cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.1403060
title: AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJmL rice

description: This is model output from LPJmL for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set. The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019). References: Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015 Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017 Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8
DOI: 10.5281/zenodo.1403205
title: AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: PEPIC wheat

description: This is model output from PEPIC for wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.

The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).

References:

Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015

Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017

Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8
DOI: 10.5281/zenodo.1403209
title: AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: PEPIC rice

description: This is model output from PEPIC for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.

The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).

References:

Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015

Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017

Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8
DOI: 10.5281/zenodo.1403211
title: AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: PEPIC maize

description: This is model output from PEPIC for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.

The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).

References:

Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015

Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017

Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8
DOI: 10.5281/zenodo.3604444
title: LiDAR Cluster Statistic of Wind Turbine Wakes

description: Mean and standard deviation of the wake velocity field generated by utility-scale wind turbines for different turbulence intensity of the incoming wind and rotor thrust coefficient. Statistics are retrieved from wind LiDAR measurements. More details in this paper https://onlinelibrary.wiley.com/doi/full/10.1002/we.2430
description: Contact: valerio.iungo@utdallas.edu
description: {"references": ["https://onlinelibrary.wiley.com/doi/full/10.1002/we.2430"]}
DOI: 10.5281/zenodo.3893897
title: Hydraulic and geochemical impact of occasional saltwater intrusions through a submarine spring in a karst and thermal aquifer (Balaruc peninsula near Montpellier, France)

description: Contains geochemical and isotopic data presented in the scientific paper "Hydraulic and geochemical impact of occasional saltwater intrusions through a submarine spring in a karst and thermal aquifer (Balaruc peninsula near Montpellier, France)". Files contain physico-chemical parameters, major ions, rare earth elements and isotopic data (Sr, B) for karst and thermal groundwater samples collected between 2010 and 2018 in the Balaruc-les-Bains (France) area.
DOI: 10.5281/zenodo.3928079
title: R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper

description: This repository contains the R code and data to reproduce figures from the "Multivariate autoregressive modelling and conditional simulation for temporal uncertainty analysis of an urban water system in Luxembourg" paper.
description: For updates please check https://github.com/ArturoTorres/temporal_uncertainty_paper_reproducible.git
DOI: 10.5281/zenodo.3946644
title: Data for "Source identification of atmospheric organic vapors in two European pine forests: Results from Vocus PTR-TOF observations"

description: This file consists of the time series of the measured trace gases, meteorological parameters, and the concentrations of isoprene and monoterpenes in the Landes forest and at the SMEAR Ⅱ station, which have been analyzed in the manuscript "Source identification of atmospheric organic vapors in two European pine forests: Results from Vocus PTR-TOF observations". For more details, please contact the author (haiyan.li@helsinki.fi).
DOI: 10.5281/zenodo.3994593
title: GGCMI Phase 2: Crop model emulators of irrigation water demand (IWD)

description: Polynomial emulators of irrigation water demand for globally gridded crop models from the GGCM phase 2 project. A0: no growing season adaptation. A1: with growing season adaptation. See: https://doi.org/10.5194/gmd-2019-365 and https://doi.org/10.5194/gmd-13-2315-2020 for more details. Note: the GEPIC emulator for maize and soy (A0) is not available.
DOI: 10.5281/zenodo.4071846
title: Simulation results: CryoGrid 3 multi-scale tiling scheme - Low-gradient polygon slope

description: This dataset contains numerical simulation results obtained with the CryoGrid 3 permafrost model for a field site in northeast Siberia. The model code and settings which were used to create these results are available 10.5281/zenodo.4095341. A journal article which describes the simulation results has been accepted for publication in the scientific journal <em>The Cryosphere</em> and is available as a preprint at 10.5194/tc-2020-137. This dataset contains the model output of two simulations for the <em>low-gradient polygon slope</em> setup described in the article.
DOI: 10.5281/zenodo.4139912
title: Data from: European map of groundwater pH and calcium.

