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Expression-based polygenic score from the amygdala 5HTT gene network
<p>This pipeline intends to facilitate the calculation of biologically informed polygenic scores from collected genomic data. This template can be adapted to create other expression-based polygenic risk scores. Data is 1) step by step description and 2) a list of genes that compose the gene network.</p>
The NANOGrav 12.5-year Wideband Data Set (version 12yv4)
<p>The NANOGrav 12.5-year wideband data set (public release "12yv4") is the supplemental data set accompanying Alam et al. 2021, "The NANOGrav 12.5 yr Data Set: Wideband Timing of 47 Millisecond Pulsars," The Astrophysical Journal Supplement Series, 252, 5, DOI 10.3847/1538-4365/abc6a1. It contains wideband pulse times of arrival, models describing frequency-dependent template profiles, pulsar timing models, timing residuals, and clock files.</p> <p>Details about the contents of these files are contained in NANOGrav_12yv4_wideband/README, as well as in NANOGrav_12yv4_wideband/wideband/README.wideband. The narrowband version of this dataset (published in Alam et al. 2021, ApJS, 252, 4, DOI: 10.3847/1538-4365/abc6a0) can be found at Zenodo DOI: 10.5281/zenodo.4312297. Both the narrowband and wideband data sets are also available at <a href="http://data.nanograv.org">data.nanograv.org</a>.</p>
Trees and alignments for: A robust phylogenomic framework for the calamoid palms
<p>Target file, alignments, gene trees and species trees from phylogenomic analyses in Kuhnhäuser et al. (2021), A robust phylogenomic framework for the calamoid palms, Molecular Phylogenetics and Evolution. <a href="https://doi.org/10.1016/j.ympev.2020.107067">https://doi.org/10.1016/j.ympev.2020.107067</a>.</p> <p>Raw sequence data are deposited in the European Nucleotide Archive of the European Bioinformatics Institute (<a href="https://www.ebi.ac.uk/ena">https://www.ebi.ac.uk/ena</a>) under project number PRJEB40689. Scripts for all phylogenetic analyses are available at <a href="https://github.com/BenKuhnhaeuser/PhyloFrame">https://github.com/BenKuhnhaeuser/PhyloFrame</a>.</p>
Atmospheric profiling data collected from radiosondes in the Southern Ocean in the austral summer of 2016/2017 during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>The data set consists of the vertical profiles of the atmospheric variables measured using radiosondes (i-Met) during the Antarctic Circumnavigation Expedition from November 2016 to April 2017. The data include the raw variables measured directly by the radiosondes and derived parameters: altitude (km), air pressure (mb), air temperature (ºC), relative humidity (%), frostpoint (ºC), potential temperature (ºK), water vapour mixing ratio (ppmv), total column water (mm w.e.), wind speed (m/s) and wind direction (deg).</p> <p><strong>Dataset contents</strong></p> <ul> <li>aceNNN_yyyymmdd, directory <ul> <li>aceNNN_yyyymmdd.csv, data file, comma-separated values</li> <li>aceNNN_yyyymmdd.kml, metadata, XML</li> <li>aceNNN_yyyymmdd.raw, data file, raw, ASCII DOS</li> <li>aceNNN_yyyymmdd.raw_config, metadata, XML</li> <li>aceNNN.de1, metadata, ASCII text format</li> <li>aceNNNflt.dat, data file, ASCII text format</li> <li>aceNNNpre.dat, data file, ASCII text format</li> </ul> </li> <li>plots, directory <ul> <li>Sounding_ACENNN.png, metadata, portable network graphics</li> </ul> </li> <li>data_file_header_csv.txt, metadata, text format</li> <li>data_file_header_dat.txt, metadata, text format</li> <li>data_file_header_launches.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>overview_radiosonde_launches.csv, metadata, comma-separated value</li> </ul> <p>where NNN is the launch number yyyy is the year, mm is the month and dd is the day. Dates are in UTC.</p> <p>json files make up a Frictionless Data package.</p> <p><strong>Dataset citation</strong></p> <p>Please cite this dataset as:</p> <p>Gorodetskaya, I.V., Thurnherr, I., Tsukernik, M., Graf, P., Aemisegger, F., Wernli, H. and Ralph, F.M. (2021). Atmospheric profiling data collected from radiosondes in the Southern Ocean in the austral summer of 2016/2017 during the Antarctic Circumnavigation Expedition. (Version 1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4382460</p>
Supraglacial features of debris covered glaciers in the Himalaya from Landsat-8 spectral umixing and Pleiades
