cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5880/gfz.6.2.2018.003
title: Fibre-optic distributed acoustic sensing data from Reykjanes, Iceland

description: Imaging the internal structure of faults remains challenging using conventional seismome-ters. Here, the authors use deployed fibre-optic cables to obtain strain data and identify faults and volcanic dykes in Iceland. Such fibre-optic networks are pervasive for telecommu-nication and could be used for hazard assessment.
      Natural hazard prediction and efficient crustal exploration requires dense seismic observa-tions both in time and space. Seismological techniques provide ground-motion data, whose accuracy depends on sensor characteristics and spatial distribution. In the manuscript Jousset et al. (2018), we demonstrate that strain determination is possible with conventional fibre-optic cables deployed for telecommunication. Extending recently distributed acoustic sensing (DAS) studies, we present high resolution spatially un-aliased broadband strain data.
      We recorded seismic signals from natural and man-made sources with 4-m spacing along a 15-km-long fibre-optic cable layout on Reykjanes Peninsula, SW Iceland.
      This data publication contains data used for plotting several figures of Jousset et al. (2018). For further explanation of the data and related processing steps, please refer to Jousset et al. (2018). A theoretical study with respect to the coupling of the cable to the ground has been published by Reinsch et al. (2017).
DOI: 10.15468/dl.a4jdpt
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bubalus mindorensis",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 25 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044483-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17879000
title: LCA4BIO_D2.2_AnnexII_Set of circularity indicators for Bio-based systems and guidance document for sustainability assessment of circular bio based strategies
DOI: 10.5281/zenodo.17878999
title: LCA4BIO_D2.2_AnnexII_Set of circularity indicators for Bio-based systems and guidance document for sustainability assessment of circular bio based strategies
DOI: 10.5880/gfz.1.4.2018.001
title: Heat flux and energy balance data of an arctic thermokarst lake

description: The dataset comprises three tables: - Data Set 1: Half-hourly measurement dataset (quality controlled and filtered), derived variables and energy balance components (Franz_ds01.csv) - Data Set 2: Half-hourly fluxes and transfer coefficients derived by bulk aerodynamic transfer models (Franz_ds02.csv) - Data Set 3. Daily courses and cumulative sums of energy balance components for the average day per subperiod including measured and modelled H and LE (Franz_ds03.csv)
description: Eddy covariance measurements were conducted from 23 April to 16 August 2014 on a thermokarst lake in the Siberian Lena River Delta, yielding direct measurements of sensible (H) and latent (LE) heat flux on half-hourly basis. Ancillary measurements including meteorological variables and water temperature measurements were gathered during the campaign. We derived bulk aerodynamic transfer coefficients in order to parameterize the heat fluxes and compare this in-situ model with independent heat flux parameterization schemes, which are also based on the common bulk transfer algorithm. We further investigated the components of a simple energy balance including measured and modelled H and LE. The dataset was created within the framework of a publication of the study results in Journal of Geophysical Research - Atmospheres (Lake-atmosphere heat flux dynamics of a thermokarst lake in arctic Siberia, by Franz et al.)
DOI: 10.5880/gfz.2.3.2018.003
title: Hourly mean values of the horizontal component at the geomagnetic observatory Huancayo (HUA), 1958 to 2013

