Find research datasets worth reusing

opencc-by-4.0Apr 2020View details →

Hybrid gridded demographic data for the world, 1950-2020

This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.5 degree grid resolution. This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data. Demographic fractions are given for the time period covered by the UN WPP model (1950-2050) while demographic totals are given for the time period covered by the combination of GPWv4 and Histsoc (1950-2020) Method - demographic fractions Demographic breakdown of country population by grid cell is calculated by combining the GPWv4 demographic data given for 2010 with the yearly country breakdowns from the UN WPP. This combines the spatial distribution of demographics from GPWv4 with the temporal trends from the UN WPP. This makes it possible to calculate exposure trends from 1980 to the present day. To combine the UN WPP demographics with the GPWv4 demographics, we calculate for each country the proportional change in fraction of demographic in each age band relative to 2010 as: \(\delta_{year,\ country,age}^{\text{wpp}} = f_{year,\ country,age}^{\text{wpp}}/f_{2010,country,age}^{\text{wpp}}\)   Where: -   \(\delta_{year,\ country,age}^{\text{wpp}}\) is the ratio of change in demographic for a given age and and country from the UN WPP dataset. -   \(f_{year,\ country,age}^{\text{wpp}}\) is the fraction of population in the UN WPP dataset for a given age band, country, and year. -   \(f_{2010,country,age}^{\text{wpp}}\) is the fraction of population in the UN WPP dataset for a given age band, country for the year 2020.   The gridded demographic fraction is then calculated relative to the 2010 demographic data given by GPWv4. For each subset of cells corresponding to a given country c, the fraction of population in a given age band is calculated as: \(f_{year,c,age}^{\text{gpw}} = \delta_{year,\ country,age}^{\text{wpp}}*f_{2010,c,\text{age}}^{\text{gpw}}\) Where: -   \(f_{year,c,age}^{\text{gpw}}\) is the fraction of the population in a given age band for given year, for the grid cell c. -   \(f_{2010,c,age}^{\text{gpw}}\) is the fraction of the population in a given age band for 2010, for the grid cell c. The matching between grid cells and country codes is performed using the GPWv4 gridded country code lookup data and country name lookup table. The final dataset is assembled by combining the cells from all countries into a single gridded time series. This time series covers the whole period from 1950-2050, corresponding to the data available in the UN WPP model.   Method - demographic totals Total population data from 1950 to 1999 is drawn from ISIMIP Histsoc, while data from 2000-2020 is drawn from GPWv4. These two gridded time series are simply joined at the cut-over date to give a single dataset covering 1950-2020. The total population per age band per cell is calculated by multiplying the population fractions by the population totals per grid cell. Note that as the total population data only covers until 2020, the time span covered by the demographic population totals data is 1950-2020 (not 1950-2050).   Disclaimer This dataset is a hybrid of different datasets with independent methodologies. No guarantees are made about the spatial or temporal consistency across dataset boundaries. The dataset may contain outlier points (e.g single cells with demographic fractions >1). This dataset is produced on a 'best effort' basis and has been found to be broadly consistent with other approaches, but may contain inconsistencies which not been identified.

opencc-by-4.0Jan 2021View details →

NAPv1.0: A seasonal hydrographic gridded data set for the Northern Antarctic Peninsula, Southern Ocean

