cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.kvpfgj
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anoplosyllis edentula",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 9 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044486-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w53537
title: Occurrence Download

description: A dataset containing 46 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((102.483 2.776176, 102.412655 2.770607, 102.344045 2.754037, 102.278859 2.726876, 102.218704 2.689792, 102.165063 2.643699, 102.119256 2.589734, 102.082411 2.529225, 102.055434 2.463664, 102.038987 2.394665, 102.033475 2.323928, 102.039031 2.253194, 102.055518 2.184206, 102.082527 2.11866, 102.119392 2.058171, 102.165206 2.004227, 102.21884 1.958156, 102.278974 1.921092, 102.344129 1.893946, 102.412699 1.877387, 102.483 1.871821, 102.553301 1.877387, 102.621871 1.893946, 102.687026 1.921092, 102.74716 1.958156, 102.800794 2.004227, 102.846608 2.058171, 102.883473 2.11866, 102.910482 2.184206, 102.926969 2.253194, 102.932525 2.323928, 102.927013 2.394665, 102.910566 2.463664, 102.883589 2.529225, 102.846744 2.589734, 102.800937 2.643699, 102.747296 2.689792, 102.687141 2.726876, 102.621955 2.754037, 102.553345 2.770607, 102.483 2.776176))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 46 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044485-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5880/pik.2017.002
title: ISIMIP2a Simulation Data from Biomes 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 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 all these data is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/#input-data-bias-correction).
      The ISIMIP2a biome outputs are based on simulations from 8 global vegetation (biomes) models (CARAIB, DLEM, JULES-B1, LPJ-GUESS, LPJmL, ORCHIDEE, VEGAS, VISIT) according to the ISIMIP2a protocol (https://www.isimip.org/protocol/#isimip2a).
description: The ISIMIP2a biome outputs are based on simulations by different global vegetation models (CARAIB, DLEM, JULES-UoE, LPJ-GUESS, LPJmL, ORCHIDEE, VEGAS, VISIT) following the ISIMIP2a protocol. The biome models simulate biogeochemical processes, biogeography and ecosystem dynamics of natural vegetation and managed lands based on soil, climate and land-use 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.2017.003
title: Continuous national Gross Domestic Product (GDP) time series for 195 countries:  past observations (1850-2005) harmonized with future projections according the Shared Socio-economic Pathways (2006-2100)

description: We here provide three different economic time series that amend or combine various existing time series for Gross Domestic Product (GDP), GDP per capita, and population to create consistent and continuous economic time series between 1850 and 2009 for up to 195 countries: 
      
(1)	A continuous table of global income data (in 1990 Geary-Khamis $) based on the Maddison Project data base (MPD) for 160 individual countries and 3 groups of countries from 1850-2010: Maddison_Project_data_completed_1850-2010.csv.(2)	A continuous table of global income data (in 2005 PPP $, PPP = purchasing power parity) for 195 countries based on a merged and harmonized dataset between MPD and Penn World Tables (PWT, version v8.1) from 1850-2009, and additionally extended using PWT v9.0 and World Development Indicators (WDI), that is consistent with future GDP per capita projections from the Shared Socioeconomic Pathways (SSPs): GDP-per-capita-national_PPP2005_SSP-harmonized_1850-2009.csv.(3)	A continuous table of global GDP data (in 2005 PPP $) for 195 countries from 1850-2009 based on the second income data set multiplied by country population data, again consistent with future SSP GDP projections: GDP-national_PPP2005_SSP-harmonized_1850-2009.csv.
      
These data are supplemented by a masking table indicating MPD original data and amended data based on current country definitions (Maddison_data_availability_masked_1850-2010.csv) and a file with PPP conversion factors used in this study (PPP_conversion_factors_PPP1990-PPP2005.csv).
      
We use various interpolation and extrapolation methods to handle missing data and discuss the advantages and limitations of our methodology. Despite known shortcomings this data set aims to provide valuable input, e.g., for climate impact research in order to consistently analyze economic impacts from pre-industrial times to the distant future. More information about data sources and data format description is given in the data description file (Data-Description-GDP_1850-2009.pdf).
      
