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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Terjanika, Viktorija;

    LCA and Social LCA data for CO2 valorisation (utilisation) scenarios - ethanol, methanol, cement and algal biomass production. Data includes various sources, all of them are listed in the file accordingly

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Nederhoff, Kees; Hoek, Jasper; van Ormondt, Maarten; Caires, Sofia; +1 Authors

    This dataset comprises 10,000 years of synthetic tropical cyclone tracks, meticulously generated using the Tropical Cyclone Wind Statistical Estimation Tool (TCWiSE) algorithm, as detailed in Nederhoff et al., 2021. Focused on the North Atlantic Ocean Basin, these tracks are constructed based on historical data and designed to mirror the present-day climate conditions. The dataset offers a comprehensive and detailed simulation of tropical cyclone activity, serving as a valuable resource for climate research and analysis in the context of current climate patterns.

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    4TU.ResearchData
    Dataset . 2024
    License: CC 0
    Data sources: 4TU.ResearchData
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    4TU.ResearchData
    Dataset . 2024
    License: CC 0
    Data sources: 4TU.ResearchData
    4TU.ResearchData | science.engineering.design
    Dataset . 2024
    License: CC 0
    Data sources: Datacite
    4TU.ResearchData | science.engineering.design
    Dataset . 2024
    License: CC 0
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2024
      License: CC 0
      Data sources: 4TU.ResearchData
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData
      Dataset . 2024
      License: CC 0
      Data sources: 4TU.ResearchData
      4TU.ResearchData | science.engineering.design
      Dataset . 2024
      License: CC 0
      Data sources: Datacite
      4TU.ResearchData | science.engineering.design
      Dataset . 2024
      License: CC 0
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Escusa Dos Santos, Luis Filipe; Thomson, Erik S.;

    This data set includes measurement data from laboratory engine experiments where different fuel types and exhaust wets scrubbing was investigated for their impact on engine exhaust particle formation. Data includes information on particle number size distributions, effective densities, chemical characterization, hygroscopicity and ice nucleation activity of exhaust particles. Detta dataset innehåller mätdata från motorexperiment i laboratorium. För mer detaljerad beskrivning se engelsk text.

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    Swedish National Data Service
    Dataset . 2024
    Data sources: Datacite
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    Swedish National Data Service
    Dataset . 2024
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Swedish National Dat...arrow_drop_down
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      Swedish National Data Service
      Dataset . 2024
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Swedish National Data Service
      Dataset . 2024
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Helfenstein, Anatol; Mulder, Vera L.; Hack-ten Broeke, Mirjam J.D.; van Doorn, Maarten; +3 Authors

    This dataset contains 206 spatially explicit environmental variables, also termed covariates, at 25m resolution that cover the entire Netherlands (national scale). The raster data are comprised of covariates related to the soil-forming factors (climate, organism/land use/land cover, relief/topography, parent material/geology) for the purpose of using them for digital soil mapping. However, since the covariates cover a wide range of environmental variables, they can potentially be used for spatial modelling in the Netherlands also outside the field of soil science. All covariates can also be found from the original source, but the potential strength and practicality of this dataset lies in the broad range of readily available, collected, prepared and harmonized raster data.The metadata of all the covariates in this dataset can be found in the "00_covariates_metadata.csv" file, including information about the names, category, value types, specific value types, type of geospatial data, file type, whether its static or dynamic, temporal coverage, date/version, resolution (all 25m), origin, source, access/license, description, processing steps and comments. The dataset includes 3 different types of files:GeoTIFF (.tif): the covariates as raster data at 25m resolution in the EPSG:28992 (Amersfoort / RD New) spatial projectionText (.txt): README files for each covariate with additional metadata information (filename ending in "_readme.txt")Tabular data (.csv): Classification and re-classification table for categorical covariates (filename ending in "_reclassify.csv")Note that the reclassification tables contain potential ways to reclassify the data provided, but can be altered by the user. Reclassification may be useful for categorical covariates with a large number of classes/categories. Note that covariates with CC BY-ND 4.0 licenses, covariates that are not open data or for which the license was unknown are not shared in this dataset.More information about these covariates can be found in the associated scientific paper "BIS-4D: Mapping soil properties and their uncertainties at 25m resolution in the Netherlands" (Helfenstein et al., 2024, under review). Different ways of pre-processing and preparing the covariates for subsequent modelling can be found in R scripts 20-25 in the associated code repository on GitLab. This includes assembling and preparing covariates using GDAL ("20_cov_prep_gdal.R"), computing digital elevation model (DEM) derivatives using SAGA GIS ("21_cov_dem_deriv_saga.R"), deriving spectral indices from RGBNIR bands of Sentinel 2 images ("22_cov_sensing_deriv.R"), preparing categorical covariates using GDAL ("23_cov_cat_recl_gdal.R"), deriving dynamic covariates ("24_cov_dyn_prep_gdal.R") and exploratory analysis of the covariates ("25_cov_expl_analysis_clorpt.Rmd", "25_cov_expl_analysis_cont_cat.Rmd").

