cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3891722
title: Global Energy Virtual Water Trade Network and Country Electricity Water Footprints

description: Global energy trade accounts for between 22 and 32% of total energy consumption, approximately 2.0 x 10<sup>11</sup> GJ of energy. Based on concepts of the energy-water nexus and water footprinting, we determine the water intensity of the energy trade for 11 different energy commodities, including primary (i.e., fossil fuels) and secondary (i.e., electricity) energy. In this data description, we present a database on the water footprint of country-to-country energy trade for 2010--2018 and water intensity values of electricity for each country. The database includes the country of origin, trade partner, type of energy commodity, quantity traded, value of the energy trade, a mean water footprint value in m<sup>3</sup>, and an estimated range of uncertainty water footprint. These data provide the basis for assessing international virtual water trade, water scarcity concerns, and the environmental implications for a changing global energy system.
DOI: 10.5281/zenodo.3891721
title: Global Energy Virtual Water Trade Network and Country Electricity Water Footprints

description: Global energy trade accounts for between 22 and 32% of total energy consumption, approximately 2.0 x 10<sup>11</sup> GJ of energy. Based on concepts of the energy-water nexus and water footprinting, we determine the water intensity of the energy trade for 11 different energy commodities, including primary (i.e., fossil fuels) and secondary (i.e., electricity) energy. In this data description, we present a database on the water footprint of country-to-country energy trade for 2010--2018 and water intensity values of electricity for each country. The database includes the country of origin, trade partner, type of energy commodity, quantity traded, value of the energy trade, a mean water footprint value in m<sup>3</sup>, and an estimated range of uncertainty water footprint. These data provide the basis for assessing international virtual water trade, water scarcity concerns, and the environmental implications for a changing global energy system.
DOI: 10.5281/zenodo.1315471
title: G³M 1.0 steady-state model output

description: Steady-state model output of the global gradient-based groundwater model G³M 1.0. Related to the publication in GMD "Beyond the bucket - Developing a global gradient-based groundwater model (G³M v1.0) for a global hydrological model from scratch" Contains the following global outputs: * hydraulic head * surface water body to/from groundwater flows * conductance values for surface water bodies
DOI: 10.5281/zenodo.1315470
title: G³M 1.0 steady-state model output

description: Steady-state model output of the global gradient-based groundwater model G³M 1.0. Related to the publication in GMD "Beyond the bucket - Developing a global gradient-based groundwater model (G³M v1.0) for a global hydrological model from scratch" Contains the following global outputs: * hydraulic head * surface water body to/from groundwater flows * conductance values for surface water bodies
DOI: 10.5281/zenodo.3538207
title: Dataset of Floods in Germany 1950-2010

description: This is a data set comprising 29,248 flood events which were sampled from time-series of daily streamflow of 374 catchments across Germany over the period 1950-2010. The flood events were sampled by applying a Peak-Over-Threshold}approach on each catchment, separately. For each of the events, a set of preconditions was collected: First, preconditions in catchment averages of daily precipitation sum [mm/d], daily mean soil moisture [\%] and daily mean temperature of each flood event were sampled: Going back in time from the day of the flood event, the means of predictor values over multiple time periods, "delta t" = [0, 1, 3, 5, 7], were extracted. Note that P is the cumulative sum of rainfall and snowmelt. Both P and SM are model output from the "Mesoscale Hydrological Model" (see references below). Second, data on 10 catchment attributes of various types, i.e. static predictors, are included for each of the catchments. These include data on the average climatic conditions (aridity index, mean annual precipitation), topography (slope, elevation), geomorphology (channel slope, drainage density).
description: Further Information to the paper that originates from this dataset will be linked in a future version of this dataset. The code for the above paper can be found at https://gitlab.com/lennartschmidt/floodmagnitude/.
description: {"references": ["Zink, Matthias, et al. \"A high-resolution dataset of water fluxes and states for Germany accounting for parametric uncertainty.\" (2017)."]}
DOI: 10.5281/zenodo.3538206
title: Dataset of Floods in Germany 1950-2010

