cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5880/gfz.3.1.2020.004
title: Chemical and Isotopic Composition of Gas in Salt Beds of a Potash Mine

description: In eastern Hesse and western Thuringia, Germany hosts significant potassium-bearing salt deposits industrially excavated in the Werra-Fulda mining district. The salt belongs to the upper Permian (Zechstein) and was deposited around 258 to 252.5 Ma ago. In the Werra-Fulda mining district, the halite rocks (Werra-Rocksalt, z1NA) contain two minable potash seams, potash Seam Thüringen (z1KTh) and potash Seam Hessen (z1KHe), with an average thickness between 2 m and 3 m (Figure 1). To investigate the chemical and isotopic composition of the gas phase of Seam Hessen, gaseous samples were collected from five, 2-meter deep, horizontal boreholes drilled in the potash horizon which is mined at a depth of 540 m. About 4 weeks in advance of the gas sampling, the packer-closed boreholes were evacuated to about 3kPa and the pressure gradient inside the holes was continuously monitored in the boreholes D1 to D5. Selected gas samples were analyzed for their noble gas isotopic composition and the δ13C values of CO2 and CH4. The noble gas isotopic compositions were determined using a VG 5400 noble gas mass spectrometer after purification in a preparation line. The carbon isotope compositions were analysed with a GC-IRMS, comprising a GC 6890N connected to a GC-C/TC III combustion device and coupled to a MAT 253 mass spectrometer. The standard deviation of the δ13C values (in ‰ vs. VPDB) is ± 0.5 ‰. The data are provided as one, Excel file with three spreadsheets as well as three individual, tab-delimited text files.
DOI: 10.15468/dl.uwgvsz
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Amblyosyllis lineata",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044481-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5880/gfz.2.3.2016.001
title: Geomagnetic Observatory St. Helena, preliminary hourly mean values 2010, XYZ components

description: The data set contains preliminary hourly mean values of the geomagnetic observatory St. Helena for 2010. The hourly means are calculated from minute means, which are based on 2 Hz raw data from an observatory grade, three component fluxgate magnetometer of type DTU FGE. The raw data is calibrated by means of weekly absolute measurements of the geomagnetic field at the observatory main pillar. The data set is provisional. Specifically, the data is visually controlled, but the applied baselines and formulas for calibration are not yet adopted in their final form. We expect the present preliminary data to be within a few nT of the final data that will eventually be distributed through the World Data Centres and INTERMAGNET.The data file is in ASCII format and contains blank separated first the month, day and hour of the time stamp of the data in the format MM DD HH followed by the hourly mean value of the geomagnetic field components X (North), Y (East) and Z (down) in nanotesla (nT). The time stamp is given for the beginning of the hour, for which the mean value of the field component is given.Geomagnetic observatories are described in e.g. Jankowski and Sucksdorf (1996). The St. Helena geomagnetic observatory was established by GFZ in 2007 and is operated by GFZ since. The network of geomagnetic observatories operated by GFZ and the observatories' instrumentation are described in Matzka (2016, http://doi.org/10.17815/jlsrf-2-136). Jankowski, J., and C. Sucksdorff, 1996, Guide for Magnetic Measurements and Observatory Practice, International Association of Geomagnetism and Aeronomy, Warsaw, 235 pp. ISBN 0-9650686-2-5, http://www.iaga-aiga.org/data/uploads/pdf/guides/iaga-guide-observatories.pdf
DOI: 10.5880/gfz.1.4.2020.003
title: Background correction for EOG VIIRS-DNB monthly composites (data and scripts)