description: The dataset contains hourly mean values (HMV) of the horizontal magnetic field component H as measured at the geomagnetic observatory Huancayo for 1958 to 2013. Huancayo observatory (IAGA code HUA) is operated by Instituto Geofisico del Peru. This dataset is based on and partly supersedes the data publication Matzka et al. (2017a), which is described in Matzka et al. (2017b). It will be superseded by another data publication (Soares et al., in preparation) that we intend to publish at GFZ Data Services as well as the World Data Centres for Geomagnetism.
      As already described in Matzka et al. (2017a, 2017b), the HMVs were taken from the World Data Centre Kyoto (WDC Kyoto) and data gaps (in total some 19 years from the 1960ies, 1970ies and 1980ies) were filled in by typing handwritten records of the HMV at GFZ. These handwritten records were monthly tables that were received as digital images from geomagnetic observatory Huancayo or that were received as microfilms from World Data Centre Boulder. We also produced digital images of these microfilms. The values from the WDC Kyoto are definitive values; the monthly tables presumably also contain definitive values. Corrections to HUA HMVs from WDC Kyoto: There is a known error in the time stamping of the HUA HMVs prior to 1948 (before 1948 the data was reported in local time, rather than universal time). This error is corrected in the present dataset. Also, an attempt was made to correct for a jump in the HMV time series at this time. 
      Further corrections, made to the dataset by Matzka et al. (2017a), are mostly the correction or deletion of outliers and the correction of shifts in the data.
      Again, please note that a dataset based on the data provided here will be submitted to the World Data Centres for Geomagnetism at a later stage and will have some additional modifications (Soares et al., in preparation).
      The data file is in ASCII format and contains blank-separated first the year (YYYY), the month (MM), the day (DD) followed by the 24 HMVs of H (format HHHHH) in nanotesla (nT), starting with the HMV for 00 to 01 universal time. Geomagnetic observatories are described in e.g. Jankowski and Sucksdorf (1996), in Matzka et al., (2010) and Matzka (2016).
DOI: 10.5880/pik.2018.004
title: ISIMIP2a Simulation Data from Fisheries & Marine Ecosystems (Fish-MIP; regional) 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 will 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 https://www.isimip.org/gettingstarted/#input-data-bias-correction).This entry refers to the ISIMIP2a simulation data from fisheries & marine ecosystems (regional) models: Atlantis (Southeastern Australia) EwE (Adriatic Sea), EwE (Baltic Sea), EwE (Catalan Sea or NW Mediterranean Sea), EwE (Cook Strait, New Zealand), EwE (East Bass Strait, Australia), EwE (Mediterranean Sea), EwE (North Sea), OSMOSE (Northern Humboldt).
description: The ISIMIP2a Fisheries & Marine Ecosystems (Fish-MIP; regional) outputs are based on simulations from 9 regional fisheries & marine ecosystems models (see listing) according to the ISIMIP2a protocol (https://www.isimip.org/protocol/#isimip2a). The models simulate total ecosystem biomass, total consumer biomass, biomass by different size classes, and fisheries catches based on prescribed input fields information. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
DOI: 10.5880/pik.2018.009
title: Bias-corrected CORDEX precipitation, min/mean/max temperature for Ethiopia, RCP 4.5 and RCP 8.5

description: State-of-the-art hydrological impact studies are based on weather data from regional climate model ensembles and require a certain degree of accuracy.
      Despite regional downscaling to finer resolution, RCM simulations often show considerable biases when compared to observed data and bias-correction is an attempt to improve the quality and reliability.
However, correction methods are based on the stationarity assumption which presumes that future physical processes in the atmosphere are comparable to the period used to correct the simulations.
      This dataset provides bias-corrected daily precipitation, min/mean/max air temperature of ten CORDEX RCM runs covering the country of Ethiopia for historical (1970-1999) and over the 21st century for RCP 4.5 and RCP 8.5.
description: Precipitation biases in most CORDEX RCMs show a high seasonality for grid boxes within the evaluation domain of Ethiopia. This limits a bias correction based on seasonal or annual means. However, as some of these grid boxes do show almost no precipitation events for single months, a harmonic-based bias correction method analogously to the one applied to temperature is not feasible for precipitation. Furthermore, this results in a large uncertainty in the estimation of the corresponding monthly biases. Thus, a bias correction is only applied on months and grid boxes with more than 100 rainy days (rainfall above 1 mm/day) within the calibration period (1951--2001). 
      The method applied is based on a local rainy day intensity scaling, correcting the frequency of rainy days and the mean precipitation on rainy days to fit the observed values in a specific calibration period. The underlying idea is the assumption of a smooth seasonal cycle for the variables simulated by the RCM and the observational reference (WATCH Forcing Data, WFD). These cycles are modelled with a series of harmonic functions using vector generalised linear models and the difference in cycles between an RCM reference simulation and the observational product is used for bias correction of the RCM projection.
DOI: 10.5880/gfz.2.6.2018.001
title: The Source Model of the Probabilistic Seismic Hazard Assessment (PSHA) of Germany - Version 2016

description: The main input data of the earthquake model for the probabilistic seismic hazard assessment of Germany, version 2016, are provided in form of: (1) the  geometry of five areal source zone models and one composite fault model for the Lower Rhine graben (ESRI shape files) and (2) the seismicity rates of all sources given as Mmax-depending Gutenberg-Richter parameters a and b with their uncertainties (EXCEL and csv files).The assignment of individual sources to the superzone models of Mmax, b-value, depth, tectonic regime and smoothing kernel arer described in the accompanying Scientific Technical Report Data (Grünthal et al. 2017).
DOI: 10.5880/pik.2018.003
title: The PRIMAP-hist national historical emissions time series (1850-2015)