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Research@WUR; 4TU.Re...arrow_drop_down
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    4TU.ResearchData
    Dataset . 2024
    License: CC BY NC SA
    Data sources: 4TU.ResearchData
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    4TU.ResearchData
    Dataset . 2024
    License: CC BY NC SA
    Data sources: 4TU.ResearchData
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    4TU.ResearchData
    Dataset . 2024
    License: CC BY NC SA
    Data sources: 4TU.ResearchData
    4TU.ResearchData | science.engineering.design
    Dataset . 2024
    License: CC BY NC SA
    Data sources: Datacite
    4TU.ResearchData | science.engineering.design
    Dataset . 2024
    License: CC BY NC SA
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Research@WUR; 4TU.Re...arrow_drop_down
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      4TU.ResearchData
      Dataset . 2024
      License: CC BY NC SA
      Data sources: 4TU.ResearchData
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData
      Dataset . 2024
      License: CC BY NC SA
      Data sources: 4TU.ResearchData
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData
      Dataset . 2024
      License: CC BY NC SA
      Data sources: 4TU.ResearchData
      4TU.ResearchData | science.engineering.design
      Dataset . 2024
      License: CC BY NC SA
      Data sources: Datacite
      4TU.ResearchData | science.engineering.design
      Dataset . 2024
      License: CC BY NC SA
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Angelis, Georgios-Fotios;

    The primary environmental health threat in the WHO European Region is air pollution, impacting the daily health and well-being of its citizens significantly. To effectively understand the impact, and dynamics of air quality a detailed investigation of different environmental, weather, and land cover indices is appropriate. To this end, this paper introduces three European cities’ spatiotemporal datasets, customized for air pollution monitoring at a regional level. The datasets are composed of major air quality, weather measurements and land use information. The duration is approximately from 2020 to 2023 with an hourly temporal resolution and a spatial resolution of 0.005◦. The temporal and spatiotemporal datasets are publicly released aiming to provide a solid foundation for researchers, analysts, and practitioners to conduct in-depth analyses of air pollution dynamics.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Davi Ferreira, A;

    DWL scripts, and stylolites datasets for the paper entitled: "Dominant wavelength: A tool for morphological simplification of stylolites". Any questions please send an email to: adrdavi123@gmail.com or andredavi@ufpr.brSupplementary Material A: Folder with three folders: Folder1: "octave or matlab executable"; Folder2: "output files", Folder3: "source code"--1) Folder1: "octave or matlab executable"Three files:"dwl_script.m": Script for extracting dominant wavelength in digitalized stylolites. Steps to reproduce: 1. Read CSV files, with X and Y coordinates; 2. Remove trends by polynomial regression; 3. Spatial grid information; 4. Fabricate new grid by Y-interpolation; 5. Frequency grid; 6. Fast Fourier Transform; 7. Dominant frequency and wavelength; 8. Bandpass filter (Call function Bandpass); 9. Show results."Bandpass.m": Apply bandpass filter to the profile f(x), considering the frequency interval [kappa0,kappa1].Steps to reproduce: 1. Spatial grid information; 2. Frequency grid; 3. Fast Fourier Transform; 4. Multiplication by the filter and inverse transform."working_sample_1.csv": Cartesian coordinates of a digitized stylolite.Dwl script, Bandpass function and "Working_sample_1.csv" must be in the same folder.--2) Folder2: "output files"Four output figures:"figure_1_bandpass_filter.jpg": Results of applying the bandpass filter in the real frequency domain. "figure_2_stylolite_grid.jpg": Stylolite grid"figure_3_dwl_final_image.jpg": Comparison between the dominant wavelengths and the digitized stylolite.--3) Folder3: "source code"Three files:"bandpass_filter_source_code.txt""dwl_script_source_code.txt" "working_sample_1.csv": Cartesian coordinates of a digitized stylolite. Supplementary Material B: DWL, geometric data and morphological classification of stylolites. This material consists of four tables:Table 1: Stylolites hosted in Calcitic Breccia;Table 2: Stylolites hosted in Dolomitic Breccia; Table 3: Stylolites hosted in Calcitic Marble;Table 4: Stylolites hosted in Metamarl."Code and data availability.rar": All code and supplementary materials compressed (.zip). THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    DANS-EASY
    Dataset . 2024
    Data sources: B2FIND
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    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    Authors: Schrauth, P;