description: This is a data set comprising 29,248 flood events which were sampled from time-series of daily streamflow of 374 catchments across Germany over the period 1950-2010. The flood events were sampled by applying a Peak-Over-Threshold}approach on each catchment, separately. For each of the events, a set of preconditions was collected: First, preconditions in catchment averages of daily precipitation sum [mm/d], daily mean soil moisture [\%] and daily mean temperature of each flood event were sampled: Going back in time from the day of the flood event, the means of predictor values over multiple time periods, "delta t" = [0, 1, 3, 5, 7], were extracted. Note that P is the cumulative sum of rainfall and snowmelt. Both P and SM are model output from the "Mesoscale Hydrological Model" (see references below). Second, data on 10 catchment attributes of various types, i.e. static predictors, are included for each of the catchments. These include data on the average climatic conditions (aridity index, mean annual precipitation), topography (slope, elevation), geomorphology (channel slope, drainage density).
description: Further Information to the paper that originates from this dataset will be linked in a future version of this dataset. The code for the above paper can be found at https://gitlab.com/lennartschmidt/floodmagnitude/.
description: {"references": ["Zink, Matthias, et al. \"A high-resolution dataset of water fluxes and states for Germany accounting for parametric uncertainty.\" (2017)."]}
DOI: 10.5281/zenodo.3381112
title: Rosalia Times Series Database

description: <strong>Rosalia Times Series Database</strong> The BOKU (University of Natural Resources and Life Sciences Vienna) university demonstration forest Rosalia with an area of 950 ha has been used for research and education since 1875. In 2013 – upon an initiative of a group of researchers in various disciplines – it was decided to extend the so far mainly forestry oriented activities by implementing a hydrological experimental research watershed. The overall objective is to collect data that support the study of transport processes in the system of soil, water, plants and atmosphere. More specifically, emphasis is on bridging the gap between point related measurements and effective values and parameters required for modelling flow and transport processes in watersheds. 2 Objectives The main objectives for the research watershed are to collect data that support the study of transport processes in the system of soil, water, plants and atmosphere emphasis is on bridging the gap between point related measurements and effective values and parameters for modelling watersheds of various sizes to generate comprehensive reference information for research projects on future management and climate change impacts Operation is planned for a period of at least 10 years using only internal resources of the university, to avoid potential interruptions due to project-based short-term availability of personal and financial resources. The objective of this article is to present the research watershed, the data collected and to make these data accessible to the research community.
DOI: 10.5281/zenodo.3381111
title: Rosalia Times Series Database

description: <strong>Rosalia Times Series Database</strong> The BOKU (University of Natural Resources and Life Sciences Vienna) university demonstration forest Rosalia with an area of 950 ha has been used for research and education since 1875. In 2013 – upon an initiative of a group of researchers in various disciplines – it was decided to extend the so far mainly forestry oriented activities by implementing a hydrological experimental research watershed. The overall objective is to collect data that support the study of transport processes in the system of soil, water, plants and atmosphere. More specifically, emphasis is on bridging the gap between point related measurements and effective values and parameters required for modelling flow and transport processes in watersheds. 2 Objectives The main objectives for the research watershed are to collect data that support the study of transport processes in the system of soil, water, plants and atmosphere emphasis is on bridging the gap between point related measurements and effective values and parameters for modelling watersheds of various sizes to generate comprehensive reference information for research projects on future management and climate change impacts Operation is planned for a period of at least 10 years using only internal resources of the university, to avoid potential interruptions due to project-based short-term availability of personal and financial resources. The objective of this article is to present the research watershed, the data collected and to make these data accessible to the research community.
DOI: 10.5281/zenodo.3251555
title: Large repeated measures experiments of acoustic Doppler current profiler (ADCP) streamflow measurements under steady flow conditions (Chauvan 2016 regatta)

