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2,208 results for “emissions”
Monthly CO2 emissions projections from 2015-2025: modified SSP2-4.5 to account for COVID-19 impacts on sector activity
<p>Monthly CO2 emissions projections 2015-2025, modified by country-specific impacts of COVID-19 lockdown in 2020-2023, with 4 different projections for the period 2024-2025. </p> <p>This repository holds the netcdf files for CO2 emissions from ground-level and aviation sources from the MESSAGE_GLOBIOM scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown for 2020. Sector activity level in 2020 is based on data up until June, and a fixed estimate is used thereafter. This is the monthly equivalent of <a href="https://zenodo.org/record/3951601#.XxYBsihKhPY">https://zenodo.org/record/3951601#.XxYBsihKhPY</a> for this time period.</p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p> <p>see <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a> for more details.</p>
EPITOME ship emissions: Projections of shipping emissions towards 2050.
<p>As part of the EPITOME project, we have setup global shipping emission scenarios. They are based on a combination of the global CO<sub>2</sub> ship emission inventory for 2015 produced with the Ship Traffic Emissions Assessment Model (STEAM) (Johansson et al., 2017) and Arctic fuel consumption and emission scenarios calculated with the DCE ship emission model (Winther et al., 2017).</p> <p>The scenarios include a Baseline scenario, a SO<sub>x</sub> Emission Control Area (SECA) and a heavy fuel oil (HFO) ban scenario. The Baseline scenario is calculated in two variants involving Business As Usual (BAU) and High Growth (HiG) traffic growths. The SECA and HFO ban scenarios are given with the BAU traffic development.</p> <p>Additionally a Polar route scenario is included, with new (diversion) ship traffic routes in the future Arctic with less sea ice. The applied traffic growths and the polar routes are Corbett et al. (2010).</p> <p>The emissions are monthly on a spatial resolution of 0.1º×0.1º. </p> <p>Base year is 2015 and the scenarios are for 2050.</p> <p>A scientific paper providing details on the methodology behind these data will be submitted to ACPD (Geels et al, submitted). In this paper we apply the data to assess the contribution from shipping emissions to air pollution in the Nordic and Arctic area and the potential benefits of the mitigation options included in the shipping emission scenarios. This paper should be referenced if the data is used. </p> <p>The data are given as netcdf files for a number of components. The emission related to the diversion routes is given as a separate field and can be added the other field. <br> </p>
State of Wildfires 2024-25: Regional Summaries of Burned Area, Fire Emissions, and Individual Fire Characteristics for National, Administrative and Biogeographical Regions
<p>This dataset supports the State of Wildfires 2024-25 report under review at <em>Earth System Science Data</em> (Kelley et al., <em>under review)</em>. It is an update of the State of Wildfires 2023-24 report (Jones et al. 2024). The dataset provides annual data and final-year anomalies in burned area (BA), fire carbon (C) emissions, and fire properties (e.g. distributional statistics for fire count, size, rate of growth). Annual data relate to the global fire season defined as March-February (e.g., March 2024-February 2025), aligning with an annuall lull in the global fire calendar (see Jones et al., 2024). The complete methodology is described by Kelley et al. (<em>under review</em>).</p> <h3>Citation</h3> <p>Work utilising our regional summaries should <strong>cite both Kelley et al. (under review) AND the primary reference for the variable(s) of interest</strong> as follows:</p> <ul> <li>Giglio et al. (2018) for MODIS MCD64A1 BA.</li> <li>van der Werf et al. (2017) for GFED4.1s fire C emissions.</li> <li>Kaiser er al. (2012) for GFAS fire C emissions.</li> <li>van der Werf et al. (2017) AND Kaiser er al. (2012) for the average of GFED4.1s and GFAS fire C emissions.</li> <li>Andela et al. (2019) for the Global Fire Atlas.</li> <li>Giglio et al. (2016) for the Fire Radiative Power (FRP) observations.</li> <li>Chuvieco et al. (2024) for FireCCIS311 BA.</li> <li>Giglio et al. (2024) for VIIRS VNP64A1 BA.</li> </ul> <h3>Input Data</h3> <p><strong>Burned Area (BA)</strong></p> <ul> <li>BA data from NASA’s MODIS BA product (MCD64A1) are extended from Giglio et al. (2018) and are available from <a href="https://lpdaac.usgs.gov/products/mcd64a1v061/">Giglio et al. (2021)</a>. <ul> <li>Period: 2002-February 2025</li> <li>Resolution: 500m, daily</li> </ul> </li> <li>BA data from ESA's Climate Change Initiative BA product (FireCCIS311) are extended from Lizundia-Loiola et al. (2022) and are available from <a href="Chuvieco,%20E.;%20Pettinari,%20M.L.;%20Lizundia-Loiola,%20J.;%20Khairoun,%20A.;%20Danne,%20O.;%20Boettcher,%20M.;%20Storm,%20T.%20(2024):%20ESA%20Fire%20Climate%20Change%20Initiative%20(Fire_cci):%20Sentinel-3%20SYN%20Burned%20Area%20Grid%20product,%20version%201.1.%20NERC%20EDS%20Centre%20for%20Environmental%20Data%20Analysis,%2029%20February%202024.%20https://catalogue.ceda.ac.uk/uuid/da8e669a74334c82a56e0b470bc4ef04">Chuvieco et al. (2024)</a>. <ul> <li>Period: 2019-February 2025</li> <li>Resolution: 300m, daily</li> </ul> </li> <li>BA data from NASA’s VIIRS BA product (VNP64A1) are available from <a href="https://lpdaac.usgs.gov/products/vnp64a1v002/">Giglio et al. (2024)</a>. <ul> <li>Period: 2012-February 2025 (only the data after 2019 are used for consistency in the comparisons between MCD64A1, FireCCIS311, and VNP64A1).</li> <li>Resolution: 500m, daily</li> </ul> </li> </ul> <p><strong>Fire Carbon (C) Emissions</strong></p> <ul> <li>GFED4.1s fire C emissions data are extended from van der Werf and are available at <a href="https://globalfiredata.org/">https://globalfiredata.org/</a>. <ul> <li>Period: 2003-February 2025</li> <li>Resolution: 0.25 degree, daily</li> </ul> </li> </ul> <ul> <li>GFAS fire C emissions data are extended from Kaiser et al. (2012) and are available from the <a href="https://confluence.ecmwf.int/display/CKB/CAMS+global+biomass+burning+emissions+based+on+fire+radiative+power+%28GFAS%29%3A+data+documentation">ECMWF Confluence Server</a>. <ul> <li>Period: 2003-February 2025</li> <li>Resolution: 0.1 degree, daily</li> </ul> </li> </ul> <p><strong>Global Fire Atlas (Individual Fire Properties)</strong></p> <ul> <li>Global Fire Atlas data are extended from Andela et al. (2019) and are available from the repository maintained by <a href="https://doi.org/10.5281/zenodo.11400062">Andela and Jones (2025)</a>. <br> <ul> <li>Period: 2002-February 2025</li> <li>Driven by 500m MODIS BA data (collection 6.1)</li> </ul> </li> </ul> <p><strong>Fire Intensities</strong></p> <ul> <li>FRP data are extended from MOD14A1 and MYD14A1 (Giglio et al., 2016) and are available at <a href="https://lpdaac.usgs.gov/products/mod14a1v061/">Giglio and Justice (2021)</a>.