    Replication folder including data and code for JUE Insight: Ticket to Paradise? The Effect of a Public Transport Subsidy on Air Quality THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Authors: Fang, Hua;

    The measured data and the code used for the study "Characterizing ozone pollution and the relationship to its precursors since Clean Air Action Plan Phase II in China: A case study" THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Authors: Bauduin, Thomas; Gypens, Nathalie; Borges, Alberto V.;

    The dataset comprises two files, each containing geo-referenced information with corresponding timestamps.The names of the ponds are written in French according to the official name defined by Brussels Environment (BE). Brussels_ponds_all.xlsx contains pH, Conductivity, dissolved oxygen saturation, CO2, CH4, N2O dissolved concentrations, chlorophyll-a concentrations, inorganic nutrients (NO3-, NO2-, NH4+, PO43-) concentrations, total phosphorus and total suspended matter concentrations collected during four surveys (November 2021, February 2022, May 2022 and August 2022) on 22 ponds Brussels_ponds_Chla_temporal.xlsx contains chlorophyll-a concentration of two ponds monitored on a recurring basis (1-2 times a month) from June 2021 to December 2022. All ponds have pontoons, raised above the water surface, which allow sampling at least 2m further from the banks. Water pH, temperature, conductivity and oxygen saturation level (%O2) were measured by a VWR MU 6100 H probe. Water was collected in 2L polypropylene bottles for subsequent analysis of chlorophyll a (Chl-a), total suspended matter (TSM) and dissolved nutrients (ammonium (NH4+), nitrite (NO2-), nitrate (NO3-), soluble reactive phosphorus (SRP)). Three 50mL falcons were filled with unfiltered water and stabilised with 200µL HNO3 (65%) for total phosphorus (Ptot) analysis. Filtration on Whatman filters 0.7µm GF/F glass microfibres with diameter of 47mm was carried out. For TSM, 1 pre-weighed filter was weighed after drying at 100°C for 48h in order to obtain the TSM concentration. For Chl-a, a filter was placed after filtration in 90% acetone to extract the Chl-a, which was then measured by fluorimetry (Kontron SFM 25 model) at an excitation length of 430nm and emission length of 664nm (Yentsch and Menzel, 1963). Nutrient concentrations were determined spectrophotometrically (Perkin-Elmer Lambda 650 S model) by coloration of the filtrates obtained after filtration through 0.7µm Whatman GF/F filters. NH4+ was determined by coloration with nitroprusside-hypochlorite-phenol (Grasshoff and Johannsen, 1972) at 630nm. NO2- and NO3- are determined before and after reduction of NO3- to NO2- by passage through a cadmium-copper column. The NO2- is then reacted with Griess' reagent in acidic medium (Grasshoff et al., 1983) at 540nm. The SRP is determined after colorimetric reaction with ammonium molybdate, ascorbic acid and potassium antimony tartrate (Koroleff, 1983) at 885nm. Ptot was determined by inductively coupled plasma spectroscopy (ICP) on an ICP-OES Perkin Elmer Avio 200 model. The assay protocol was based on the US EPA (1994) method 200.7 for analysis of metals and trace elements in water by ICP with prior microwave acid digestion based on US EPA, (2007) method 3015A. CO2 measurements were carried out on the field with a Li-Cor Li-840 IR-CO2/H2O gas analyser calibrated before each campaign, using the headspace technique with 4 polypropylene syringes (Abril et al., 