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The PRIMAP-hist national historical emissions time series (1750-2024) v2.7
<p><strong>Recommended citation</strong></p> <p>Gütschow, J.; Busch, D.; Pflüger, M. (2025): The PRIMAP-hist national historical emissions time series v2.7 (1750-2024). zenodo. doi:10.5281/zenodo.<em>17090760</em>.</p> <p>Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016</p> <p><strong>Content</strong></p> <ul> <li><a href="#use-of-the-dataset-and-full-description">Use of the dataset and full description</a></li> <li><a href="#abstract">Abstract</a></li> <li><a href="#support">Support</a></li> <li><a href="#sources">Sources</a></li> <li><a href="#files-included-in-the-dataset">Files included in the dataset</a></li> <li><a href="#notes">Notes</a></li> <li><a href="#data-format-description-columns">Data format description (columns)</a></li> <li><a href="#references">References</a></li> <li><a href="#changelog">Changelog</a></li> </ul> <p><strong>Abstract</strong></p> <p>The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas, covering the years 1750 to 2024, and almost all UNFCCC (United Nations Framework Convention on Climate Change) member states as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO2, CH4, and N2O subsector data for Energy, Industrial Processes and Product Use (IPPU), and Agriculture are available. The "country reported data priority" (CR) scenario of the PRIMAP-hist datset prioritizes data that individual countries report to the UNFCCC.</p> <p>For developed countries, AnnexI in terms of the UNFCCC, this is the data submitted anually in the "National Inventory Submissions". Until 2023 data was submitted in the "Common Reporting Format" (CRF). Since 2024 the new "Common Reporting Tables" (CRT) are used.For developing countries, non-AnnexI in terms of the UNFCCC our preferred data source are the Common Reporting Tables (CRT) submitted with the Biannial Transparency Reports (BTR). When countries do not provide the tables we read available data from the pdf reports and use additional submissions (Biannial Update Reports (BUR), National Communications (NC), and National Inventory Reports (NIR)) read from pdf and xlsx/csv files and for older submissions obtained from the UNFCCC DI portal (di.unfccc.int). For a list of these submissions please see below. For South Korea the 2024 official GHG inventory has not yet been submitted to the UN but is included in PRIMAP-hist. PRIMAP-hist also includes official data for Taiwan which is not recognized as a party to the UNFCCC. As the USA have not submitted any data to the UNFCCC this year we use the draft inventory report which the Environmental Defense Fund (EDF) obtained from the US Environmental Protection Agency (EPA) through the Freedom of Information Act.</p> <p>Gaps in the country reported data are filled using third party data such as CDIAC, EI (fossil CO2), Andrew cement emissions data (cement), FAOSTAT (agriculture), and EDGAR 2024 (all sectors for CO2, CH4, N2O, HFCs, PFCs, SF6, NF3, except energy CO2). Lower priority data are harmonized to higher priority data in the gap-filling process.</p> <p>For the third party priority time series gaps in the third party data are filled from country reported data sources.</p> <p>Data for earlier years which are not available in the above mentioned sources are sourced from EDGAR-HYDE, CEDS, and RCP (N2O only) historical emissions.</p> <p>The v2.4 release of PRIMAP-hist reduced the time-lag from 2 to 1 years for the October release. Thus the present version 2.7 includes data for 2024. For energy CO2 growth rates from the Energy Institute's *Statistical Review of World Energy* are used to extend the country reported data to 2024. For CO2 from cement production Andrew cement data are used for a few countries. For all other sectors and gases no emission estimates exist. Thus PRIMAP-hist relies on numerical methods and uses a linear extrapolation based on the last 5 years. COVID-19 has primarily impacted energy related emissions and in tests with CRF data no impact of COVID in the performance of linear extrapolation of emissions data in the other sectors has been detected. For the few cases where extrapolation is needed for energy CO2 we use a 15 year trend for the extrapolation.</p> <p>Version 2.7 of the PRIMAP-hist dataset does not include emissions from Land Use, Land-Use Change, and Forestry (LULUCF) in the main file. LULUCF data are included in the file with increased number of significant digits and have to be used with care as they are constructed from different sources using different methodologies and are not harmonized.</p> <p>The PRIMAP-hist v2.7 dataset is an updated version of</p> <p>Gütschow, J.; Pflüger, M.; Busch, D. (2025): The PRIMAP-hist national historical emissions time series v2.6.1 (1750-2023). zenodo. doi:10.5281/zenodo.15016289.</p> <p>The <a href="#changelog">Changelog</a> indicates the most important changes. You can also check the issue tracker on <a href="https://github.com/JGuetschow/PRIMAP-hist">github.com/JGuetschow/PRIMAP-hist</a> for additional information on issues found after the release of the dataset. Detailed per country information is available from the detailed changelog which is available on the <a href="https://primap.org/primap-hist">primap.org</a> website and on zenodo.</p> <p><strong>Use of the dataset and full description</strong></p> <p>Before using the dataset, please read this document and the article describing the methodology, especially the section on uncertainties and the section on limitations of the method and use of the dataset.</p> <p>Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016</p> <p>Please notify us (support@johannes-guetschow@.de) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.