The Northern Antarctic Peninsula (NAP) climatology version 1 (NAPv1.0) was built by optimally interpolate hydrographic data sets from the CTD, MEOP and Argo floats profiles sampled in the NAP and adjacent regions during the period of 1990-2019. The database consists of data from the World Ocean Database, Pangaea, Hutchinson et al. (2020), Brazilian High Latitude Oceanography Group (GOAL; http://goal.furg.br/), Marine Mammals Exploring the Oceans Pole to Pole consortium (MEOP), and Argo floats. The climatology has outputs for summer (Jan-Mar), autumn (Apr-Jun), winter (Jul-Sep) and spring (Oct-Dec). The profiles were first linearly interpolated onto 90 depth levels, and then optimally interpolated in space using a grid of ~10 km resolution. The grid spacing is 0.09˚ along latitudes and 0.2˚ along longitudes (i.e., 0.09˚ latitude x 0.09˚/cos(63˚S) longitude, where 63˚S is the mean latitude of our domain). A series of tests were made to find the appropriate smoothing lengthscale and the a priori relative error in order to find a balance between smoothness and feature representativeness. The final smoothing lengthscale (i.e. the radius of influence of the interpolation) chosen was 1˚ in latitude and longitude, and the a priori relative error allowed was set to 0.2 for the objective interpolation algorithm. The same constants were set for all depth levels and all variables. The regions where the mapping relative error was higher than 0.5 were excluded. The NAPv1.0 climatology can be used for several applications, including input data for ocean and climate models initialization/assessment and ocean reanalysis evaluation, as well as to produce and reconstruct biogeochemical properties. The NAPv1.0 climatology represents the ocean mean-state for the NAP for the end of the 20th and early 21st-century.   Reference:   Dotto, T. S., Mata, M. M., Kerr, R., and Garcia, C. A. E.: A novel hydrographic gridded data set for the northern Antarctic Peninsula, Earth Syst. Sci. Data, 13, 671–696, https://doi.org/10.5194/essd-13-671-2021, 2021.

opencc-by-4.0Dec 2023View details →

Magnetic Anomaly Map of Paraná State - Final gridded data

This gridded data is part of the article entitled: "THE MAGNETIC ANOMALY MAP OF PARANÁ STATE: AN INTEGRATION OF AIRBORNE SURVEYS PERFORMED OVER THE YEARS", which was submitted in December 2023 to the Brazilian Journal of Geophysics. The article is still under review.  These files include airborne magnetic data integrated at 1800m altitude, and the upwarded data to 2700m. Details of the integration and general interpretations are described in the related article.

opencc-by-4.0Jan 2023View details →

Global taxonomic occurrence grids using GBIF data for species distribution models.

To achieve large geographic coverage, species occurrence databases that are composed of ad hoc species data collections such as that provided by the Global Biodiversity Information Facility (GBIF) are often used. A drawback to using these data is their geographic sampling bias, in which some regions are more intensively sampled than others, while other areas have very little to none reported sampling effort. Uneven sampling effort can mislead conclusions about biodiversity patterns and species distributions (Gotelli & Colwell, 2001; Lobo, 2008). Here we provide taxonomic occurrence grids to help mitigate the effects of sampling bias in species distribution modeling. These grids can be used to exclude areas of (a custom-defined) low sampling effort from the background when sampling for pseudo-absences’ (Phillips et al., 2009; Barbet-Massin et al.,2012). The occurrence grids have a 1 degree spatial resolution using WGS 84 as the geographic coordinate system. Each 1 degree grid cell contains the number of records present in GBIF corresponding to a specific taxonomic group: plants, mammals, reptiles, amphibians, birds and molluscs. To construct the occurrence grids, we used the 1- by 1-degree world latitude and longitude vector grid provided by ESRI (Redlands, California). It has a custom license which permits it reuse as long as ESRI is cited. It was downloaded from : <a href="https://www.arcgis.com/home/item.html?id=f11bcdc5d484400fa926dcce68de3df7">https://www.arcgis.com/home/item.html?id=f11bcdc5d484400fa926dcce68de3df7</a> To map spatial sampling effort, the number of georeferenced occurrences corresponding to each taxonomic group contained by each 1- by 1-degree grid cell were counted. The grids were then converted to GeoTIFFs. The raster values correspond to the number of occurrences reported for the grid cells. For the purposes of the <a href="https://osf.io/7dpgr/">TrIAS project</a>, grid cells with fewer than 5 occurrences were removed. The TrIAS taxonomic occurrence grids are used as inputs to the TrIAS risk modelling and mapping workflow: https://github.com/trias-project/risk-modelling-and-mapping. Full (with all grid cells containing at least one occurrence) taxonomic occurrence grids are also provided. GBIF data for each taxonomic group were downloaded using the following criteria: “Basis of Record”: Observation, Machine Observation, Human Observation, Specimen, Material sample, Literature Occurrence, Unknown evidence., "HasCoordinate is true", "HasGeospatialIssue is false", "TaxonKey is Amphibia", "Year 1975-2005". Raster Attributes <table> <tbody> <tr> <td> Attribute </td> <td> Description </td> </tr> <tr> <td> OID </td> <td> numeric row ID </td> </tr> <tr> <td> Value </td> <td> the number of records contained in the grid cell </td> </tr> <tr> <td> Count </td> <td> the number of times the value appears in the raster </td> </tr> </tbody> </table>     The extent of each taxonomic occurrence grid: <ul> <li> longitude -180.0; latitude -90.0 (southwest corner) </li> <li> longitude 180.0; latitude 90.0 (northeast corner) </li> </ul>   Files: TrIAS taxonomic occurrence grids amphib_1deg_min5.tif birds_1deg_min5.tif mammals_1deg_min5.tif molluscs_1deg_min5.tif reptiles_1deg_min5.tif   Raw taxonomic occurrence grids amphib_1deg_grid.tif birds_1deg_grid.tif mammals_1deg_grid.tif molluscs_1deg_grid.tif reptiles_1deg_grid.tif