description: The datasets contain low resolution image data [1] that can be used to approximately remove natural light from the monthly composite nighttime images produced by the Earth Observation Group (EOG) using the Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) [2]. Natural light includes airglow and reflected light from stars, as well as polar light at high latitudes. The low-resolution images can be expanded and then subtracted from the EOG monthly composites, using python scripts included in this data publication [1]. More details are provided in the data description and in Coesfeld et al. (2020). The background correction is based on the VIIRS-DNB monthly composites data from April 2012 to December 2019 without stray light correction produced by the EOG, and the associated file containing the number of cloud-free observations (Earth Observation Group, 2015). Locations away from human settlements and artificial lights were selected using the Global Human Settlement Layer (GHSL) population density map from 2015 [3] and the VIIRS-DNB 2015 vcm-orm annual composite by the EOG (Earth Observation Group, 2015). The python script we used in Coesfeld et al. (2020) to select locations on an equally spaced grid over the global DNB extent (from 75N to 65S) is included. The six images of the EOG composite are processed separately. This code identifies locations on a 72x28 grid (2016 locations total). In order to optimize the location of the specified grid points, the Global Human Settlement Layer (GHSL) population density and VIIRS-DNB annual composite of 2015 are consulted.
DOI: 10.5880/pik.2018.012
title: A global gridded data set on tillage

description: Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on type and spatial distribution. This dataset is the result of a study on global classification of tillage practices and the spatially explicit mapping of crop-specific tillage systems for around the year 2005.
      This global gridded tillage system data set is dedicated to modeling communities interested in the quantitative assessment of biophysical and biogeochemical impacts of land use and soil management on cropland. The data set is complemented by the publication of the R- code and can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as Conservation Agriculture (Porwollik et al. 2018, http://doi.org/10.5880/PIK.2018.013). Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD).
      We present the mapping result of six tillage systems for 42 crop types and potential suitable Conservation Agriculture area as the following variables:
      We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = potential suitable Conservation Agriculture area
      Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF
      Dataset sources (with indication of reference):
      1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. Water erosion: Nachtergaele et al. (2011)
description: This tillage dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/.
DOI: 10.6084/m9.figshare.28151570.v1
title: The Trichonectriaceae multi-gene matrix (nITS+LSU+<i>tef1</i>+<i>rpb1</i><i>+rpb2</i>)

description: The Trichonectriaceae multi-gene matrix (nITS+LSU+<i>tef1</i>+<i>rpb1</i><i>+rpb2</i>) included target lichenicolous species (48 specimens of 26 species), as well as three species of the Niessliaceae were selected as an outgroup.
DOI: 10.6084/m9.figshare.28151570
title: The Trichonectriaceae multi-gene matrix (nITS+LSU+<i>tef1</i>+<i>rpb1</i><i>+rpb2</i>)

description: The Trichonectriaceae multi-gene matrix (nITS+LSU+<i>tef1</i>+<i>rpb1</i><i>+rpb2</i>) included target lichenicolous species (48 specimens of 26 species), as well as three species of the Niessliaceae were selected as an outgroup.
DOI: 10.15468/dl.mxmtdd
title: Occurrence Download