description: Description of the files: <strong>ADDITIONAL_RAW_DATA_E-Ukraine_Ca</strong>: Complementary data with Ca measurements compiled from Eastern Ukraine (Excel) <strong>ADDITIONAL_RAW_DATA_E-Ukraine_pH</strong>: Complementary data with pH measurements compiled from Eastern Ukraine (Excel) <strong>Ca_imp_RAW_DATA_used_for_modelling</strong>: Calcium-imputed data used for spatial modelling (Excel) <strong>detailed_maps-panels_Ca_pH</strong>: Final map outputs with details, by European zones (JPEG) <strong>pH_RAW_DATA_used_for_modelling</strong>: Coordinates and pH values used for modeling (Excel) <strong>Raster_data_for_GIS</strong>: Model outputs for GIS (ASCII with ArcGIS project and legends) <strong>sites_ca_GIS_points</strong>: Point data with Calcium measurements used for modelling (shapefile) <strong>sites_pH_GIS_points</strong>: Point data with pH measurements used for modelling (shapefile)
DOI: 10.5281/zenodo.4569847
title: Data and code from the publication "Performance of open-path GasFinder3 devices for CH4 concentration measurements close to ambient levels."

description: This dataset contains R code and data from the publication "Performance of open-path GasFinder3 devices for CH4 concentration measurements close to ambient levels." published in AMT (Atmospheric Measurement Techniques).
DOI: 10.5281/zenodo.4600691
title: Simulation results: CryoGrid 3 multi-scale tiling scheme - Single-tile

description: This dataset contains numerical simulation results obtained with the CryoGrid 3 permafrost model for a field site in northeast Siberia. The model code and settings which were used to create these results are available 10.5281/zenodo.4095341. A journal article which describes the simulation results has been accepted for publication in the scientific journal <em>The Cryosphere</em> and is available as a preprint at 10.5194/tc-2020-137. This dataset contains the model output of two simulations for the <em>single-tile</em> setup described in the article.
DOI: 10.5281/zenodo.4600819
title: Simulation results: CryoGrid 3 multi-scale tiling scheme - Polygon

description: This dataset contains numerical simulation results obtained with the CryoGrid 3 permafrost model for a field site in northeast Siberia. The model code and settings which were used to create these results are available 10.5281/zenodo.4095341. A journal article which describes the simulation results has been accepted for publication in the scientific journal <em>The Cryosphere </em>and is available as a preprint at 10.5194/tc-2020-137. This dataset contains the model output of two simulations for the <em>polgyon</em> setup described in the article.
DOI: 10.5285/8e0a573d-61a4-4a6f-9fca-fc34cbd5fb45
title: Sentinel-2 imagery, S6, south-west Greenland, July 2017: Broadband albedo and surface type classification

description: This dataset consists of (1) broadband albedo calculated using a narrowband-to-broadband approximation and (2) surface type classification into snow, clean ice, light algae, heavy algae, cryoconite and water, as determined by a supervised classification algorithm, as applied to Sentinel-2 overpasses of S6, K-transect, south-west Greenland on 20 and 21 July 2017. Funding was provided by the NERC standard grant NE/M021025/1.
description: Sentinel-2 imagery (European Space Agency) downloaded from Sinergise, Slovenia and converted from L1C to L2A reflectance using ESA Sen2Cor at 20 m resolution. Reflectance data ingested into a Random Forests classifier which was first trained on field data acquired in "Multi-spectral unmanned aerial system imagery, S6, south-west Greenland, July 2017: Levels 2 (ground reflectance) and 3 (broadband albedo and surface type classification)" (DOI: 10.5285/77ca631f-a3a4-4f26-bc90-57bb17baa6fc), in order to generate classification maps of ice sheet surface type across the study area. A narrowband-to-broadband albedo conversion was also applied following Liang (2001, Remote Sensing of Environment).
description: Instrumentation: MicaSense RedEdge multispectral camera integrated onto Steadidrone Mavrik-M quadcopter. ASD FieldSpec Pro 3.
description: Good. Sentinel-2 acquisitions used were clear-sky over study area of interest. See Tedstone et al. (2020, TC) for details of classifier algorithm accuracy.
DOI: 10.5676/dwd_gpcc/fd_m_v2018_050
title: GPCC Full Data Monthly Version 2018.0 at 0.5°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data
title: Gridded Monthly Totals