Version history: Please use the updated version of this dataset which contains correction of errors in the original dataset. For a detailed description of the changes please consult the CHANGELOG included in the data description document of the new version.
DOI: 10.6084/m9.figshare.30118942
title: Growth-Form Classification of Species Used in Flammability Trait Analyses

description: This dataset supports the paper <i>Distribution of flammability-related traits in different fire regimes</i>. It includes the original species names as listed in the TRY database and the associated growth forms for each species.Species were classified into five growth-form categories: grass, geophyte/herb, shrub, shrub/tree, and tree, using information from Li and Prentice (2024) and Plants of the World Online (https://powo.science.kew.org/). Grass classifications follow Watson and Dallwitz (1992).References: Li, J. & Prentice, I.C. (2024) Global patterns of plant functional traits and their relationships to climate. Zenodo.Watson, L. & Dallwitz, M. (1992) The grass genera of the world. Wallingford, Oxfordshire, C.A.B. International.
DOI: 10.6084/m9.figshare.30118942.v1
title: Growth-Form Classification of Species Used in Flammability Trait Analyses

description: This dataset supports the paper <i>Distribution of flammability-related traits in different fire regimes</i>. It includes the original species names as listed in the TRY database and the associated growth forms for each species.Species were classified into five growth-form categories: grass, geophyte/herb, shrub, shrub/tree, and tree, using information from Li and Prentice (2024) and Plants of the World Online (https://powo.science.kew.org/). Grass classifications follow Watson and Dallwitz (1992).References: Li, J. & Prentice, I.C. (2024) Global patterns of plant functional traits and their relationships to climate. Zenodo.Watson, L. & Dallwitz, M. (1992) The grass genera of the world. Wallingford, Oxfordshire, C.A.B. International.
DOI: 10.15468/dl.s28j7k
title: Occurrence Download

description: A dataset containing 1892 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hippotragus niger",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 1892 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044487-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5880/pik.2020.002
title: Model output for the publication: "Climatic fluctuations modeled for carbon and sulfur emissions from end-Triassic volcanism"

description: In "Climatic fluctuations modeled for carbon and sulfur emissions from end-Triassic volcanism" we study perturbations of Earth's climate and biogeochemical cycles by volcanism of the Central Atlantic Magmatic Province (CAMP) during the latest Triassic, about 201 million years ago, using a coupled climate model. The data presented here is the model output on which that manuscript is 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.
description: The data is model output from 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.
DOI: 10.5880/igets.la.l1.001
title: Superconducting Gravimeter Data from Larzac Observatory - Level 1

description: The International Geodynamics and Earth Tide Service (IGETS) was established in 2015 by the International Association of Geodesy. IGETS continues the activities of the Global Geodynamics Project (GGP) between 1997 and 2015 to provide support to geodetic and geophysical research activities using superconducting gravimeter (SG) data within the context of an international network. As part of this network, the Larzac station (code LA) was established in 2011 by GM - OSU OREME. Continuous time-varying gravity and atmospheric pressure data from LA are integrated in the IGETS data base hosted by ISDC (Information System and Data Centre) at GFZ. The gravimetry laboratory is located at 50 km at the West of Montpellier (longitude: 3.22 E, latitude: 43.97 N, height above MSL: 670 m) in the Larzac Observatory (https://deims.org/83b01fa5-747f-47be-9185-408d73a90fb2). It has been designed to monitor hydro-meteorological parameters in a karstic and Mediterranean environment. To monitor groundwater resources, an SG manufactured by GWR Instruments, the iGrav#002, has been installed in the observatory at the begin of June 2011. Research activities are aimed at both validate gravimeters (eg Gphone in Fores et al., 2019 or AQG-A in Menoret et al., 2018). The time series of gravity and barometric pressure from the gravimeter iGrav-002 starts in June 2011. The time sampling of the raw gravity and barometric pressure data of IGETS Level 1 is both 1 minute and 1 second. For a detailed description of the IGETS data base and the provided files see Voigt et al. (2016, http://doi.org/10.2312/GFZ.b103-16087). Moreover the observatory is also equipped with a permanent GNSS (Global Navigation Satellite Systems) antenna HOLA, a large band seismometer and an eddy-correlation flux tower belonging of the RENAG network (RESIF-RENAG French National Geodetic Network, RESIF – Réseau Sismologique et Géodésique Français, https://doi.org/10.15778/resif.rg, 2017) which is the French contribution to EPOS for the Seismology and Geodesy components.
DOI: 10.5880/igets.ru.l1.001
title: Superconducting Gravimeter Data from Rustrel (LSBB) - Level 1