openCC0Dec 2023View details →

Gridded 1-hectare estimates of shrub community structure at the Jornada Basin LTER site derived from NAIP (2011) and LiDAR (2019) data

This dataset contains four raster maps of shrub community structure at the Jornada Basin LTER site in southern New Mexico U.S.A. These shrub structure estimates were created by combining an existing categorical shrub map (Ji et al. 2019) with USGS LiDAR shrub height estimates from 2019. The resulting raster dataset includes four bands of spatially aligned shrub volume, cover, height, and density estimates at one hectare resolution. Data are also included in tabular format, extracted from the 1 hectare grid upon which estimates were created. These shrub structure estimates are intended to facilitate analyses of habitat structure and community dynamics within the northern Chihuahuan Desert.

Frog grid data (Bisley Experimental Watershed)

Population estimates from 1987 to 1995 are reported for the terrestrial anuran, Eleutherodactylus coqui, from four long-term study plots in the Luquillo Experimental Forest of northeastern Puerto Rico. The major factor influencing population size during this time was Hurricane Hugo, which deposited much of the canopy onto the forest floor in 1989. Population densities since Hurricane Hugo have been influenced by succession, with continued high densities associated with thickets of Cecropia and Heliconia. Trefalls, which are similar to hurricanes on a local scale, also were shown to influence population sizes. Years with prolonged dry periods reduced numbers of juvenile frogs, but rainfall patterns alone did not explain most population variation. Population levels of invertebrate predators were related to variation in frog numbers. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.

Regeneration after Hurricane Hugo, woody species > 10 cm tall (9Ha grid, El Verde) (9Ha Plots Small Data Set)

The purpose of this data set is to document vegetation damage and recovery following Hurricane Hugo, a borderline category 3-4 hurricane with winds from 130 to 160mph (110kts) and a pressure of 945 to 946.1 mb, which hit Puerto Rico in September 18th, 1989 Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.

Bisley Grid Habitat data 1994, 1999

The data set consists of one file containing data from the summers of 1994 and 1999. Various habitat characteristics are presented, as well as the apparency of common plant taxa at 7 heights (every 0.5 m from ground level to 3 m). However, the data for some plant species were not divided by height in 1994; only total apparency of those species is available for that year. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.

Bisley Grid Invertebrate Data, 1989-1999

The data set consists of counts of terrestrial invertebrates from the grid at Bisley Watersheds #1 and 2, for the years 1989, 1990, 1994, and 1999. Data for 1989 and 1990 are confined to 4 species of terrestrial snails: Caracolus caracolla, Gaeotis nigrolineata, Nenia tridens, and Polydontes acutangula. Counts for other snail species and the walking stick Lamponius portoricensis are available for 1994 and 1999. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.

openCC0Aug 2021View details →

Core Site Grid Quadrat Data for the Net Primary Production Study at the Sevilleta National Wildlife Refuge, New Mexico

