cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5084644
title: Model output of the Simple Biosphere Model, version 4 (SiB4) used in "Evaluation of carbonyl sulfide biosphere exchange in the Simple Biosphere Model (SiB4)"

description: Model output of the Simple Biosphere Model, version 4 (SiB4) used in "Evaluation of carbonyl sulfide biosphere exchange in the Simple Biosphere Model (SiB4)". The dataset includes COS ecosystem, vegetation and soil fluxes, as well as CO2 exchange fluxes. COS soil fluxes are calculated through the Ogee et al. (2016) soil model, and COS mole fractions vary spatially and temporally. The site-specific files provide SiB4 output only for the plant functional type that is represented by this site. The plant functional type is included in the file name. Datasets include 3 hourly output between 2000-2020 for site simulations, and monthly output between 2000-2020 for global simulations (at 0.5x0.5 degrees). The SiB4 model code is available online: https://gitlab.com/kdhaynes/sib4_corral
DOI: 10.5281/zenodo.5652263
title: Daily 1-km gap-free PM10 grids in China, v1 (2000–2020)

description: A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP aerosol dataset (LGHAP.v1), we provide a 21-year-long (2000–2020) gap free PM10 concentration product with daily 1-km resolution covering the land area of China. The dataset was generated from the daily gap free AOD (https://doi.org/10.5281/zenodo.5652257) that was derived through an integration of a set of data tensors of AOD and other related datasets such as air pollutants concentration and atmospheric visibility acquired from diversified sensors or platforms via a machine learned regression model. The dataset was provided in the NetCDF format, while data in each individual year were archived in a zip file. Python, Matlab, R, and IDL codes were also provided to help users read and visualize the LGHAP data.
DOI: 10.5281/zenodo.5787915
title: Flexural and compressive strength of the landfast sea ice in the Prydz Bay, East Antarctic

description: This dataset includes the results of flexural and compression tests of sea ice samples conducted in the cold laboratory. The ice samples were prepared using a landfast sea ice extracted through the whole ice thickness in the Prydz Bay, East Antarctic. The files contain the following data fields: <strong>Readme</strong> – a detailed description of all data <strong>Compression test</strong> – specific details of sea ice compression test including sample information, physical properties, and mechanical results <strong>Flexural test</strong> – specific details of sea ice flexural test including sample information, physical properties, and mechanical results
DOI: 10.5281/zenodo.6954142
title: Assessing methods for representing soil heterogeneity through a flexible approach within the Joint UK Land Environment Simulator (JULES) at version 3.4.1

description: This is the JULES input and output data and plotting scripts used in the paper titled "Assessing methods for representing soil heterogeneity through a flexible approach within the Joint UK Land Environment Simulator (JULES) at version 3.4.1. The tar file contains the following: outputs/cardwfdei_gl4_uncompressed.mon.nc<br> outputs/cardwfdei_gl4_surf_ft_dom_soil.mon.nc<br> outputs/cardwfdei_gl4_gb_dom_soil_decompr_loam.mon.nc<br> outputs/cardwfdei_gl4_surf-soil_consolidated.mon.nc<br> outputs/cardwfdei_gl4_gb_dom_soil_compr_loam.mon.nc<br> plotting_scripts/utils.py<br> plotting_scripts/gb_flux_summary.py<br> plotting_scripts/tile_fsmc_summary.py<br> plotting_scripts/gb_smcl_closure_summary.py<br> plotting_scripts/colors_names.py<br> inputs/drive-file-WFDEI-79-10.txt<br> inputs/WFDEI_data/README.txt<br> inputs/extract_cardwfdei_point.py uncompressed is the output for experiment HResTex.<br> surf_ft_dom_soil is the output for experiment SurfDom.<br> gb_dom_soil_decompr_loam is the output for experiment SurfGB.<br> surf-soil_consolidated is the output for experiment HResTexAgg.<br> gb_dom_soil_compr_loam is the output for experiment DC. Descriptions about the individual plotting scripts are given within each file. The WFDEI data is freely available from a separate source and inputs/WFDEI_data/README.txt describes how to obtain this dataset. inputs/extract_cardwfdei_point.py extracts the WFDEI data for the single latitude/longitude point used in this paper. inputs/drive-file-WFDEI-79-10.txt is the input file required by JULES to read in the WFDEI driving data files.
DOI: 10.5281/zenodo.7650360
title: Harmonising the land-use flux estimates of global models and national inventories for 2000-2020: background data