<p>This dataset contains the spectral unmixing output files for the debris covered glacier surfaces based on Landsat-8 OLI imagery and Pleiades imagery of 2015. Files are provided for two domains, the Khumbu reference region of Nepal and the greater Himalaya region (76.3 to 92.6° W and 26.3 to 34.2° N), which covers covering most area from Himachal/Jammu and Kashmir border to Bhutan Himalaya. </p> <ul> <li>Landsat surface reflectance : Himalaya_L8_6S_surface_reflectance_scenes_2015 .zip <ul> <li>Contains surface reflectance images of Landsat-8 OLI scenes mostly from 2015 (two images are from 2014 and 2016 due to clouds in 2015) </li> <li>Collection 1 Level 1 (L1TP)</li> <li>Atmospherically and topographically corrected using the ARCSI routine, supplied in .kea format. These can be converted to GeoTifs using the GDAL command.</li> <li>Naming structure: LS8_yyyymmdd_latYYlongXXXX_rRRpPPP_vmsk_topshad_rad_srefdem_stdsref.kea</li> <li>Projection is UTM (zones depending on the image), from the original Landsat L1TP files</li> <li>The file naming convention, which is a standard output from ARCSI routine, include the image date ("yyyy" = year, mm = "month", "dd" = day), latitude ("YY") and longitude ("XXXX") of the image center, path/row ("PPP" = path, "RRR" = row), and the output products generated by ARCSI ("rad" = radiation, "topshad" = topographic shadows, "srefdem" indicates the use of elevation data, "stdsref" = standardized surface reflectance)</li> </ul> </li> <li>Fractional maps for the Khumbu: LS8_20150930_r41p140_frac_files. zip <ul> <li>Raster format (GeoTiffs) </li> <li>Non-normalized fractional water, light and dark debris and vegetation maps for the Khumbu reference image (Sept 30, 2015, path 140 row 40)</li> <li>Output from the linear mixing model routine used to produce binary maps of surfaces with values ranging from 0 to 1 (0% to 100% pixel coverage)</li> </ul> </li> <li>Binary surface maps for the Himalaya: Himalaya_L8_raw_binary_surface_maps.zip <ul> <li>Vector format (ArcGIS shapefiles)</li> <li>Raw, unprocessed binary maps of ponds, vegetation debris, ice and clouds over the debris covered glacier tongues in the Himalaya around the year 2015 (binary files) </li> <li>Derived from tresholding the fractional maps using a variable threshold (see publication)</li> <li>Maps in this pre-release version have not been manually corrected for misclassified areas due to confusion of classes, and the ice and cloud classes are not highly accurate</li> <li>These are not the final coverages of these surfaces over the domain and should not be used as such</li> <li>The supraglacial pond maps will undergo manual corrections and the datasets will be updated on this page</li> </ul> </li> <li>Dataset for analysis, glacier-by-glacier: Himalaya_SDC_LS_for_analysis_gt1km2_with_frac_and_debris_attributes.txt <ul> <li>original data from the SupraGlacial Debris Cover dataset (Sherler et al 2018)</li> <li>updated with the preliminary fractional cover of each surface (in %) on a glacier-by-glacier basis</li> <li>contains only debris covered tongues >1 km2 </li> <li>debris covered attributes were calculated from the ALOS Global Digital Surface Model (AW3D30 DEM) for each debris covered tongue <ul> <li>DC_area_km2 = recalculated debris covered area</li> <li>DCmin = minimum debris cover elevation (meters)</li> <li>DCmax = maximum debris cover elevation (meters)</li> <li>DCrange = altitudinal range (meters)</li> <li>DCmed = median elevation (meters)</li> <li>SLmean = mean slope (degrees)</li> <li>SLrange = slope range (degrees)</li> <li>SLmin = min slope (degrees)</li> <li>SLmax = max slope (degrees)</li> </ul> </li> </ul> </li> </ul>
Dataset to Manuscript: Key drivers of pyrogenic carbon redistribution during a simulated rainfall event, Bellè et al. 2021 (Biogeosciences)
<p>Dataset to manuscript: Bellè, S-L., Berhe, A., Hagedorn, F., Santin, C., Schiedung, M., van Meerveld, I. and Abiven, S.: Key drivers of pyrogenic carbon redistribution during a simulated rainfall event, Biogeosciences, https://doi.org/10.5194/bg-2020-361, 2021. </p> <p>All parameters and variables are described in the "var_names" file.</p>
Monthly aerosol emissions and GHG concentration projections from 2020-2025: modified SSP2-4.5 to account for COVID-19 impacts on sector activity
<p>This repository holds the netcdf files for emissions and concentrations projected by the scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown, projected out for 5 years after 2020. The details of these activity estimates are available from <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>.</p> <p>The methodology behind these calculations is based on <a href="https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/">https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/tree/endof2020</a>, a slight modification of the approach used in <a href="https://zenodo.org/record/3947917#.XxR_qyhKhPZ">https://zenodo.org/record/3947917#.XxR_qyhKhPZ</a> to have a different timeframe. </p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p>
Four-year blip emissions changes due to COVID-19: modified SSP2-4.5 to account for sector activity level
