ministry of Economic Warfare. It lists towns and cities of the German Reich with more than a thousand inhabitants and information on their war-related infrastructure, industrial and production facilities. Only four verified copies still exist worldwide and none of them have been accessible for scholarly digital use until now. “The Bomber's Baedeker” was re-discovered in 2019 in the library of the Leibniz Institute of European History (IEG), digitised in cooperation with the Mainz University Library and made accessible and processed by the Digital Historical Research | DH Lab and the Darmstadt University of Applied Sciences as part of a cross-institutional cooperation (including courses with students) so that “The Bomber's Baedeker” can now be used, analysed and processed as an open, machine-readable data source in compliance with FAIR principles.</p>
Data for "Hot-carrier transfer across a nanoparticle-molecule junction: The importance of orbital hybridization and level alignment"
<p>This upload includes the data presented and analyzed in the article "Hot-carrier transfer across a nanoparticle-molecule junction: The importance of orbital hybridization and level alignment" by Jakub Fojt, Tuomas P. Rossi, Mikael Kuisma, and Paul Erhart.</p> <p>The codes for reproducing the data are provided at <a href="https://doi.org/10.5281/zenodo.7118376">doi:10.5281/zenodo.7118376</a>.</p> <p>See <em>README.md</em> in <em>data.zip</em> for a detailed description.</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.