description: This online repository includes all the relevant data used in the paper "Harmonizing the land-use flux estimates of global models and national inventories for 2000-2020" (Grassi et al. 2023), plus some additional methodological information, organised in the following files: 1) "<strong>Global model</strong><strong>s</strong> <strong>land CO2 </strong><strong>data 2000-2020</strong>" (MS Excel Format), including for each country data for: a. Land-use CO2 fluxes from each of three Bookkeeping Models (BMs) used, and for different categories (net LULUCF, deforestation, forest, other transitions, organic soils). b. The ensemble mean of the ‘natural terrestrial sink’ estimated by 16 Dynamic Global Vegetation Models (DGVMs), filtered with maps of intact/non-intact forest. The global model data included here are consistent with those included in the Global Carbon Budget 2022 (Friedlingstein et al., 2022). 2) “<strong>National inventories LULUCF data 2000-2020</strong>” (version Dec 2022, MS Excel Format), including a comprehensive collection of LULUCF CO2 data based on countries' submissions to the United Nations Framework Convention on Climate Change (UNFCCC). The data here represent a slight update of the dataset included in Grassi et al. (2022). 3) “<strong>Processing steps for DGVM results”,</strong> describing the protocol used to filter the results of DGVMs with maps of intact/non-intact forest and further details on the maps (PDF Format). 4) “<strong>Intact and non-intact forest maps</strong>”, available in two files with different resolutions (0.5 and 0.05 degrees) in NetCDF format. Grassi et al. (2023) used the 0.5 degree resolution. 5) "<strong>IntactAndNonIntactForest_0.5deg_script.js</strong>", the Google Earth Engine Java script to produce the forest maps (.js/text format) For further details, please refer to: Grassi et al. (2023) Harmonising the land-use flux estimates of global models and national inventories for 2000-2020. Earth Syst. Sci. Data. Other references: Friedlingstein et al. (2022) Global Carbon Budget 2022, Earth Syst. Sci. Data, 14, 4811–4900. Grassi et al (2022) Carbon fluxes from land 2000–2020: bringing clarity to countries' reporting. Earth Syst. Sci. Data, 14, 4643-4666.
DOI: 10.5281/zenodo.8369202
title: CCClim - A machine-learning powered cloud class climatology

description: CCClim is based on cloud property retrievals from the European Space Agency's (ESA) Cloud\_cci dataset, adding relative occurrences of eight major cloud types as defined by the World Meteorological Organization (WMO) at 1° resolution. 
The cloud types are predicted using a two stage machine learning framework, in which a 1 km pixel-level classifier is followed up with a grid-box scale Random Forest regression model.
CCClim's global coverage being almost gapless from 1982 to 2016 allows for performing process-oriented analyses of clouds on a climatological time scale. Similarly, the moderate spatial and temporal resolutions make it a lightweight dataset while enabling straightforward comparison to climate models.
The compressed tarball contains 35 netCDF files, each covering one calendar year. Each file provides daily averages of nine cloud-related variables and the nine classes (eight cloud types+undetermined) as per 1° grid box fractional amounts.
Cloud-related variables:

cloud water path
ice water path
liquid water path
cloud optical depth
effective liquid droplet radius at cloud top
effective ice particle radius at cloud top
cloud top pressure
surface temperature
cloud area fraction
Cloud types:

Ci: Cirrus/Cirrostratus
As: Altostratus
Ac: Altocumulus
St: Stratus
Sc: Stratocumulus
Cu: Cumulus
Ns: Nimbostratus
Dc: Deep convective
DOI: 10.5285/0613b74ecc574fa7b6ac8a22838c5f81
title: Exploring the sensitivity of atmospheric nitrate concentrations to nitric acid uptake rate using the Met Office’s Unified Model: nitrate and nitric acid simulation data

description: This dataset contains nitrate and nitric acid simulation data to explore the sensitivity of atmospheric nitrate concentrations to nitric acid uptake rate using the Met Office’s Unified Model. The files are seperated into directories by simulation name - 1. A control simulation with no nitrate aerosol (CNTL); 2. A simulation with NH4·NO3 reaching equilibrium instantaneously (INSTANT); 3. A simulation with the HNO3 uptake rate set to 0.193 (FAST); and 4. A simulation with the HNO3 uptake rate 0.001 (SLOW). All simulations are performed with the Met Office Unified Model (UM or MetUM) in an N96L85 resolution.
DOI: 10.5439/1025284
title: ARM: Balloon-borne Sounding System (BBSS): meterological data profile

description: Balloon-borne Sounding System (BBSS): meterological data profile
DOI: 10.5439/1097547
title: ARM: WACRARSCL: multiple outputs from first Kollias algorithm

description: WACRARSCL: multiple outputs from first Kollias algorithm
DOI: 10.5440/1765733
title: Soil CO2 and CH4 Chamber Fluxes in Tussock Tundra, Council Road Mile Marker 71, Seward Peninsula, Alaska, 2016-2019

description: In August-September 2016, June-August 2017, June-August 2018, and June 2019, co-located measurements were made of surface CH4 and CO2 flux, soil temperature, moisture, and thaw depth. Measurements were made at 35 chamber locations at the Council Mile Marker 71 Site (CN_MM71) on the Seward Peninsula, Alaska. Chamber locations include upland moist acidic tussock tundra, thermo-erosional slopes, and periodically inundated lowland water channels. The dataset includes three *.csv data files with measurements using transparent and opaque chambers (CO2 and CH4 fluxes in light and dark) plus one *.pdf user guide.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
DOI: 10.5441/002/edbt.2014.40
title: Exploiting the query structure for efficient join ordering in SPARQL queries
DOI: 10.6084/m9.figshare.14686656.v1
title: Intercomparison Davos Hohenpeißenberg

description: TOC Data of amt-2021-52 <br>
DOI: 10.6084/m9.figshare.28239560
title: Supplementary Material for: Safety, tolerability, pharmacokinetics and effects of gene polymorphisms on Furaprevir (TG-2349), a novel hepatitis C inhibitor: a randomized phase Ι study

description: Introduction: The purpose of our study was to evaluate the safety, tolerability, and pharmacokinetics of furaprevir, a new highly selective hepatitis C virus NS3/4A protease inhibitor. Methods: The study was divided into 2 parts: Part A (single ascending-dose study, SAD) and Part B (multiple ascending-dose study, MAD). A total of 62 healthy subjects were enrolled in the studies. DNA samples were extracted from all subjects and genotypes of CYP3A5*3, CYP3A4*1G and POR*28 were analyzed by ligase detection reaction (LDR). Results: We used nonlinear mixed effects model (NONMEM) to construct furaprevir population pharmacokinetics model. (1) In SAD, Cmax and AUC were greater than dose increased ratio in the dose rang of 100-400 mg; (2) In MAD, Cmax and AUC increased in an approximately dose-proportional manner in the dose range of 200-600 mg; (3) A one-compartment model with transit absorption described the plasma concentrations of furaprevir. The apparent clearance (CL) was estimated at 33.4 L/h. The distribution volume of compartment (V2) was 219.0 L. No serious adverse event occurred in the studies. But other screening gene mutations had no statistically significant effects on the pharmacokinetics of furaprevir. Conclusion: Food significantly impacts the bioavailability of furaprevir. Furaprevir does not accumulate in vivo after multiple rising doses and has demonstrated safety and tolerability in healthy subjects, supporting its further investigation in patients with hepatitis C.
DOI: 10.1594/pangaea.921923
title: RECOG-EQ RL01 Earthquake correction for CSR, GFZ and ITSG solutions of GRACE level 3 total water storage anomalies from 2003-01 to 2016-12 correcting for the Sumatra-Andaman (2004) and Tohoku (2011) earthquakes