2015). Samples for CH4 and N2O were collected via a silicone tube into 60mL borosilicate serum bottles and poisoned with 200µL of saturated HgCl2 solution. The vials were sealed with a butyl stopper and crimped with an aluminium cap. Measurements were carried out using the headspace technique (Weiss, 1974) and a gas chromatography measurement (model SRI 8610C) with a flame ionisation detector (FID) for CH4 and an electron capture detector (ECD) for N2O. References: Abril, G., Bouillon, S., Darchambeau, F., Teodoru, C.R., Marwick, T.R., Tamooh, F., Omengo, Ochieng Omengo, F., Geeraert, N., Deirmendjian, L., Polsenaere, P., Borges, A.V., (2015). Technical Note : Large overestimation of p CO2 calculated from pH and alkalinity in acidic , organic-rich freshwaters Biogeosciences 12, 67–78. https://doi.org/10.5194/bg-12-67-2015 Grasshoff, K., & Johannsen, H. (1972). A new sensitive and direct method for the automatic determination of ammonia in sea water. ICES Journal of Marine Science, 34(3), 516-521. https://doi.org/10.1093/icesjms/34.3.516 Grasshoff, K., Kremling, K., & Ehrhardt, M. (Eds.). (1983). Methods of seawater analysis : Determination of nitrite. John Wiley & Sons Koroleff, J. (1983). Determination of total phosphorus by alkaline persulphate oxidation. Methods of Seawater Analysis. Verlag Chemie, Wienheim, 136-138. U.S. EPA. 1994. “Method 200.7: Determination of Metals and Trace Elements in Water and Wastes by Inductively Coupled Plasma-Atomic Emission Spectrometry,” Revision 4.4. Cincinnati, OH. https://www.epa.gov/esam/method-2007-determination-metals-and-trace-elements-water-and-wastes-inductively-coupled U.S. EPA. 2007. “Method 3015A (SW-846): Microwave Assisted Acid Digestion of Aqueous Samples and Extracts,” Revision 1. Washington, DC https://www.epa.gov/esam/epa-method-3015a-microwave-assisted-acid-digestion-aqueous-samples-and-extracts Weiss, R. (1974). Carbon dioxide in water and seawater: the solubility of a non-ideal gas. Marine chemistry, 2(3), 203-215. https://doi.org/10.1016/0304-4203(74)90015-2 Yentsch, C. S., & Menzel, D. W. (1963, July). A method for the determination of phytoplankton chlorophyll and phaeophytin by fluorescence. In Deep Sea Research and Oceanographic Abstracts (Vol. 10, No. 3, pp. 221-231). Elsevier. https://doi.org/10.1016/0011-7471(63)90358-9

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    ZENODO
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    ZENODO
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    Authors: Super, Ingrid; Scarpelli, Tia; Droste, Arjan; Palmer, Paul;

    Provided are several datasets and two pieces of code that can be used to analyse/visualise the data. TNO_GHGco_v4_0_2018_uncertainties_v1_0_Edinb.nc: contains emissions uncertainties related to the TNO-GHGco-v4 European emission inventory. inv_out_*: 4 files with output from numerical experiments with GEOS-Chem (v12.5) and with either default uncertainties (base) or the advanced ones provided in TNO_GHGco_v4_0_2018_uncertainties_v1_0_Edinb.nc (tno) and with only CO2 (co2) or CO2 and CO combined (co2_co). Other files are input files for the numerical experiments that provide additional data needed for plotting. The python codes are used to analyse the output from the numerical experiments. The scalar version provides scatter plots for the scaling factors resulting from the inversions and the emis version provides maps of the difference between absolute prior and posterior deviations from the true emissions.

