cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5067/trmm/tmpa/3h-e-ir/7
title: TRMM (TMPA-RT) Near Real-Time IR precipitation estimate L3 1-hour 0.25 degree x 0.25 degree V7
DOI: 10.5067/viirs/vnp02mod.001
title: VIIRS/NPP Moderate Resolution Bands L1B 6-Min Swath 750m
DOI: 10.5281/zenodo.1044951
title: Coupling Of Hydropso And Phreeqc

description: The folder contains input and output files for coupling hydroPSO and PHREEQC. 

The input files for  running PHREQC and hydroPSO are 'U.phrq', 'ParamFiles.txt',  '<em>ParamRanges.txt'</em>, and (iii) ‘<em>PSO_OBS.txt</em>’.

The out files in folder called PSO.out are 'png folder', 'Velocities', 'Particles', 'model_out', and others.
DOI: 10.5281/zenodo.13898611
title: Revised TOPAS input files for the simulation of WAXS, SAXS and PDF

description: TOPAS input files for discrete and low-dimensional structure models: (1) a benzene molecule, (2) a PbS quantum dot, (4) a hydroxyapatite nano-fibril and (5) turbostratic carbon

TOPAS calculations can be performed with the blank data file or yobs_eqn =1; min 0 max 157.5204 del 0.1

the file path of the blank data file has to be updated

models can be found at https://doi.org/10.5281/zenodo.8169025
DOI: 10.5281/zenodo.1403173
title: AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: pDSSAT soy

description: This is model output from pDSSAT for soy 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.1403183
title: AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: pAPSIM wheat

description: This is model output from pAPSIM 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.1408577
title: AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: GEPIC maize

description: This is model output from GEPIC 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.3677542
title: TECA BARD Parameters

description: Markov-chain Monte Carlo samples of parameters for the TECA Bayesian Atmospheric River Detector described by O'Brien et al. (2020, GMD).
DOI: 10.5281/zenodo.3727224
title: OU/NSSL CLAMPS AERIoe Temperature and Water Vapor Profile Data from LAPSE-RATE

description: The AERIoe algorithm (Turner and Loehnert 2014, Turner and Blumberg 2018) retrieves profiles of temperature and water vapor mixing ratio, together with cloud properties for a single-layer cloud (i.e., LWP, effective radius), from AERI-observed infrared radiance spectrum. The data can be used to characterize the evolution of the planetary boundary layer and boundary layer clouds. This dataset was collected at the Moffat Consolidated School in Moffat, CO during the LAPSE-RATE field campaign. The AERIoe retrieval was run at 15-minute resolution to match the cadence of the UAS that was colocated with the CLAMPS facility. This is a physical-iterative retrieval method. The retrieval of thermodynamic profiles from spectral infrared radiance observations is an ill-posed problem, and thus constraints need to be included in the retrieval algorithm to provide physically plausible results. Here, we use a climatology of 2022 radiosonde profiles collected from Denver during July as our prior information in an optimal estimation framework. As the method uses an optimal estimation framework, a full error covariance matrix of each solution is included in the output file. The 1-sigma uncertainty of each retrieved variable, which is derived from the error covariance matrix, is included for each scientific field and is named "sigma_X", where "X" is the name of the scientific field (e.g., 'temperature'). The information content in the AERI observations, which is in the "dfs" field, on the thermodynamic profiles is primarily concentrated in the lowest 3 km or up to cloud base; the retrieved data should not be used above that level (or used with caution). References: Turner, D. D., and U. Löhnert, 2014: Information Content and Uncertainties in Thermodynamic Profiles and Liquid Cloud Properties Retrieved from the Ground-Based Atmospheric Emitted Radiance Interferometer (AERI). <em>J. Appl. Meteor. Climatol.</em>, <strong>53</strong>, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1. Turner, D. D., and W. G. Blumberg, 2019: Improvements to the AERIoe Thermodynamic Profile Retrieval Algorithm. <em>IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing</em>, <strong>12</strong>, 1339–1354, https://doi.org/10.1109/JSTARS.2018.2874968.
DOI: 10.5281/zenodo.3886911
title: The case of a southern European glacier disappearing under recent warming that survived Roman and Medieval warm periods

description: We upload there the dataset needed to build up the chronological model of the ice core presented in the discussion paper "The case of a southern European glacier disappearing under recent warming that survived Roman and Medieval warm periods" . The readme pdf file presents the methods to create such an age-depth model using the package CLAM v2.2 (Blaauw, 2010) and the settings chose to run it.
DOI: 10.5281/zenodo.3972882
title: Lagrangian moisture sources for an arid region in northeast Greenland