description: RECOG - REgional COrrections for GRACE: The dataset contains a monthly global earthquake correction for GRACE-level 3 total water storage anomalies (TWSA) for CSR, GFZ and ITSG solutions. The correction is monthly available from January 2003 to December 2016 and is used to correct for the Sumatra-Andaman (2004) and Tohoku (2011) earthquake by using a method described in Einarsson et al. (2010), where a co- and a post-seismic signal is modeled. The files contain longitude, latitude, time and the correction field of TWSA on an 0.5° grid. In the processing of the data, we included replacing lower degree coefficients, DDK3 filtering, reduced a temporal mean from 2003 to2016 and applied a correction for glacial isostatic adjustment. More information about the computation and application of the earthquake correction can be found in Deggim et al. (2020, in preparation). For more information about the GRACE processing visit following website: https://www.apmg.uni-bonn.de/daten-und-modelle/grace_level3_monthly_solutions, last access 12.08.2020.
DOI: 10.17592/001.2022081901
title: Accumulation rate, volcanic tie points, ECM, DEP, nssSO4, density and chronology from shallow ice cores around Dome Fuji, East Antarctica

description: We reconstructed surface mass balance (SMB) around Dome Fuji, Antarctica, over the last 5000 years using the data from 15 shallow ice cores and 7 snow pits. The depth-age relationships for the ice cores were determined by synchronizing them with a layer-counted ice core from West Antarctica (WAIS Divide ice core) using volcanic signals. The reconstructed SMB records for the last 4000 years show spatial patterns that may be affected by their locations relative to the ice divides around Dome Fuji, proximity to the ocean, and wind direction. The SMB records from the individual ice cores and snow pits were stacked to reconstruct the SMB history in the Dome Fuji area. The stacked record exhibits a long-term decreasing trend at -0.037 kg m-2 per century over the last 5000 years in the preindustrial period. The decreasing trend may be the result of long-term surface cooling over East Antarctica and Southern Ocean, and sea-ice expansion in the water vapor source areas. The multidecadal to centennial variations of the Dome Fuji SMB after detrending the record show four distinct periods during the last millennium: mostly negative period before 1300 C.E., slightly positive for 1300-1450 C.E., slightly negative for 1450-1850 C.E. with a weak maximum around 1600 C.E., and strong increase after 1860 C.E. These variations are consistent with those of previously reconstructed SMB records in the East Antarctic plateau. The low SMB periods tend to coincide with the combination of strong volcanic forcings and solar minima for the last 1000 years, but the correspondence is not clear for the older periods, possibly because of the lack of coincidence of volcanic and solar forcings, or the deterioration of the SMB record due to smaller number of stacked cores.
DOI: 10.17632/c4xj5rv6kv.1
title: Raster Circumpolar Arctic Vegetation Map

description: Authors: Martha K. Raynolds, Donald A. Walker, Andrew Balser, Christian Bay, Mitch Campbell, Mikhail M. Cherosov, Fred J. A. Daniëls, Pernille Bronken Eidesen, Ksenia A. Ermokhina, Gerald V. Frost, Birgit Jedrzejek, M. Torre Jorgenson, Blair E. Kennedy, Sergei S. Kholod, Igor A. Lavrinenko, Olga V. Lavrinenko, Borgþór Magnússon, Nadezhda V. Matveyeva, Sigmar Metúsalemsson, Lennart Nilsen, Ian Olthof, Igor N. Pospelov, Elena B. Pospelova, Darren Pouliot, Vladimir Razzhivin, Gabriela Schaepman-Strub, Jozef Šibík, Mikhail Yu. Telyatnikov, Elena Troeva. Land cover maps are the basic data layer required for understanding and modeling ecological patterns and processes. The Circumpolar Arctic Vegetation Map (CAVM), produced in 2003, has been widely used as a base map for studies in the arctic tundra biome. However, the relatively coarse resolution and vector format of the map were not compatible with many other data sets. We present a new version of the CAVM, building on the strengths of the original map, while providing a finer spatial resolution, raster format, and improved mapping. The Raster CAVM uses the legend, extent and projection of the original CAVM. The legend has 16 vegetation types, glacier, saline water, freshwater, and non-arctic land. The Raster CAVM divides the original rock-water-vegetation complex map unit that mapped the Canadian Shield into two map units, distinguishing between areas with lichen- and shrub-dominated vegetation. In contrast to the original hand-drawn CAVM, the new map is based on unsupervised classifications of seventeen geographic/floristic sub-sections of the Arctic, using AVHRR and MODIS data (reflectance and NDVI) and elevation data. The units resulting from the classification were modeled to the CAVM types using a wide variety of ancillary data. The map was reviewed by experts familiar with their particular region, including many of the original authors of the CAVM from Canada, Greenland (Denmark), Iceland, Norway (including Svalbard), Russia, and the U.S. The analysis presented here summarizes the area, geographical distribution, elevation, summer temperatures, and NDVI of the map units. The greater spatial resolution of the Raster CAVM allowed more detailed mapping of water-bodies and mountainous areas. It portrays coastal-inland gradients, and better reflects the heterogeneity of vegetation type distribution than the original CAVM. Accuracy assessment of random 1-km pixels interpreted from 6 Landsat scenes showed an average of 70 % accuracy, up from 39 % for the original CAVM. The distribution of shrub-dominated types changed the most, with more prostrate shrub tundra mapped in mountainous areas, and less low shrub tundra in lowland areas. This improved mapping is important for quantifying existing and potential changes to land cover, a key environmental indicator for modeling and monitoring ecosystems.
DOI: 10.17632/hkkzbn4zv3.1
title: Evaluation of the contribution of new particle formation to cloud droplet in urban atmosphere