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    ZENODO
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    Authors: Terjanika, Viktorija;

    LCA and Social LCA data for CO2 valorisation (utilisation) scenarios - ethanol, methanol, cement and algal biomass production. Data includes various sources, all of them are listed in the file accordingly

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    ZENODO
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Nederhoff, Kees; Hoek, Jasper; van Ormondt, Maarten; Caires, Sofia; +1 Authors

    This dataset comprises 10,000 years of synthetic tropical cyclone tracks, meticulously generated using the Tropical Cyclone Wind Statistical Estimation Tool (TCWiSE) algorithm, as detailed in Nederhoff et al., 2021. Focused on the North Atlantic Ocean Basin, these tracks are constructed based on historical data and designed to mirror the present-day climate conditions. The dataset offers a comprehensive and detailed simulation of tropical cyclone activity, serving as a valuable resource for climate research and analysis in the context of current climate patterns.

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    4TU.ResearchData
    Dataset . 2024
    License: CC 0
    Data sources: 4TU.ResearchData
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    4TU.ResearchData
    Dataset . 2024
    License: CC 0
    Data sources: 4TU.ResearchData
    4TU.ResearchData | science.engineering.design
    Dataset . 2024
    License: CC 0
    Data sources: Datacite
    4TU.ResearchData | science.engineering.design
    Dataset . 2024
    License: CC 0
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2024
      License: CC 0
      Data sources: 4TU.ResearchData
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      4TU.ResearchData
      Dataset . 2024
      License: CC 0
      Data sources: 4TU.ResearchData
      4TU.ResearchData | science.engineering.design
      Dataset . 2024
      License: CC 0
      Data sources: Datacite
      4TU.ResearchData | science.engineering.design
      Dataset . 2024
      License: CC 0
      Data sources: Datacite
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    Authors: Escusa Dos Santos, Luis Filipe; Thomson, Erik S.;

    This data set includes measurement data from laboratory engine experiments where different fuel types and exhaust wets scrubbing was investigated for their impact on engine exhaust particle formation. Data includes information on particle number size distributions, effective densities, chemical characterization, hygroscopicity and ice nucleation activity of exhaust particles. Detta dataset innehåller mätdata från motorexperiment i laboratorium. För mer detaljerad beskrivning se engelsk text.

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    Swedish National Data Service
    Dataset . 2024
    Data sources: Datacite
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    Swedish National Data Service
    Dataset . 2024
    Data sources: Datacite
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      Swedish National Data Service
      Dataset . 2024
      Data sources: Datacite
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      Swedish National Data Service
      Dataset . 2024
      Data sources: Datacite
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    Authors: Helfenstein, Anatol; Mulder, Vera L.; Hack-ten Broeke, Mirjam J.D.; van Doorn, Maarten; +3 Authors

    This dataset contains 206 spatially explicit environmental variables, also termed covariates, at 25m resolution that cover the entire Netherlands (national scale). The raster data are comprised of covariates related to the soil-forming factors (climate, organism/land use/land cover, relief/topography, parent material/geology) for the purpose of using them for digital soil mapping. However, since the covariates cover a wide range of environmental variables, they can potentially be used for spatial modelling in the Netherlands also outside the field of soil science. All covariates can also be found from the original source, but the potential strength and practicality of this dataset lies in the broad range of readily available, collected, prepared and harmonized raster data.The metadata of all the covariates in this dataset can be found in the "00_covariates_metadata.csv" file, including information about the names, category, value types, specific value types, type of geospatial data, file type, whether its static or dynamic, temporal coverage, date/version, resolution (all 25m), origin, source, access/license, description, processing steps and comments. The dataset includes 3 different types of files:GeoTIFF (.tif): the covariates as raster data at 25m resolution in the EPSG:28992 (Amersfoort / RD New) spatial projectionText (.txt): README files for each covariate with additional metadata information (filename ending in "_readme.txt")Tabular data (.csv): Classification and re-classification table for categorical covariates (filename ending in "_reclassify.csv")Note that the reclassification tables contain potential ways to reclassify the data provided, but can be altered by the user. Reclassification may be useful for categorical covariates with a large number of classes/categories. Note that covariates with CC BY-ND 4.0 licenses, covariates that are not open data or for which the license was unknown are not shared in this dataset.More information about these covariates can be found in the associated scientific paper "BIS-4D: Mapping soil properties and their uncertainties at 25m resolution in the Netherlands" (Helfenstein et al., 2024, under review). Different ways of pre-processing and preparing the covariates for subsequent modelling can be found in R scripts 20-25 in the associated code repository on GitLab. This includes assembling and preparing covariates using GDAL ("20_cov_prep_gdal.R"), computing digital elevation model (DEM) derivatives using SAGA GIS ("21_cov_dem_deriv_saga.R"), deriving spectral indices from RGBNIR bands of Sentinel 2 images ("22_cov_sensing_deriv.R"), preparing categorical covariates using GDAL ("23_cov_cat_recl_gdal.R"), deriving dynamic covariates ("24_cov_dyn_prep_gdal.R") and exploratory analysis of the covariates ("25_cov_expl_analysis_clorpt.Rmd", "25_cov_expl_analysis_cont_cat.Rmd").