<p>This repository holds the netcdf files for aerosol emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database ( <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown, projected out for 5 years after 2020 before returning to baseline. The details of these activity estimates runs in parallel to those described in <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>, except instead of a 2-year blip, we have done a 4-year blip. Note that it is one year after the blip has finished before things return to baseline.</p> <p>The methodology behind these calculations is based on <a href="https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/">https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/</a>, a slight modification of the approach used in <a href="https://zenodo.org/record/3947917#.XxR_qyhKhPZ">https://zenodo.org/record/3947917#.XxR_qyhKhPZ</a> for aerosols emissions. We present only a single scenario (called 4-year blip, featuring a one year recovery after the end of the 4 years) compared to the baseline.</p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p>
ECOBREED WP4 soybean data related to Randelovic et al. (2020)
<p>Data related to the publication of Randelovic et al. (2020) Agronomy 10, 1108. doi: 10.3390/agronomy10081108. Data include the following files: (a) Excel file with two sheets (2018 & 2019) including trial information (plot allocation) and number of plants per square meter; (b) RGB image of soybean trial 2018 taken at V4 stage (four unfolded trifoliolate leaves); (c) RGB image of soybean trial 2018 taken at R3 stage (beginning pod); (d) RGB image of soybean trial 2019 taken at V4 stage; (e) RGB image of soybean trial 2019 taken at R3 stage. [Growth stages according to Fehr WR, Caviness CE (1977) Stages of soybean development. Iowa State Univ. Cooperative Ext. Serv., Spec. Rep. 80.]</p>
Continental Europe Digital Terrain Model geomorphometry derivatives at 30 m, 100 m and 250 m
<p>Digital Terrain Model geomorphometry derivatives based on the DTM for Continental Europe using the <a href="https://epsg.io/3035">EPSG:3035</a> projection system. Processed using <a href="http://www.saga-gis.org/">SAGA GIS</a>, <a href="https://grass.osgeo.org/grass78/">GRASS 7 GIS</a> and <a href="https://gdal.org/programs/gdaldem.html">GDAL</a> at 3 standard spatial resolutions: 30-m, 100-m and 250-m. Derivatives include:</p> <ul> <li>devmean = deviation from mean value derived using <a href="http://www.saga-gis.org/saga_tool_doc/7.4.0/statistics_grid_1.html">SAGA GIS</a>,</li> <li>downlocal / down = downslope local and general curvature derived using <a href="http://www.saga-gis.org/saga_tool_doc/7.1.1/ta_morphometry_26.html">SAGA GIS</a>,</li> <li>hillshade = hillshading derived using using GDAL <a href="https://gdal.org/programs/gdaldem.html">gdaldem</a> functions,</li> <li>mnr = Module Melton Ruggedness Number derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.2.4/ta_hydrology_23.html">SAGA GIS</a>,</li> <li>northerness/easterness = derived using <a href="https://grass.osgeo.org/grass78/manuals/addons/r.northerness.easterness.html">GRASS 7 GIS</a>,</li> <li>openp / openn = openness positive negative derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.2.5/ta_lighting_5.html">SAGA GIS</a>,</li> <li>slope = slope in percent derived using GDAL <a href="https://gdal.org/programs/gdaldem.html">gdaldem</a> functions,</li> <li>topidx = a topographic index (wetness index) derived using <a href="https://grass.osgeo.org/grass76/manuals/r.topidx.html">GRASS 7 GIS</a>,</li> <li>tpi = Topographic Wetness Index derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.1.3/ta_hydrology_20.html">SAGA GIS</a>,</li> <li>vbf = Multiresolution Index of Valley Bottom Flatness derived using <a href="http://www.saga-gis.org/saga_tool_doc/2.2.6/ta_morphometry_8.html">SAGA GIS</a>,</li> </ul> <p>Detailed processing steps can be found <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers"><strong>here</strong></a>. Read more about the processing steps <a href="https://opendatascience.eu/building-continental-europe-digital-terrain-model-30-m-resolution-using-machine-learning"><strong>here</strong></a>.</p> <p>Derivatives were chosen aiming to support soil and vegetation mapping projects. The slope.percent map at 30-m has been converted from 0-100% scale to 0-200% (Byte format) to help decrease the file size.</p>
Large-scale Ridesharing DARP Instances Based on Real Travel Demand
<p>This repository presents a set of large-scale Dial-a-Ride Problem (DARP) instances. The instances were created as a standardized set of ridesharing DARP problems for the purpose of benchmarking and comparing different solution methods.</p><p>The instances are based on real demand and realistic travel time data from 3 different US cities, Chicago, New York City and Washington, DC. The instances consist of real travel requests from the selected period, positions of vehicles with their capacities and realistic shortest travel times between all pairs of locations in each city.</p><p>The instances and results of two solution methods, the Insertion Heuristic, and the optimal Vehicle-group Assignment method, can be found in the dataset. The dataset and methodology used to create it are described in the paper <a href="https://arxiv.org/abs/2305.18859">Large-scale Ridesharing DARP Instances Based on Real Travel Demand</a>.</p>
Lexical Relations from the Wisdom of the Crowd 1.0