<br> <ul> <li>Period: 2002-February 2025</li> <li>Resolution: 1km, daily</li> </ul> </li> </ul> <h3>Regional Analysis</h3> <p>We performed "cookie-cutting" (spatial and temporal masking) of the above input data sets to features in each of the following regional layers (e.g. per country in the "Countries" layer). </p> <p>The statistics derived from cookie-cutting are listed below. Full details in Kelley et al. (2025).</p> <div> <table> <tbody> <tr> <td> <p>Layer</p> </td> <td> <p>Short Form </p> </td> <td> <p>Source</p> </td> </tr> <tr> <td> <p>Biomes</p> </td> <td> <p>NA</p> </td> <td> <p>Olson et al. (2001)</p> </td> </tr> <tr> <td> <p>Ecoregions</p> </td> <td> <p>NA</p> </td> <td> <p>Olson et al. (2001)</p> </td> </tr> <tr> <td> <p>Continents</p> </td> <td> <p>NA</p> </td> <td> <p>ArcGIS Hub (2024)</p> </td> </tr> <tr> <td> <p>Continental Biomes</p> </td> <td> <p>NA</p> </td> <td> <p>See above</p> </td> </tr> <tr> <td> <p>Countries</p> </td> <td> <p>NA</p> </td> <td> <p>EU Eurostat (2020)</p> </td> </tr> <tr> <td> <p>UC Davis Global Administrative Areas (GADM) Level 1</p> </td> <td> <p>GADM-L1</p> </td> <td> <p>UC Davis (2022)</p> <br><br></td> </tr> <tr> <td> <p>Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) Working Group I (WGI) Reference Regions </p> </td> <td> <p>IPCC AR6 WGI Regions</p> </td> <td> <p>Iturbide et al. (2020)</p> </td> </tr> <tr> <td> <p>Global C Project Regional C Cycle Assessment and Processes (RECCAP2) Reference Regions</p> </td> <td> <p>RECCAP2 Regions</p> </td> <td> <p>Ciais et al. (2022)</p> </td> </tr> <tr> <td> <p>Global Fire Emissions Database (GFED) Basis Regions</p> </td> <td> <p>GFED4.1s Regions</p> </td> <td> <p>van der Werf et al. (2006)</p> </td> </tr> </tbody> </table> </div> <h3> </h3> <h3>Regional Statistics and Anomalies</h3> <ul> <li><strong>Burned Area (BA)</strong> <ul> <li>Calculated regional totals for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranking amongst all recorded fire seasons.</li> <li>Onset, peak, and cessation based on monthly deviations from climatological means.</li> </ul> </li> </ul> <ul> <li><strong>Carbon Emissions</strong> <ul> <li>Calculated regional totals for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2003).</li> <li>Ranking amongst all recorded fire seasons.</li> <li>Onset, peak, and cessation based on monthly deviations from climatological means.</li> <li>Statistics available for GFAS, GFED, and their mean.</li> </ul> </li> </ul> <ul> <li><strong>Individual Fire Properties</strong> <ul> <li>Based on values of individual fire size and rate of growth ignition from the ignition point vectors of the Global Fire Atlas.</li> <li>Calculated regional count.</li> <li>Calculated regional maxima and 95th percentiles of fire size and rate of growth for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranked anomalies among all recorded fire seasons.</li> </ul> </li> </ul> <ul> <li><strong>Fire Intensity</strong> <ul> <li>Based on active fire observations of FRP, which are pooled within each fire of the Global Fire Atlas.</li> <li>For each fire, the 95th percentile value of all FRP observations is the assigned intensity value (i.e. a "peak fire intensity" omitting any spurious high-end values).</li> <li>Regionally, the peak fire intensity values are averaged across individual fires.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranked anomalies among all recorded fire seasons.</li> </ul> </li> </ul>
Thermal infrared emissivity spectral library of silicates measured under the Mercury simulated environment
<p>This is the thermal emissivity spectral library of silicates measured as a function of temperature under Mercury simulated environment. Data is measured at the Planetary Spectroscopy Laboratory (PSL), Institute of Planetary Research, German Aerospace Center (DLR), Berlin. The spectral library will be used for mineral identification of Mercury surface using MERTIS datasets. The manuscript related to this work is submitted to Icarus on the title "<strong>Thermal Infrared Spectroscopy (7-14 µm) of Silicates under Simulated Mercury Daytime Surface Conditions and their Detection: Supporting MERTIS onboard the BepiColombo Mission".</strong></p>
Global ammonia emissions from CAMEO throughout the century for 3 scenarios (2000-2100)
<p><strong>Global ammonia emissions from the CAMEO process-based model </strong>(general model description and evaluation can be found in Beaudor et al., 2023, GMD; https://doi.org/10.5194/gmd-16-1053-2023).</p><p>Monthly files containing global NH3 emissions and Manure application rates in gN.m2.yr-1 (2.5° lon x 1.27° lat; IPSL-CM6A-LR Earth System Model resolution):</p><p>1) total agricultural emissions (TOT_AGRI; the sum of manure management and agricultural soil emissions)</p><p>2) manure management emissions (MANURE_MANAG.)</p><p>3) agricultural soil emissions (SOIL_AGRI)</p><p>4) natural soil emissions (SOIL_NAT) corrected for baresoil (excluding Sahara in this new version)</p><p>5) Fraction of continent (CONT_FRAC) from the model to use for CTM prescription or global budget calculation</p><p>6) TAN and non TAN applied to grassland from ruminants during grazing (tan_input_graz, nontan_input_graz)</p><p>7) TAN and non TAN applied to grassland from ruminants and considered as fertilizers (tan_input_manureApp_grass, nontan_input_manureApp_grass)</p><p>8) TAN and non TAN applied to cropland from all types of animal and considered as fertilizers (tan_input_manureApp_crop, nontan_input_manureApp_crop)</p><p>9) Grazing intensity (grazing_intensity, unitless)</p><p>10) Net Primary Production of grassland and grass biomass dedicated to livestock feed (NPP_grass, Cgrass_ingested in gC.m2.yr-1)</p><p>The four files correspond to a specific simulation using input4MIPs forcing files :</p><p>- Present-day simulation from 2000 to 2014 </p><p>- Future simulation from 2015 to 2100 under scenario SSP-2.45</p><p>- Future simulation from 2015 to 2100 under scenario SSP-4.34</p><p>- Future simulation from 2015 to 2100 under scenario SSP-5.85</p><p>Note that these datasets have been prepared in the scope of a publication to be submitted.</p><p><i><strong>Beaudor, M., N. Vuichard, J. Lathière, D. Hauglustaine., Historical and future ammonia emissions database (2000-2100) from the CAMEO process-based model, in preparation.</strong></i></p>