description: dataset of lagrangian moisture sources (February 1979-May 2017) that was used in a research article with the title "Lagrangian detection of precipitation moisture sources for an arid region in northeast Greenland: relations to the North Atlantic Oscillation, sea ice cover and temporal trends from 1979 to 2017" published in the Weather and Climate Dynamics journal (https://wcd.copernicus.org/articles/2/1/2021/, more information in readme.md) <strong>When using this dataset, please refer to the original publication in addition to this Zenodo repository:</strong> - Schuster, L., Maussion, F., Langhamer, L., Moseley, G.E.: Lagrangian detection of precipitation moisture sources for an arid region in northeast Greenland: relations to the North Atlantic Oscillation, sea ice cover, and temporal trends from 1979 to 2017, Weather and Climate Dynamics, 2, 1-17, https://doi.org/10.5194/wcd-2-1-2021, 2021
description: {"references": ["Schuster, L., Maussion, F., Langhamer, L., Moseley, G.E.: Lagrangian detection of precipitation moisture sources for an arid region in northeast Greenland: relations to the North Atlantic Oscillation, sea ice cover, and temporal trends from 1979 to 2017, Weather and Climate Dynamics, 2, 1-17, https://doi.org/10.5194/wcd-2-1-2021, 2021"]}
DOI: 10.5281/zenodo.3995136
title: Raw dataset for publication 10.5194/gchron-2020-1

description: Raw data used to generate figures and tables in doi:10.5194/gchron-2020-1 "The Isotopx NGX and the ATONA Faraday Amplifiers"
DOI: 10.5281/zenodo.4064128
title: Snow cover in the European Alps: Station observations of snow depth and depth of snowfall

description: Auxiliary files, code, and data for paper published in The Cryosphere: Observed snow depth trends in the European Alps 1971 to 2019 https://doi.org/10.5194/tc-15-1343-2021 <strong>Auxiliary files:</strong> aux_paper.zip: Auxiliary figures to the paper (time series showing the consistency of averaging monthly mean snow depth of stations within 500 m elevation bins; times of seasonal snow depth and snow cover duration indices). aux_paper_crocus_comparison.zip: Time series comparing spatial statistical gap filling from paper to gap filling using snow depth assimilation into Crocus snow model (only for subset of stations in the French Alps) aux_paper_monthly_time_series.zip: Plots of monthly time series of snow depth, for each station. aux_paper_spatial_consistency.zip: Aggregate results from spatial consistency (statistical simulation using neighboring stations), and time series of observed versus simulated monthly snow depths. <strong>Code </strong>(working copy, not cleaned, all written in R statistical software): code.zip to read in the different data sources to do quality checks and data processing to perform statistical analyses as in paper to produce figures and tables as in paper <strong>Data</strong>: &gt; 2000 stations from Austria, Germany, France, Italy, Switzerland, and Slovenia Daily stations snow depth and depth of snowfall, as .zips, grouped by data provider. Information on column content is provided in "data_daily_00_column_names_content.txt". Monthly stations mean snow depth, sum of depth of snowfall, maximum snow depth, days with snow cover (1-100cm thresholds), as .zips, grouped by data provider. Information on column content is provided in "data_monthly_00_column_names_content.txt". Meta data (name, latitude, longitude, elevation) in "meta_all.csv", along with an interactive map "meta_interactive_map.html", and column information in "meta_00_column_names_content.txt". If you <strong>use the data you agree to adhere to the respective data provider's terms</strong> as listed in "00_DATA_LICENSE_AND_TERMS.PDF" The license terms especially (and additionally to any other terms of the single data providers) include: <strong>Attribution</strong> — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. [from CC BY 4.0] <strong>Version history:</strong> v1.3: added maxHS and SCD (with various 1-100cm thresholds) to monthly data v1.2: uploaded data v1.1: changes to aux-paper.zip and code.zip as consequence from submitting a revised manuscript v1.0: initial upload
DOI: 10.5285/bbdfd09a04304158b366777eba0d2aeb
title: High resolution Standardized Precipitation Evapotranspiration Index (SPEI) dataset for Africa

description: This dataset consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM). The SPEI dataset covers the whole of the African continent for a 36-year-long period (1981–2016) at a horizontal resolution of 5 km (0.05 deg) and a monthly time resolution. The dataset is provided in NetCDF format with in a Geographic Lat/Lon projection. Due to the lower reliability of SPEI over areas with low hydro-climatic variability, the areas with barren or sparsely vegetated areas in Africa were masked out based on data from the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface type product (MCD12Q1).
DOI: 10.5285/f91cd3ee7b6243d5b7d41b9beaf397e1
title: ESA Sea State Climate Change Initiative (Sea_State_cci): Global remote sensing multi-mission along-track significant wave height, L2P product, version 1.1

description: The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 2P (L2P) data) with a particular focus for use in climate studies. This dataset contains the Version 1.1 Remote Sensing Significant Wave Height product, along-track at approximately 6 km spatial resolution, separated per satellite and pass, including all measurements with flags, corrections and extra parameters from other sources. These are expert products with rich content and no data loss. This first version of the Sea State CCI products is inherited from the GlobWave project building on experience and existing outputs. It extends and improves the GlobWave products which were a post-processing over existing L2 altimeter agency products with additional filtering, corrections and variables. A major improvement consists in a new denoised sea surface height variable using Empirical Mode Decomposition. The altimeter data used in the Sea State CCI dataset v1.1 come from multiple satellite missions spanning from 1991 to 2018 (ERS-1, ERS-2, Topex, Envisat, GFO, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL). Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in the Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band).
DOI: 10.5445/ir/1000099232
title: Training data and emulators for the analysis of sensitivity of deep convective clouds and hail to environmental conditions and microphysics (updated version)