<p>A set of 300 most frequent nouns has been extracted from the Russian National Corpus. Then, each method or resource, including RuThes, produced at most five hypernyms, if possible. In case it is not possible, missing answers treated as empty results. This resulted in 9 322 unique non-empty subsumption pairs that have been passed for crowdsourcing annotation on the Yandex.Toloka microtask platform. Each pair has been annotated by seven different annotators whose mother tongue is Russian and the age is at least 20 by February 1, 2017.</p> <p>The layout of the human intelligence task (HIT) design assumes the direct answer to a simple question: does the given pair of words represent a meaningful <em>is-a</em> relation? Since the crowd workers are not expert lexicographers and this question might be difficult for them, it has been rephrased as “Is it correct that a <em>kitten</em> is a kind of <em>mammal</em>?” (in Russian).</p> <p>The answers have been aggregated using the Yandex.Toloka proprietary answer aggregation mechanism. As the result, 3 940 out of 9 322 pairs have been annotated as positive while the rest 5 382 have been annotated as negative.</p> <p>Interestingly, the workers were more confident in negative answers rather than in the positive ones. These negative answers are extremely useful for both training and testing different relation extraction methods. To the best of our knowledge, this is the first dataset of this kind made for the Russian language using microtask-based crowdsourcing.</p>
InnoVine WP3: 105 phenolic compound quantification of 2014 and 2015 mature grape berries from a core-collection of 279 irrigated and non-irrigated Vitis vinifera cultivars
<p>FP7/311775 InnoVine (Innovation in vineyard): Combining innovation in vineyard management and genetic diversity for a sustainable European viticulture</p> <p>WP3: Exploiting the genetic diversity in grapevine</p> <p>105 phenolic or related compounds, from 2014 and 2015 mature grape berries from a core-collection of 279 irrigated and non-irrigated <em>Vitis vinifera</em> cultivars, were quantified by UPLC-TQ-MRM Mass Spectrometry (Lambert M<em> et al., Molecules</em> <strong>2015</strong>, <em>20</em>(5), 7890-7914; doi:10.3390/molecules20057890 & Pinasseau L <em>et al.</em>, <em>Molecules</em> <strong>2016</strong>, <em>21</em>(10), 1409; doi:10.3390/molecules21101409).</p> <p>3 parameters were added:<br> - water/drought status (delta C13)<br> - sugar content (refractive index, brix degree)<br> - weight of 100 grape berries</p> <p>All plant material was collected at the Vassal repository: French National Grapevine Germplasm Collection, INRA Domaine de Vassal, 34340 Marseillan-Plage, France (Centre de Ressources Biologiques de la Vigne (CRB-Vigne) de Vassal-Montpellier).</p>
Updated DEVOTES indicator catalogue of MSFD indicator systems targeting descriptors D1, D2, D4, and D6
<p>This is version 8 of the Catalogue of Indicators of the FP7 project DEVOTES (grant number 308392) that aims at supporting the implementation of the EU MSFD. This catalogue of indicators is an inventory of existing methods. The metadata have been updated and extended since deiverable D3-1 of the DEVOTES project. You can learn more about the DEVOTES project at: http://www.devotes-project.eu</p> <p>All data are provided without a guarantee of correctness or completeness. We are aware of some errors in the database content and are continuously working on correcting these and on supplementing the content with new metadata.</p> <p>The data can be best viewed with the free DEVOTool software, available at http://www.devotes-project.eu/devotool. By using the DEVOTool software, you accept the license conditions as outlined in section 6 of the software manual distributed together with DEVOTool. </p>
ChinaHighNO₂: Daily Seamless 1 km Ground-Level NO₂ Dataset for China (2019–Present)
<p>ChinaHighNO<sub>2</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level NO<sub>2</sub> dataset for China <strong>from 2019 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.93, a root-mean-square error (RMSE) of 4.89 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 3.48 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighNO<sub>2</sub> dataset in your scientific research, please cite the following references (Wei et al., EST, 2022; Wei et al., ACP, 2023):</p> <ul> <li> <p>Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R., Dickerson, R., Lin, J., Boersma, K., Sun, L., Li, R., Xue, W., Cui, Y., Zhang, C., and Wang, J. <a href="https://weijing-rs.github.io/publications/Wei_et_al-EST-2022.pdf">Ground-level NO<sub>2</sub> surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence</a>. <em>Environmental Science & Technology</em>, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834</p> </li> <li> <p>Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2023.pdf">Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations</a>. <em>Atmospheric Chemistry and Physics</em>, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023</p> </li> </ul> <p><strong>Note that the ChinaHighNO<sub>2 </sub>dataset is also available for periods prior to 2019, but at a spatial resolution of 10 km:</strong></p> <p> all (including <strong>daily</strong>) data for the years <strong>2008–2018 </strong>is accessible at: <strong><a href="https://doi.org/10.5281/zenodo.4641542">https://doi.org/10.5281/zenodo.4641542</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