description: The International Geodynamics and Earth Tide Service (IGETS) was established in 2015 by the International Association of Geodesy. IGETS continues the activities of the Global Geodynamics Project (GGP) between 1997 and 2015 to provide support to geodetic and geophysical research activities using superconducting gravimeter (SG) data within the context of an international network. As part of this network, the Rustrel station (code RU) was established in 2015 thanks to the financial support of the EQUIPEX MIGA (Matter wave-laser based Interferometer Gravitation Antenna) ANR-11-EQPX-0028 and by the European FEDER 2006-2013 “PFM LSBB - Développement des qualités environnementales du LSBB”. Continuous time-varying gravity and atmospheric pressure data from RU are integrated in the IGETS data base hosted by ISDC (Information System and Data Centre) at GFZ. The operation and maintenance of the RU instrumentation is done by staff at EOST/IPG Strasbourg. The RU station is located about 500 m beneath the surface in the underground research laboratory of the LSBB (« Laboratoire Souterrain à Bas Bruit ») built in 1997. The LSBB is located within the Regional Natural Park of Luberon (longitude: 5.4837 E, latitude: 43.9411 N, height above MSL: 499.3 m). The LSBB gallery is located in the unsaturated zone of the Fontaine de Vaucluse karst hydrosystem. There is no industry around the site so the anthropogenic noise is very low. The time series of gravity and barometric pressure from iOSG-24 starts in October 2015 and is going on. The time sampling of the raw gravity and barometric pressure data of IGETS Level 1 is 1 minute. For a detailed description of the IGETS data base and the provided files see Voigt et al. (2016, https://doi.org/10.2312/GFZ.b103-16087).
DOI: 10.5880/gfz.1.3.2020.002
title: Terrestrial water storage on the South American continent: Data from numerical simulations, observations, and deep learning

description: In Irrgang et al. (2020), we have trained a convolutional neural network to perform a so-called downscaling task. This downscaling aims to recover the fine-structure continental water storage distribution on the South American continent from coarse-resolution space-borne gravimetry observations. Here, we share data sets that were used for training the neural network, namely (1) monthly pairs of gridded terrestrial water storage anomalies (TWSA) of the South American continent and (2) surface water storage anomalies (SWSA) in the Amazonas region for the time period 2003-2019. TWSAs were used as target (output) values of the neural network and were derived from the Land Surface Discharge Model (LSDM, Dill, 2008). The corresponding input values were calculated by spatially smoothing the TWSA fields with a 600 km Gaussian filter. After training the neural network over the time period of 2003 to 2018, its performance was tested and compared to LSDM for the subsequent year 2019.
DOI: 10.5880/gfz.2.3.2020.002
title: Hourly mean values of the geomagnetic field horizontal component H at the geomagnetic observatories Tatuoca (TTB) and Huancayo (HUA), 1957 to 2019.

description: This dataset comprises hourly mean values of the horizontal geomagnetic field component H measured at the geomagnetic observatories Tatuoca (TTB, latitude: -1.205°, longitude: 311.487°, elevation: 5 m, Brazil) and Huancayo (HUA, latitude: -12.050°, longitude: -284.670°, elevation: 3313 m, Peru) for the period January 1st, 1957 to December 31st, 2019. TTB data from 1957 to May 2008 is described in the scientific publication Soares et al. (2020), to which this data publication is supplement to. This data comes from Observatório Nacional's archives and we expect to submit a final version of this data to WDC Edinburgh at a later point in time. From June 2008 to December 2017 we use hourly mean values calculated from the minute means available at Soares et al (2018). For 2018, we use quasi-definitive data from Observatório Nacional's archives. From January 2019 to July 22nd 2019, we use preliminary data from Observatório Nacional's archives. From July 23rd 2019 to December 2019, we use preliminary data from INTERMAGNET. For HUA from 1957 to 2001 we give new corrected data here that are based on earlier data publications (Matzka et al, 2017; Matzka et al., 2018). We plan to submit a final version of this data to WDC Edinburgh at a later point in time. From 2002 to 2014 we use definitive data from INTERMAGNET/WDC Edinburgh. From January 2015 to April 2015, we use quasi-definitive data from INTERMAGNET. From May 2015 to December 2019 we use variation data from INTERMAGNET. The data files are provided in the IAGA-2002 format (https://www.ngdc.noaa.gov/IAGA/vdat/IAGA2002/iaga2002format.html). Geomagnetic observatories are described in e.g. Jankowski and Sucksdorff (1996).
DOI: 10.5880/gfz.2.6.2020.001
title: Buildings data from Remote Rapid Visual survey (RRVS) for exposure modelling in Kyrgyzstan and Tajikistan