description: This data was measured at the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (39.98◦ N, 116.39◦ E) from May 25, 2017 to June 18, 2017. The number size distribution of particles in the size range from 10 to 550 nm (scanned range) were measured with time resolution of 5 minutes by a scanning mobility particle sizer (SMPS) used to calculated the particles number concentration. And the non-refractory chemical composition of PM1 is measured by an Aerosol Chemical Speciation Monitor (ACSM) used to calculate the hygroscopicity parameters of aerosol particles and analyse it's source. The wind speed and wind direction data were download from National Climate Data Center （https://www.ncdc.noaa.gov/）, from 25 May to 15 June 2017.
DOI: 10.17882/94148
title: Lagoon hydrodynamics of pearl farming islands in French Polynesia : the case of Gambier Islands

description: Gambier Islands has been instrumented over a period of 9 months (from June 2019 to late February 2020) separated into 2 Legs. Physical process such as water level, wave climate, currents ciruclation in deep channels, exchanges between intra-lagoons and water column stratification have been measured to characterized the hydrodynamic functioning of this opened pearl farming group of islands. The sampling strategy principally focused on the Rikitea lagoon and on lagoons surroundings Mangareva and Aukena Islands. To do so, autonomous oceanographic instruments measuring at high sampling frequency were deployed on bottom moored stations.
Sampling strategy available on Sextant - Marine Geographic Information System.
More informations on ANR MANA project.
DOI: 10.25625/cvuepo
title: Cacao grafting increases crop yield without compromising biodiversity

description: This dataset includes all data tables and R scripts used in the data preparation and statistical analyses for the paper "Cacao grafting increases crop yield without compromising biodiversity" by Carolina Ocampo-Ariza, Sophie Müller, et al. Unfortunately, Gro.Data does not allowed for the files to be organized in subfolders. Therefore, readers who download this will have to rewrite the location of all files accordingly.
DOI: 10.25984/1876417
title: End-Use Load Profiles for the U.S. Building Stock

description: The United States is embarking on an ambitious transition to a 100% clean energy economy by 2050, which will require improving the flexibility of electric grids. One way to achieve grid flexibility is to shed or shift demand to align with changing grid needs. To facilitate this, it is critical to understand how and when energy is used. High quality end-use load profiles (EULPs) provide this information, and can help cities, states, and utilities understand the time-sensitive value of energy efficiency, demand response, and distributed energy resources. Publicly available EULPs have traditionally had limited application because of age and incomplete geographic representation. To help fill this gap, the U.S. Department of Energy (DOE) funded a three-year project, End-Use Load Profiles for the U.S. Building Stock, that culminated in this publicly available dataset of calibrated and validated 15-minute resolution load profiles for all major residential and commercial building types and end uses, across all climate regions in the United States. These EULPs were created by calibrating the ResStock and ComStock physics-based building stock models using many different measured datasets, as described in the "Technical Report Documenting Methodology" linked in the submission.