cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7419306
title: Data sources for the groundwater depletion manuscript in Water Resources Research

description: Here, you can access the source files for the figures (and tables) of the publication (see reference). Basically, you find the model output (WaterGAP 2.2a) for global scaled groundwater storage, total water storage, baseflow, groundwater recharge (diffuse and below surface water bodies) and a table where location of grid cell and belonging continental area (e.g. to convert values into km³) is given. In addition, an Excel-File for the diagram of HPA (Figure 2) is accessible. First part of the file name represents the model variant (IRR100, IRR100_S, IRR70_S, NOUSE_S, for details see the manuscript), then the variable name and unit is given (Total Water Storages [mm], groundwater storage [mm], Qb (baseflow) [mm], Rg (diffuse groundwater recharge) [mm], Rg_swb (groundwater recharge below surface water bodies) [mm]). File format is a zipped netCDF. The table "lat_lon_cont_area.txt" contains the ArcID (internal grid cell number), coordinates and the continental area which is used for WaterGAP calculations. Original data description: https://www.uni-frankfurt.de/49903932/6__GW_depletion
description: Model output is given in netCDF format. There are some nice tools to manipulate or convert netCDF (as CDO, Panoply, GrADS). Anyhow, if you struggle with this format, please send a request for a different file format to hannes.mueller.schmied@em.uni-frankfurt.de. In addition, a table with coordinates and area used in WaterGAP is provided in case of the need to convert the units. Source for Figure 2 (HPA) is available as MS Excel document.
description: {"references": ["D\u00f6ll, P., H. M\u00fcller Schmied, C. Schuh, F. T. Portmann, and A. Eicker (2014), Global-scale assessment of groundwater depletion and related groundwater abstractions: Combining hydrological modeling with information from well observations and GRACE satellites, Water Resour. Res., 50, doi:10.1002/2014WR015595."]}
DOI: 10.5281/zenodo.7419383
title: Data sources for the manuscript on groundwater stress indicators published in Water Resources Research

description: We computed seven global-scale groundwater stress indicators at the 0.5° grid-cell level and for transboundary aquifers&gt; 20,000 km². All indicators were calculated for current conditions (1981-2010 or 2001-2010) based on a homogenized version of the concatenated WATCH Forcing Data ERA-40 (WFD) and WFD ERA-Interim data sets (WFDEI). In addition, four of the indicators were computed for the 2050s (2041-2070) under the worst-case greenhouse gas emissions scenario RCP8.5 applying ten climate and irrigation scenarios. The scenarios were derived by combining two irrigation scenarios (“AAI constant” and “AAI LandSHIFT”) with model output from the five global climate models GFDL-ESM2M, HadGEM2 -ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M. Here, we provide the WaterGAP model output used to compute the groundwater stress indicators. A description of the indicators and the underlying data can be found in the reference below. Moreover, a table with coordinates and grid-cell area [km²] used in WaterGAP is provided for the conversion of units. The model output comprises: 1. Monthly groundwater recharge (GWR and GWRswb) 1981-2010 and 2041-2070 [mm/month, km³/month] 2. Monthly groundwater withdrawals (WWg) 1981-2010 (constWU, transWU) and 2041-2070 (constWU) [m³/month] 3. Monthly net abstractions from groundwater (NAg) 1981-2010 (constWU, transWU) and 2041-2070 (constWU) [m³/month] 4. Monthly groundwater discharge (“gwrunoff”) 2001-2010 from a model run with human water use (transWU) [mm/month] 5. Monthly groundwater discharge (“gwrunoff”) 2001-2010 from a model run without human water use (NAT) [mm/month] 6. Monthly groundwater storage 2001-2010 from a model run with human water use (transWU) [mm] 7. Monthly groundwater storage 2001-2010 from a model run without human water use (NAT) [mm]
description: Purpose: Computation of groundwater stress indicators at the 0.5° grid-cell level or for larger spatial units such as transboundary aquifers. We advise the use for global assessments or for spatial units larger than 20,000 km².
description: {"references": ["Herbert, C. & D\u00f6ll, P. (2019): Global assessment of current and future groundwater stress with a focus on transboundary aquifers. Water Resour. Res., 55. doi: 10.1029/2018WR023321."]}
DOI: 10.5281/zenodo.7419382
title: Data sources for the manuscript on groundwater stress indicators published in Water Resources Research