Open-source traffic and CO2 emission dataset for commercial aviation
<p>This record is a global open-source passenger air traffic dataset primarily dedicated to the research community. <br>It gives a seating capacity available on each origin-destination route for a given year, 2019, and the associated aircraft and airline when this information is available. </p> <p>Context on the original work is given in the related articles (<a href="https://doi.org/10.59490/joas.2024.7365">https://doi.org/10.59490/joas.2024.7365,</a> <a href="https://doi.org/10.59490/joas.2023.7201">https://doi.org/10.59490/joas.2023.7201)</a> and on the associated GitHub page (<a href="https://github.com/AeroMAPS/AeroSCOPE/">https://github.com/AeroMAPS/AeroSCOPE/</a>).<br>A simple data exploration interface will be available at <a href="www.aeromaps.eu/aeroscope">www.aeromaps.eu/aeroscope.</a><br>The dataset was created by aggregating various available open-source databases with limited geographical coverage. It was then completed using a route database created by parsing Wikipedia and Wikidata, on which the traffic volume was estimated using a machine learning algorithm (XGBoost) trained using traffic and socio-economical data.<br> </p> <h4><br><strong>1- DISCLAIMER</strong></h4> <p><br>The dataset was gathered to allow highly aggregated analyses of the air traffic, at the continental or country levels. At the route level, the accuracy is limited as mentioned in the associated article and improper usage could lead to erroneous analyses. </p> <p>Although all sources used are open to everyone, the Eurocontrol database is only freely available to academic researchers. It is used in this dataset in a very aggregated way and under several levels of abstraction. As a result, it is not distributed in its original format as specified in the contract of use.</p> <p>As a general rule, we decline any responsibility for any use that is contrary to the terms and conditions of the various sources that are used. In case of commercial use of the database, please contact us in advance.</p> <h4><br><strong>2- DESCRIPTION</strong></h4> <p>Each data entry represents an (Origin-Destination-Operator-Aircraft type) tuple.</p> <p><em>Please </em>refer<em> to </em>the<em> support article for more details (see above).</em></p> <p>The dataset contains the following columns:</p> <ul> <li>"First column" : index</li> <li><strong>airline_iata : </strong>IATA code of the operator in nominal cases. An ICAO -> IATA code conversion was performed for some sources, and the ICAO code was kept if no match was found.</li> <li><strong>acft_icao : </strong>ICAO code of the aircraft type</li> <li><strong>acft_class : </strong>Aircraft class identifier, own classification. <ul> <li>WB: Wide Body</li> <li>NB: Narrow Body</li> <li>RJ: Regional Jet</li> <li>PJ: Private Jet</li> <li>TP: Turbo Propeller</li> <li>PP: Piston Propeller</li> <li>HE: Helicopter</li> <li>OTHER</li> </ul> </li> <li><strong>seymour_proxy: </strong>Aircraft code for Seymour Surrogate (https://doi.org/10.1016/j.trd.2020.102528), own classification to derive proxy aircraft when nominal aircraft type unavailable in the aircraft performance model.</li> <li><strong>source: </strong>Original data source for the record, before compilation and enrichment. <ul> <li>ANAC: Brasilian Civil Aviation Authorities</li> <li>AUS Stats: Australian Civil Aviation Authorities</li> <li>BTS: US Bureau of Transportation Statistics T100</li> <li>Estimation: Own model, estimation on Wikipedia-parsed route database</li> <li>Eurocontrol: Aggregation and enrichment of R&D database</li> <li>OpenSky</li> <li>World Bank</li> </ul> </li> <li><strong>seats: </strong>Number of seats available for the data entry, AFTER airport residual scaling</li> <li><strong>n_flights: </strong>Number of flights of the data entry, when available</li> <li><strong>iata_departure</strong>, <strong>iata_arrival : </strong>IATA code of the origin and destination airports. Some BTS inhouse identifiers could remain but it is marginal.</li> <li><strong>departure_lon</strong><em>, </em><strong>departure_lat</strong><em>, </em><strong>arrival_lon</strong><em>, </em><strong>arrival_lat : </strong>Origin and destination coordinates, could be NaN if the IATA identifier is erroneous</li> <li><strong>departure_country, arrival_country</strong>: Origin and destination country ISO2 code. <strong>WARNING: </strong>disable NA (Namibia) as default NaN at import</li> <li><strong>departure_continent, arrival_continent: </strong>Origin and destination continent code. <strong>WARNING: </strong>disable NA (North America) as default NaN at import</li> <li><strong>seats_no_est_scaling: </strong>Number of seats available for the data entry, BEFORE airport residual scaling</li> <li><strong>distance_km: </strong>Flight distance (km)</li> <li><strong>ask: </strong>Available Seat Kilometres</li> <li><strong>rpk: </strong>Revenue Passenger Kilometres (simple calculation from ASK using IATA average load factor)</li> <li><strong>fuel_burn_seymour: </strong>Fuel burn <em>per flight</em> (kg) when seymour proxy available</li> <li><strong>fuel_burn: </strong>Total fuel burn of the data entry (kg)</li> <li><strong>co2: </strong>Total CO2 emissions of the data entry (kg)</li> <li><strong>domestic: </strong>Domestic/international boolean (Domestic=1, International=0)</li> </ul> <p> </p> <h4><strong>3- Citation</strong></h4> <p>Please cite the support paper instead of the dataset itself. </p> <blockquote> <p>Salgas, A., Sun, J., Delbecq, S., Planès, T., & Lafforgue, G. (2024). Compilation and Applications of an Open-Source Dataset on Global Air Traffic Flows and Carbon Emissions. <em>Journal of Open Aviation Science</em>. <a href="https://doi.org/10.59490/joas.2024.7365">https://doi.org/10.59490/joas.2023.7201</a></p> </blockquote>
Carbon Price Scenarios: Projecting prices for emission certificates
<p>This dataset consists of three different carbon price development scenarios. Each is represented by two growth rates which results in a total of 6 time series. The time frame is from 2020 to 2050. The units of the values are given in € / t CO₂. All values are nominal.</p> <p>Overall, it should be noted that an estimate of the development of CO2 prices in the german nEHS and EU-ETS is subject to great uncertainty due to the major influence of regulatory intervention, a less liquid market towards 2030 and a lack of markets after 2030.</p> <p>The data provided is delivered in frictionless data format (see 2024-03-25_metadata_carbon-price-scenarios.package.json) and can be accessed using the frictionless software (https://frictionlessdata.io/).</p>