Global monthly catches from tuna surface fisheries by 1° grid (1958-2023) (FIRMS level 0)
<p>We compiled a comprehensive dataset of geo-referenced catches from global tuna fisheries that use fishing gears set at the water's surface. This dataset was created by harmonizing public domain data from the five tuna Regional Fisheries Management Organizations (t-RFMOs) for the period 1958-2023. Under the auspices of the Fisheries and Resources Monitoring System (FIRMS) of the United Nations Food and Agriculture Organization (FAO), we developed a systematic data flow process in collaboration with the t-RFMO Secretariats. This process involved the implementation of a data exchange format adhering to the standards of the FAO Coordinating Working Party on Fishery Statistics (CWP), facilitating the seamless integration of data into the dataset.</p> <p>Geo-referenced catch data from tuna surface fisheries are reported in either the number of fish or live-weight equivalent (metric tonnes), with some strata providing catches in both units. The catches primarily represent the quantities of retained fish either landed or transhipped at sea and in ports. The data are stratified by year, month, fishing fleet, fishing gear, fishing mode, 1° grid area of longitude and latitude, and taxon.</p> <p>The dataset encompasses 42 medium- and large-sized pelagic species found in both neritic and oceanic habitats of the world's oceans. This includes 14 species of tunas, 9 species of billfish, 4 species of Spanish mackerels, 2 species of bonitos, and wahoo. Despite uncertainties and incomplete data due to under-reporting, the dataset also includes reported catches for 12 species of pelagic sharks and rays that may be either targeted or incidentally caught in tuna and tuna-like fisheries.</p> <p>The dataset serves as a benchmark for the monitoring and assessment of both artisanal and industrial fisheries using surrounding nets, gillnets, entangling nets, and pole-and-lines from over 70 fishing fleets across 69 countries that have exploited tuna and tuna-like species for subsistence and commercial purposes over more than six decades.</p>
Inanspruchnahme von Routineimpfungen in Deutschland - Ergebnisse aus der KV-Impfsurveillance
<p>Dem Robert Koch-Institut (RKI) obliegt die Aufgabe, Daten zur Inanspruchnahme von Schutzimpfungen in der Bevölkerung in Deutschland zu erheben, aufzubereiten und national wie international zu berichten.<br> Die wichtigste Datenquelle zur Berechnung von Impfquoten stellen die vertragsärztlichen Abrechnungsdaten dar, die von den Kassenärztlichen Vereinigungen (KVen) im Rahmen der "KV-Impfsurveillance" (KVIS) an das RKI übermittelt werden. Begonnen als Gemeinschaftsprojekt mit den KVen im Jahr 2004, ist die KVIS seit dem Jahr 2020 im Infektionsschutzgesetz (IfSG) gesetzlich verankert (<a href="https://www.gesetze-im-internet.de/ifsg/__13.html">§13 (5) IfSG</a>).<br> Neben der jährlichen Berichterstattung zu aktuellen Impfquoten im <a href="https://www.rki.de/DE/Content/Infekt/EpidBull/epid_bull_node.html">Epidemiologischen Bulletin</a>, ergänzt <a href="https://www.rki.de/vacmap">VacMap</a> als interaktives Dashboard die Kommunikation der Impfquoten in Deutschland und ermöglicht die Nachnutzung der Daten durch Akteure der Impfprävention. Anhand der Darstellung der Impfquoten nach Altersgruppen, im Zeitverlauf und auf regionaler Ebene können Defizite in der Umsetzung der Impfempfehlungen identifiziert und in der Folge zielgruppenspezifisch adressiert werden.</p>
Storm Database Files for CLIMK–WINDS: A New Database of Extreme European Winter Windstorms
<p>This database is comprised of the four netCDF files containing the 50 most extreme European winter windstorms identified within the four input sources, with one netCDF file per source: ERA5 reanalysis, CCLM_ERA5_EUR-11 regional climate model simulation, COSMO-REA6 reanalysis, and CCLM_ERA5_CEU-3 regional climate model. This database was created by Clare Marie Flynn and its creation is described in the following paper: Flynn, C. M., Moemken, J., Pinto, J., Schutte, M., and Messori, G.: CLIMK–WINDS: A New Database of Extreme European Winter Windstorms, under review for final submission, Earth System Science Data, 2025.</p>
SWOT River Database (SWORD)