description: A dataset containing 2009468 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-71.20456 46.90862, -71.21722 46.90862, -71.20941 46.89928, -71.22206 46.89928, -71.23471 46.89928, -71.22691 46.88995, -71.27751 46.88995, -71.28532 46.89928, -71.32327 46.89928, -71.31546 46.88995, -71.3281 46.88995, -71.3203 46.88061, -71.33294 46.88061, -71.34559 46.88061, -71.33778 46.87128, -71.35043 46.87128, -71.36308 46.87128, -71.35527 46.86195, -71.36791 46.86195, -71.38056 46.86195, -71.37275 46.85261, -71.41068 46.85261, -71.41849 46.86195, -71.43114 46.86195, -71.42333 46.85261, -71.43597 46.85261, -71.42816 46.84328, -71.41552 46.84328, -71.40772 46.83395, -71.39508 46.83395, -71.37948 46.81529, -71.39212 46.81529, -71.36876 46.7873, -71.38139 46.7873, -71.37361 46.77797, -71.38625 46.77797, -71.37847 46.76864, -71.3911 46.76864, -71.37555 46.74999, -71.38818 46.74999, -71.38041 46.74066, -71.39304 46.74066, -71.38527 46.73134, -71.34739 46.73134, -71.35516 46.74066, -71.3299 46.74066, -71.32214 46.73134, -71.30952 46.73134, -71.30176 46.72201, -71.28914 46.72201, -71.29689 46.73134, -71.28427 46.73134, -71.29202 46.74066, -71.22889 46.74066, -71.21626 46.74066, -71.23953 46.76864, -71.22689 46.76864, -71.23466 46.77797, -71.20939 46.77797, -71.20163 46.76864, -71.17637 46.76864, -71.18413 46.77797, -71.17149 46.77797, -71.15886 46.77797, -71.16662 46.7873, -71.15398 46.7873, -71.16174 46.79663, -71.14911 46.79663, -71.15687 46.80596, -71.14423 46.80596, -71.152 46.81529, -71.12672 46.81529, -71.11409 46.81529, -71.12185 46.82462, -71.13449 46.82462, -71.14226 46.83395, -71.1549 46.83395, -71.17046 46.85261, -71.15781 46.85261, -71.16559 46.86195, -71.15295 46.86195, -71.1403 46.86195, -71.14809 46.87128, -71.13544 46.87128, -71.15881 46.89928, -71.17146 46.89928, -71.17926 46.90862, -71.19191 46.90862, -71.19972 46.91796, -71.21237 46.91796, -71.20456 46.90862))",
 "Year is greater than or equal to 2010",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 2009468 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044432-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17873894
title: Dataset for "High-resolution photoelectron spectroscopy and photodetachment rotational simulations of SeH- and TeH-"
DOI: 10.5880/pik.2018.006
title: ISIMIP2a Simulation Data from Permafrost Sector

description: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors.
      ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This may serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming.
      The focus topic for ISIMIP2a is model evaluation and validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to the input data for the impact models is provided through a central ISIMIP archive (see ISIMIP 2a Input Data & Bias Correction at https://www.isimip.org/gettingstarted/#input-data-bias-correction).
      This entry refers to the ISIMIP2a simulation data from permafrost models: JULES-B1 (formerly JULES_UoE), LPJmL, IAPRAS-DSS.
description: The ISIMIP2a Permafrost outputs are based on simulations from 3 permafrost models (see listing) according to the ISIMIP2a Simulation Protocol (https://www.isimip.org/protocol/#isimip2a). The models simulate coupled water and carbon processes, like the soil carbon storage on permafrost soils, non-linear effects in changing vegetation and fire, and the physical state of the permafrost based on soil, climate and physio-geographical information. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
DOI: 10.5281/zenodo.17873893
title: Dataset for "High-resolution photoelectron spectroscopy and photodetachment rotational simulations of SeH- and TeH-"
DOI: 10.5880/gfz.3.3.2018.002
title: Data supplement to: Laser ablation in situ silicon stable isotope analysis of phytoliths