Dataset envolved in "Evaluation of the single-component thermal dust emission model in CMB experiments"
<h1>Dataset envolved in "Evaluation of the single-component thermal dust emission model in CMB experiments"</h1> <p>See http://arxiv.org/abs/2411.04543.</p> <p>This data set contains the .fits files envolved in our work, from <a href="https://irsa.ipac.caltech.edu/data/Planck/" target="_blank" rel="noopener">Planck release</a> and <a href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/" target="_blank" rel="noopener">Irfan et. al., 2019</a>: </p> <p>In order to use these data files, </p> <p>please follow: (github readme)</p> <h2>Data from <em>Planck</em> release</h2> <h3><em>Planck</em> Release 1, 2013</h3> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map for thermal dust model (optical depth, spectral index, and temperature)<br></strong></p> <p><strong>Relation to this work: provide parameters of model Planck 2013</strong></p> <p>HFI_CompMap_ThermalDustModel_2048_R1.20.fits</p> <p><a title="HFI_CompMap_ThermalDustModel_2048_R1.20.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_CompMap_ThermalDustModel_2048_R1.20.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_CompMap_ThermalDustModel_2048_R1.20.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: full-sky maps at 217 GHz with zodiacal light and without zodiacal light</strong></p> <p><strong>Relation to this work: used to filter out regions with strong zodiacal emission</strong></p> <p>HFI_SkyMap_217_2048_R1.10_nominal.fits<br><a title="HFI_SkyMap_217_2048_R1.10_nominal.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal.fits</a></p> <p>HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits<br><a title="HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_1/all-sky-maps/maps/HFI_SkyMap_217_2048_R1.10_nominal_ZodiCorrected.fits</a></p> <h3> </h3> <h3><em>Planck</em> Release 2, 2015</h3> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of synchrotron emission</strong></p> <p><strong>Relation to this work: used to remove synchrotron emission from full-sky maps</strong></p> <p>COM_CompMap_Synchrotron-commander_0256_R2.00.fits<br><a title="COM_CompMap_Synchrotron-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Synchrotron-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Synchrotron-commander_0256_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of free-free emission</strong></p> <p><strong>Relation to this work: used to remove free-free emission from full-sky maps</strong></p> <p>COM_CompMap_freefree-commander_0256_R2.00.fits<br><a title="COM_CompMap_freefree-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_freefree-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_freefree-commander_0256_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of carbon monoxide</strong></p> <p><strong>Relation to this work: used to remove carbon monoxide emission from full-sky maps</strong></p> <p>COM_CompMap_CO21-commander_2048_R2.00.fits<br><a title="COM_CompMap_CO21-commander_2048_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_CO21-commander_2048_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_CO21-commander_2048_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of 94/100 GHz molecular emission lines</strong></p> <p><strong>Relation to this work: used to remove 94/100 GHz emission lines from full-sky maps</strong></p> <p>COM_CompMap_xline-commander_0256_R2.00.fits<br><a title="COM_CompMap_xline-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: Galactic plane masks with no apodization</strong></p> <p><strong>Relation to this work: used to mask Galactic plane</strong></p> <p>HFI_Mask_GalPlane-apo0_2048_R2.00.fits<br><a title="COM_CompMap_xline-commander_0256_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_xline-commander_0256_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_GalPlane-apo0_2048_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: point source masks</strong></p> <p><strong>Relation to this work: used to mask point sources in full-sky maps and inpaint them </strong></p> <p>HFI_Mask_PointSrc_2048_R2.00.fits<br><a title="HFI_Mask_PointSrc_2048_R2.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_PointSrc_2048_R2.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/ancillary-data/masks/HFI_Mask_PointSrc_2048_R2.00.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: maps for thermal dust model (optical depth, spectral index, and temperature)<br></strong></p> <p><strong>Relation to this work: provide parameters of model Planck 2015 (GNILC pipeline, without CIB contamination)</strong></p> <p>COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits<br><a title="COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Opacity_2048_R2.01.fits</a></p> <p>COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits<br><a title="COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Spectral-Index_2048_R2.01.fits</a></p> <p>COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits<br><a title="COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/all-sky-maps/maps/component-maps/foregrounds/COM_CompMap_Dust-GNILC-Model-Temperature_2048_R2.01.fits</a></p> <p><strong>Format: .FITS file (table)</strong></p> <p><strong>Type: <em>Planck</em> catalogue of compact sources at 30, 44, 70, 100, 143, 217, 353, 545, and 857 GHz</strong></p> <p><strong>Relation to this work: to mask compact sources</strong></p> <p>COM_PCCS_030_R2.04.fits<br><a title="COM_PCCS_030_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_030_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_030_R2.04.fits</a></p> <p>COM_PCCS_044_R2.04.fits<br><a title="COM_PCCS_044_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_044_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_044_R2.04.fits</a></p> <p>COM_PCCS_070_R2.04.fits<br><a title="COM_PCCS_070_R2.04.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_070_R2.04.