cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5880/gfz.3.3.2019.001
title: Data set of major, minor and trace elements in Co-coloured Egyptian glass objects from the 18th dynasty workshops at Amarna, Egypt

description: During the Egyptian 18th dynasty (c. 1550–1292 BC), cobalt ore was mined, processed and used as a colourant for glass, faience and blue-painted pottery. Co-coloured glass objects have a mid- to dark blue colour and were produced in order to imitate the semi-precious stone lapis lazuli. During this period, the glass objects were manufactured predominantly at two sites: Malqata (25°42'51.2"N 32°35'33.4"E) and Amarna (27°38'40.3"N 30°53'55.0"E). Major, minor and trace element concentration data from 38 blue glass objects from Amarna in the collection of Egyptian Museum and Papyrus Collection in Berlin are reported in this data publication. For comparison, glass objects from the same period and location, but of different colours (one red, two each of colourless, green and turquoise-blue glass) were analysed with the same method. These objects were originally brought to Berlin subsequent to the 1911–1914 excavations at Amarna carried out under the direction of Ludwig Borchardt on behalf of the Deutsche Orient-Gesellschaft. Unfortunately, most of these have by now lost their specific finds location. In addition, two recent samples of cobalt ore from the region of Ain Asil, near the Dakhla oasis (25°30'59.6"N 29°09'59.8"E), were included in the analysis.
DOI: 10.5880/wsm.2020.003
title: Modelled stress state in the Bavarian Molasse Basin with quantified uncertainties

description: These data are supplementary material to Ziegler & Heidbach (2020) and present the results of a 3D geomechanical-numerical model of the stress state with quantified uncertainties. The average modelled stress state is provided for each of the six components of the full stress tensor. In addition, the associated standard deviation for each component is provided. The modelling approach uses a published lithological model and the used data is described in the publication Ziegler & Heidbach (2020). The reduced stress tensor is derived using the Tecplot Addon GeoStress (Stromeyer & Heidbach, 2017).
description: The model results are provided in a comma-separated ascii file. Each line in the file represents one of the approx. 3 million finite elements that comprise the model. The following data are provided for each element. 1 - Location X / Easting (UTM 32)[m]2 - Location Y / Northing (UTM 32)[m]3 - Location Z / Depth below sea level [m]4 - Average of Sigma XX component of the stress tensor [Pa]5 - Standard Deviation of Sigma XX component of the stress tensor [Pa]6 - Average of Sigma YY component of the stress tensor [Pa]7 - Standard Deviation of Sigma YY component of the stress tensor [Pa]8 - Average of Sigma ZZ component of the stress tensor [Pa]9 - Standard Deviation of Sigma ZZ component of the stress tensor [Pa]10 - Average of Sigma XY component of the stress tensor [Pa]11 - Standard Deviation of Sigma XY component of the stress tensor [Pa]12 - Average of Sigma YZ component of the stress tensor [Pa]13 - Standard Deviation of Sigma YZ component of the stress tensor [Pa]14 - Average of Sigma ZX component of the stress tensor [Pa]15 - Standard Deviation of Sigma ZX component of the stress tensor [Pa]16 - Average of the maximum horizontal stress component (SHmax) [Pa]17 - Standard Deviation of the maximum horizontal stress component (SHmax) [Pa]18 - Average of the minimum horizontal stress component (Shmin) [Pa]19 - Standard Deviation of the minimum horizontal stress component (Shmin) [Pa]20 - The vertical stress component (Sv) [Pa]21 - Average of the differential stress (S1-S3) [Pa]22 - Standard Deviation of the differential stress (S1-S3) [Pa]
DOI: 10.5880/gfz.4.2.2020.001
title: Seismic moment and injection efficiency evolution during Enhanced Geothermal System (EGS) projects

description: The here provided data are part of a broader analysis of past and present stimulation projects, revealing that the temporal evolution and growth of maximum observed moment magnitudes may be linked directly to the injected fluid volume and hydraulic energy. Analyzed projects include the most prominent European Enhanced Geothermal System (EGS) projects in Basel, Switzerland (BAS) and Soultz-sous-Forêts (STZ), France. In Soultz, three different stimulations over the course of 10 years were performed in different wells and different depths. Therefore, we differentiate between the injections in 1993 (STZ93), 2000 (STZ00), and in 2003 (STZ03). We also included the deepest EGS Project to date (St1), located in Helsinki, Finland. Furthermore, we included the fluid-injection experiment from the German super deep scientific drilling hole (KTB), two Australian EGS projects, located at Paralana (Para) and the 2003 Cooper Basin (CBN) injection, as well as the EGS project near Pohang, South Korea. Finally, we also considered a single well injection period at the Berlín geothermal field (BGF), El Salvador, representing the only hydrothermal site considered here. For each project the cumulative volume injected is provided along with the applied hydraulic energy, maximum observed seismic moment, cumulative seismic moment, and injection efficiency as tab separated ASCII files with the .csv extension. All stimulation files are combined into a single .zip archive. More details on processing steps and references herein can be found in the accompanying data description.
DOI: 10.5880/gfz.4.4.2019.002
title: Pan-European probabilistic flood loss data for residential buildings