<p><strong>VERSION NOTES:</strong></p> <p><strong>v17 versus v17b</strong></p> <ul> <li>"Type" change for 1662 reaches and associated nodes globally. Please reference the Product Description Document for the "Type" identifier definition. </li> <li>Updates to reach and node lengths and distance-from-outlet variable to correct a bug in the node length calculation in select reaches (<2% of reaches were impacted globally).</li> <li>SWORD v17b is the official version for SWOT <strong>Version D</strong> <a href="https://podaac.jpl.nasa.gov/SWOT?tab=datasets-information&sections=about"><strong>RiverSP Vector Products</strong></a>.</li> </ul> <p>The project and public versions of SWORD were kept separate while algorithms were being developed in preparation for SWOT's launch in 2022. Now that the SWOT mission is here, the project version of SWORD is published as the public version which is why the version numbers jump after v2. The primary difference between the project and public versions of SWORD are extra "filler" variables in the NetCDF format that will be used for calculating discharge. For details on the filler variables please reference the Product Description Document provided with the downloads. </p> <p>If you use the SWORD Database in your work, please cite: Altenau et al., (2021) The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A Global River Network for Satellite Data Products. <em>Water Resources Research</em>. <a href="https://doi.org/10.1029/2021WR030054">https://doi.org/10.1029/2021WR030054</a></p> <p>You can also visit <a href="http://www.swordexplorer.com"><strong>www.swordexplorer.com</strong></a> to explore the current version of SWORD before downloading. </p> <p><strong>1. Summary:</strong></p> <p>The Surface Water and Ocean Topography (SWOT) satellite mission vastly expands observations of river water surface elevation (WSE), width, and slope. In order to facilitate a wide range of new analyses with flexibility, the SWOT mission provides a range of relevant data products. One product the SWOT mission provides are river vector products stored in shapefile format for each SWOT overpass (JPL Internal Document, 2020b). The <strong>SWO</strong>t <strong>R</strong>iver <strong>D</strong>atabase (<strong>SWORD</strong>) combines multiple global river- and satellite-related datasets to define the nodes and reaches that constitute SWOT river vector data products. SWORD provides high-resolution river nodes (200 m) and reaches (~10 km) in shapefile and netCDF formats with attached hydrologic variables (WSE, width, slope, etc.) as well as a consistent topological system for global rivers 30 m wide and greater.</p> <p><strong>2. Data Formats:</strong></p> <p>The SWORD database is provided in netCDF, geopackage, and shapefile formats. All files start with a two-digit continent identifier ("af" – Africa, "as" – Asia / Siberia, "eu" – Europe / Middle East, "na" – North America, "oc" – Oceania, "sa" – South America). File syntax denotes the regional information for each file and varies slightly between netCDF and shapefile formats.</p> <p>NetCDF files are structured in 3 groups: centerlines, nodes, and reaches. The centerline group contains location information and associated reach and node ids along the original GRWL 30 m centerlines (Allen and Pavelsky, 2018). Node and reach groups contain hydrologic attributes at the ~200 m node and ~10 km reach locations (see description of attributes below). NetCDFs are distributed at continental scales with a filename convention as follows: [continent]_sword_v17.nc (<em>i.e. na_sword_v17.nc</em>).</p> <p>SWORD shapefiles consist of four main files (.dbf, .prj, .shp, .shx). There are separate shapefiles for nodes and reaches, where nodes are represented as ~200 m spaced points and reaches are represented as polylines. All shapefiles are in geographic (latitude/longitude) projection, referenced to datum WGS84. Shapefiles are split into HydroBASINS (Lehner and Grill, 2013) Pfafstetter level 2 basins (hbXX) for each continent with a naming convention as follows: [continent]_sword_[nodes/reaches]_hb[XX]_v17.shp (<em>i.e. na_sword_nodes_hb74_v17.shp; na_sword_reaches_hb74_v17.shp</em>).</p> <p>SWORD geopackage files are split into two files for nodes and reaches per continental region, where nodes are represented as 200 m spaced points and reaches are represented as polylines. All geopackage files are in geographic (latitude/longitude) projection, referenced to datum WGS84. Geopackage file names are distributed at continental scales and are defined by a two-digit identifier (Table 2): [continent]_sword_[nodes/reaches]_v17.gpkg (i.e. na_sword_nodes_v17.gpkg; na_sword_reaches_v17.gpkg).</p> <p><strong>3. Attribute Description:</strong></p> <p>This list contains the primary attributes contained in the SWORD database.</p> <ul> <li><strong>x:</strong> Longitude of the node or reach ranging from 180°E to 180°W (units: decimal degrees).</li> <li><strong>y:</strong> Latitude of the node or reach ranging from 90°S to 90°N (units: decimal degrees).