description: We computed seven global-scale groundwater stress indicators at the 0.5° grid-cell level and for transboundary aquifers&gt; 20,000 km². All indicators were calculated for current conditions (1981-2010 or 2001-2010) based on a homogenized version of the concatenated WATCH Forcing Data ERA-40 (WFD) and WFD ERA-Interim data sets (WFDEI). In addition, four of the indicators were computed for the 2050s (2041-2070) under the worst-case greenhouse gas emissions scenario RCP8.5 applying ten climate and irrigation scenarios. The scenarios were derived by combining two irrigation scenarios (“AAI constant” and “AAI LandSHIFT”) with model output from the five global climate models GFDL-ESM2M, HadGEM2 -ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M. Here, we provide the WaterGAP model output used to compute the groundwater stress indicators. A description of the indicators and the underlying data can be found in the reference below. Moreover, a table with coordinates and grid-cell area [km²] used in WaterGAP is provided for the conversion of units. The model output comprises: 1. Monthly groundwater recharge (GWR and GWRswb) 1981-2010 and 2041-2070 [mm/month, km³/month] 2. Monthly groundwater withdrawals (WWg) 1981-2010 (constWU, transWU) and 2041-2070 (constWU) [m³/month] 3. Monthly net abstractions from groundwater (NAg) 1981-2010 (constWU, transWU) and 2041-2070 (constWU) [m³/month] 4. Monthly groundwater discharge (“gwrunoff”) 2001-2010 from a model run with human water use (transWU) [mm/month] 5. Monthly groundwater discharge (“gwrunoff”) 2001-2010 from a model run without human water use (NAT) [mm/month] 6. Monthly groundwater storage 2001-2010 from a model run with human water use (transWU) [mm] 7. Monthly groundwater storage 2001-2010 from a model run without human water use (NAT) [mm]
description: Purpose: Computation of groundwater stress indicators at the 0.5° grid-cell level or for larger spatial units such as transboundary aquifers. We advise the use for global assessments or for spatial units larger than 20,000 km².
description: {"references": ["Herbert, C. & D\u00f6ll, P. (2019): Global assessment of current and future groundwater stress with a focus on transboundary aquifers. Water Resour. Res., 55. doi: 10.1029/2018WR023321."]}
DOI: 10.5281/zenodo.7422380
title: Output of the Global Crop Water Model GCWM

description: Crop water requirements of irrigated crops and crop water use of rainfed crops is calculated by the Global Crop Water Model (GCWM) for the period 1998-2002 based on a soil water balance performed in daily time steps.Crop yields of rainfed and irrigated crops are distinguished by downscaling yields reported in national and subnational agricultural statistics (Monfreda et al., 2008) by considering drought stress simulated for rainfed agriculture. Virtual water contents are calculated as ratio between crop water use and crop yield. Domestic virtual water flows are determined by calculating a balance between crop production and crop consumption at grid cell level and establishing flows from surplus cells (major production areas) to deficit cells (highly populated regions). Crop growing areas, sowing and harvest dates were derived from the global data set of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000). Consequently, crop water use (blue and green) is computed for the crops wheat, rice, maize, barley, rye, millet, sorghum, soybeans, sunflower, potatoes, cassava, sugar cane, sugar beet, oil palm, rapeseed/canola, groundnuts/peanuts, pulses, citrus, date palm, grapes/vine, cocoa, coffee, cotton and unspecified other crops (other perennial crops, other annual crops, fodder grasses). For the calculation of crop yields, virtual water contents and virtual water flows we distinguish in addition the usage of maize, rye and sorghum for grain or forage. Virtual water flows are computed for crops traded mainly as primary commodities (all specific crops except of sugar cane, sugar beet and cotton). The data set has a spatial resolution of 5 arc-minutes by 5 arc-minutes. Format: ASCII-grids (global, 5 arc-minute) or text files (country data).
description: Purpose: Assessments of the impact ofproduction and consumption of crops on water resources or, vice versa, the impact of water management on agricultural production. We advise to use the data set for global assessments or at the scale of world regions.
description: {"references": ["Hoff, H., D\u00f6ll, P., Fader, M., Gerten, D., Hauser, S., & Siebert, S. (2014): Water footprints of cities - indicators for sustainable consumption and production. Hydrology and Earth System Sciences 18, 213-226, 2014. DOI:10.5194/hess-18-213-2014", "Siebert, S. & D\u00f6ll, P. (2010): Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation. Journal of Hydrology 384, 198-217. DOI:10.1016/j.jhydrol.2009.07.031."]}
DOI: 10.5281/zenodo.7422379
title: Output of the Global Crop Water Model GCWM