description: Increasing flood losses over the last decades emphasize the need towards significantly improved and more efficient flood risk management. One key requirement is reliable risk assessment in conjunction with consistent flood loss modeling. Current risk assessments and flood loss estimations for Europe are until now based on regional approaches using deterministic depth-damage function and do rarely report associated uncertainties. To reduce these shortcomings, we present the results of a novel, consistent approach based on the Bayesian Network Flood Loss Estimation MOdel for the private sector (BN-FLEMOps). The dataset is consistent in terms of the input data used to drive the model and because we use the same vulnerability model to derive the flood loss estimation. Essential inputs for any flood loss estimation are hazard (usually water depth), asset (value of objects at risk) and flood experience parameters. The hazard input was given by a European inundation scenario for a continent-wide flood with 100 years return period (Alfieri et al., 2014). Asset values were computed following the the approach by Huizinga et al. (2017) and the flood experience was derived using the database of the Dartmouth Flood Observatory (DFO) (Brakenridge, 2018). The provided dataset comprises a flood loss estimation covering the European continent, spatially aggregated on level three of the standard territorial units for statistics NUTS-3 (https://ec.europa.eu/eurostat/web/nuts/background). The data set reports the summary statistics as a flood loss distribution per NUTS-3 region in 10 per cent quantile steps. The flood loss estimations are given in Million Euro. In addition, the NUTS-3 code, the underlying version of the standard territorial unit and the associated NUTS level are provided. This data publication includes the exact dataset as reported in Lüdtke et al (2019) [filename_1], which is single model application. Supplementary, we provide the summary statistics from an ensemble of 1000 model runs to account for the inherent variability of the probabilistic model [filename_2]. The ensemble model application reports the same statistical measures as the single model application (flood loss distribution per NUTS-3 region in 10 per cent quantile steps), but the given numbers show the median of 1000 model runs for each quantile step (10%, 20%, … 90%). The dateset is provided as a multi-polygon vector. All polygons that belong to the same standard territorial unit share the same attributes. The spatial reference system is defined by EPSG:4326. We provide two formats, (I) an ESRI shape file and (ii) a GEOjson representation. For more information please refer to the associated data description.
DOI: 10.5880/pik.2019.019
title: The PRIMAP-hist Socio-Eco national historical GDP and population time series v2.1, (1850 - 2017)

description: The PRIMAP-hist Socio-Eco dataset combines several published datasets to create a comprehensive set of population and Gross domestic product (GDP) pathways for every country covering the years 1850 to 2017, and all UNFCCC (United Nations Framework Convention on Climate Change) member states, as well as most non-UNFCCC territories. The data has no sector resolution. List of datasets included in this data publication: (1) PMHSOCIOECO21_GDP_26-Jul-2019.csv: contains the GDP data for all countries(2) PMHSOCIOECO21_Population_26-Jul-2019.csv: contains the population data for all countries(3) PRIMAP-hist_SocioEco_data_description.pdf: including CHANGELOG(all files are also included in the .zip folder) When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset. Please consider also citing the relevant original sources when using the PRIMAP-hist Socio-Eco dataset. See the full citations in the References section further below. A data description article is in preparation. Until it is published we refer to the description article of the PRIMAP-hist emissions time series for the methodology used. SOURCES: - UN World Population Prospects 2019 (UN2019)- World Bank World Development Indicators 2019 (July) (WDI2019B). We use the *NY.GDP.MKTP.PP.KD* variable for GDP.- Penn World Table version 9.1 (PWT91). We use the *cgdpe* variable for GDP (Robert and Feenstra, 2019; Feenstra et al., 2015)- Maddison Project Database 2018 (MPD2018). We use the *cgdppc* variable for GDP (Bolt et al,, 2018)- Anthropogenic land use estimates for the Holocene – HYDE 3.2 (HYDE32)(Klein Goldewijk, 2017)- Continuous national gross domestic product (GDP) time series for 195 countries: past observations (1850–2005) harmonized with future projections according to the Shared Socio-economic Pathways (2006–2100) (Geiger2018, Geiger and Frieler, 2018)Full references are available in the data description document.
description: Methods:Country resolved data is combined from different sources using the PRIMAP emissions module (Nabel et. al., 2011). It is supplemented with growth rates from regionally resolved sources and numerical extrapolations.
DOI: 10.5880/pik.2019.020
title: Data sets for the identification of a global typology for coastal urban vulnerability under rapid urbanization