description: The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS methodology in Kyrgyzstan and Tajikistan, within the framework of the projects EMCA (Earthquake Model Central Asia), funded by GEM, and "Assessing Seismic Risk in the Kyrgyz Republic", funded by the World Bank. The survey has been carried out between 2012 and 2016 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images and footprints from OpenStreetMap. 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 description lon longitude in fraction of degrees lat latitude in fraction of degrees object_id unique id of the building surveyed MAT_TYPE Material Type MAT_TECH Material Technology MAT_PROP Material Property LLRS Type of Lateral Load-Resisting System LLRS_DUCT System Ductility HEIGHT Height YR_BUILT Date of Construction or Retrofit OCCUPY Building Occupancy Class - General OCCUPY_DT Building Occupancy Class - Detail POSITION Building Position within a Block PLAN_SHAPE Shape of the Building Plan STR_IRREG Regular or Irregular STR_IRREG_DT Plan Irregularity or Vertical Irregularity STR_IRREG_TYPE Type of Irregularity NONSTRCEXW Exterior walls ROOF_SHAPE Roof Shape ROOFCOVMAT Roof Covering ROOFSYSMAT Roof System Material ROOFSYSTYP Roof System Type ROOF_CONN Roof Connections FLOOR_MAT Floor Material FLOOR_TYPE Floor System Type FLOOR_CONN Floor Connections. For each building an EMCA vulnerability class has been assigned following the fuzzy scoring methodology described in Pittore et al., 2018. The related class definition schema (as a .json document) is included in the data package.
DOI: 10.5880/fidgeo.2020.036
title: SpecWa: Spectral remote sensing data and chlorophyll a values of inland waters

description: This dataset contains data of measurements at several inland water bodies in the region around Karlsruhe, Germany, including spectral data and mainly chlorophyll a values. The measurements are motivated by the idea to link remote sensing data and in-situ acquired data for an estimation of selected water parameters solely based on spectral data with data-driven machine learning approaches. To address this topic, we monitored the selected inland water bodies with a spectrometer as a remote sensing device and two in-situ measurement devices to collect among others the chlorophyll a values during the summers of the years 2018 and 2019. The eleven selected water bodies are characterized by a relatively small size and a variety of different chlorophyll a concentrations. In sum, the dataset of the year 2018 consists of 1,305 datapoints and the dataset of the year 2019 includes 2,830 datapoints. Each datapoint is defined by the spectral data in the range of 389 nm to 910 nm and all in-situ measured reference values such as the chlorophyll a concentration of the respective year. Depending on the applied in-situ measurement device, the chlorophyll a concentration or additional water parameters were either measured directly on-site or taken as water samples and analyzed in the laboratory. We provide the data in nearly raw format including information about the water bodies. The data are organized along the year of the measurements. In addition, exemplary scripts for reading and processing the data are included.
DOI: 10.5880/gfz.2.6.2018.003
title: SHARE European Earthquake Catalogue (SHEEC) 1900-2006

description: The SHARE European Earthquake Catalogue (SHEEC) 1900-2006 has been compiled by GFZ Potsdam in the frame of an independant project and represents a temporal and spatial excerpt of "The European-Mediterranean Earthquake Catalogue" (EMEC) for the last millennium (Grünthal & Wahlström, 2012) with a few modifications, which are described in Grünthal et al. (2013). It was compiled under the coordination of INGV, Milan. It builds on the data contained in AHEAD (Archive of Historical Earthquake Data) and with the methodology developed in the frame of the I3, EC project "Network of Research Infrastructures for European Seismology" (NERIES), module NA4. The catalogue (SHEEC) 1000-1899 and the Greek part of 1900-2006 have been partially supported by the EC 7th framework programme project SHARE. Background Information The SHARE European Earthquake Catalogue (SHEEC) 1900-2006 is basically an excerpt of the European-Mediterranean Earthquake Catalogue (EMEC) by Grünthal & Wahlström (2012), the latter covering also historical time in a larger area. Minor differences between SHEEC and EMEC in this time period are described by Grünthal et al. (2013). In both catalogues., the magnitudes threshold is Mw = 3.5 for earthquake locations at latitudes ≥ 44° N and Mw = 4.0 at latitudes &lt; 44° N. The parameters of the catalogue have been determined by: - special algorithms to select one out of several possible focal parameter solutions - location, time, depth (optional), Mw magnitude, and intensity - if given by different sources - transformation equations to calculate Mw, if not original, from other magnitude types according to a strict hierarchy - routines to eliminate fake and other non-tectonic events, and to avoid duplicates