description: Crop water requirements of irrigated crops and crop water use of rainfed crops is calculated by the Global Crop Water Model (GCWM) for the period 1998-2002 based on a soil water balance performed in daily time steps.Crop yields of rainfed and irrigated crops are distinguished by downscaling yields reported in national and subnational agricultural statistics (Monfreda et al., 2008) by considering drought stress simulated for rainfed agriculture. Virtual water contents are calculated as ratio between crop water use and crop yield. Domestic virtual water flows are determined by calculating a balance between crop production and crop consumption at grid cell level and establishing flows from surplus cells (major production areas) to deficit cells (highly populated regions). Crop growing areas, sowing and harvest dates were derived from the global data set of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000). Consequently, crop water use (blue and green) is computed for the crops wheat, rice, maize, barley, rye, millet, sorghum, soybeans, sunflower, potatoes, cassava, sugar cane, sugar beet, oil palm, rapeseed/canola, groundnuts/peanuts, pulses, citrus, date palm, grapes/vine, cocoa, coffee, cotton and unspecified other crops (other perennial crops, other annual crops, fodder grasses). For the calculation of crop yields, virtual water contents and virtual water flows we distinguish in addition the usage of maize, rye and sorghum for grain or forage. Virtual water flows are computed for crops traded mainly as primary commodities (all specific crops except of sugar cane, sugar beet and cotton). The data set has a spatial resolution of 5 arc-minutes by 5 arc-minutes. Format: ASCII-grids (global, 5 arc-minute) or text files (country data).
description: Purpose: Assessments of the impact ofproduction and consumption of crops on water resources or, vice versa, the impact of water management on agricultural production. We advise to use the data set for global assessments or at the scale of world regions.
description: {"references": ["Hoff, H., D\u00f6ll, P., Fader, M., Gerten, D., Hauser, S., & Siebert, S. (2014): Water footprints of cities - indicators for sustainable consumption and production. Hydrology and Earth System Sciences 18, 213-226, 2014. DOI:10.5194/hess-18-213-2014", "Siebert, S. & D\u00f6ll, P. (2010): Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation. Journal of Hydrology 384, 198-217. DOI:10.1016/j.jhydrol.2009.07.031."]}
DOI: 10.6084/m9.figshare.21707603
title: Land use raster data.zip

description: Land use raster data in the middle and lower reaches of the Yangtze River, 2005-2020
DOI: 10.6084/m9.figshare.21707603.v1
title: Land use raster data.zip

description: Land use raster data in the middle and lower reaches of the Yangtze River, 2005-2020
DOI: 10.5281/zenodo.7422506
title: MIRCA2000

description: Crop water requirements of irrigated crops and crop water use of rainfed crops is calculated by the Global Crop Water Model (GCWM) for the period 1998-2002 based on a soil water balance performed in daily time steps.Crop yields of rainfed and irrigated crops are distinguished by downscaling yields reported in national and subnational agricultural statistics (Monfreda et al., 2008) by considering drought stress simulated for rainfed agriculture. Virtual water contents are calculated as ratio between crop water use and crop yield. Domestic virtual water flows are determined by calculating a balance between crop production and crop consumption at grid cell level and establishing flows from surplus cells (major production areas) to deficit cells (highly populated regions). Crop growing areas, sowing and harvest dates were derived from the global data set of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000). Consequently, crop water use (blue and green) is computed for the crops wheat, rice, maize, barley, rye, millet, sorghum, soybeans, sunflower, potatoes, cassava, sugar cane, sugar beet, oil palm, rapeseed/canola, groundnuts/peanuts, pulses, citrus, date palm, grapes/vine, cocoa, coffee, cotton and unspecified other crops (other perennial crops, other annual crops, fodder grasses). For the calculation of crop yields, virtual water contents and virtual water flows we distinguish in addition the usage of maize, rye and sorghum for grain or forage. Virtual water flows are computed for crops traded mainly as primary commodities (all specific crops except of sugar cane, sugar beet and cotton). The data set has a spatial resolution of 5 arc-minutes by 5 arc-minutes. Format: ASCII-grids (global, 5 arc-minute) or text files (country data). For a detailed description of each folder see https://www.uni-frankfurt.de/45218023/MIRCA and https://www.uni-frankfurt.de/45218031/Data_download_center_for_MIRCA2000.
description: Purpose: Modelling of water use by irrigated and rainfed agriculture, modelling of blue and green virtual water content, modelling crop productivities, development of data sets on global cropland and crop types, modelling of the impact of irrigated agriculture on global (or regional) climate. We advise to use the data set for global assessments or at the scale of world regions.
description: {"references": ["Portmann, F. T., Siebert, S. & D\u00f6ll, P. (2010): MIRCA2000 \u2013 Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling, Global Biogeochemical Cycles, 24, GB1011, doi:10.1029/2008GB003435."]}
DOI: 10.5281/zenodo.7422505
title: MIRCA2000