</p> <p>When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the PRIMAP-hist dataset. See the full citations in the References section further below.</p> <p>Since version 2.3 we use the data formats developed for the PRIMAP2 climate policy analysis suite: <a href="https://github.com/pik-primap/primap2">PRIMAP2 on GitHub</a>. The data are published both in the interchange format which consists of a csv file with the data and a yaml file with additional metadata and the native NetCDF based format. For a detailed description of the data format we refer to the <a href="https://primap2.readthedocs.io/en/stable/">PRIMAP2 documentation</a>.</p> <p>We have also included files with more than three significant digits. These files are mainly aimed at people doing policy analysis using the country reported data scenario (HISTCR). Using the high precision data they can avoid questions on discrepancies with the reported data. The uncertainties of emissions data do not justify the additional significant digits and they might give a false sense of accuracy, so please use this version of the dataset with extra care.</p> <p><strong>Support</strong></p> <p>If you encounter possible errors or other things that should be noted, please check our issue tracker at <a href="https://github.com/JGuetschow/PRIMAP-hist">github.com/JGuetschow/PRIMAP-hist</a> and report your findings there. Please use the tag "v2.7" in any issue you create regarding this dataset.</p> <p>If you need support in using the dataset or have any other questions regarding the dataset, please contact nc-support@johannes-guetschow.de.</p> <p>Basic support is free for non-commercial users and most questions can be answered with a short e-mail. However, we do not have the resources to provide extensive support free of charge. For commercial users support will be included with the commercial license (see below).</p> <p><strong>License</strong></p> <p>Since v2.7 PRIMAP-hist is published under a non-commercial license (CC BY-NC-SA). This means that commercial users can not use it freely and have to obtain a commercial license. The commercial license is only available for the country reported priority (CR) time-series as the third party priority (TP) time-series builds heavily on EDGAR and FAOSTAT data. For commercial customers wanting to use the TP time-series we offer to develop custom code to generate the data locally. Please contact commercial-support@johannes-guetschow.de for more information.</p> <p><strong>Sources</strong></p> <ul> <li><strong>Global CO2 emissions from cement production v250226</strong> <a href="https://doi.org/10.5281/zenodo.14931651">data</a>, <a href="https://doi.org/10.5194/essd-11-1675-2019">paper</a>: Andrew<br>(2025), Andrew (2019)</li> <li><strong>EI Statistical Review of World Energy 2025</strong> <a href="https://www.energyinst.org/statistical-review">website</a>: Energy Institute (2025)</li> <li><strong>CDIAC</strong> <a href="https://energy.appstate.edu/cdiac-appstate/data-products">data</a>: Hefner and Marland (2023), <a href="https://doi.org/10.5281/zenodo.10607882">data</a>: Hefner (2024), <a href="https://essd.copernicus.org/articles/13/1667/2021/">paper</a>: Gilfillan and Marland (2021)</li> <li><strong>CEDS</strong>: <a href="http://doi.org/10.5281/zenodo.3606753">data:</a> Hoesly et al. (2020), <a href="https://gmd.copernicus.org/articles/11/369/2018/">paper</a>: Hoesly et al. (2018)</li> <li><strong>EDGAR 2024</strong>: <a href="https://edgar.jrc.ec.europa.eu/report_2024">data/website</a>: European Commission, European Commision, JRC (2024), report: European Commission. Joint Research Centre & IEA. (2024)</li> <li><strong>EDGAR-HYDE 1.4</strong> <a href="http://themasites.pbl.nl/tridion/en/themasites/edgar/emission_data/edgar-hyde-100yr/edgar-hyde-1-4.html">data</a>: Van Aardenne et al. (2001), Olivier and Berdowski (2001)</li> <li><strong>FAOSTAT database</strong> <a href="http://www.fao.org/faostat/en/#data">data</a>: Food and Agriculture Organization of the United Nations (2024)</li> <li><strong>RCP historical data</strong> <a href="http://www.pik-potsdam.de/~mmalte/rcps/">data</a>, <a href="https://doi.org/10.1007/s10584-011-0156-z">paper</a>: Meinshausen et al. (2011)</li> <li><strong>UNFCCC National Communications and National Inventory Reports for developing countries available from the UNFCCC DI portal</strong> <a href="http://di.unfccc.int/detailed_data_by_party">website</a>, <a href="https://doi.org/10.5281/zenodo.12664477">data</a>: UNFCCC (2024e), Pflüger and Gütschow (2024), <a href="https://github.com/primap-community/FAOSTAT_data_primap/">github</a></li> <li><strong>UNFCCC Biannial Update Reports, National Communications, and National Inventory </strong><br><strong>Reports for developing countries</strong> <a href="https://unfccc.int/BURs">website-BURs</a>, <a href="https://unfccc.int/non-annex-I-NCs">website-NCs</a>, <a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/main/extracted_data/UNFCCC">data</a>: UNFCCC (2024d), UNFCCC (2024b). <ul> <li>Notes: <ol> <li>Not all BUR and NC submissions are included as reading the data is time consuming and not all submission contain sufficient data to be used in PRIMAP-hist.</li> <li>Not all submissions included in PRIMAP-hist are available in the github repository as we do not (yet) have code that we can publish for all submissions.</li> <li>for a list of added submissions see section "Data source updates (v2.7)" in the chnagelogin the pdf data description.