description: This study aims to identify whether model parameters describing atmospheric conditions such as wind shear or model parameters related to cloud microphysics such as the fall velocity of hail lead to larger uncertainties in the prediction of deep convective clouds. In an idealized setup of a cloud-resolving model including a two-moment microphysics scheme we use the approach of statistical emulation to allow for a Monte Carlo sampling of the parameter space, which enables a comprehensive sensitivity analysis. We analyze the impact of three sets of input parameters (environmental conditions, microphysics, combined) on cloud properties (vertically integrated content of six hydrometeor classes), precipitation, the size distribution of hail and diabatic heating rates. This dataset contains the processed model output and the generated emulators when the convection is triggered by a warm bubble.
DOI: 10.5676/dwd_gpcc/fd_d_v1_100
title: GPCC Full Data Daily Version 1.0 at 1.0°: Daily Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data
title: Gridded Daily Totals

description: This is the GPCC Full Data Daily product of daily global land-surface precipitation based on data provided by national meteorological and hydrological services, global and regional data collections as well as WMO GTS-data. This product contains the daily totals on a regular grid with a spatial resolution of 1.0° x 1.0° latitude by longitude. Relative precipitation anomalies at the stations are interpolated by means of ordinary block kriging and then superimposed on the GPCC Full Data Reanalysis V7 (DOI: 10.5676/DWD_GPCC/FD_M_V7_100). The temporal coverage of the dataset ranges from January 1988 until December 2013.
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 Monthly (GPCC_FG_M) and First Guess Daily (GPCC_FG_D) with high timeliness, to be applied for, e.g., drought monitoring.
DOI: 10.5676/eum_saf_cm/sarah/v002_01
title: Surface Radiation Data Set - Heliosat (SARAH) - Edition 2.1

description: The first update of the second edition of the Surface Solar Radiation Data Set - Heliosat
      (SARAH-2) is a satellite-based climate data record of the solar surface irradiance, the
      surface direct irradiance (direct horizontal and direct normalized), the sunshine duration,
      spectral information, and the effective cloud albedo derived from satellite-observations of
      the visible channels of the MVIRI and the SEVIRI instruments onboard the geostationary
      Meteosat satellites. The data are available from 1983 to 2017 and cover the region
      ±65° longitude and ±65° latitude (±60° longitude and
      ±60° latitude for the spectral information). The products are available as
      monthly and daily means, and as 30-min instantaneous data (sunshine duration is available as
      monthly and daily sum) on a regular latitude/longitude grid with a spatial resolution of
      0.05° x 0.05° degrees. The data record is complemented with a comprehensive
      documentation of the algorithms used and the generation of the data record. Validation report
      and user guidance are available as well. This data set includes the data (1983 to 2015) from
      Edition 2.0 of the SARAH data record (doi: 10.5676/EUM_SAF_CM/SARAH/V002).
DOI: 10.6084/m9.figshare.26819887
title: Data final assignment recognition program (RTA)-2024

description: Gelatin-chitosan-TEOS-Ca(OH)<sub>2</sub> composite material has potential as anti-inflammatory a medicament in the exposed pulp
DOI: 10.6084/m9.figshare.28080359
title: Supplementary Material for: “The Road Less Traveled” - Ciliary Sulcus Implantation of the Preserflo MicroShunt

description: Introduction Herein, we report on the option and clinical advantage of the Preserflo MicroShunt insertion into the posterior chamber ciliary sulcus in a patient with advanced pseudo-exfoliation glaucoma at a high risk of corneal decompensation.
Case presentation We describe a 64-year-old advanced pseudoexfoliation glaucoma patient who despite maximal tolerated medical therapy and two failed glaucoma surgeries, still suffered from uncontrolled intraocular pressure (IOP) of 36mmHg in his left eye. The patient underwent the implantation of the Preserflo MicroShunt. The shunt was placed nasally into an area of unscarred conjunctiva, with the anterior part of the shunt inserted into the ciliary sulcus. Postoperatively, the patient’s IOP dropped to 6 mmHg on the first day and stabilized at 11 mmHg at the 6-month mark. An elevated, posteriorly located bleb was observed, and the patient no longer required additional topical medications. Endothelial cell (EC) count remained stable with no signs of corneal edema. The patient did not experience any serious post-operative complications.
Conclusion Implantation of the Preserflo MicroShunt into the ciliary sulcus appears to be a viable option for patients at high risk of corneal decompensation, hence, offering effective IOP control while minimizing EC loss. Further studies with larger patient groups are warranted to better evaluate the safety and efficacy of this technique.