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_070_R2.04.fits</a></p> <p>COM_PCCS_100-excluded_R2.01.fits<br><a title="COM_PCCS_100-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100-excluded_R2.01.fits</a></p> <p>COM_PCCS_100_R2.01.fits<br><a title="COM_PCCS_100_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_100_R2.01.fits</a></p> <p>COM_PCCS_143-excluded_R2.01.fits<br><a title="COM_PCCS_143-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143-excluded_R2.01.fits</a></p> <p>COM_PCCS_143_R2.01.fits<br><a title="COM_PCCS_143_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_143_R2.01.fits</a></p> <p>COM_PCCS_217-excluded_R2.01.fits<br><a title="COM_PCCS_217-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217-excluded_R2.01.fits</a></p> <p>COM_PCCS_217_R2.01.fits<br><a title="COM_PCCS_217_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_217_R2.01.fits</a></p> <p>COM_PCCS_353-excluded_R2.01.fits<br><a title="COM_PCCS_353-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353-excluded_R2.01.fits</a></p> <p>COM_PCCS_353_R2.01.fits<br><a title="COM_PCCS_353_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_353_R2.01.fits</a></p> <p>COM_PCCS_545-excluded_R2.01.fits<br><a title="COM_PCCS_545-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545-excluded_R2.01.fits</a></p> <p>COM_PCCS_545_R2.01.fits<br><a title="COM_PCCS_545_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_545_R2.01.fits</a></p> <p>COM_PCCS_857-excluded_R2.01.fits<br><a title="COM_PCCS_857-excluded_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857-excluded_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857-excluded_R2.01.fits</a></p> <p>COM_PCCS_857_R2.01.fits<br><a title="COM_PCCS_857_R2.01.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857_R2.01.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_2/catalogs/COM_PCCS_857_R2.01.fits</a></p> <h3> </h3> <h3><em>Planck</em> Release 3, 2018</h3> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: map of CMB anisotropies (SMICA from <em>Planck</em> 2018)</strong></p> <p><strong>Relation to this work: used to remove CMB anisotropies from full-sky maps</strong></p> <p>COM_CMB_IQU-smica_2048_R3.00_full.fits<br><a title="COM_CMB_IQU-smica_2048_R3.00_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/component-maps/cmb/COM_CMB_IQU-smica_2048_R3.00_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/component-maps/cmb/COM_CMB_IQU-smica_2048_R3.00_full.fits</a></p> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: <em>Planck</em> 2018 full-sky maps at 100, 143, 217, 353, 545, and 857 GHz</strong></p> <p><strong>Relation to this work: used to obtain dust data maps at these bands</strong></p> <p>HFI_SkyMap_100_2048_R3.01_full.fits<br><a title="HFI_SkyMap_100_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_100_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_100_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_143_2048_R3.01_full.fits<br><a title="HFI_SkyMap_143_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_143_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_143_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_217_2048_R3.01_full.fits<br><a title="HFI_SkyMap_217_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_217_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_217_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_353_2048_R3.01_full.fits<br><a title="HFI_SkyMap_353_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_353_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_353_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_545_2048_R3.01_full.fits<br><a title="HFI_SkyMap_545_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_545_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_545_2048_R3.01_full.fits</a></p> <p>HFI_SkyMap_857_2048_R3.01_full.fits<br><a title="HFI_SkyMap_857_2048_R3.01_full.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_857_2048_R3.01_full.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/all-sky-maps/maps/HFI_SkyMap_857_2048_R3.01_full.fits</a></p> <p>HFI_RIMO_R3.00.fits<br><a title="HFI_RIMO_R3.00.fits" href="https://irsa.ipac.caltech.edu/data/Planck/release_3/ancillary-data/HFI_RIMO_R3.00.fits" target="_blank" rel="noopener">https://irsa.ipac.caltech.edu/data/Planck/release_3/ancillary-data/HFI_RIMO_R3.00.fits</a></p> <h2> </h2> <h2>Thermal dust model from Melis O. Irfan et al. <a href="https://www.aanda.org/articles/aa/abs/2019/03/aa34394-18/aa34394-18.html" target="_blank" rel="noopener">A&A 623, A21 (2019)</a></h2> <p><strong>Format: .FITS file</strong></p> <p><strong>Type: maps for thermal dust model (optical depth, spectral index, and temperature)<br></strong></p> <p><strong>Relation to this work: provide parameters of model Melis O. Irfan et al. 2019</strong></p> <p>beta.fits<br><a title="beta.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/beta.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/beta.fits</a></p> <p>tau.fits<br><a title="tau.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/tau.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/tau.fits</a></p> <p>temp.fits<br><a title="temp.fits" href="https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/temp.fits" target="_blank" rel="noopener">https://cdsarc.cds.unistra.fr/ftp/J/A+A/623/A21/fits/temp.fits</a></p> <p> </p>
A global dataset of specialty crop biomass and N2O emissions
<div> <p>We reviewed global field studies of vineyard, orchard, and vegetable cropping systems, which were also included in a meta-analysis (<a href="https://doi.org/10.1111/gcb.17233">https://doi.org/10.1111/gcb.17233</a>). We narrowed down the studies to those with field measurements of adequate variables (biomass C, N, and N<sub>2</sub>O) covering at least one growing season. As a result, cumulative N₂O emission measurements (per growing rotation, season, or year), along with biomass data of different plant organs from the same regions, were compiled for grape (<em>Vitis vinifera</em>), almond [<em>Prunus dulcis</em> (Mill.) D.A. Webb], peach (<em>Prunus persica</em> L.), walnut (<em>Juglans regia</em>), lettuce (<em>Lactuca sativa</em>), broccoli (<em>Brassica oleracea</em> var. <em>italica </em>P.), cauliflower (<em>Brassica oleracea</em> var. <em>botrytis </em>L.), and tomato (<em>Lycopersicon esculentum</em> L.) planting system. These observations were collected from fields spanning seven Koppen-Geiger climate types and five countries (the United States, Germany, Spain, France, and Australia). When only dry mass was measured, biomass C content for aboveground vegetable crops and berry fruit was assumed at 43%; nut fruit and woody organs of orchard tree at 48%. Area-weighted averages of N<sub>2</sub>O emissions were used (tree/vine row and interrow).</p> <p> </p> <p>Corresponding author: Mu Hong (mu.hong@colostate.edu)</p> </div> <p> </p>
COMPAIR carbon footprint calculations and greenhouse gas emissions reduction scenarios