</li> </ol> </li> </ul> </li> <li><strong>UNFCCC First Biannial Transparency Reports</strong> <a href="https://unfccc.int/first-biennial-transparency-reports">website</a>, [<a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/main/extracted_data/UNFCCC">data</a>] UNFCCC (2025) <ul> <li>Notes: <ol> <li>For a list of added submissions see section "Data source updates (v2.7)" in the changelog in the pdf data description. </li> </ol> </li> </ul> </li> <li><strong>UNFCCC National Inventory Submissions 2025 (CRTAI)</strong> <a href="https://unfccc.int/ghg-inventories-annex-i-parties/2025">website</a>, <a href="https://doi.org/10.5194/essd-10-1427-2018">paper</a>, <a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/main/extracted_data/UNFCCC">data</a>: UNFCCC (2024c) (processed based on Jeffery et al. (2018))</li> <li><strong>Official country repositories (non-UNFCCC)</strong> <ul> <li><strong>Belarus</strong>: Greenhouse gas statistics (1990-2022) <a href="https://www.belstat.gov.by/ofitsialnaya-statistika/makroekonomika-i-okruzhayushchaya-sreda/okruzhayuschaya-sreda/sovmestnaya-sistema-ekologicheskoi-informatsii2/b-izmenenie-klimata/b-3-vybrosy-parnikovyh-gazov/?_x_tr_sl=zh-TW&_x_tr_tl=en&_x_tr_hl=en-US&_x_tr_pto=wapp">website</a>: National Statistical Committee of the<br>Republic of Belarus (2024)</li> <li><strong>Mexico</strong>: 2023 Inventory <a href="https://historico.datos.gob.mx/busca/dataset/inventario-nacional-de-emisiones-de-gases-y-compuestos-de-efecto-invernadero-inegycei">website</a>,<a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/master/extracted_data/non-UNFCCC/Mexico"> data</a>: Instituto Nacional de Ecologia y Gambio Climatico (2025)</li> <li><strong>South Korea</strong>: 2024 Inventory <a href="https://www.gir.go.kr/home/index.do?menuId=36">website</a>, <a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/master/extracted_data/non-UNFCCC/Republic_of_Korea">data</a>: Republic of Korea (2024)</li> <li><strong>Taiwan / Republic of China</strong>: 2024 Inventory <a href="https://www.cca.gov.tw/en/information-service/publications/national-ghg-inventory-report/12003.html">website</a>, <a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/master/extracted_data/non-UNFCCC/Taiwan">data</a>: Republic of China - Environmental<br>Protection Administration (2024)</li> <li><strong>Pakistan</strong>: 2016 inventory report <a href="https://www.gcisc.org.pk/GHGINVENTORY2011-2012_FINAL_GCISCRR19.pdf">website</a>, <a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/master/extracted_data/non-UNFCCC/Pakistan">data</a>: Mir and Ijaz (2016)</li> <li><strong>Philippines</strong>: NICCDIES National GHG Inventory <a href="https://niccdies.climate.gov.ph/ghg-inventory/national">website</a>, <a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/master/extracted_data/non-UNFCCC/Philippines">data</a>: NICCDIES (2025)</li> <li><strong>USA</strong>: Draft 2025 National Inventory <a href="https://www.edf.org/freedom-information-act-documents-epas-greenhouse-gas-inventory">website</a>, <a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/master/extracted_data/non-UNFCCC/United_States_of_America">data</a>: US EPA (2025)</li> </ul> </li> </ul> <p>For the pre-1990 LULUCF time-series we use the following additional data sources:</p> <ul> <li><strong>Houghton land use CO2</strong> <a href="http://cdiac.ornl.gov/trends/landuse/houghton/houghton.html">website</a>: Houghton (2008)</li> <li><strong>HYDE land cover data</strong> <a href="http://themasites.pbl.nl/tridion/en/themasites/hyde/download/index-2.html">website</a>: Klein Goldewijk et al. (2010), Klein Goldewijk et al. (2011)</li> <li><strong>SAGE Global Potential Vegetation Dataset</strong> <a href="https://nelson.wisc.edu/sage/data-and-models/global-potential-vegetation/index.php">website</a>: Ramankutty and Foley (1999)</li> <li><strong>FAO Country Boundaries</strong> <a href="http://ref.data.fao.org/map?entryId=18329470-472d-11db-88e0-000d939bc5d8">website</a>: Food and Agriculture Organization of the United Nations<br>(2015)</li> </ul> <p> </p> <p><strong>Files included in the dataset</strong></p> <p>For each dataset we have three files: the <em>.nc</em> file contains the data and metadata in the native PRIMAP2 netCDF based format. The <em>.csv</em> file contains the data in a csv format following the specifications of the PRIMAP2 interchange format. The metadata for the interchange format file is included in the <em>.yaml</em> file.</p> <ul> <li><strong>Guetschow-et-al-2025a-PRIMAP-hist_v2.7_final_22-Aug-2025.X</strong>: The main dataset with numerical extrapolation of all time series to 2024 and three significant digits.</li> <li><strong>Guetschow-et-al-2025a-PRIMAP-hist_v2.7_final_no_extrap_22-Aug-2025.X</strong>: Variant without numerical extrapolation of missing values and not including the country groups mentioned in section ["area (ISO3)"] (three significant digits).</li> <li><strong>Guetschow-et-al-2025a-PRIMAP-hist_v2.7_final_no_rounding_22-Aug-2025.X</strong>: The main dataset with numerical extrapolation of all time series to 2024 and eleven significant digits.</li> <li><strong>Guetschow-et-al-2025a-PRIMAP-hist_v2.7_final_no_extrap_no_rounding_22-Aug-2025.X</strong>: Variant without numerical extrapolation of missing values and not including the country groups mentioned in section ["area (ISO3)"] (eleven significant digits).</li> <li><strong>PRIMAP-hist_v2.7_data-description.pdf</strong>: Data description including changelog.</li> <li><strong>PRIMAP-hist_v2.7_updated_figures.pdf</strong>: Updated figures from the PRIMAP-hist paper published in ESSD.</li> <li><strong>changes_PRIMAP-hist_v2.7_final_nr_to_v2.6_final_nr.xlsx: </strong>xlsx file which lists relative changes per country, gas and main sector for 2005, 2015, 2020, 2022, 2023, and 1990-2023 (cumulative) from v2.6 to v2.7 (comparison between current version and October 2024 version).