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    4TU.ResearchData
    Dataset . 2024
    License: CC BY NC SA
    Data sources: 4TU.ResearchData
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    4TU.ResearchData
    Dataset . 2024
    License: CC BY NC SA
    Data sources: 4TU.ResearchData
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    4TU.ResearchData
    Dataset . 2024
    License: CC BY NC SA
    Data sources: 4TU.ResearchData
    4TU.ResearchData | science.engineering.design
    Dataset . 2024
    License: CC BY NC SA
    Data sources: Datacite
    4TU.ResearchData | science.engineering.design
    Dataset . 2024
    License: CC BY NC SA
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2024
      License: CC BY NC SA
      Data sources: 4TU.ResearchData
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      4TU.ResearchData
      Dataset . 2024
      License: CC BY NC SA
      Data sources: 4TU.ResearchData
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      4TU.ResearchData
      Dataset . 2024
      License: CC BY NC SA
      Data sources: 4TU.ResearchData
      4TU.ResearchData | science.engineering.design
      Dataset . 2024
      License: CC BY NC SA
      Data sources: Datacite
      4TU.ResearchData | science.engineering.design
      Dataset . 2024
      License: CC BY NC SA
      Data sources: Datacite
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    Authors: Angelis, Georgios-Fotios;

    The primary environmental health threat in the WHO European Region is air pollution, impacting the daily health and well-being of its citizens significantly. To effectively understand the impact, and dynamics of air quality a detailed investigation of different environmental, weather, and land cover indices is appropriate. To this end, this paper introduces three European cities’ spatiotemporal datasets, customized for air pollution monitoring at a regional level. The datasets are composed of major air quality, weather measurements and land use information. The duration is approximately from 2020 to 2023 with an hourly temporal resolution and a spatial resolution of 0.005◦. The temporal and spatiotemporal datasets are publicly released aiming to provide a solid foundation for researchers, analysts, and practitioners to conduct in-depth analyses of air pollution dynamics.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Davi Ferreira, A;