Begun in spring 2013, this project is part of a long-term study at the Sevilleta LTER measuring net primary production (NPP) across three distinct ecosystems: creosote-dominant shrubland (Site C), black grama-dominant grassland (Site G), and blue grama-dominant grassland (Site B). Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. Above-ground net primary production is the change in plant biomass, represented by stems, flowers, fruit and foliage, over time and incorporates growth as well as loss to death and decomposition. To measure this change the vegetation variables in this dataset, including species composition and the cover and height of individuals, are sampled twice yearly (spring and fall) at permanent 1m x 1m plots within each site. A third sampling at Site C is performed in the winter. The data from these plots is used to build regressions correlating biomass and volume via weights of select harvested species obtained in SEV999, "Net Primary Productivity (NPP) Weight Data." This biomass data is included in SEV999, "Seasonal Biomass and Seasonal and Annual NPP for Core Grid Research Sites."

opencc-by-4.0Dec 2020View details →

ICESat-2 sea ice ancillary data - Mean Sea Surface Height Grids

File format: NetCDF Mean Sea Surface (MSS) Height data grids used for the production of ICESat-2 sea ice data products (ATL07, ATL10, ATL20, ATL21). Blended data from CryoSat-2 and DTU13.

opencc-by-4.0Sep 2020View details →

GO-SHIP Easy Ocean: Formatted and gridded ship-based hydrographic section data

<a href="https://www.go-ship.org">GO-SHIP</a> (The Global Ocean Ship-based Hydrographic Investigations Program) has developed the protocols and methods to generate a data product that concatenates all occupations of individual sections into a time-series; the GO-SHIP Easy Ocean. Here we provide access to the analysis-ready gridded GO-SHIP Easy Ocean product that enhances the accessibility of this unique data set that spans four decades, comprised of more than 40 cross-ocean transects, many with multiple repeats. This product, of uniformly calibrated CTD (temperature, salinity and oxygen) data, provides easy access to and use of the high-quality hydrographic temperature and salinity data that span more than 40 years. The GO-SHIP Easy Ocean product will underpin the quality control of autonomous platforms, provide a ready assessment of ocean-only and coupled climate model simulations, and be used in specific research projects. The GO-SHIP Easy Oceanis a companion to the GLODAP inorganic and carbon product. The section data are available from Zenodo in two standard arrangements: Uninterpolated (reported) and interpolated (gridded). For both arrangements, five quantities are recorded; in situ temperature in ITS-90 scale, in situ salinity in PSS-78 scale, the dissolved oxygen concentration in μmol/kg, Conservative Temperature in °C, and Absolute Salinity in g/kg. The data are available in various formats. Cite <a href="https://doi.org/10.1038/s41597-022-01212-w">Katsumata et al (2022)</a> when using this product and include the following acknowledgment statement in any publication or derived product: Data were collected and made publicly available by the International Global Ship-based Hydrographic Investigations Program GO-SHIP (https://www.go-ship.org/) and the national programs that contribute to it.

opencc-by-4.0Sep 2023View details →

Data for: Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3

We present all of the data across our SNR and abundance study for the molecules O2 and O3 for an exoEarth twin. The wavelength range is from 0.515-1 micron, with 25 evenly spaced 20% bandpasses in this range. The SNR ranges from 3-20, and the abundance values range in log space in steps of 0.5 and/or 0.25 (all presented in VMR in the associated table). We present the lower and upper wavelength per bandpass, the input O2 and O3 values (abundance case), the retrieved O2 and O3 values (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for O2 and O3. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) 2: Using Grid-Based Nested Sampling in Coronagraphy Observation Simulations for O2 and O3, accepted and currently available on arXiv.  To open this csv as a Pandas dataframe, use the following command: your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input O2': str, {'Input O3': str}})

opencc-by-4.0Nov 2024View details →

Soil grid data for 3 agricultural fields in Italy (Soil Moisture, soil organic carbon)

Soil data collected in an agricultural area with annual crops in Italy (west-central Lombardia Po Valley, province of Pavia). The data refers to soil properties of 320 soil samples for Soil Moisture and  120 for SOC, collected in the topsoil (around 5-10 cm), considering a regular sampling grid, within three agricultural field with different crops (spring-summer cycle) and soils type, Rice-Loamy, Sorghum-Sandy and, Maize-Clay, in a period (before the seeding of the crops),  when the soil was bare, in the framework of the EJP Steropes project. The aim of the collected dataset was to be able to analyse the influence of soil moisture in SOC (WP2 of the STEROPES project) prediction models from remote sensing. Data in the form of shape file (one shapefile for each agricultural field, for oth Soil Moisture and SOC), and pictures of the soil surface in .jpg format.