description: Coastal areas are urbanizing at unprecedented rates, particularly in low- and middle-income countries. Combinations of long-standing and emerging problems in these urban areas generate vulnerability for human well-being and ecosystems alike. Based on the presented data sets a spatially explicit global systematization of these problems into typical urban vulnerability profiles can be derived for the year 2000 using largely sub-national data. We present here 11 indicator datasets for urban expansion, urban population growth, marginalization of poor populations, government effectiveness, exposures and damages to climate-related extreme events, low-lying settlement, and wetlands prevalence. Applying a standard k-means cluster analysis to this input data reveals a global typology of seven clearly distinguishable clusters, or urban profiles of vulnerability (for results see Sterzel et. al., 2019).
DOI: 10.5281/zenodo.17878983
title: Gait Under Cognitive Load
DOI: 10.5281/zenodo.17650143
title: Gait Under Cognitive Load
DOI: 10.5880/gfz.1.3.2019.001
title: Monthly complex amplitudes of global M2 ocean tide induced electromagnetic field signals from 1990 to 2016

description: An electric current is induced by the motion of electrical conducting seawater through the ambient geomagnetic field. The periodic oceanic tidal flow induces an electric current that emits periodical time-variable electromagnetic field signals. The radial component of the ocean tide induced magnetic field signals has successfully been extracted from magnetic field observations of the satellite missions CHAMP and Swarm. It is known that the amplitudes of these electromagnetic signals are modulated by, among other influences, variations of the electrical seawater conductivity distribution of the ocean. The electrical seawater conductivity in return depends on seawater temperature and salinity. In order to analyse the influence of variations in oceanic temperature and salinity, we modelled a complete set of monthly time slices of three dimensional global complex amplitudes of these electromagnetic field signals for the years 1990 to 2016. In order to analyse solely the influence of variations in the climate sensitive seawater temperature and salinity on the ocean tide induced magnetic field signals, the influences of the secular variation of the geomagnetic field and temporal variations in ocean tide transports have been neglected. The data set is a supplement to the article of Petereit et al. (2019). The detailed method used to create this data set can be found in the data and methods section of the article and the associated data description file. Several datasets and models have been combined in order to compute the necessary models for the electrical conductivity of the Earth's surface and the ocean tide induced electric currents. These are the two main components needed for the modelling of the electromagnetic field signals that are emitted by the ocean tide induced electric currents. The model for the electrical conductivity of the Earth is composed of three components: a 1-D mantle conductivity distribution (Grayver et al., 2017), the time constant sediment conductivity (Laske & Masters, 1997) and the time-varying ocean conductivity. Ocean conductivity values were derived from a dataset of monthly global seawater temperature and salinity distributions that were derived from in-situ observations (Cabanes et al., 2013) using the TEOS-10 Toolbox (IOC, SCOR & APSO, 2010) to solve the Gibbs-seawater equation. The ocean-tide induced electric current density was computed as the vector product of the oceanic seawater conductivity, the tidal transports of the TPXO8-atlas (Egbert & Erofeeva, 2002) and ambient geomagnetic field of the IGRF-12 (Thébault et al., 2015). While, the oceanic seawater conductivity was variable in time, the tidal transports and the field strength of the ambient geomagnetic field have been kept constant.
DOI: 10.5880/fidgeo.2019.028
title: Decollement depth of Active thrust faults in Italy

description: Based on available geological and geophysical data, the depth of the basal thrust decollement for compressional areas of Italy is collected. The proposed dataset is useful to a large scientific and risk-management audience (e.g., input for numerical modelling of regional studies, or providing the maximum depth of brittle crust useful to constraints maximum expected magnitudes for the study region). The dataset is presented as a long table (2019-028_Petricca_Table1.txt) in tab-separated text format. The table contains three columns indicating 1) the longitude, 2) the latitude and 3) the depth (in km) values of the maximum thrust faulting depth. Obtained depths range between 1 and 17 km. Conceptual model for the definition of the active thrust decollement depths (see Petricca et al., 2019): to define the basal decollement depth of active thrust faults are selected 75 published geological and seismic sections plus two maps of basal decollement (Table 1 in Petricca et al., 2019 for references). The study domain is gridded with nodes every 10x10 km. At each node coinciding with a seismic or geological section, the punctual value of the basal decollement depth with respect to the sea level is assigned. For the Calabrian Arc and part of Sicily, we used values picked from depth maps. Depth values at empty nodes are assigned by interpolation criteria using the minimum curvature method (Briggs, 1974), generalized by Smith and Wessel (1990) including the tension factor (i.e., the smoothing grade - 0.5 in this case). Further, the trend of the obtained isodepth contours is recalibrated following the composite sources (i.e. the maximum depth of seismogenic sources given in the DISS database - see Basili et al., 2008). Depth correction is obtained adding/subtracting the topography/bathymetry elevation/depth at nodes using values interpolated from ETOPO1 Global Relief Model. Due to the fact that the brittle-ductile transition (BDT) depth is possibly and locally shallower than the basal thrust depth (zbt), further correction is necessary. For this purpose, the BDT depths from Petricca et al. (2015) is compared with the basal thrust depths zbt from this study to select at each node of the computation grid the shallower value. The majority of the studied areas show a basal thrust depth (zmax) shallower than the BDT. An exception occurs offshore in the southern Tyrrhenian Sea, Sicily, where the BDT depth (10-12 km) is considerably shallower than the basal thrust depth (zmax&lt;30 km). Limited portions of the northern Apennines and the part of the Calabrian arc close to the coast show comparable depths between the basal thrust (zmax) and BDT (i.e., 14-17 km).
DOI: 10.5880/gfz.3.1.2019.002
title: XRD analyses on cuttings samples from IODP Expeditions 338 and 348 (Nankai Trough accretionary prism)