description: From 8 to 10 November 2016, 50 laboratories or teams (from 8 different countries) with 50 ADCPs, simultaneously conducted more than 600 ADCP discharge measurements in steady flow conditions (around 14 m<sup>3</sup>/s released by a dam), during three half-days, over 500 m along the Taurion River at Saint-Priest-de-Taurion, France. 26 cross-sections with various shapes and flow conditions, and more or less favourable conditions, were distributed along the river. A specific experiment procedure, which consisted of circulating every team over half of the cross-sections, was implemented in order to quantify the impact of site selection on the discharge measurement<br> uncertainty. The experimental design is presented in Despax et al (2017) available at https://irsteadoc.irstea.fr/cemoa/PUB00055007 Despax, A., Hauet, A., Le Coz, J., Dramais, G., Blanquart, B., Besson, D., & Belleville, A. (2017). Inter-laboratory comparison of discharge measurements with Acoustic Doppler Current Profilers Chauvan field experiments. 8, 9 and 10th November 2016. (Technical report). Lyon, France: Groupe Doppler. (92 p.)
description: The tree structure of files is presented in a pdf file included in the zip file.
DOI: 10.5281/zenodo.3251554
title: Large repeated measures experiments of acoustic Doppler current profiler (ADCP) streamflow measurements under steady flow conditions (Chauvan 2016 regatta)

description: From 8 to 10 November 2016, 50 laboratories or teams (from 8 different countries) with 50 ADCPs, simultaneously conducted more than 600 ADCP discharge measurements in steady flow conditions (around 14 m<sup>3</sup>/s released by a dam), during three half-days, over 500 m along the Taurion River at Saint-Priest-de-Taurion, France. 26 cross-sections with various shapes and flow conditions, and more or less favourable conditions, were distributed along the river. A specific experiment procedure, which consisted of circulating every team over half of the cross-sections, was implemented in order to quantify the impact of site selection on the discharge measurement<br> uncertainty. The experimental design is presented in Despax et al (2017) available at https://irsteadoc.irstea.fr/cemoa/PUB00055007 Despax, A., Hauet, A., Le Coz, J., Dramais, G., Blanquart, B., Besson, D., & Belleville, A. (2017). Inter-laboratory comparison of discharge measurements with Acoustic Doppler Current Profilers Chauvan field experiments. 8, 9 and 10th November 2016. (Technical report). Lyon, France: Groupe Doppler. (92 p.)
description: The tree structure of files is presented in a pdf file included in the zip file.
DOI: 10.5281/zenodo.3262172
title: A rainfall data inter-comparison dataset for Germany

description: The rainfall data inter-comparison dataset is a collection of precipitation statistics calculated from the hourly nationwide German radar climatology (RADKLIM) and radar online adjustment (RADOLAN) composites provided by the German Weather Service (Deutscher Wetterdienst, DWD), which were combined with rainfall statistics derived from rain gauge data for inter-comparison. Moreover, additional information on parameters that can potentially influence radar data quality, such as the height above sea level, information on wind energy plants and the distance to the next radar station, were included in the dataset. The dataset consists of two point shapefiles which are readable with all common GIS. It constitutes a spatially highly resolved rainfall statistics geodataset for the period 2006 - 2017, which can be used for statistical rainfall analyses or for the derivation of model inputs. Furthermore, this data collection has the potential to benefit all users who intend to use precipitation data for any purpose in Germany and to identify the rainfall dataset that is best suited for their application by a straightforward comparison of three rainfall datasets without any tedious data processing and georeferencing. Spatial extent: Germany Spatial Resolution: 1 x 1 km Time period: 2006 - 2017 Data Format: Two point shapefiles Compared precipitation datasets: RADKLIM, RADOLAN, rain gauge data Selection of calculated precipitation statistics: Annual precipitation sum Mean annual precipitation sum Mean seasonal precipitation sums Number of days exceeding a daily precipitation of 1 mm Number of days exceeding a daily precipitation of 20 mm Mean daily precipitation sum of all days exceeding a precipitation sum of 1 mm Mean daily precipitation sum of all days exceeding a precipitation sum of 20 mm Number of NoData entries An Excel file with detailed information on all parameters and on original data sources is also included in the dataset. Original data source URLs: RADKLIM (DWD): https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/reproc/2017_002/bin/ RADOLAN (DWD): ftp://ftp-cdc.dwd.de/pub/CDC/grids_germany/hourly/radolan/historical/bin/ Rain Gauge Data (DWD): https://opendata.dwd.de/climate_environment/CDC/observations_ germany/climate/1_minute/precipitation/ Location of wind energy plants (UFZ): https://www.ufz.de/record/dmp/archive/5467/de/ SRTM DEM (DLR): https://geoservice.dlr.de/egp/
DOI: 10.5281/zenodo.2565836
title: SWAF HR