</li> <li><strong>changes_PRIMAP-hist_v2.7_final_nr_to_v2.6.1_final_nr.xlsx</strong>: xlsx file which lists relative changes per country, gas and main sector for 2005, 2015, 2020, 2022, 2023, and 1990-2023 (cumulative) from v2.6.1 to v2.7 (comparison between latest version and current version).</li> <li><strong>changes_PRIMAP-hist_v2.7_final_nr_CR_to_TP.xlsx</strong>: xlsx file which lists relative differences per country, gas and main sector for 2005, 2015, 2020, 2023, 2024, and 1990-2024 (cumulative) between country reported priority and third party priority scenarios.</li> <li><strong>changelog_v2.6_final_to_v2.6.1_final.zip</strong>: Detailed changelog in html format containing plots and a breakdown of changes to sectors and gases for all countries. Country specific notes ar also included. To use the changelog locally unpack the zip file and open the file in the "html" directory in your browser. The changelog is also available on the <a href="https://primap.org/primap-hist/">primap.org</a> website.</li> </ul> <p><strong>Notes</strong></p> <ul> <li>Emissions from international aviation and shipping are not included in the dataset.</li> <li>Emissions from Land Use, Land-Use Change, and Forestry (LULUCF) are not included in the main version of this dataset. They are included in the version without rounding as users need to take extra care when using LULUCF data because changes some of the year-to-year changes in the data come from using different sources or methodology changes within a source rather than changes in actual emissions.</li> </ul> <p><strong>Data format description</strong></p> <p>The PRIMAP-hist data in the comma-separated values (CSV) files is formatted consistently with the PRIMAP2 interchange format.</p> <p>The data contained in each column are as follows:</p> <p><em>"source"</em></p> <p>Name of the data source. Here: <em>PRIMAP-hist_v2.7</em> with suffixes <em>_ne</em> for no extrapolation and <em>_nr</em> for no rounding.</p> <p><em>"scenario (PRIMAP)"</em></p> <ul> <li>HISTCR: In this scenario country-reported data (CRT, BUR / NIR / NC / UNFCCCDI) are prioritized over third-party data (CDIAC, FAO, Andrew, EDGAR, BP).</li> <li>HISTTP: In this scenario third-party data (CDIAC, FAO, Andrew, EDGAR, BP) are prioritized over country-reported data (CRT, BUR / NIR / NC / UNFCCCDI)</li> </ul> <p><em>"provenance"</em></p> <p>Provenance of the data. Here: "derived" as it is a composite source.</p> <p><em>"country"</em></p> <p>ISO 3166 three-letter country codes or custom codes for groups:</p> <table> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Region description</strong></td> </tr> <tr> <td>EARTH</td> <td>Aggregated emissions for all countries.</td> </tr> <tr> <td>ANNEXI</td> <td>Annex I Parties to the Convention</td> </tr> <tr> <td>NONANNEXI</td> <td>Non-Annex I Parties to the Convention</td> </tr> <tr> <td>AOSIS</td> <td>Alliance of Small Island States</td> </tr> <tr> <td>BASIC</td> <td>BASIC countries (Brazil, South Africa, India and China)</td> </tr> <tr> <td>EU27BX</td> <td>European Union post Brexit</td> </tr> <tr> <td>LDC</td> <td>Least Developed Countries</td> </tr> <tr> <td>UMBRELLA</td> <td>Umbrella Group</td> </tr> </tbody> </table> <p>Table: Additional "country" codes.</p> <p><em>"category"</em></p> <p>IPCC (Intergovernmental Panel on Climate Change) 2006 categories for emissions. Some aggregate sectors have been added to the hierarchy. These begin with the prefix <em>M</em>.</p> <table> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Description</strong></td> <td><strong>Gases covered</strong></td> </tr> <tr> <td>M.0.EL</td> <td>National Total excluding LULUCF</td> <td>all</td> </tr> <tr> <td>1</td> <td>Energy</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>1.A</td> <td>Fuel Combustion Activities</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>1.B</td> <td>Fugitive Emissions from Fuels</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>1.B.1</td> <td>Solid Fuels</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>1.B.2</td> <td>Oil and Natural Gas</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>1.B.3</td> <td>Other Emissions from Energy Production</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>1.C</td> <td>Carbon Dioxide Transport and Storage</td> <td>CO2</td> </tr> <tr> <td>2</td> <td>Industrial Processes and Product Use (IPPU)</td> <td>CO2, CH4, N2O, f-gases</td> </tr> <tr> <td>2.A</td> <td>Mineral Industry</td> <td>CO2</td> </tr> <tr> <td>2.B</td> <td>Chemical Industry</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>2.C</td> <td>Metal Industry</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>2.D</td> <td>Non-Energy Prod. from Fuels and Solvent Use</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>2.E</td> <td>Electronics Industry</td> <td>N2O (f-gases emitted but not resolved)</td> </tr> <tr> <td>2.F</td> <td> <p>Product uses as Substitutes for Ozone Depleting Substances </p> <p>(no data available as the category is only used for fluorinated gases which are only resolved at the level of category 2)</p> </td> <td>--</td> </tr> <tr> <td>2.G</td> <td>Other Product Manufacture and Use</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>2.H</td> <td>Other</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>M.AG</td> <td>Agriculture, sum of 3.A and M.AG.ELV</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>3.A</td> <td>Livestock</td> <td>CH4, N2O</td> </tr> <tr> <td>M.AG.ELV</td> <td>Agriculture excluding Livestock</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>4</td> <td>Waste</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>5</td> <td>Other</td> <td>CO2, CH4, N2O</td> </tr> </tbody> </table> <p>Table: Category descriptions using IPCC 2006 terminology.