    DWL scripts, and stylolites datasets for the paper entitled: "Dominant wavelength: A tool for morphological simplification of stylolites". Any questions please send an email to: adrdavi123@gmail.com or andredavi@ufpr.brSupplementary Material A: Folder with three folders: Folder1: "octave or matlab executable"; Folder2: "output files", Folder3: "source code"--1) Folder1: "octave or matlab executable"Three files:"dwl_script.m": Script for extracting dominant wavelength in digitalized stylolites. Steps to reproduce: 1. Read CSV files, with X and Y coordinates; 2. Remove trends by polynomial regression; 3. Spatial grid information; 4. Fabricate new grid by Y-interpolation; 5. Frequency grid; 6. Fast Fourier Transform; 7. Dominant frequency and wavelength; 8. Bandpass filter (Call function Bandpass); 9. Show results."Bandpass.m": Apply bandpass filter to the profile f(x), considering the frequency interval [kappa0,kappa1].Steps to reproduce: 1. Spatial grid information; 2. Frequency grid; 3. Fast Fourier Transform; 4. Multiplication by the filter and inverse transform."working_sample_1.csv": Cartesian coordinates of a digitized stylolite.Dwl script, Bandpass function and "Working_sample_1.csv" must be in the same folder.--2) Folder2: "output files"Four output figures:"figure_1_bandpass_filter.jpg": Results of applying the bandpass filter in the real frequency domain. "figure_2_stylolite_grid.jpg": Stylolite grid"figure_3_dwl_final_image.jpg": Comparison between the dominant wavelengths and the digitized stylolite.--3) Folder3: "source code"Three files:"bandpass_filter_source_code.txt""dwl_script_source_code.txt" "working_sample_1.csv": Cartesian coordinates of a digitized stylolite. Supplementary Material B: DWL, geometric data and morphological classification of stylolites. This material consists of four tables:Table 1: Stylolites hosted in Calcitic Breccia;Table 2: Stylolites hosted in Dolomitic Breccia; Table 3: Stylolites hosted in Calcitic Marble;Table 4: Stylolites hosted in Metamarl."Code and data availability.rar": All code and supplementary materials compressed (.zip). THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    DANS-EASY
    Dataset . 2024
    Data sources: B2FIND
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    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      DANS-EASY
      Dataset . 2024
      Data sources: B2FIND
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      Mendeley Data
      Dataset . 2024
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    Authors: Schrauth, P;

    Replication folder including data and code for JUE Insight: Ticket to Paradise? The Effect of a Public Transport Subsidy on Air Quality THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Authors: Fang, Hua;

    The measured data and the code used for the study "Characterizing ozone pollution and the relationship to its precursors since Clean Air Action Plan Phase II in China: A case study" THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Authors: Bauduin, Thomas; Gypens, Nathalie; Borges, Alberto V.;