description: Crop water requirements of irrigated crops and crop water use of rainfed crops is calculated by the Global Crop Water Model (GCWM) for the period 1998-2002 based on a soil water balance performed in daily time steps.Crop yields of rainfed and irrigated crops are distinguished by downscaling yields reported in national and subnational agricultural statistics (Monfreda et al., 2008) by considering drought stress simulated for rainfed agriculture. Virtual water contents are calculated as ratio between crop water use and crop yield. Domestic virtual water flows are determined by calculating a balance between crop production and crop consumption at grid cell level and establishing flows from surplus cells (major production areas) to deficit cells (highly populated regions). Crop growing areas, sowing and harvest dates were derived from the global data set of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000). Consequently, crop water use (blue and green) is computed for the crops wheat, rice, maize, barley, rye, millet, sorghum, soybeans, sunflower, potatoes, cassava, sugar cane, sugar beet, oil palm, rapeseed/canola, groundnuts/peanuts, pulses, citrus, date palm, grapes/vine, cocoa, coffee, cotton and unspecified other crops (other perennial crops, other annual crops, fodder grasses). For the calculation of crop yields, virtual water contents and virtual water flows we distinguish in addition the usage of maize, rye and sorghum for grain or forage. Virtual water flows are computed for crops traded mainly as primary commodities (all specific crops except of sugar cane, sugar beet and cotton). The data set has a spatial resolution of 5 arc-minutes by 5 arc-minutes. Format: ASCII-grids (global, 5 arc-minute) or text files (country data). For a detailed description of each folder see https://www.uni-frankfurt.de/45218023/MIRCA and https://www.uni-frankfurt.de/45218031/Data_download_center_for_MIRCA2000.
description: Purpose: Modelling of water use by irrigated and rainfed agriculture, modelling of blue and green virtual water content, modelling crop productivities, development of data sets on global cropland and crop types, modelling of the impact of irrigated agriculture on global (or regional) climate. We advise to use the data set for global assessments or at the scale of world regions.
description: {"references": ["Portmann, F. T., Siebert, S. & D\u00f6ll, P. (2010): MIRCA2000 \u2013 Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling, Global Biogeochemical Cycles, 24, GB1011, doi:10.1029/2008GB003435."]}
DOI: 10.6084/m9.figshare.21707606
title: China Urban Statistical Annual report.zip

description: Data on China's urban development indicators from 2005 to 2020e maps from 2005 to 2020
DOI: 10.26190/unsworks/24031
title: PAART study data for cognitive change analysis

description: This two-year cohort study known as the Predictors of Adherence to Antiretroviral Therapy (PAART) study was conducted between September 2013 and November 2015 to evaluate factors influecncing sub-optimal adherance to anti-retroviral trreatment among people living with HIV (PLHIV). A total of 522 PLHIV who had a plasma viral load &lt;50 copies/ml participated in this study. Participants were recruited from 17 HIV care services across Australia. As part of the study assessment, cognitive screening data was collected among participants. An anlysis was conducted using the data from this study to determine clinically significant cognitive change among virally suppressed PLHIV. This deidentified individual participant dataset was used for this analysis and is made publicly avialalbe for other researchers.
description: Data was collected at baseline, month-12 and month-24. CogState computerized screening tool was used for the cognitive assessment. Data collected from participants included demographic and socioecnomic factors, healthcare seeking behaviors, life-style factors, general health, comorbidites, HIV-related factors, and quality of life.This published dataset is formatted in one line per patient for each time point.
DOI: 10.26190/unsworks/24124
title: Long-term health outcomes among CNS HAND Study cohort