description: This data publication uses XRD bulk rock analyses carried out on cuttings aboard D/V Chikyu during the International Ocean Discovery Program (IODP) Expeditions 338 and 348 of the Nankai Trough Seismogenic Zone Experiment (NanTroSEIZE) project (Strasser et al, 2014, Tobin et al., 2015). More data on clay minerals in the C0002F and C0002P holes are published by Underwood and Song (2016a and 2016b), and Underwood (2017). These data are supplementary material for Schleicher and Jurado (2019). XRD data of the clay size fraction were analyzed at the University of Michigan, USA, and the GFZ Potsdam, Germany. All XRD analyses of the random powder and texture (oriented) preparation followed the analytical methods described in Moore and Reynolds (1997). Oriented clay size samples were measured under air-dried and glycolated conditions, the latter treatment caused interlayer expansion of swelling clays, allowing the recognition of discrete smectite and mixed-layer smectitic phases. In order to compare the clay mineral content, and the mineral amount relative to the adjacent material, exactly 45 μg of the material was mixed with 1.5 ml deionized water and dropped on a round glass slide (diameter 32 mm). All air-dried samples were measured at a relative humidity (RH) of ~30%, and afterward stored in a desiccator filled with ethylene glycol, in order to investigate the final swelling stage of the smectitic phases. The data are provided as tab delimited table (2019-002_Schleicher-Jurado_XRD-data.txt, see also Table 1 in Schleicher and Jurado, 2019) with the following columns:- Hole: name of the C0002 subhole- Depth (mbsf): depth in meter below surface (mbsf)- Sample (SMW): sample number SMW (solid cuttings taken from drilling mud)- Smectite (int./cps): intensity of smectite in counts per second (cps)- Illite (int./cps): intensity of smectite in counts per second (cps) In addition, the original XRD measurements are provided in raw and text formats (2019-002_Schleicher-Jurado_original-XRD-measurements.zip). All science data from these expeditions are also accesible via the Database of the science data acquired by International Ocean Discovery Program and Integrated Ocean Drilling Program expeditions of D/V Chikyu (http://sio7.jamstec.go.jp/).
DOI: 10.5880/gfz.1.4.2019.002
title: Analysis boundaries and lighting trends (2012-2018) for selected International Dark Sky Places

description: This dataset is related to the question of whether communities inside of certified "International Dark Sky Places" have different levels of lighting change in comparison to communities of similar size that are located further away. It is a supplement to Coesfeld et al. (2019), in which this question was examined for the time period 2012-2018. This dataset contains the boundaries of the analysis areas (i.e. community boundaries) in the directory "Coordinates and Graphs". These boundaries are stored as polygons in plain text format. Additional data related to the publication (e.g. Excel tables containing summary data of measured lighting trends for each community) are also included. Details of the data are available in the data description file.
DOI: 10.5880/pik.2019.002
title: Model output for the publication: "On the sensitivity of the Devonian climate to continental configuration, vegetation cover, orbital configuration, CO_2 concentration and insolation"

description: In "On the sensitivity of the Devonian climate to continental configuration, vegetation cover, orbital configuration, CO_2 concentration and insolation" we study the sensitivity of the Devonian (419 to 359 million years ago) to several parameters using a coupled climate model. The data presented here is the model output the results of this manuscript are based on. Additionally, the figures of the publication and scripts (Python and Yorick) to analyse the model output and generate the figures are contained. The model output is provided in different netcdf files. The structure of the model output is explained in a readme file. The data is generated using the coupled ocean-atmosphere model CLIMBER3alpha which models climate globally on a 3.75°x3.75° (ocean) and 22.5° (longitude) x 7.5° (latitude) (atmosphere) grid. More information about the model can be found in the manuscript.