    The dataset comprises two files, each containing geo-referenced information with corresponding timestamps.The names of the ponds are written in French according to the official name defined by Brussels Environment (BE). Brussels_ponds_all.xlsx contains pH, Conductivity, dissolved oxygen saturation, CO2, CH4, N2O dissolved concentrations, chlorophyll-a concentrations, inorganic nutrients (NO3-, NO2-, NH4+, PO43-) concentrations, total phosphorus and total suspended matter concentrations collected during four surveys (November 2021, February 2022, May 2022 and August 2022) on 22 ponds Brussels_ponds_Chla_temporal.xlsx contains chlorophyll-a concentration of two ponds monitored on a recurring basis (1-2 times a month) from June 2021 to December 2022. All ponds have pontoons, raised above the water surface, which allow sampling at least 2m further from the banks. Water pH, temperature, conductivity and oxygen saturation level (%O2) were measured by a VWR MU 6100 H probe. Water was collected in 2L polypropylene bottles for subsequent analysis of chlorophyll a (Chl-a), total suspended matter (TSM) and dissolved nutrients (ammonium (NH4+), nitrite (NO2-), nitrate (NO3-), soluble reactive phosphorus (SRP)). Three 50mL falcons were filled with unfiltered water and stabilised with 200µL HNO3 (65%) for total phosphorus (Ptot) analysis. Filtration on Whatman filters 0.7µm GF/F glass microfibres with diameter of 47mm was carried out. For TSM, 1 pre-weighed filter was weighed after drying at 100°C for 48h in order to obtain the TSM concentration. For Chl-a, a filter was placed after filtration in 90% acetone to extract the Chl-a, which was then measured by fluorimetry (Kontron SFM 25 model) at an excitation length of 430nm and emission length of 664nm (Yentsch and Menzel, 1963). Nutrient concentrations were determined spectrophotometrically (Perkin-Elmer Lambda 650 S model) by coloration of the filtrates obtained after filtration through 0.7µm Whatman GF/F filters. NH4+ was determined by coloration with nitroprusside-hypochlorite-phenol (Grasshoff and Johannsen, 1972) at 630nm. NO2- and NO3- are determined before and after reduction of NO3- to NO2- by passage through a cadmium-copper column. The NO2- is then reacted with Griess' reagent in acidic medium (Grasshoff et al., 1983) at 540nm. The SRP is determined after colorimetric reaction with ammonium molybdate, ascorbic acid and potassium antimony tartrate (Koroleff, 1983) at 885nm. Ptot was determined by inductively coupled plasma spectroscopy (ICP) on an ICP-OES Perkin Elmer Avio 200 model. The assay protocol was based on the US EPA (1994) method 200.7 for analysis of metals and trace elements in water by ICP with prior microwave acid digestion based on US EPA, (2007) method 3015A. CO2 measurements were carried out on the field with a Li-Cor Li-840 IR-CO2/H2O gas analyser calibrated before each campaign, using the headspace technique with 4 polypropylene syringes (Abril et al., 2015). Samples for CH4 and N2O were collected via a silicone tube into 60mL borosilicate serum bottles and poisoned with 200µL of saturated HgCl2 solution. The vials were sealed with a butyl stopper and crimped with an aluminium cap. Measurements were carried out using the headspace technique (Weiss, 1974) and a gas chromatography measurement (model SRI 8610C) with a flame ionisation detector (FID) for CH4 and an electron capture detector (ECD) for N2O. References: Abril, G., Bouillon, S., Darchambeau, F., Teodoru, C.R., Marwick, T.R., Tamooh, F., Omengo, Ochieng Omengo, F., Geeraert, N., Deirmendjian, L., Polsenaere, P., Borges, A.V., (2015). Technical Note : Large overestimation of p CO2 calculated from pH and alkalinity in acidic , organic-rich freshwaters Biogeosciences 12, 67–78. https://doi.org/10.5194/bg-12-67-2015 Grasshoff, K., & Johannsen, H. (1972). A new sensitive and direct method for the automatic determination of ammonia in sea water. ICES Journal of Marine Science, 34(3), 516-521. https://doi.org/10.1093/icesjms/34.3.516 Grasshoff, K., Kremling, K., & Ehrhardt, M. (Eds.). (1983). Methods of seawater analysis : Determination of nitrite. John Wiley & Sons Koroleff, J. (1983). Determination of total phosphorus by alkaline persulphate oxidation. Methods of Seawater Analysis. Verlag Chemie, Wienheim, 136-138. U.S. EPA. 1994. “Method 200.7: Determination of Metals and Trace Elements in Water and Wastes by Inductively Coupled Plasma-Atomic Emission Spectrometry,” Revision 4.4. Cincinnati, OH. https://www.epa.gov/esam/method-2007-determination-metals-and-trace-elements-water-and-wastes-inductively-coupled U.S. EPA. 2007. “Method 3015A (SW-846): Microwave Assisted Acid Digestion of Aqueous Samples and Extracts,” Revision 1. Washington, DC https://www.epa.gov/esam/epa-method-3015a-microwave-assisted-acid-digestion-aqueous-samples-and-extracts Weiss, R. (1974). Carbon dioxide in water and seawater: the solubility of a non-ideal gas. Marine chemistry, 2(3), 203-215. https://doi.org/10.1016/0304-4203(74)90015-2 Yentsch, C. S., & Menzel, D. W. (1963, July). A method for the determination of phytoplankton chlorophyll and phaeophytin by fluorescence. In Deep Sea Research and Oceanographic Abstracts (Vol. 10, No. 3, pp. 221-231). Elsevier. https://doi.org/10.1016/0011-7471(63)90358-9

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    Authors: Super, Ingrid; Scarpelli, Tia; Droste, Arjan; Palmer, Paul;

    Provided are several datasets and two pieces of code that can be used to analyse/visualise the data. TNO_GHGco_v4_0_2018_uncertainties_v1_0_Edinb.nc: contains emissions uncertainties related to the TNO-GHGco-v4 European emission inventory. inv_out_*: 4 files with output from numerical experiments with GEOS-Chem (v12.5) and with either default uncertainties (base) or the advanced ones provided in TNO_GHGco_v4_0_2018_uncertainties_v1_0_Edinb.nc (tno) and with only CO2 (co2) or CO2 and CO combined (co2_co). Other files are input files for the numerical experiments that provide additional data needed for plotting. The python codes are used to analyse the output from the numerical experiments. The scalar version provides scatter plots for the scaling factors resulting from the inversions and the emis version provides maps of the difference between absolute prior and posterior deviations from the true emissions.

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