description: This is an updated version of Gütschow et al. (2017, http://doi.org/10.5880/pik.2017.001). Please use this version which incorporates updates to input data as well as correction of errors in the original dataset and its first update. For a detailed description of the changes please consult the CHANGELOG included in the data description document.
      This dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas covering the years 1850 to 2015 and all UNFCCC (United Nations Framework Convention on Climate Change) member states as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 1996 categories. For CO2‚‚ from energy and industry time series for subsectors are available.
      List of datasets included in this data publication:(1) PRIMAP-hist_v1.2_14-Dec-2017.csv: With numerical extrapolation of all time series to 2014. (only in .zip folder)(2) PRIMAP-hist_no_extrapolation_v1.2_14-Dec-2017.csv: Without numerical extrapolation of missing values. (only in .zip folder)(3) PRIMAP-hist_v1.2_data-format-description: including CHANGELOG(4) PRIMAP-hist_v1.2_updated_figures: updated figures of those published in Gütschow et al. (2016)(all files are also included in the .zip folder)
      When using this dataset or one of its updates, please also cite the data description article (Gütschow et al., 2016, http://doi.org/10.5194/essd-8-571-2016) to which this data are supplement to. Please consider also citing the relevant original sources.
      SOURCES:- UNFCCC National Communications and National Inventory Reports for developing countries: UNFCCC (2017B)- UNFCCC Biennal Update Reports: UNFCCC (2016)- UNFCCC Common Reporting Format (CRF): UNFCCC (2016), UNFCCC (2017)- BP Statistical Review of World Energy: BP (2017)- CDIAC: Boden et al. (2017)- EDGAR versions 4.2 and 4.2 FT2010: JRC and PBL (2011), Olivier and Janssens-Maenhout (2012)- FAOSTAT database: Food and Agriculture Organization of the United Nations (2016)- Houghton land use CO2: Houghton (2008)- RCP historical data: Meinshausen et al. (2011)- EDGAR-HYDE 1.4: Van Aardenne et al. (2001), Olivier and Berdowski (2001)- HYDE land cover data: Klein Goldewijk et al. (2010), Klein Goldewijk et al. (2011)- SAGE Global Potential Vegetation Dataset: Ramankutty and Foley (1999)- FAO Country Boundaries: Food and Agriculture Organization of the United Nations (2015)
description: 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. Regional deforestation emissions are downscaled to country level using estimates of the deforested area obtained from potential vegetation and calculations for the needed agricultural land.
DOI: 10.15468/dl.pxyh94
title: Occurrence Download

description: A dataset listing the 240 species recorded in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 "Geometry POLYGON((-73.47570487472754 6.253449502542793,-73.47570487472754 6.210386416759761,-73.35846224667543 6.210386416759761,-73.35846224667543 6.253449502542793,-73.47570487472754 6.253449502542793))"
 ]
}

 The dataset's 240 records were derived from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044467-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5880/gfz.2.1.2018.002
title: 40-year record of radon, water temperature, and discharge rate from a hot spring in China

description: A high-fidelity radon record covering nearly 40 years from the hot spring site of BangLazhang (BLZ), Southwestern China allows to study multi-year periodicities. At BLZ, radon dissolved in water (Radon), water temperature (WT), and spring discharge rate (DR) were measured daily from 1976 until 2015. Barometric pressure, regional rainfall, galactic cosmic rays (GCR flux is modulated by solar wind and thus a proxy for solar activity), and regional seismicity from the same period were considered to identify potentially influencing factors controlling the changes in radon [Yan et al., 2017]. Various wavelet techniques indicate that the long-period radon concentration is characterized by a quasi-decadal (8-11 years) cycle, matching well with the concurrent periodicity in water temperature, spring discharge rates.
      The BLZ hot spring monitoring site is maintained and operated by the China Earthquake Administration of Yunnan Province. Water from the spring is sampled once daily and measurements of radon have been performed routinely in a laboratory since 1976 April 6. The sample time is designated to occur at 8 o’clock in the morning in order to reduce the effect of daily variations. The radon concentration has been measured with three types of radon measurement instruments during the past 40 years. From 1976 April 6, to 1982 June 5, a FD-105 type radon gas detector was used, reporting the radon concentration in Eman. Eman is converted to the metric unit Bq/L using the relationship 1 Eman = 3.7 Bq/L. From 1982 June 6 to 2012 April 11, a FD-105K type electrometer (manufactured by Shanghai Electronic Instrument, co.) was used, the measurements given in Bq/L. Since 2012 April 12, a FD-125 type Radon & Thorium analyzer, manufactured by Beijing Nuclear Instrument Factory, sponsored by CNNC (China National Nuclear Corporation), has been used.
      Water sampled from the spring is degassed by bubbling air and transported into a chamber, where the radon concentration is measured in a ZnS cell connected to a photomultiplier detector, and a scintillation counter. The measurement precision of the instruments is 0.1 Bq/L. A solid radium source (226Ra) with a known radioactive radon content is used for the calibration of the water radon under normal working conditions. This source is used to measure and calculate the calibration value of the instrument.
      In addition to radon, water temperature and spring discharge rate are measured at the spring site when the water is sampled for radon. Temperature is measured using a mercury thermometer with a resolution of 0.1°C. Discharge rate is measured using the stopwatch capacity method, i.e., the required time per unit volume of water is measured. Barometric pressure has been measured since 1997. Regional rainfall data were downloaded through the CPC Merged Analysis of Precipitation (CMAP) for the same period to evaluate its possible influence on radon in the present study.
DOI: 10.5880/pik.2018.001
title: PRIMAP-crf: UNFCCC CRF data in IPCC 2006 categories