description: Silicon is a beneficial element for many plants, and is deposited in plant tissue as amorphous bio-opal (phytoliths). The biochemical processes of uptake and precipitation induce isotope fractionation: the mass-dependent shift in the relative abundances of the stable isotopes of silicon. At the bulk scale, the silicon isotope composition reported as δ30Si span from -2 to +6 ‰. To further constrain these variations, at the scale of individual phytolith fragments we applied in situ femtosecond laser ablation multicollector inductively coupled plasma mass spectrometry (fsLA-MC-ICP-MS) to a set of 7 natural phytolith samples.
      Two phytoliths samples (Norway spruce Picea abies and European beech Fagus sylvatica L.) were extracted from the organic-rich topsoil horizon (O) of two studies sites in Germany (Beerenbusch, close to village Rheinsberg and Wildmooswald, in the southern Black Forest). The other five phytolith samples (bushgrass Calamagrostis epigejos, common reed Phragmites australis, common horsetail Equisetum arvense, annual and perennial rough horsetail Equisetum hyemale) were separated from plant materials.
      The individual phytolith fragments were analysed by fsLA-MC-ICP-MS and Si isotope results are reported in the δ-notation (delta) as permil deviation relative to NIST SRM610, which is isotopically indistinguishable from the reference material NBS28 (quartz NIST SRM8546 alias NBS28, δ29Si ≡ 0 and δ30Si ≡ 0). Raw data processing and background corrections were made according to the protocol described in Schuessler and von Blanckenburg (2014) that also involves application of several data rejection/acceptance criteria. Of these, the most important ones are that A) only 30/28Si and 29/28Si ratios are used for the calculation which deviate less than 3 standard deviation from the mean and B) only results which follow the mass-depended terrestrial fractionation line in a three-isotope-plot of δ29Si vs. δ30Si within analytical uncertainties and C) have a mass bias drift between the two bracketing standards of less than 0.30 ‰ in 30/28Si are accepted and reported in this study.
      Detailed description of the sample origin, preparation steps, and the measurement protocol can be found in Frick, D. A.; Schuessler, J. A.; Sommer, M.; von Blanckenburg, F. (2018): Laser ablation in situ silicon stable isotope analysis of phytoliths. Geostandards and Geoanalytical Research. https://doi.org/10.1111/ggr.12243. With this supplement we aim to provide a comprehensive dataset for in situ stable silicon isotope composition of individual phytolith fragments.
DOI: 10.5880/gfz.5.2.2018.001
title: A global database of radiogenic Nd and Sr isotopes in marine and terrestrial samples

description: The database presented here contains radiogenic neodymium and strontium isotope ratios measured on both terrestrial and marine sediments. It was compiled to help assessing sediment provenance and transport processes for various time intervals. This can be achieved by either mapping sediment isotopic signature and/or fingerprinting source areas using statistical tools (see supplemental references).
      The database has been built by incorporating data from the literature and the SedDB database and harmonizing the metadata, especially units and geographical coordinates. The original data were processed in three steps. Firstly, a specific attention has been devoted to provide geographical coordinates to each sample in order to be able to map the data. When available, the original geographical coordinates from the reference (generally DMS coordinates, with different precision standard) were transferred into the decimal degrees system. When coordinates were not provided, an approximate location was derived from available information in the original publication. Secondly, all samples were assigned a set of standardized criteria that help splitting the dataset in specific categories. We defined categories associated with the sample location ("Region", "Sub-region", "Location", which relate to location at continental to city/river scale) or with the sample types (terrestrial samples – “aerosols”, “soil sediments”, “river sediments” - or marine samples –“marine sediment” or “trap sample”). Thirdly, samples were discriminated according to their deposition age, which allowed to compute average values for specific time intervals (see attached table "Age_determination_Sediment_Cores.csv"). The dataset will be updated bi-annually and might be extended to reach a global geographical extent and/or add other type of samples.
      This dataset contains two csv tables: "Dataset_Nd_Sr_isotopes.csv" and "Age_determination_Sediment_Cores.csv". "Dataset_Nd_Sr_isotopes.csv" contains the assembled dataset of marine and terrestrial Nd and/or Sr concentration and isotopes, together with sorting criteria and geographical locations. "Age_determination_Sediment_Cores.csv" contains all background information concerning the determination of the isotopic signature of specific time intervals (depth interval, number of samples, mean and standard deviation). Column headers are explained in respective metadata comma-separated files. A human readable data description is provided in portable document format, as well. Finally, R code for mapping the data and running statistical analyses is also available for this dataset (see supplemental references).
DOI: 10.5880/gfz.3.3.2017.002
title: Supplementary data to: Nutrient cycling in a tropical montane rainforest under a supply-limited weathering regime traced by elemental mass balances and Mg stable isotopes