description: This is the centennial GPCC Full Data Monthly Product of monthly global land-surface precipitation based on ~ 80,000 stations world-wide that feature record durations of 10 years or longer.
			The data coverage per month varies from ~10,000 to more than 50,000 stations.
			This product contains the monthly totals on a regular grid with a spatial resolution of 0.25° 0.5° x 0.5° latitude by longitude.
			Precipitation anomalies at the stations are interpolated and then superimposed on the GPCC Climatology V2018 in the corresponding resolution (DOI: 10.5676/DWD_GPCC/CLIM_M_V2018_025).
			The temporal coverage of the dataset ranges from January 1891 until December 2016.
			The GPCC Full Data Monthly Product is the most accurate in situ precipitation reanalysis data set of GPCC.
			Moreover it supports regional climate monitoring, model validation, climate variability analysis and water resources assessment studies.
description: General user recommendations: Users are referred to following gridded Global Precipitation Climate Centre (GPCC) products: GPCC Climatology 1951-2000 (GPCC_CLIM) with high representativeness,
			for application as a reference, and for utilization of the anomaly interpolation method. GPCC Full Data Reanalysis (GPCC_FD) with high accuracy, for, e.g., verification of models,
			or for analysis of historic global precipitation, or for research concerning the global water cycle, e.g., trend and teleconnection analyses.
			GPCC Monitoring Product (GPCC_MP) with high availability and reasonable timeliness, for applications like calibration of satellite data, or early annual reporting.
			GPCC First Guess Product (GPCC_FG) with high timeliness, to be applied for, e.g., drought monitoring.
DOI: 10.5676/gruan/dpkg-2019-1
title: Data package related to article 'Progress in managing the transition from the RS92 to the Vaisala RS41 as the operational radiosonde within the GCOS Reference Upper-Air Network' (10.5194/gi-2019-36)
DOI: 10.5683/sp2/pgyqev
title: SSAR magnitude correlation code, catalog, and plot data

description: The files contain the code used to carry out the statistical analysis of magnitude correlations for the self-similar aftershock rates (SSAR) model, the catalog ("TimeMagLT.txt") used in the analysis, along with the data for the plots.
DOI: 10.6084/m9.figshare.11371455
title: hpp-data.zip

description: -----------------------DATA README-----------------------The data in this package is from a paper:<br>Signal Processing for In-Situ Detection of Effective Heat Pulse Probe Spacing Radius as the Basis of a Self-Calibrating Heat Pulse Probeby Nicholas J. Kinar, John W. Pomeroy, B.-C. Si<br>Please see the paper and associated source code for further details.<br>Data contact:Dr. Nicholas J. KinarSmart Water Systems LabUniversity of Saskatchewann.kinar@usask.ca<br>-----------------------<br>The data in each file is stored in CSV format. Each file name describes the nominal heat strength used for the heating pulse and the duration of heating.The total time of experiment is also encoded in the file name. The associated source code uses the file name to extract the metadata shown below.<br>YYY-Xwm-Xsec-heating-Xmin-ZZZZ.csv<br>YYY is the material typeXwm is the heating strength (W/m)Xsec is the time of heating (s)Xmin is the total time of experiment (minutes)ZZZZ is the repetition identifier<br>An example of this file format follows:<br>sand-45wm-8sec-heating-3min-total-rep1.csv<br>-----------------------<br>Inside each CSV file, a single time sample is stored per line. Commas delineate columns. Column #1in the table below is the leftmost column in the CSV file. The time series begins at index 0.The two sets below show the mapping between column number and variables in each column.<br>{1, 2, 3, 4, 5, 6, 7, 8, 9}{Sample number, Vout, dV, I, Rw, T1, T2, DAC tuning code, qprime}<br>-------------------------------------------------------Column | Identifier-------------------------------------------------------1 Time series sample number (starting at 0)2 Output voltage to the heating element (V)3 Voltage drop over the current sense resistor connected to the heating element (V)4 Current through the heating element (A)5 Resistance of the heating element6 Temperature inside of the heating needle7 Temperature of the needle at an offset distance to the heating needle8 DAC tuning code used for control in the PID loop9 Heat strength at each time step
DOI: 10.6084/m9.figshare.26347657.v1
title: Supplementary Materials: SM Table 1