description: The current dataset presentes a 1km resolution inundation map over the Amazon Basin based on the use of multi-sattelite data sources: SAWF product based on SMOS L3TB, MERIT product based on SRTM datasets and GSWO based on LandSat datasets. A dataset for 2012 is presented here. The product is presented in matlab format with the associated longitue and latitude positions vectors
description: {"references": ["Parrens M., Kerr Y., Al Bitar A., SWAF-HR: A High Spatial and Temporal Resolution Water Surface Extent Product Over the Amazon Basin, IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium"]}
DOI: 10.5281/zenodo.3262171
title: A rainfall data inter-comparison dataset for Germany

description: The rainfall data inter-comparison dataset is a collection of precipitation statistics calculated from the hourly nationwide German radar climatology (RADKLIM) and radar online adjustment (RADOLAN) composites provided by the German Weather Service (Deutscher Wetterdienst, DWD), which were combined with rainfall statistics derived from rain gauge data for inter-comparison. Moreover, additional information on parameters that can potentially influence radar data quality, such as the height above sea level, information on wind energy plants and the distance to the next radar station, were included in the dataset. The dataset consists of two point shapefiles which are readable with all common GIS. It constitutes a spatially highly resolved rainfall statistics geodataset for the period 2006 - 2017, which can be used for statistical rainfall analyses or for the derivation of model inputs. Furthermore, this data collection has the potential to benefit all users who intend to use precipitation data for any purpose in Germany and to identify the rainfall dataset that is best suited for their application by a straightforward comparison of three rainfall datasets without any tedious data processing and georeferencing. Spatial extent: Germany Spatial Resolution: 1 x 1 km Time period: 2006 - 2017 Data Format: Two point shapefiles Compared precipitation datasets: RADKLIM, RADOLAN, rain gauge data Selection of calculated precipitation statistics: Annual precipitation sum Mean annual precipitation sum Mean seasonal precipitation sums Number of days exceeding a daily precipitation of 1 mm Number of days exceeding a daily precipitation of 20 mm Mean daily precipitation sum of all days exceeding a precipitation sum of 1 mm Mean daily precipitation sum of all days exceeding a precipitation sum of 20 mm Number of NoData entries An Excel file with detailed information on all parameters and on original data sources is also included in the dataset. Original data source URLs: RADKLIM (DWD): https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/reproc/2017_002/bin/ RADOLAN (DWD): ftp://ftp-cdc.dwd.de/pub/CDC/grids_germany/hourly/radolan/historical/bin/ Rain Gauge Data (DWD): https://opendata.dwd.de/climate_environment/CDC/observations_ germany/climate/1_minute/precipitation/ Location of wind energy plants (UFZ): https://www.ufz.de/record/dmp/archive/5467/de/ SRTM DEM (DLR): https://geoservice.dlr.de/egp/
DOI: 10.5281/zenodo.2565835
title: SWAF HR

description: The current dataset presentes a 1km resolution inundation map over the Amazon Basin based on the use of multi-sattelite data sources: SAWF product based on SMOS L3TB, MERIT product based on SRTM datasets and GSWO based on LandSat datasets. A dataset for 2012 is presented here. The product is presented in matlab format with the associated longitue and latitude positions vectors
description: {"references": ["Parrens M., Kerr Y., Al Bitar A., SWAF-HR: A High Spatial and Temporal Resolution Water Surface Extent Product Over the Amazon Basin, IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium"]}
DOI: 10.5281/zenodo.1466038
title: Snowpack ensemble simulation at the Kühtai snow monitoring station

description: This dataset presents an ensemble of 230000 point-scale snowpack simulations at the snow monitoring station Kühtai (Tyrol, Austria) over the course of 25 winter seasons. We provide time series of simulated snow water equivalent, errors of these simulations and the corresponding sensitivity indices. The dataset splits in simulations for various sensitivity analysis designs: i.e. to assess the sensitivity of a) forcing data error, model structure, and parametrization, b) different forcing variables while perturbing model structure and parametrization, c) model structures while perturbing forcing errors and parametrization.
DOI: 10.5281/zenodo.1466037
title: Snowpack ensemble simulation at the Kühtai snow monitoring station