<p>Citizens' carbon footprint calculation results and citizen-created scenarios on how Greenhouse Gas emissions can be reduced by 55% by 2030 are available that were gathered as part of the <a href="https://cordis.europa.eu/project/id/101036563">EU Horizon2020 COMPAIR project</a> in Europe. The pilot cities/regions are Berlin, Athens, Sofia, Plovdiv, and Flanders.</p>
Data for paper publication "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3"
<p>The dataset presented here is related to the article by Leon-Marcos et al. 2025: "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3" accepted for publication in GMD. It comprises global fields of the FESOM2.1-REcoM3 biogeochemistry model tracers employed to calculate the ocean biomolecule concentration that serve as input data for the aerosol model. Additionally, the ECHAM6.3–HAM2.3 code of the marine aerosol implementation and the required scripts to run the model experiments are provided here. The aerosol-climate model simulation results of the marine aerosol emission, as well as the evaluation of the model results compared to observations, are also included. For further information, please refer to the attached data description. </p> <p> </p> <h2> </h2>
Atmospheric Halocarbon Observations at Beromünster, Switzerland, and Bayesian Inverse Modeling to assess Emissions
<p>Atmospheric halocarbon (CFCs, halons, HCFCs, HFCs, PFCs, SF<sub>6</sub>, NF<sub>3</sub>, HFOs) and carbon monoxide (CO) observations (mole fractions) from the tall tower site at Beromünster, Switzerland (47.2 °N, 8.2 °E, 797 m a.s.l., 212 m a.g.l.), covering the period September 2019 to September 2020. The halocarbon measurements were conducted using a Medusa pre-concentration unit, coupled to gas chromatography (Agilent 6890N) and mass spectrometry (Agilent 5975, GC-MS).</p> <p>For further details see: Miller, B. R., Weiss, R. F., Salameh, P. K., Tanhua, T., Greally, B. R., Mühle, J., and Simmonds, P. G.: Medusa: A Sample Preconcentration and GC/MS Detector System for in Situ Measurements of Atmospheric Trace Halocarbons, Hydrocarbons, and Sulfur Compounds, Anal. Chem., 80, 1536–1545, https://doi.org/10.1021/ac702084k, 2008).</p> <p>The data format follows that used within the AGAGE network (see AGAGE data archive: <a href="http://agage.mit.edu/data/agage-data">http://agage.mit.edu/data/agage-data</a>).</p> <p>Data results for the Bayesian inversion conducted based on the measurement data from Beromünster to assess Swiss halocarbon emissions. Files are provided in netCDF format for the 28 individual substances discussed in (Rust, D. et al., 2022, <em>Swiss halocarbon emissions for 2019 to 2020 assessed from regional atmospheric observations</em>, Atmospheric Chemistry and Physics). Each file contains the a priori and a posteriori emissions as used or calculated in the Bayesian inversion. Data are provided on the grid used in the inversion (irregular longitude/latitude). Metadata are included as netCDF attributes. The netCDF files follow the CF conventions and are readable with any netcdf interface/tool.</p>
Code for "New land-use-change emissions indicate a declining CO2 airborne fraction"
<p>Data and programming scripts for reproducing the results from the Nature publication titled:</p> <p>"New land-use-change emissions indicate a declining CO2 airborne fraction".</p> <p>Authors: Margreet J. E. van Marle*, Dave van Wees*, Richard A. Houghton, Robert D. Field, Jan Verbesselt, and Guido R. van der Werf<br> * These authors contributed equally.</p> <p>DOI: https://doi.org/10.1038/s41586-021-04376-4</p> <p> </p> <p>This dataset includes the following (All files are preceded by "Marle_et_al_Nature_AirborneFraction_"):</p> <p>- "Datasheet.xlsx": Excel dataset containing all annual and monthly emissions and CO2 time series used for the analysis, and the resulting airborne fraction time series.</p> <p>- "Script.py":<br> BEFORE RUNNING THE SCRIPT: change the 'wdir' variable to the directory containing the provided script and files.<br> NOTE: This script requires the Python module: 'pymannkendall'<br> Python script used for reproducing the results and figures from the paper. The provided Datasheet.xlsx file and the .zip and .npz files are required for this program. In case all these files are found by the script, it should run within several seconds. Successful execution of the script will save Figures 1-4 from the main text and print the data from Table 1. In case script execution takes longer, please check if the .xlsx, .zip and .npz files are correctly present in the assigned 'wdir' directory. Otherwise the script will start recalculating these files, which might take a while (see notes below).</p> <p>- "MC10000_MK_ts_TRENDabs.zip": .zip file containing all results from the Monte-Carlo simulation for trend estimation for Figure 3 (calculated using Python function 'calc_AF_MonteCarlo()'). This .zip file contains multiple .npz files for different emission scenarios and data treatments. This .zip file is managed by the Python script function 'calc_AF_MonteCarlo_filemanager()', there is no need to unzip the file manually. In case the .zip file is not found by the Python script (e.g. because the .zip file was unpacked manually and deleted), the program will start recalculating and save a new .zip file. This can take several minutes dependent on the computer used. Recalculated results could differ very slightly due to the random factor in the Monte-Carlo approach, even though the 10,000 iterations bring this variation to a minimum.</p> <p>- "MC1000_MK_run50x50_TRENDabs.npz": .npz file containing the Monte-Carlo results used for producing Figure 4 (calculated using Python function 'calc_AF_MonteCarlo_ARR()'). In case the .npz file is not found by the Python script (e.g. because it was deleted or not downloaded), the program will start recalculating and save a new file. This can take around 30 hours(!) dependent on the computer used. Recalculated results could differ slightly due to the random factor in the Monte-Carlo approach.</p> <p>- "tol_colors.py": Additional Python module used in script.py, required for producing the colors used in the Main text figures. Source: https://personal.sron.nl/~pault/</p> <p>- Figure files: Figures 1-4 from the Main text saved as .pdf files. Figure 3 is saved as three independent panels. The Figures are also reproduced by script.py if executed successfully.</p> <p> </p>
Characterization of the angular-dependent emission of nitrogen-vacancy centers in nanodiamond