</p> <p>The categories are organized in a hierarchy where children of each category sum to the parent category. The full IPCC2006 category hierarchy contains many more subcategories, but data availability is not sufficient to resolve the full IPCC category tree in PRIMAP-hist. However, we will regularly assess if the available data allows for resolving addition subcategories. The categories currently resoved by PRIMAP-hist are organized as follows.<br><br> M.0.EL Total emissions excluding LULUCF<br> ├1 Energy<br> │├1.A Fuel Combustion Activities <br> │├1.B Fugitive Emissions from Fuels<br> ││├1.B.1 Solid Fuels<br> ││├1.B.2 Oil and Natural Gas<br> ││╰1.B.3 Other Emissions from Energy Production<br> │╰1.C Carbon Dioxide Transport and Storage<br> ├2 Industrial Processes and Product Use<br> │├2.A Mineral Industry<br> │├2.B Chemical Industry<br> │├2.C Metal Industry<br> │├2.D Non-Energy Products from Fuels and Solvent Use<br> │├2.E Electronics Industry<br> │├2.F Product Uses as Substitutes for Ozone Depleting Substances<br> │├2.G Other Product Manufacture and Use<br> │╰2.H Other<br> ├4 Waste<br> ├5 Other<br> ╰M.AG Agriculture<br> ├3.A Livestock<br> ╰M.AG.ELV Agriculture excluding Livestock<br><br><br>The "no_rounding" version of the dataset additionally contains data for the "Land Use, Land Use Change, and Forestry" sector and sectors sums including this sectors</p> <table> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Description</strong></td> <td><strong>Gases covered</strong></td> </tr> <tr> <td>0</td> <td>National Total including LULUCF</td> <td>CO2, CH4, N2O, f-gases</td> </tr> <tr> <td>3</td> <td>Agriculture, Forestry and Other Land Use (AFOLU)</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>M.LULUCF</td> <td>Land Use, Land Use Change, and Forestry</td> <td>CO2, CH4, N2O</td> </tr> </tbody> </table> <p>Table: Additional category descriptions using IPCC 2006 terminology.<br><br>PRIMAP-hist uses a distinction between agriculture and Land Use, Land Use Change and Forestry which are combined into the AFOLU sector in IPCC2006 categories. Thus, in contrast to the standard IPCC 2006 categories, category 3 "Agriculture, Forestry, and Other Land Use" is subdivided as follows:<br><br> 3 Agriculture, Forestry, and Other Land Use<br> ├M.AG Agriculture<br> │├3.A Livestock<br> │╰M.AG.ELV Agriculture excluding Livestock<br> ╰M.LULUCF Land Use, Land Use Change, and Forestry<br><br>Where a national total including LULUCF is reported, it is calculated as the sum of "M.0.EL Total emissions excluding LULUCF" and "M.LULUCF Land Use, Land Use Change, and Forestry":<br><br> 0 National Total<br> ├M.0.EL Total emissions excluding LULUCF<br> │├1 Energy<br> │├2 Industrial Processes and Product Use<br> │├4 Waste<br> │├5 Other<br> │╰M.AG Agriculture<br> ╰M.LULUCF Land Use, Land Use Change, and Forestry<br><br><br>"<em>entity</em>"<br><br>Gas baskets using global warming potentials (GWP) from the Second Assessment Report (SAR), Fourth Assessment Report (AR4), Fifth Assessment Report (AR5), Sixth Assessment Report (AR6).</p> <table> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>CH4</td> <td>Methane</td> </tr> <tr> <td>CO2</td> <td>Carbon Dioxide</td> </tr> <tr> <td>N2O</td> <td>Nitrous Oxide</td> </tr> <tr> <td>HFCS (SARGWP100)</td> <td>Hydrofluorocarbons (SAR)</td> </tr> <tr> <td>HFCS (AR4GWP100)</td> <td>Hydrofluorocarbons (AR4)</td> </tr> <tr> <td>HFCS (AR5GWP100)</td> <td>Hydrofluorocarbons (AR5)</td> </tr> <tr> <td>HFCS (AR6GWP100)</td> <td>Hydrofluorocarbons (AR6)</td> </tr> <tr> <td>PFCS (SARGWP100)</td> <td>Perfluorocarbons (SAR)</td> </tr> <tr> <td>PFCS (AR4GWP100)</td> <td>Perfluorocarbons (AR4)</td> </tr> <tr> <td>PFCS (AR5GWP100)</td> <td>Perfluorocarbons (AR5)</td> </tr> <tr> <td>PFCS (AR6GWP100)</td> <td>Perfluorocarbons (AR6)</td> </tr> <tr> <td>SF6</td> <td>Sulfur Hexafluoride</td> </tr> <tr> <td>NF3</td> <td>Nitrogen Trifluoride</td> </tr> <tr> <td>FGASES (SARGWP100)</td> <td>Fluorinated Gases (SAR): HFCs, PFCs, SF6, NF3</td> </tr> <tr> <td>FGASES (AR4GWP100)</td> <td>Fluorinated Gases (AR4): HFCs, PFCs, SF6, NF3</td> </tr> <tr> <td>FGASES (AR5GWP100)</td> <td>Fluorinated Gases (AR5): HFCs, PFCs, SF6, NF3</td> </tr> <tr> <td>FGASES (AR6GWP100)</td> <td>Fluorinated Gases (AR6): HFCs, PFCs, SF6, NF3</td> </tr> <tr> <td>KYOTOGHG (SARGWP100)</td> <td>Kyoto greenhouse gases (SAR)</td> </tr> <tr> <td>KYOTOGHG (AR4GWP100)</td> <td>Kyoto greenhouse gases (AR4)</td> </tr> <tr> <td>KYOTOGHG (AR5GWP100)</td> <td>Kyoto greenhouse gases (AR5)</td> </tr> <tr> <td>KYOTOGHG (AR6GWP100)</td> <td>Kyoto greenhouse gases (AR6)</td> </tr> </tbody> </table> <p>Table: Gas categories and underlying global warming potentials</p> <p><em>"unit"</em></p> <p>Unit is either <em><substance> * gigagram / a</em> where substance is the entity or for CO2 equivalent units <em>CO2 * gigagram / a</em>. The CO2-equivalent is calculated according to the global warming potential indicated by the entity (see above).</p> <p><em>Remaining columns</em></p> <p>Years from 1750-2024.</p> <p><strong>Changelog</strong></p> <p>For the changelog we refer to the pdf version of the data description available for download here</p> <p><strong>References</strong></p> <p>For the full references we refer to the references section and the pdf version of the data description available for download here.</p>
FIGURE 78 in The concept of Compsoneuria Eaton, 1881 revisited in light of historical and new material from the Sunda Islands (Ephemeroptera: Heptageniidae: Ecdyonurinae)
FIGURE 78. SEM pictures of eggs extracted from a mature nymph of Afronurus taipokauensis comb. nov. from Hong Kong, China; a: egg in toto; b: micropyle and detail of the chorionic structure.