opencc-by-4.0Nov 2024View details →

Data from: Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster

<h2>Data from: Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster</h2> <ul> <li>Authors: Matteo Guaita, Alberto Marín-Cebrián, Eduardo Ahedo, Mario Merino, Fabrice Cipriani, Käthe Dannenmayer</li> <li>Contact email: mguaita@pa.uc3m.es</li> <li>Date: 15/11/2024</li> <li>Keywords: Plasma Physics, Plasma Plumes, Gridded Ion Thruster, Cathode, Facility Effects, Particel in Cell</li> <li>Version: 1.0.0</li> <li>Digital Object Identifier (DOI): 10.5281/zenodo.14165272</li> <li>License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0/" target="_blank" rel="noopener">Open Data Commons Attribution License</a></li> </ul> <h2>Abstract</h2> This dataset contains the data from the simulations presented in the article submitted for pubblicaiton in the Journal: Plasma Sources Science and Technology (PSST): "Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster" The data in this repository is the result of several hybrid PIC simulations as described in the reference. For further information on the setup, numerical parameters and physical meaning of the simulations please refer to the article <h2>Dataset description</h2> The simulations that produced the datasets in this repository were run with the full PIC code Picaso. The majority of the data is at steady-state, and has been averaged over the last 7000 simulation time-steps to reduce numerical noise. This averaging has been performed as a first step directly by the code through time-step accumulation techniques, and at a later stage in post-processing by averaging over the last 20 print-outs of the code. The data inside the "time_dependent" folder is instead time-varying. <h2>Data files</h2> Each HDF5 data-group contains the mesh and time coordinates and plasma properties of a specific simulation. In particular, the naming convention is the following: <ul> <li>Ref_planar.hdf5: Contains the results of the "reference planar simulation" presented in Sections III and IV of the article.</li> <li>2Te_planar.hdf5: Contains the results of the simulation with a doubled electron temperature at the cathode presented in Section V of the article.</li> <li>2Ie_planar.hdf5: Contains the results of the simulation with a doubled electron current at the cathode presented in Section V of the article.</li> <li>No_coll_planar.hdf5: Contains the results of the simulation without inelastic electron collisions presented in Section V of the article.</li> <li>Ref_axisym.hdf5: Contains the results of the non-accelerated axis-symmetric simulation presented in Section VI of the article</li> <li>fcol_2.5_axisym.hdf5: Contains the results of the axis-symmetric simulation,accelerated by a factor 2.5, presented in Section VI of the article</li> <li>fcol_5_axisym.hdf5: Contains the results of the axis-symmetric simulation,accelerated by a factor 5, presented in Section VI of the article</li> <li>fcol_7.5_axisym.hdf5: Contains the results of the axis-symmetric simulation,accelerated by a factor 7.5, presented in Section VI of the article</li> <li>fcol_10_axisym.hdf5: Contains the results of the axis-symmetric simulation,accelerated by a factor 10, presented in Section VI of the article</li> </ul> In each of these files the data is organized in a series of subfolders: <ul> <li>Electrons_prim: Contains the steady-state properties of primary electrons</li> <li>Electrons_trap: Contains the steady-state properties of trapped electrons</li> <li>Ions_fast: Contains the steady-state properties of fast ions (ions injected through the thruster grids)</li> <li>Ions_slow: Contains the steady-state properties of slow ions (ions produced by collisions in the plume)</li> <li>Time_dependent: Contains the vector of time-stamps and spatially global data saved at the corresponding time</li> </ul> The data files found in the outer simulation folder are: <ul> <li>xs: Physical x coordinates [cm]</li> <li>zs: Physical z coordinates [cm]</li> <li>phi: electric potential [V]</li> <li>rho_el: space charge density [C/m³]</li> <li>nn: Total neutral density [1/m³]</li> </ul> The data files for each particle population are: <ul> <li>n: Plasma (ion) density [1/m³]</li> <li>f_x: Particle flux along x [1/(m² s)]</li> <li>f_y: Particle flux along y [1/(m² s)]</li> <li>f_z: Particle flux along z [1/(m² s)]</li> <li>p_xx: xx component of the pressure tensor [J/m³]</li> <li>p_yy: yy component of the pressure tensor [J/m³]</li> <li>p_zz: zz component of the pressure tensor [J/m³]</li> </ul> The data files in the time dependent folder are: <ul> <li>t: Time coordinates [s]</li> <li>phi_W: Potential of the vacuum chamber walls [V]</li> <li>phi_max: Maximum value of the potential in the plume [V]</li> <li>nte_frac: Fraction between the number of trapped electrons and ions in the plume bulk [%]</li> <li>nu_te_ela: globally averaged trapped electron-neutral elastic collision frequency [Hz]</li> <li>nu_te_ion: globally averaged trapped electron-neutral ionization collision frequency [Hz]</li> <li>nu_te_exc: globally averaged trapped electron-neutral excitation collision frequency [Hz]</li> <li>nu_te_cou: globally averaged trapped electron-neutral Coulomb collision frequency [Hz]</li> <li>nu_pe_ela: globally averaged primary electron-neutral elastic collision frequency [Hz]</li> <li>nu_pe_ion: globally averaged primary electron-neutral ionization collision frequency [Hz]</li> <li>nu_pe_exc: globally averaged primary electron-neutral excitation collision frequency [Hz]</li> <li>nu_pe_cou: globally averaged primary electron-neutral Coulomb collision frequency [Hz]</li> </ul>   Note that all the other quantities shown in the article may be obtained from the ones saved here. We remind here that the gas employed is Xenon and that all ions are considered to be singly charged. <h2>Citation</h2> Any works using this dataset or any part of it in any form shall cite it as follows. The BibTeX entry s provided for convenience: @dataset{sim_data_guai25b,   author       = {Matteo Guaita and Alberto Marín-Cebrián and Mario Merino and Eduardo Ahedo and Fabrice Cipriani and Käthe Dannenmayer},   title            = {Data from: Electron populations and neutralization process in the plume of a gridded ion thruster},   month       = November,   year            = 2024,   publisher  = {Zenodo},   version      = {1.0.1},   doi              = {10.5281/zenodo.14165272},   url              = {https://doi.org/10.5281/zenodo.12751281} } The journal article associated with this data-set shall also be cited as follows: @article{guai25b,     doi = {10.1088/1361-6595/adc482},     year = {2025},     month = {mar},     publisher = {IOP Publishing},     author = {Matteo Guaita and Alberto Marín-Cebrián and Mario Merino and Eduardo Ahedo and Fabrice Cipriani and Käthe Dannenmayer},     title = {Electron populations and neutralization process in the plume of a gridded ion thruster},     journal = {Plasma Sources Science and Technology }, }   <h2>Acknowledgments</h2> This work, and the corresponding dataset, has been supported by the ECOMODIS project, funded by the European Space Agency, under contract 4000137869/22/NL/RA

opencc-by-4.0Nov 2024View details →

Hydrographic gridded data set for the South Brazil Bight and Southern Brazilian Shelf

This dataset includes climatological and seasonal maps, spanning data from 1972 to 2024, across 8 different depth levels: 5, 10, 25, 50, 100, 200, 500, and 1000 dBar, with a spatial resolution of 10 km. The maps were generated using the griddata function with triangulation-based natural neighbor interpolation. The variables included in this dataset are conservative temperature (°C), absolute salinity (g kg⁻¹), neutral density (kg m⁻³), dissolved oxygen (mL L⁻¹), total alkalinity (µmol kg⁻¹), total dissolved inorganic carbon (µmol kg⁻¹), pH (total scale), partial pressure of carbon dioxide (µatm), nitrate (µmol kg⁻¹), and phosphate (µmol kg⁻¹). The name description of each variable is provided in the readme_DatasetATLAS.txt. The dataset can be directly accessed using Ocean Data View (ODV) software.