</li> <li><strong>node_id:</strong> ID of each node. The format of the id is as follows: CBBBBBRRRRNNNT where C = Continent (the first number of the Pfafstetter basin code), B = Remaining Pfafstetter basin code up to level 6, R = Reach number (assigned sequentially within a level 6 basin starting at the downstream end working upstream), N = Node number (assigned sequentially within a reach starting at the downstream end working upstream), T = Type (1 – river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost node).</li> <li><strong>node_length </strong><em>(node files only</em>): Node length measured along the GRWL centerline points (units: meters).</li> <li><strong>reach_id:</strong> ID of each reach. The format of the id is as follows: CBBBBBRRRRT where C = Continent (the first number of the Pfafstetter basin code), B = Remaining Pfafstetter basin codes up to level 6, R = Reach number (assigned sequentially within a level 6 basin starting at the downstream end working upstream, T = Type (1 – river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost reach).</li> <li><strong>reach_length </strong>(<em>reach files only</em>): Reach length measured along the GRWL centerline points (units: meters).</li> <li><strong>wse:</strong> Average water surface elevation (WSE) value for a node or reach. WSEs are extracted from the MERIT Hydro dataset (Yamazaki et al., 2019) and referenced to the EGM96 geoid (units: meters).</li> <li><strong>wse_var:</strong> WSE variance along the GRWL centerline points used to calculate the average WSE for each node or reach (units: square meters).</li> <li><strong>width:</strong> Average width for a node or reach (units: meters).</li> <li><strong>width_var:</strong> Width variance along the GRWL centerline points used to calculate the average width for each node or reach (units: square meters).</li> <li><strong>max_width: </strong>Maximum width value across the channel for each node or reach that includes island and bar areas (units: meters).</li> <li><strong>facc: </strong>Maximum flow accumulation value for a node or reach. Flow accumulation values are extracted from the MERIT Hydro dataset (Yamazaki et al., 2019) (units: square kilometers).</li> <li><strong>n_chan_max:</strong> Maximum number of channels for each node or reach.</li> <li><strong>n_chan_mod:</strong> Mode of the number of channels for each node or reach.</li> <li><strong>obstr_type: </strong>Type of obstruction for each node or reach based on the Globale Obstruction Database (GROD, Whittemore et al., 2020) and HydroFALLS data (http://wp.geog.mcgill.ca/hydrolab/hydrofalls). Obstr_type values: 0 - No Dam, 1 - Dam, 2 - Channel Dam, 3 - Lock, 4 - Low Permeable Dam, 5 - Waterfall.</li> <li><strong>grod_id:</strong> The unique GROD ID for each node or reach with obstr_type values 1-4.</li> <li><strong>hfalls_id:</strong> The unique HydroFALLS ID for each node or reach with obstr_type value 5.</li> <li><strong>dist_out:</strong> Distance from the river outlet for each node or reach (units: meters).</li> <li><strong>type:</strong> Type identifier for a node or reach: 1 – river, 2 – lake off river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost reach/node.</li> <li><strong>lakeflag</strong>: GRWL water body identifier for each reach: 0 – river, 1 – lake/reservoir, 2 – canal, 3 – tidally influenced river.</li> <li><strong>manual_add </strong>(<em>node files only</em>): Binary flag indicating whether the node was manually added to the public GRWL centerlines (Allen and Pavelsky, 2018). These nodes were originally given a width = 1, but have since been updated to have the reach width values.</li> <li><strong>meand_len </strong>(<em>node files only</em>): Length of the meander that a node belongs to, measured from beginning of the meander to its end in meters. For nodes longer than one meander, the meander length will represent the average length of all meanders belonging to the node (units: meters).</li> <li><strong>sinuosity </strong>(<em>node files only</em>): The total reach length the node belongs to divided by the Euclidean distance between the reach end points.</li> <li><strong>slope </strong>(<em>reach files only</em>): Reach average slope calculated along the GRWL centerline points. Slopes are calculated using a linear regression (units: meters/kilometer).</li> <li><strong>n_nodes</strong> (<em>reach files only</em>): Number of nodes associated with each reach.</li> <li><strong>n_rch_up</strong> (<em>reach files only</em>): Number of upstream reaches for each reach.</li> <li><strong>n_rch_down</strong> (<em>reach files only</em>): Number of downstream reaches for each reach.</li> <li><strong>rch_id_up</strong> (<em>reach files only</em>): Reach IDs of the upstream neighboring reaches.</li> <li><strong>rch_id_dn</strong> (<em>reach files only</em>): Reach IDs of the downstream neighboring reaches.</li> <li><strong>swot_obs </strong>(<em>reach files only</em>): The maximum number of SWOT passes to intersect each reach during the 21 day orbit cycle.</li> <li><strong>swot_orbits </strong>(<em>reach files only</em>): A list of the SWOT orbit tracks that intersect each reach during the 21 day orbit cycle.