FIGURES 68–77. Compsoneuriella spp, nymphal structures. Figs 68-70 in The concept of Compsoneuria Eaton, 1881 revisited in light of historical and new material from the Sunda Islands (Ephemeroptera: Heptageniidae: Ecdyonurinae)
FIGURES 68–77. Compsoneuriella spp, nymphal structures. Figs 68-70: Compsoneuriella sp. 1 from Thailand. Fig. 68: Outline of the labium (left side paraglossa only drawn); Fig. 69: Bristles on the upper face of hind femora; Fig. 70: Posterior margin of tergite IV. Figs 71-73: Compsoneuriella langensis. Fig. 71: Outline of the labrum; Fig. 72: Outline of the glossae; Fig. 73: Outline of gill VII. Figs 74-77: Compsoneuriella tagbanua. Fig. 74: Outline of the labrum; Fig. 75: Outline of the glossae; Fig. 76: Detail of the left mandible; Fig. 77: Scattered setae on the ventral side of the galea.
FIGURES 60–67 in The concept of Compsoneuria Eaton, 1881 revisited in light of historical and new material from the Sunda Islands (Ephemeroptera: Heptageniidae: Ecdyonurinae)
FIGURES 60–67. Nymphal leg structures of Compsoneuria spectabilis (Figs 60-63) and Compsoneuriella thienemanni (Figs 64-67) with SEM. Figs 60 and 64: Dorsal view of the hind leg; Figs 61 and 65: Detail view of the posterior margin of the hind femur and the hind tibia; Figs 62 and 66: Bristle on the upper face of femora; Figs 63 and 67: Tarsal claw.
FIGURES 55–59 in The concept of Compsoneuria Eaton, 1881 revisited in light of historical and new material from the Sunda Islands (Ephemeroptera: Heptageniidae: Ecdyonurinae)
FIGURES 55–59. Nymphal maxillae details of Notonurus sp. from Madagascar. Fig. 55: SEM view of the dentisetae, proximal dentiseta in red, distal dentisetae in yellow; Fig. 56: Same view in optical microscope; Fig. 57: SEM view of scattered setae on the ventral face of the galea; Fig. 58: Same view in optical microscope; Fig. 59: SEM view of the comb-shape setae on the crown of the galea.
FIGURES 36–40 in The concept of Compsoneuria Eaton, 1881 revisited in light of historical and new material from the Sunda Islands (Ephemeroptera: Heptageniidae: Ecdyonurinae)
FIGURES 36–40. Compsoneuriella thienemanni, thoracic and abdominal parts. Fig. 36: Hind leg; Fig. 37: Bristles on the upper face of hind femora; Fig. 38: Gill V (only the plate drawn); Fig. 39: Gill VII; Fig. 40: posterior margin of tergite IV.
FIGURES 30–35. Compsoneuriella thienemanni, nymphal mouthparts. Fig. 30 in The concept of Compsoneuria Eaton, 1881 revisited in light of historical and new material from the Sunda Islands (Ephemeroptera: Heptageniidae: Ecdyonurinae)
FIGURES 30–35. Compsoneuriella thienemanni, nymphal mouthparts. Fig. 30: Dorsal view of the head; Fig. 31: Labrum, with structure on the dorsal face (left) and ventral face (right); Fig. 32: Right mandible; Fig. 33: Left mandible; a: general view, b: details of the incisors. c: detail of the right incisor of another specimen; Fig. 34: Hypopharynx (left side); Fig. 35: Labium (left side).
FIGURES 25–29. Compsoneuriella thienemanni Ulmer, male imago lectotype. Fig. 25 in The concept of Compsoneuria Eaton, 1881 revisited in light of historical and new material from the Sunda Islands (Ephemeroptera: Heptageniidae: Ecdyonurinae)
FIGURES 25–29. Compsoneuriella thienemanni Ulmer, male imago lectotype. Fig. 25: Forewing; Fig. 26: Foreleg; Fig. 27: Foreleg of a (para?) lectotype mounted on slide by Ulmer; Fig. 28: Genitalia; Fig. 29: Penis structure in ventral (left) and dorsal (right) view.
FIGURES 20–21 in The concept of Compsoneuria Eaton, 1881 revisited in light of historical and new material from the Sunda Islands (Ephemeroptera: Heptageniidae: Ecdyonurinae)
FIGURES 20–21. SEM pictures of eggs extracted from a female imago (Fig. 20) and from mature nymph (Fig. 21) of Compsoneuria spectabilis. Fig. 20: Female imago from Java, Buitenzorg, XI. 1919, Karny leg; Fig. 21: Female nymph (UN4) from Sumatra, Ombilin River, Talawi, 8.XI.2011, Balke leg.; a: egg in toto; b: detail of the pole; c: micropyle and detail of the chorionic structure.
FIGURES 22–24 in The concept of Compsoneuria Eaton, 1881 revisited in light of historical and new material from the Sunda Islands (Ephemeroptera: Heptageniidae: Ecdyonurinae)
FIGURES 22–24. Compsoneuria lieftincki (Ulmer), male imago. Fig. 22: Forewing; Fig. 23: Genitalia; Fig. 24: Penis structure in ventral (left) and dorsal (right) view.