description: This dataset presents an ensemble of 230000 point-scale snowpack simulations at the snow monitoring station Kühtai (Tyrol, Austria) over the course of 25 winter seasons. We provide time series of simulated snow water equivalent, errors of these simulations and the corresponding sensitivity indices. The dataset splits in simulations for various sensitivity analysis designs: i.e. to assess the sensitivity of a) forcing data error, model structure, and parametrization, b) different forcing variables while perturbing model structure and parametrization, c) model structures while perturbing forcing errors and parametrization.
DOI: 10.5281/zenodo.7585390
title: Changing intensity of hydroclimatic extreme events revealed by GRACE and GRACE-FO Data sets

description: This archive contains the key data files (including figure data) associated with: Rodell, M., and B. Li, 2023: Changing intensity of hydroclimatic extreme events revealed by GRACE and GRACE-FO, <em>Nature Water</em>, accepted. Figure1_time_series_data.xlsx – Data used to create the 14 inset time series plots in Figure 1. Figure1_wet_map_data.nc – NetCDF file containing the spatial information used to create the<br> “Top wet events” map in Figure 1. Figure_dry_map_data.nc - NetCDF file containing the spatial information used to create the<br> “Top dry events” map in Figure 1. Figure2_data.xlsx – Time series data used to create Figure 2. Figure3_data.xlsx – Time series data used to create Figures 3b and 3c. Figure4_data.xlsx – Location, year, and intensity data used to create the maps in Figure 4. Source data and code used in this study are available as follows. Data Availability The GRACE/FO products (CSR GRACE/GRACE-FO RL06 Mascon Solutions, version 02) used in our analyses are available from the University of Texas Center for Space Research (https://www2.csr.utexas.edu/grace/RL06_mascons.html). The output from a global GRACE/FO data assimilating instance of the Catchment land surface model (GRACEDADM_CLSM025GL_7D 3.0) used to fill the 11-month gap between the GRACE and GRACE-FO missions and 18 additional missing months is available from the Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/datasets/GRACEDADM_CLSM025GL_7D_3.0/). The climate oscillation indicator data can be downloaded from the NOAA Physical Sciences Laboratory (https://psl.noaa.gov/data/climateindices/list/ and https://psl.noaa.gov/gcos_wgsp/Timeseries/DMI/). The global mean temperature data are available from the NASA Goddard Institute for Space Studies (https://data.giss.nasa.gov/gistemp/). Code Availability The python code for the ST-DBSCAN clustering algorithm was obtained from the Github repository, https://github.com/gitAtila/ST-DBSCAN. Statistical analyses were performed and figures were generated using NCL software.
DOI: 10.17037/data.00003261
title: Diagnosis codelist - retinopathy codes (icd-10)

description: HES diagnostic codes for identifying retinopathy.
DOI: 10.17037/data.00003260
title: Diagnosis codelist - retinopathy codes (medcodes)

description: CPRD gold diagnostic codes for identifying retinopathy.
DOI: 10.5517/ccdc.csd.cc25hllw
title: CCDC 2010988: Experimental Crystal Structure Determination

description: Related Article: Xiaobin Wang, Jianqi Chai, Xiangyi Kong, Fei Jin, Min Chen, Chunlong Yang, Wei Xue|2021|Bioorg.Med.Chem.|45|116330|doi:10.1016/j.bmc.2021.116330
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc26lcqx
title: CCDC 2043449: Experimental Crystal Structure Determination

description: Related Article: Jun Sato, Hiroki Kusano, Toshiaki Aoki, Satoru Shibuya, Katsuki Yokoo, Kazuo Komano, Takuya Oguma, Shuhei Matsumoto, Takafumi Sato, Kazuya Yasuo, Kenji Yamawaki|2021|Bioorg.Med.Chem.|46|116343|doi:10.1016/j.bmc.2021.116343
description: Related Article: Jun Sato|2021|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.