description: The CNS HAND study was primarily conducted among 254 people living with HIV (PLHIV) and 72 HIV-negative persons from a primary care clinic in Sydney, Australia named Holdsworth House General Practice to assess the burden of HIV-associated neurocognitive disorder (HAND) among PLHIV and to validate the CogState computerized cognitive screening tool for the diagnosis of HAND. This baseline study took place between October 2011 and October 2012. Exclusion criteria at the entry of the study was alcohol or substance intoxication. Providing that majority of the study participants continue receiving care at the clinic, their long-term health outcome data on age-related non-AIDS conditions (major neurological and cognitive disorders, cardiovascular diseases, diabetes, osteoporosis, non-AIDS cancers, and chronic kidney, liver and lung diseases) were collected in June 2021 censoring the study end point at the end of April 2021. Three HIV-negative participants were excluded from this long-term outcome study because of seroconversion during the study follow-up period. This deidentified individual participant dataset was used for survival analyses to identify if PLHIV despite under suppressive combination antiretroviral therapy has higher risks for age-related non-AIDS conditions compared to age and lifestyle-matched HIV-negative persons.
description: Baseline data was collected between October 2010 and October 2011. Follow-up outcome data on age-related non-AIDS conditions was collected from the study entry until April the 30th 2021. CogState computerized screening tool was used for the cognitive assessment at baseline. In addition, participants’ demographics, medical history, mood status, alcohol and drug use disorders, and functional status were assessed through interviews and self-reported questionnaire at the baseline. This published dataset is formatted in wide form with each row representing a unique participant covering both baseline and follow-up visits.
DOI: 10.26190/unsworks/24125
title: CNS HAND Study data for cognitive aging analysis

description: The CNS HAND study was primarily conducted among 254 people living with HIV (PLHIV) and 72 HIV-negative persons from a primary care clinic in Sydney, Australia named Holdsworth House General Practice to assess the burden of HIV-associated neurocognitive disorder (HAND) among PLHIV and to validate the CogState computerized cognitive screening tool for the diagnosis of HAND. The study took place between October 2011 and December 2013. Exclusion criteria at the entry of the study was alcohol or substance intoxication. An analysis was conducted using the data from this study to determine premature cognitive aging among PLHIV. This deidentified individual participant dataset was used for this analysis and is made publicly available for other researchers.
description: Data was collected at baseline and Month 6. A total of 208 PLHIV and 58 HIV-negative participants attended the Month-6 follow-up visit. CogState computerized screening tool was used for the cognitive assessment. In addition, participants’ demographics, medical history, mood status, alcohol and drug use disorders, and functional status were assessed through interviews and self-reported questionnaire. This published dataset is formatted in wide form with each row representing a unique participant covering both baseline and follow-up visits.
DOI: 10.6084/m9.figshare.21707510
title: modified IEEE-24 bus electricity system and Belgian 20-node gas system dataset

description: modified IEEE-24 bus electricity system coupled with Belgian 20-node gas system dataset
DOI: 10.6084/m9.figshare.21707510.v1
title: modified-IEEE-24-bus-electricity-system-coupled-with-Belgian-20-node-gas-system

description: modified-IEEE-24-bus-electricity-system-coupled-with-Belgian-20-node-gas-system
DOI: 10.6084/m9.figshare.21707630.v1
title: Small to Moderate Earthquakes and Microseisms recorded at Huainan Deep Underground Lab

description: This data set is several three-component seismic data observed in Huainan Deep Underground Lab, including several small to moderate earthquakes and microseisms. All the data are recorded by CMG-40TDE broadband seismometers, and has been preprocessed by removing the linear trend and mean, removing instrument response and filtering.
DOI: 10.34740/kaggle/dsv/4707137
title: Elon Musk Tweets
DOI: 10.6084/m9.figshare.21707528
title: Data submission-Phuong Van Nguyen.xls

description: Data include 2 factors (soil type and plant species) with variables encompassing shoot dry matter yield, phosphorus uptake, phosphorus fractions and root low molecular weight organic anions. Depletion or accumulation of phosphorus in the rhizosphere is the difference of phosphorus concentrations between rhizosphere and bulk soils.