FIGURES 41–42 in The concept of Compsoneuria Eaton, 1881 revisited in light of historical and new material from the Sunda Islands (Ephemeroptera: Heptageniidae: Ecdyonurinae)
FIGURES 41–42. SEM pictures of eggs extracted from a female imago (Fig. 41) and from a mature nymph (Fig. 42) of Compsoneuriella thienemanni. Fig. 41: Female imago from Sumatra, Wai Lima, Lampongs, XI–XII. 1921, Karny leg; Fig. 42: Female nymph (UN11) from Sumatra, Harau Canyon, 27.IX.2009, Balke leg; a: egg in toto; b: detail of the pole; c: micropyle and detail of the chorionic structure.
FIGURES 13–19 in The concept of Compsoneuria Eaton, 1881 revisited in light of historical and new material from the Sunda Islands (Ephemeroptera: Heptageniidae: Ecdyonurinae)
FIGURES 13–19. Compsoneuria spectabilis, thoracic and abdominal parts. Fig. 13: Hind leg; Fig. 14: Bristles on the upper face of hind femora; Fig. 15: Schematic cross section of the tibia and the respective position of the two rows of setae; Fig. 16: Gill I (only the plate drawn); Fig. 17: Gill V (only the plate drawn); Fig. 18: Gill VII; Fig. 19: Posterior margin of tergite IV.
FIGURES 1–7. Compsoneuria spectabilis Eaton, male imago. Fig. 1 in The concept of Compsoneuria Eaton, 1881 revisited in light of historical and new material from the Sunda Islands (Ephemeroptera: Heptageniidae: Ecdyonurinae)
FIGURES 1–7. Compsoneuria spectabilis Eaton, male imago. Fig. 1: Forewing; Fig. 2: Hindwing; Fig.3: Foreleg; Figs 4–5: Fore- and hindlegs of a 1919 specimen mounted on slide by Ulmer; Fig. 6: Genitalia; Fig. 7: Penis structure in ventral (left) and dorsal (right) view. Abbreviations: as: apical sclerite; ass: spine on the apical sclerite; bss: basal sclerite spine; ls: lateral sclerite; sas: subapical spine; tit: titillator
FIGURES 8–12. Compsoneuria spectabilis, nymphal mouthparts. Fig. 8 in The concept of Compsoneuria Eaton, 1881 revisited in light of historical and new material from the Sunda Islands (Ephemeroptera: Heptageniidae: Ecdyonurinae)
FIGURES 8–12. Compsoneuria spectabilis, nymphal mouthparts. Fig. 8: Labrum, with structure on the dorsal face (left) and ventral face (right); Fig. 9: Right mandible; Fig. 10: Left mandible; a: general view, b: details of the incisors; Fig. 11: Hypopharynx (left side); Fig. 12: Labium (left side).
FIGURES 48–54 in The concept of Compsoneuria Eaton, 1881 revisited in light of historical and new material from the Sunda Islands (Ephemeroptera: Heptageniidae: Ecdyonurinae)
FIGURES 48–54. Nymphal maxillae details of Compsoneuriella thienemanni. Fig. 48: SEM view of the dentisetae, proximal dentiseta in red, distal dentisetae in yellow, with first one entire; Figs 49–50: Different angle views of the dentisetae in SEM, to show the outer distal dentiseta always entire; Fig. 51: Same view in optical microscope; Fig. 52: SEM view of fimbriate scattered setae on the ventral face of the galea; Fig. 53: Same view in optical microscope; Fig. 54: SEM view of the combshape setae on the crown of the galea; abbreviations: dp: proximal dentiseta, dd1, first distal dentiseta, dd2, second distal dentiseta.
FIGURES 43–47 in The concept of Compsoneuria Eaton, 1881 revisited in light of historical and new material from the Sunda Islands (Ephemeroptera: Heptageniidae: Ecdyonurinae)
FIGURES 43–47. Nymphal maxillae details of Compsoneuria spectabilis. Fig. 43: SEM view of the dentisetae, proximal dentiseta in red, distal dentisetae in yellow, with first one fimbriate; Fig 44: Same view in optical microscope; Fig. 45: SEM view of fimbriate scattered setae on the ventral face of the galea; Fig. 46: Same view in optical microscope; Fig. 47: SEM view of the comb-shape setae on the crown of the galea; abbreviations: dp: proximal dentiseta, dd1, first distal dentiseta, dd2 second distal dentiseta.