description: PRIMAP-crf is a processed version of data reported by countries to the United Nations Framework Convention on Climate Change (UNFCCC) in the Common Reporting Format (CRF). The processing has three key aspects: 1) Data from individual countries and years are combined into one file. 2) Data is re-organised to follow the IPCC 2006 hierarchical categorisation. 3) ‘Baskets’ of gases are calculated according to different global warming potential estimates from each of the three most recent IPCC reports.
      All Annex I Parties to the United Nations Framework Convention on Climate Change (UNFCCC) are required to report domestic emissions on an annual basis in a 'Common Reporting Format' (CRF). In 2015, the CRF data reporting was updated to follow the more recent 2006 guidelines from the IPCC and the structure of the reporting tables was modified accordingly. However, the hierarchical categorisation of data in the IPCC 2006 guidelines is not readily extracted from the reporting tables. We present the PRIMAP-crf data as a re-constructed hierarchical dataset according to the IPCC 2006 guidelines. Furthermore, the data is organised in a series of tables containing all available countries and years for each GHG individual gas and category reported.
      In addition to single gases, the Kyoto basket of greenhouse gases (CO2, N2O, CH4, HFCs, PFCs, SF6, and NF3) is provided according to multiple global warming potentials. The dataset was produced using the PRIMAP emissions module. Key processing steps include; extracting data from submitted CRF excel spreadsheets, mapping CRF categories to IPCC 2006 categories, constructing missing categories from available data, and aggregating single gases to gas baskets. The processed data is available under an Creative Commons Attribution 4.0 International License (CC BY 4.0).
DOI: 10.5880/pik.2017.008
title: A global data set of spatially-explicit tropical cyclone exposure (TCE-DAT)

description: Tropical cyclones (TCs) pose a major risk to societies worldwide. While data on observed cyclones tracks (location of the center) and wind speeds is publicly available these data sets do not contain information about the spatial extent of the storm and people or assets exposed. Here, we apply a simplified wind
field model to estimate all areas (grid cells) exposed to wind speeds above 34 knots. 
      Based on available spatially-explicit data on population densities and Gross Domestic Product (GDP) we estimate 1) the
number of people and 2) the sum of assets exposed to above tropical storm force wind speeds for temporal
changes in historical distribution of population and assets (TCE-hist) and assuming fixed 2015 patterns
(TCE-2015). 
      The associated spatially-explicit exposure data (TCE-DAT) covers the period 1950 to 2015. It is considered key information to 1) assess the contribution of climatological versus socio-economic drivers of changes in exposure to tropical cyclones, 2) estimate changes in vulnerability from the difference
in exposure and reported damages and calibrate associated damage functions, and 3) build improved exposure-based predictors to estimate higher-level societal impacts such as long-term effects on GDP,
employment, or migration. 
      We validate the adequateness of our methodology by comparing our exposure
estimate to estimated exposure obtained from reported wind fields available since 1988 for the United States. We expect that the free availability of the underlying model and TCE-DAT will make research on
tropical cyclone risks more accessible to non-experts and stakeholders.
      Files included in the zip folder: 
(1) TCE-DAT_single_events_historical.zip: Zipped archive containing 2707 files with exposed population
and assets by grid cell using historical socio-economic exposure estimates.(2) TCE-DAT_single_events_2015.zip: Zipped archive containing 2713 files with exposed population
and assets by grid cell using fixed socio-economic exposure at 2015 values.(3) Data-description_TCE-DAT_2017.008.pdf: full description of the data set including information on data sources and the description of variables/ data columns
      Additional information on each TC event in the zipped archive (e.g. TC name, NatCatSERVICE_ID,
genesis_basin, aggregated exposure estimates by country) are available in the exposure data sets aggregated
on country-event level (see Geiger et al., 2017; http://doi.org/10.5880/pik.2017.005 for details).
DOI: 10.15468/dl.n5ysry
title: Occurrence Download

description: A dataset listing the 1689 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is one of (Spain, Portugal)",
 "Geometry POLYGON((-9.16563 36.14004,0.21825 36.14004,0.21825 43.75452,-9.16563 43.75452,-9.16563 36.14004))",
 "TaxonKey is Aves"
 ]
}

 The dataset's 1689 records were derived from 269 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044468-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.