description: The dataset contains chemical analyses from the well-characterised Hakgala field site in the tropical Highlands of Sri Lanka. This site is located on a road cut between Nuwara Elia and Welimada (06.92923° N, 80.81834° E, 1753 m altitude), bordering a 12 km^2 natural forest reserve consisting of pristine, mature, stable upper montane rain forest, close to the Hakgala Botanical Garden. A deeply weathered regolith depth profile (ca. 10 m) developed on a hillslope underlain by charnockite bedrock. Adjacent to the regolith profile ecologically pristine catchments (&gt;1 km^2) are drained by small creeks.
      Here, data on samples of all compartments of the Critical Zone (defined as the near surface layer of the Earth extending from the bottom of the weathering zone to the top of the tree canopy) are reported. The dataset compiles published (Hewawasam et al., 2013, GCA, 118, 202-230) and new data (Schuessler et al., 2018, Chemical Geology) of element concentrations, stable Mg isotopes, and radiogenic Sr isotope ratios on stream water (time series sampling 2010-2013), vegetation, soil, saprolite (depth profile sampling), weathered bedrock (corestones), and unweathered bedrock. From this data, weathering indicators such as the chemical depletion fraction (CDF) and the element mass transfer coefficients (Tau) were calculated and reported in the dataset.
      The samples used for these analyses have been assigned with International Geo Sample Numbers (IGSN, www.igsn.org). Details on sampling locations are provided via IGSN links in the tables and in the related work section on the DOI Landing Page at GFZ Data Services. Moreover, the IGSN data can be accessed by adding the IGSN after igsn.org, e.g. igsn.org/GFFB1003V. Further details on sampling and locations are provided in Hewawasam et al. (2013, GCA, 118, 202-230).
      This publication contains an annotated summary table serving as a supplementary table for Schuessler et al. (2018, Chemical Geology) in pdf and xlsx (Microsoft Excel) formats. In addition, separate tables reflecting differing samples and methodologies for input into statistical software are provided as comma separated files. Column headers for all tables are explained in a separate csv file (Data columns headers for tables S1 to S3.csv). The analytical methodologies used to generate the data are described in the data description file.
DOI: 10.5880/gfz.2.6.2018.002
title: RRVS Building survey for exposure modelling in Alsace (France)

description: The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS (Remote Rapid Visual Screening) methodology in Alsace, France, within the framework of the DESTRESS project. The survey has been carried out between May and June 2017 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images from Google StreetView (vintage: February 2011) and footprints from OpenStreetMap.Surveyor: Konstantinos G. Megalooikonomou (GFZ German Research Centre for Geosciences)The attributes are encoded according to the GEM taxonomy v2.0 (see https://taxonomy.openquake.org).
      The following attributes are defined (not all are observable in the RRVS survey):code,descriptionlon, longitude in fraction of degreeslat, latitude in fraction of degreesobject_id, unique id of the building surveyedMAT_TYPE,Material TypeMAT_TECH,Material TechnologyMAT_PROP,Material PropertyLLRS,Type of Lateral Load-Resisting SystemLLRS_DUCT,System DuctilityHEIGHT,HeightYR_BUILT,Date of Construction or RetrofitOCCUPY,Building Occupancy Class - GeneralOCCUPY_DT,Building Occupancy Class - DetailPOSITION,Building Position within a BlockPLAN_SHAPE,Shape of the Building PlanSTR_IRREG,Regular or IrregularSTR_IRREG_DT,Plan Irregularity or Vertical IrregularitySTR_IRREG_TYPE,Type of IrregularityNONSTRCEXW,Exterior wallsROOF_SHAPE,Roof ShapeROOFCOVMAT,Roof CoveringROOFSYSMAT,Roof System MaterialROOFSYSTYP,Roof System TypeROOF_CONN,Roof ConnectionsFLOOR_MAT,Floor MaterialFLOOR_TYPE,Floor System TypeFLOOR_CONN,Floor Connections