Supplementary material 3 from: Goutte S, Reyes-Velasco J, Kassie A, Boissinot S (2022) Genetic and morphometric analyses of historical type specimens clarify the taxonomy of the Ethiopian Leptopelis gramineus species complex (Anura, Arthroleptidae). ZooKeys 1128: 63-97. https://doi.org/10.3897/zookeys.1128.82176
Maximum Likelihood phylogenetic inference, based on the ribosomal RNAs 16s for members of the Leptopelis gramineus complex
A New Tracking Algorithm Based on Foshan Total Lightning Data and the Influence of Historical Velocity and Matching Method on the Results
<p>This paper introduces a new tracking algorithm (V2CM) based on total lightning data from the Foshan Total Lightning Location System (FTLLS), and the other four algorithms are used to test the influence of historical velocity and matching method on the results during the formation of V2CM. Different methods are used to quantitatively evaluate the performance of five tracking methods. Additionally, the number of splits and merges along a track is used as a new index to describe the complexity of the track. It is found that the best tracking method V2CM has the highest probability of detection (POD), the false alarm rate (FAR) and the critical success index (CSI) being 65.6%, 39.2%, 46.4%, respectively. It also produces longer and more coherent tracks than those of other four methods. The average number of splits and merges of the best method is 2.40 and lightning clusters matching method between two successive time intervals has a greater impact on the evaluation results than the historical velocity. In addition, lightning clusters move in the northeast direction, with a mean speed of 48.48 km/h and a median speed of 39.08 km/h. Complex tracks, involving splitting or merging, move faster than simple track.</p>
[IO Islamic 3053] Collection of Tales and Historical Sketches
<ul> <li><strong>Collection of Tales.</strong></li> <li><strong>This manuscript is now IO Islamic 859 in the India Office collections.</strong></li> <li><strong>[metadata:</strong> <a href="https://en.wikipedia.org/wiki/Carl_Hermann_Eth%C3%A9"><strong>Hermann Ethé</strong></a><strong><em>, </em></strong><a href="http://doi.org/10.5281/zenodo.1323681"><strong><em>Catalogue of Persian Manuscripts in the Library of the India Office,</em></strong> </a><a href="http://doi.org/10.5281/zenodo.1323681"><strong>2 vols. (Oxford: India Office, 1903): volume 1,</strong></a> <strong>number 3053 here with notations and hyperlinks].</strong></li> </ul> <p>859</p> <p>Collection of tales and historical sketches.</p> <p>This copy contains:</p> <p>A main part on ff. 1<sup>a</sup>-70<sup>a</sup>, consisting of –</p> <p>1. Ff. 1<sup>a</sup>-16<sup>a</sup> : Ḳiṣṣ-i-Tamîm Anṣârî, identical with that in the <a href="https://doi.org/10.5281/zenodo.7372420">preceding copy</a>, but styled on the fly-leaf عمرنامه, or ‘story of a woman soliciting justice of the Khalîfa ‘Umar,’ by ‘Alî Muḥammad. Beginning, on fol. 1<sup>a</sup>:</p> <p>اندر خبر آورده اند كه در وقت امير المؤمنين عمر خطّاب رضى الله عنه روزى امير المؤمنين رضى الله عنه نماز بامداد گذارده بود الخ</p> <p>2. Ff. 17<sup>a</sup>-63<sup>a</sup>: <a href="https://en.wikipedia.org/wiki/Bakhtiyar-nama">Bakhtyârnâma</a>, in a similar redaction as No. 475, 3, in the Bodleian Cat. (col. 436), but divided into ten bâbs, like the enlarged version, No. 476, 1, in the same Cat. It was edited by <a href="http://worldcat.org/identities/lccn-n88140888/">Sir W. Ouseley</a>, text and translation, London, 1801. Beginning, on fol. 17<sup>a</sup>:</p> <p>الحمد لله .... بدانكه اين كتاب يادگار پادشاهان است و بختيارنامه كه فاضلان تصنيف كرده اند تا طالبان را و خوانندگانرا پند حاصل آيد و اين مختصر بر ده باب است و در هربابى حكمتها گفته است آغاز داستان بختيار نامه اين است چنین روایت ميكنند كه در ديار عجم پادشاه بود با داد و عدل و شكوه الخ</p> <p>3. Ff. 64<sup>a</sup>-70<sup>a</sup>: Fatḥnâma, that is, Sulṭân Muḥammad III’s campaign against Hungary in A.H. 1005 (A.D. 1596, 1597), identical with the ‘Fatḥnâma-i-Khûnkâr-i-Rûm’ in No. <a href="https://doi.org/10.5281/zenodo.3840325">572</a> above [=IO Islamic 2469]. It is translated from Turkish, see the title here:</p> <p>ترجمۀ فتحنامۀ حضرت بادينى پناه و شاه خونكار روم كه بتركى نوشته الخ</p> <p>Beginning:</p> <p>الحمد لمن لا نصر الاّ من عنده و الصلوة و السّلام على حبيبه الخ</p> <p>A date (probably of composition) appears at the end A.H. 1012 (A.D. 1603, 1604).</p> <p>B. An appendix, by two different hands, on ff. 71<sup>a</sup>-79<sup>a</sup>, consisting of –</p> <p>4. Ff. 71<sup>a</sup>-77<sup>a</sup>: Account of the district of اودگير Ûdgir, that is, Oodgherry in Telingana.</p> <p>5. Ff.78<sup>a</sup>-79<sup>a</sup>: A short account of <a href="https://en.wikipedia.org/wiki/Dhondia_Wagh">Dhundia Wâgh</a>, or as he is called here, Dhûndûjî Wâgh (دهوندوجى واگه), the famous freebooter of Mysore, who was at last subjugated and killed by the English in 1800, see <a href="http://worldcat.org/identities/lccn-n80020579/">Beale</a>, <a href="https://doi.org/10.5281/zenodo.3342520">Oriental Biogr. Dict</a>., p. 81.</p> <p>No. 3053, ff. 79, ll. 13 (on ff. 71-77, ll. 9-10); written by three different hands, the greater part in Naskhî, mixed with Shikasta; size, 8 in. by 5<sup>3/4</sup> in.</p> <p> </p> <p> </p>
Dataset supporting publication: "Environmental sustainability and Energy Efficiency in Historical Buildings: GeoFit Project Implementation in the Case Study of a medieval fortress in Perugia"
<p>Dataset supporting publication:“Environmental sustainability and Energy Efficiency in Historical Buildings: GeoFit Project Implementation in the Case Study of a medieval fortress in Perugia” (publication available for download: <a href="https://zenodo.org/record/7435513">GEOFIT Zenodo</a>)</p> <p>Italian cities are mainly constituted by buildings constructed until the mid-20th century by pre-industrial construction techniques. A HVAC system for the energy retrofit of historical buildings is evaluated when applied in the case study of Sant’Apollinare. It consists of a ground source heat pump a water tank for thermal energy storage connected to a low-temperature radiant system and air handling unit. The building thermal-energy behavior, typically influenced by thermal inertia in historical buildings, and the novel HVAC system performance interactions are comparatively assessed together with more traditional scenarios. Energy demand decreases by about one third compared to the pre-retrofit situation.</p>
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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.