<p>We report on the characterization of the angular-dependent emission of single-photon emitters based on single nitrogen-vacancy (NV-) centers in nanodiamond at room temperature. A theoretical model for the calculation of the angular emission patterns of such an NV-center at a dielectric interface will be presented. For the first time, the orientation of the NV-centers in nanodiamond was determined from back focal plane images of NV-centers and by comparison of the theoretical and experimental angular emission pattern. Furthermore, the orientation of the NV-centers was also obtained from measurements of the fluorescence intensity in dependence on the polarization angle of the linearly polarized excitation laser. The results of these measurements are in good agreement. Moreover, the collection efficiency in this setup was calculated to be higher than 80% using the model of the angular emission of the NV-centers.</p>
Dataset for the published article "ITER relevant multi-emissive sheaths at normal magnetic field inclination"
<p>The data contained in the zip files constitute the main research data of the publication entitled as "<a href="https://iopscience.iop.org/article/10.1088/1741-4326/acaabd">ITER relevant multi-emissive sheaths at normal magnetic field inclination</a>" [1]. All the datasets constitute post-processed output from the 2D3V SPICE2 Particle-In-Cell (PIC) code. All the PIC simulations have been performed by M. Komm and A. Podolnik. The input is specified by the plasma density, the electron temperature and the surface temperature. The plasma parameters are relevant to partially mitigated ITER edge-localized modes (ELMs). The output concerns the incident plasma current densities, the emitted electron current densities and their standard deviation, the normal wall electrostatic field, the average electron incident energy, the average electron incident angle with respect to the wall normal and the virtual cathode depth. </p> <p>The assumptions below are followed in all simulations: (i) The Bohm pre-sheath structure is unaltered by the escaping emitted electrons, since the ions are injected at the plasma boundary with a speed distribution satisfying the Bohm criterion. (ii) Irrespective of the emission, the wall is biased with respect to the plasma boundary with a magnitude fixed by the ambipolarity of the plasma fluxes. (iii) The sheath is collisionless. (iv) The wall is perfectly planar. (v) A homogeneous quasi-neutral plasma boundary and an infinite emitting wall with a homogeneous prescribed surface temperature are considered.</p> <p>Sheaths that form between plasma-facing components (PFCs) and standard scrape-off-layer plasmas can be described by the classical model of one-dimensional magnetized multi-positive ion sheaths. There are various conditions that need to be satisfied for this model to be valid such as negligible cross-field drifts, low collisionality and weak electron emission.</p> <p>In contemporary metallic tokamaks, the weak emission condition is violated in the divertor region during intra-ELM as well as inter-ELM periods; thermionic emission being an effective electron emission mechanism from hot tungsten PFCs. As a result of the localized ELM-wetted area, the incident plasma currents can be assumed to remain nearly ambipolar and thus the non-ambipolar current should be equal to the emitted current that escapes to the Bohm pre-sheath. This escaping current density generates a strong volumetric Lorentz force that drives melt layer motion leading to macroscopic PFC erosion. At very elevated surface temperatures, the nominal thermionic current densities are so large that they become incompatible with the classical Bohm pre-sheath structure. As a consequence, space charge accumulation in the sheath leads to the formation of a virtual cathode that limits the escaping thermionic current to a constant value causing the recapture of a fraction of the thermo-electrons. Thus, there is a transition from a monotonic to a non-monotonic potential profile, with the latter known as the space-charge limited (SCL) regime of the emissive sheath. In the case of oblique magnetic field inclination angles, the SCL transition is still realized, but further complications arise due to the suppression of the nominal thermionic current by recapture during Larmor gyration. In contemporary tokamaks, this transition generally occurs at temperatures below the tungsten melting point, thus particular attention has been paid to the SCL sheaths, since they nearly exclusively surround the molten tungsten PFCs. The thermionic emissive sheath in the SCL regime has been thoroughly investigated in our previous works, where an accurate semi-empirical expression for the limited value of the escaping thermionic current as function of the plasma conditions and magnetic field inclination angle was constructed on the basis of systematic PIC simulations [2-4].</p> <p>On the other hand, during ITER intra-ELM periods, the predicted elevated electron temperatures and high plasma densities of the pre-sheath edge should have a strong impact on the emissive sheath established above hot tungsten PFCs. In particular, the high plasma electron temperatures could enable significant contributions from electron-induced electron emission (secondary electron emission and electron backscattering), the intense normal surface electrostatic fields indicate that thermionic emission is coupled with field emission (in the Schottky regime) and the strong plasma currents suggest that virtual cathodes are formed at much higher surface temperatures (so that the monotonic potential profile regime is of primary interest for melt motion). In order to explore this novel multi-emissive sheath regime, a a comprehensive tungsten electron emission model has been implemented that features accurate analytical descriptions of the yields, energy and angular distributions for the processes of field-assisted thermionic emission, secondary electron emission and electron backscattering [5]. In the present publication [1], at normal magnetic field inclinations, highly accurate analytical semi-empirical expressions are provided for the secondary electron emission current, electron backscattering current and thermionic current in the monotonic regime as well as for the total escaping current in the SCL regime. These semi-empirical expressions have been benchmarked against comprehensive PIC simulations, whose primary post-processed data are provided herein.</p> <p>[1] P. Tolias, M. Komm, S. Ratynskaia and A. Podolnik, "ITER relevant multi-emissive sheaths at normal magnetic field inclination", Nucl. Fusion 63 (2023) 026007.<br> [2] M. Komm, S. Ratynskaia, P. Tolias, J. Cavalier, R. Dejarnac, J. P. Gunn and A. Podolnik, "On thermionic emission from plasma-facing components in tokamak-relevant conditions", Plasma Phys. Control. Fusion 59 (2017) 094002.<br> [3] M. Komm, P. Tolias, S. Ratynskaia, R. Dejarnac, J. P. Gunn, K. Krieger, A. Podolnik, R. A. Pitts and R. Panek, "Simulations of thermionic suppression during tungsten transient melting experiments", Phys. Scr. T170 (2017) 014069.<br> [4] M. Komm, S. Ratynskaia, P. Tolias and A. Podolnik, "Space-charge limited thermionic sheaths in magnetized fusion plasmas", Nucl. Fusion 60 (2020) 054002.<br> [5] P. Tolias, M. Komm, S. Ratynskaia and A. Podolnik, "Origin and nature of the emissive sheath surrounding hot tungsten tokamak surfaces", Nucl. Mater. Energy 25 (2020) 100818.</p> <p> </p>