</li> <li><strong>river_name:</strong> All river names associated with a node or reach. If there are multiple names for a node or reach they are listed in alphabetical order and separated by a semicolon.</li> <li><strong>edit_flag:</strong> Numerical flag indicating the type of update applied to SWORD nodes or reaches from the previous version. Flag descriptions are listed in the Product Description Documentation included with the file downloads.</li> <li><strong>trib_flag: </strong>Binary flag indicating if a large tributary not represented in SWORD is entering a node or reach. 0 - no tributary, 1 - tributary.</li> </ul> <p><strong>4. References:</strong></p> <p>Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. <em>Science</em>, 361(6402), 585-588.</p> <p>Altenau, E. H., Pavelsky, T. M., Durand, M. T., Yang X., Frasson, R. P. d. M., & Bendezu, L. (2021). The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A global river network for satellite data products". Water Resources Research.</p> <p>Biancamaria, S., Lettenmaier, D. P., & Pavelsky, T. M. (2016). The SWOT mission and its capabilities for land hydrology. In Remote Sensing and Water Resources (pp. 117-147). Springer, Cham.</p> <p>JPL Internal Document (2020b). Surface Water and Ocean Topography Mission Level 2 KaRIn high rate river single pass vector product, JPL D-56413, Rev. A, https://podaac-tools.jpl.nasa.gov/drive/files/misc/web/misc/swot_mission_docs/pdd/D-56413_SWOT_Product_Description_L2_HR_RiverSP_20200825a.pdf</p> <p>Lehner, B., Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems. Hydrological Processes, 27(15): 2171–2186. Data is available at www.hydrosheds.org.</p> <p>Tessler, Z. D., Vörösmarty, C. J., Grossberg, M., Gladkova, I., Aizenman, H., Syvitski, J. P. M., & Foufoula-Georgiou, E. (2015). Profiling risk and sustainability in coastal deltas of the world. Science, 349(6248), 638-643.</p> <p>Whittemore, A., Ross, M. R., Dolan, W., Langhorst, T., Yang, X., Pawar, S., Jorissen, M., Lawton, E., Januchowski-Hartley, S., & Pavelsky, T. (2020). A Participatory Science Approach to Expanding Instream Infrastructure Inventories. <em>Earth's Future</em>, <em>8</em>(11), e2020EF001558.</p> <p>Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G., & Pavelsky, T. (2019). MERIT Hydro: A high-resolution global hydrography map based on latest topography datasets. Water Resources Research. <a href="https://doi.org/10.1029/2019WR024873">https://doi.org/10.1029/2019WR024873</a>.</p> <p>Yang, X., Pavelsky, T. M., Allen, G. H. (2019). The past and future of global river ice. Nature.</p> <p>SWOT Orbits: https://www.aviso.altimetry.fr/en/missions/future-missions/swot/orbit.html</p> <p>HydroFALLS: <a href="http://wp.geog.mcgill.ca/hydrolab/hydrofalls/">http://wp.geog.mcgill.ca/hydrolab/hydrofalls/</a></p>
Global annual catches from tuna fisheries (1918-2023) (FIRMS level 0)
<p>We constructed the most comprehensive dataset of nominal catches from global tuna fisheries by compiling and harmonizing public domain data from the five tuna Regional Fisheries Management Organizations (t-RFMOs) for the period 1918-2023. Under the auspices of the Fisheries and Resources Monitoring System (FIRMS) of the United Nations Food and Agriculture Organization (FAO),we developed a systematic data flow process in collaboration with the t-RFMO Secretariats. This process involved the implementation of a data exchange format adhering to the standards of the FAO Coordinating Working Party on Fishery Statistics (CWP),facilitating the seamless integration of data into the dataset.<br><br>Nominal catch data are expressed in live-weight equivalent (metric tonnes) and primarily represent the quantities of retained fish either landed or transhipped at sea and in ports. In recent years,data from fisheries in the Atlantic and Western-Central Pacific Oceans have partially included amounts of fish discarded dead. The data are stratified by year,fishing fleet,fishing gear,large spatial area,and taxon.<br><br>The dataset encompasses 50 medium- and large-sized pelagic species found in both neritic and oceanic habitats of the world's oceans. This includes 15 species of tunas,10 species of billfish,8 species of Spanish mackerels,2 species of bonitos,and wahoo. In 2023,the global catch for these species was estimated to exceed 6.4 million metric tonnes. Despite uncertainties and incomplete data due to under-reporting,the dataset also includes reported catches for 14 species of pelagic sharks and rays that may be either targeted or incidentally caught in tuna and tuna-like fisheries. The total reported catch of these elasmobranch species was approximately 154,000 metric tonnes in 2023.<br><br>The dataset serves as a benchmark for the monitoring and assessment of both artisanal and industrial fisheries from over 161 fishing fleets across 159 countries that have exploited tuna and tuna-like species for subsistence and commercial purposes over more than seven decades.</p>
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