description: Detailed literature review data on the occurrence and fate of emerging and persistent organic pollutants in dumpsite leachate, soils, and crops.
DOI: 10.7289/v58g8j02
title: NOAA GOES-R Series Advanced Baseline Imager (ABI) Level 2 Cloud Optical Depth (COD)

description: The GOES-R Advanced Baseline Imager (ABI) Cloud Optical Depth product contains an image with pixel values identifying the measure of the extinction due to condensed water or ice clouds at a wavelength of 0.64 um. Separate algorithms are used for daytime, solar zenith angle to 82 degrees, and nighttime, solar zenith angle greater than 82 degrees, conditions. The product is generated in combination with the Cloud Particle Size product by the same algorithms. The product includes data quality information that identifies whether the daytime or nighttime algorithm generated the pixel, and provides an assessment of the cloud optical depth data values for on-earth pixels. The cloud optical depth value is a dimensionless quantity. The Cloud Optical Depth product image is produced on the ABI fixed grid at 4 km resolution for Full Disk and 2 km resolution for CONUS coverage regions. The product's spatial resolution increased from 4 km to 2 km on 22 March 2023. Product data is produced under the following conditions for the daytime algorithm: Cloudy; Geolocated source data to local zenith angles of 65 degrees and to solar zenith angles of 82 degrees. Product data generated by the nighttime algorithm is produced under the following conditions: Cloudy; Geolocated source data to local zenith angles of 70 degrees and solar zenith angles between 82 and 180 degrees. The cloud optical depth values reported range from 0 to 160. The sensitivity of the product to high optical depths is limited for nighttime conditions to the nighttime maximum threshold, which is an optical depth value of 16. Cloud Optical Depth product data is identified as degraded in the terminator region, which is a solar zenith angle between 65 and 82 degrees for the daytime algorithm, and 82 and 90 degrees for the nighttime algorithm.
DOI: 10.7289/v5w9574v
title: NOAA Next Generation Radar (NEXRAD) Level II Base Data
DOI: 10.7488/ds/7718
title: Genotype data of Sasso chicken
title: Genotype data for commercial T451A Sasso chickens raised in Ethiopia

description: This dataset pertains to genotypes of individual Sasso chickens that were raised in semi-scavenging conditions in Ethiopia as part of a project collaboration with ILRI to study these commercial chickens. Data pertains to individual SNPs obtained from imputed sequences after quality control procedures (described in the attached readme.txt file). This dataset contains the genotypes, in PLINK binary format, for 2,358 individuals genotypes with around 2.9 million SNPs across the genome. This data has been already through quality control procedures with the following steps: only bi-allelic markers, minor allele frequency threshold of 0.02, genotype and sample call rates of 0.9 and linkage disequilibrium pruning (r2 &gt; 0.80). Also, samples with IBS &gt; 0.98 were removed due to being considered as duplicates.
DOI: 10.13093/permos-2019-01
title: Swiss Permafrost Monitoring Network Database (PERMOS Database)

description: Ground temperature time series in daily, monthly and annual resolution measured in permafrost boreholes, Near ground surface temperature time series in daily, monthly and annual resolution measured at the PERMOS sites, Electrical resistivity tomography (ERT) time series in annual resolution measured at fixed profiles at the PERMOS sites, Terrestrial geodetic survey (TGS)time series in annual resolution measured on actively moving rock glaciers, related meta data.