The stellar parameters and the quantities of the residual emissions of the detected active stars in the LAMOST-K2 survey
<p>The full Table 1 in <em>Investigation of stellar magnetic activity using variational autoencoder based on low-resolution spectroscopic survey</em> (Xiang, Gu & Cao, 2022, MNRAS, 514, 4781; <a href="https://arxiv.org/abs/2206.07257">arXiv:2206.07257</a>). The columns are LAMOST obsid, K2 ID, Teff, logg, [Fe/H], EW_res_Halpha, EW_res_Ca II 8498, EW_res_Ca II 8542, EW_res_Ca II 8662, log F_Halpha, log F_Ca, log R'_Halpha, log R'_Ca. The chromospheric emissions were detected and measured with the spectral subtraction technique, which removes the inactive template spectra (photospheric contribution) from the observed stellar spectra. In this work, we used the variational autoencoder neural networks to efficiently generate the proper template spectra in a data-driven manner. More details can be found in the associated paper (<a href="https://arxiv.org/abs/2206.07257">https://arxiv.org/abs/2206.07257</a>). The demo code can be found on GitHub (<a href="https://github.com/xylib/vae-for-spectroscopic-survey">https://github.com/xylib/vae-for-spectroscopic-survey</a>).</p>
Commodity-driven deforestation, associated carbon emissions and trade 2001-2022
<p><span>This dataset contains estimates of commodity-driven deforestation and associated carbon emissions for the period 2001-2022, estimated by the Deforestation Driver and Carbon Emission (DeDuCE) model (Singh & Persson 2024), which combines remote sensing data on forest loss and land-use with agricultural statistics to identify and attribute deforestation across the world to expansion of cropland, pastures and forest plantation, and the commodities produced on this land. This also contains data on deforestation embodied in the production, exports, imports, and consumption of agricultural and forestry commodities by country, year, and commodity for the time period 2005-2022 derived using physical and monetary trade models. The data is an update of the results presented in Pendrill et al. (2022) and the differences between the two datasets are detailed in the explainer available here.</span></p>
Emissions Database for Global Atmospheric Research, version v4.3.2 part I Greenhouse gases
<p>The Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2, partim Greenhouse gases compiles anthropogenic emissions data for CO2, CH4 and N2O based on international statistics and emission factors. The version v4.3.2 of the EDGAR emission inventory provides global estimates, broken down to IPCC-relevant source-sector levels, from 1970 (the year of EU’s first Air Quality Directive) to 2012 (the end year of the first commitment period of the Kyoto Protocol (KP)). Strengths of EDGAR v4.3.2 include global geo-coverage (226 countries), continuity in time, and comprehensiveness in activities. Emission sources of the multiple gases include all human activities except the land-use, land-use change and forestry sector and are compiled following a bottom-up and IPCC-compliant approach. The dataset provides in addition to the complete timeseries 1970-2012 also annual and global gridmaps of 0.1 degree by 0.1 degree resolution for each source-sector and each year. For 2010 also 12 monthly gridmaps per source-sector are provided.</p>
Dataset for simulation studies of fleet vehicle selection in terms of pollutant emissions
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Szczepański E, Jachimowski R, Rudyk T. Simulation studies of fleet vehicle selection in terms of pollutant emissions. Combustion Engines. 2024;196(1):80-88. https://doi.org/10.19206/CE-169802 - published online: 2023-08-10, which discusses the application of simulation in solving the problem of vehicle selection and determining optimal approaches considering pollutant emissions.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset</li> <li>InputData.csv: contains the input data used in the model, including data from the COPERT model.</li> <li>OutputOptimization.csv: contains output data</li> <li>OutputSummary.xlsx: contains output data</li> <li>imulation_model_xml.fsx: contains the code of the model in XML format.</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</p>
Images and Crater Data for "Crater Detection Dependence on Resolution, Incidence Angle, Emission Angle, and Phase Angle"
<p>Images are from the LROC-NAC and have been cartographically controlled to each other and the <em>Apollo 11</em> landing site as described in Supporting Information Text S1. Images are cropped so that the cover ±0.025° from the landing site when coordinates have three significant figures. The images are provided as .png files with .pgw ("PNG World"). The images are at 1 mpp (contain a "1mpp" string in the file name), 2.5 mpp (contain a 2p5mpp" string in the file name), and 6.25 mpp (contain a "6p25mpp" string in the file name). Additionally, the three <em>e</em> > 10° images are included as unprojected .cub files; these files omit the "l2" (map projected, Level-2 data) string and any "l4" (mosaicked) string from the file name, but they instead include "trim" to indicate the image has been trimmed from its full extent to the area of interest.</p> <p>Crater data are formatted as .csv (comma-separated values) files and are one file per image per researcher. File names have the exact same name as the image file that was used to map crater data, with two differences: The initials of the author are appended, and the file extension is "csv" instead of "png". The files do not have headers, but they are formatted such that the first column is latitude (decimal degrees north), second column is longitude (decimal degrees east), and diameter (kilometers). Crater data are entirely in one .zip file.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.