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FIGURE 4 in Two new species of Aristolochia series Thyrsicae (Aristolochiaceae) from southern Central America, with comments on morphologically similar species
FIGURE 4. Photographs of living flowers of the larger-flowered species of Aristolochia series Thyrsicae in Central America, for comparison. A. Aristolochia belizensis (unvouchered; from Belize, photo by Jan Meerman). B. Aristolochia chapmaniana (T. Croat 6733, MO; from Panama, photo by T. Croat). B1. Aristolochia chapmaniana (unvouchered; from Panama, courtesy of STRI research portal). C. Aristolochia longissima (R. Aguilar 15691, USJ; from Costa Rica, photo by R. Aguilar). D. Aristolochia ornithorhyncha (J.E. Jiménez & A. Aguilar 3500, USJ; from Costa Rica, photo by J.E. Jiménez). E. Aristolochia ovalifolia (N. Morales 25, UJAT; from Tabasco, Mexico, photo by Neil Morales). F. Aristolochia tonduzii (J.E. Jiménez et al. 3444, USJ; from Costa Rica, photo by J.E. Jiménez).
FIGURE 1. Aristolochia longissima. A. Shoot segment with leaves. B in Two new species of Aristolochia series Thyrsicae (Aristolochiaceae) from southern Central America, with comments on morphologically similar species
FIGURE 1. Aristolochia longissima. A. Shoot segment with leaves. B. Detail of leaf surface showing pellucid gland dots. C. Flower in lateral view. D. Flower limb, flattened for the most part. E. Flower limb (showing natural conformation in anthesis) and distal part of tube. F. Detail of tube and limb juncture on lateral view, showing revolute margins (limb toward the left). G. Longitudinal section of the utricle. H. Gynostemium in female phase (with the stigmatic surface exposed and anthers closed). I. Gynostemium in male phase (with stigma closed and anthers dehisced). J. Capsule before dehiscence. K. Seeds. Drawings based on live specimen of R. Aguilar 15691 (USJ), with exception of B, I and K, based on live specimen of J.E. Jiménez & L.D. Arias 4040 (USJ).
Long Time-Series Glacier Outlines in the Three-Rivers Headwater Region from 1986 to 2021 Based on Deep Learning
<p>The glacier outlines in the Three-Rivers Headwater Region from 1986–2021 in a total of 12 periods were obtained based on Landsat-5 and 8 images using the M-LandsNet and through manual adjustments. The comparison with previous research results indicated that this dataset has high accuracy. This dataset can provide support for the study of the glaciers mass balance in the Three-Rivers Headwater Region.</p>
FIGURE 1. Calliandra estebanensis H.M. Hern. A in A narrowly endemic new species of Calliandra series Racemosae (Fabaceae) from Sinaloa, Mexico
FIGURE 1. Calliandra estebanensis H.M. Hern. A. Branchlet with inflorescence at anthesis and developing pod. B. Leaflet. C. Detail of calyx and corolla. D. Dissected flower showing the staminal tube and pistil. E. Detail of the ovary. F. Pod. Vouchers: A–E, E. Martínez et al. 4180 (MEXU); F, H.S. Gentry 6330-A, (MEXU). Drawn by Albino Luna.
FIGURE 2. Unacetolyzed 8 in A narrowly endemic new species of Calliandra series Racemosae (Fabaceae) from Sinaloa, Mexico
FIGURE 2. Unacetolyzed 8-grained polyad of Calliandra estebanensis. Voucher: E. Martínez et al. 4180 (MEXU). Scale bar = 100 μm.
Time-series drinking water metagenomes: Assemblies & MAGs
<p><span><span><span><span><span><span><span><span><span><span><span>Reconstructing microbial genomes from metagenomic short-read data can be challenging due to the unknown and uneven complexity of microbial communities. This complexity encompasses highly diverse populations which often includes strain variants. Reconstructing high-quality genomes is a crucial part of the metagenomic workflow as subsequent ecological and metabolic inferences depend on their accuracy, quality and completeness. In contrast to microbial communities in other ecosystems, there has been no systematic assessment of genome-centric metagenomic workflows for drinking water microbiomes. In this study, we assessed the performance of a combination of assembly and binning strategies for time-series drinking water metagenomes that were collected over 6 months. The goal of this study was to identify the combination of assembly and binning approaches that results in high quality and quantity metagenome-assembled genomes (MAGs), representing most of the sequenced metagenome. Our findings suggest that the metaSPAdes co-assembly strategies had the best performance as they resulted in larger and less fragmented assemblies with at least 85% of the sequence data mapping to contigs greater than 1kbp. Furthermore, a combination of metaSPAdes co-assembly strategies and MetaBAT2 produced the highest number of medium-quality MAGs while capturing at least 70% of the metagenomes based on read recruitment. Utilizing different assembly/binning approaches also assist in the reconstruction of unique MAGs from closely related species that would have otherwise collapsed into a single MAG using a single workflow. Overall, our study suggests that leveraging multiple binning approaches with different metaSPAdes co-assembly strategies may be required to maximize the recovery of good-quality MAGs.</span></span></span></span></span></span></span></span></span></span></span></p>
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 4 in The flat-footed fly genus Seri Kessel & Kessel (Diptera: Platypezidae)
FIGURE 4. Male genitalia of Seri dymka (Kessel), left lateral view. Abbreviations: cerc—cercus, epand—epandrium, ej apod—ejaculatory apodeme, hypd—hypandrium, hypd lb—hypandrial lobe, hyprct—hypoproct, phapod—phallapodeme, ph—phallus, spm sac—sperm sac, sur—surstylus. Scale in mm.
FIGURE 2. Tatuidris tatusia type series, showing pilosity pattern A. A–D in Additions to the taxonomy of the armadillo ants (Hymenoptera, Formicidae, Tatu i dri s)
FIGURE 2. Tatuidris tatusia type series, showing pilosity pattern A. A–D) Full face view, lateral view, dorsal view, and label of T. tatusia Holotype; E–G) Full face view, lateral view, dorsal view, and label of T. tatusia Paratype (MCZ collection Type locality: El Salvador [Brown and Kempf 1968]). Images A–D courtesy of antweb.org.
FIGURE 5 in Revision of the type series of Empis (Planempis) mandarina Frey, the type species of Planempis (Diptera: Empididae)
FIGURE 5. Hind legs of Empis (Planempis) spp., male; A-D: left hind leg excluding coxae; B: middle part of hind tibia showing ventral erect pile; E: hind femur showing pale posteroventral hairs; A, B: E. (P.) mandarina Frey, lectotype; C: E. (P.) lindebergi sp. nov., holotype; D, E: E. (P.) janssoni sp. nov., holotype.
FIGURE 6 in Revision of the type series of Empis (Planempis) mandarina Frey, the type species of Planempis (Diptera: Empididae)
FIGURE 6. Empis (Planempis) janssoni sp. nov., holotype male; A; genitalia, lateral aspect; B: cerci, dorsal aspect; C: ditto, posterior aspect; D: phallus (excluding part in coelom), lateral aspect; E. ditto, ventral aspect.
FIGURE 3 in Revision of the type series of Empis (Planempis) mandarina Frey, the type species of Planempis (Diptera: Empididae)
FIGURE 3. Wings of Empis (Planempis) spp. A: E. (P.) mandarina Frey, lectotype male; B: E. (P.) lindebergi sp. nov., holotype male; C: E. (P.) janssoni sp. nov., holotype male; D: ditto, paratype female 1; E: ditto, paratype female 2; F: E. (P.) sp. female syntype of E. (P.) mandarina sensu Frey from Kuatun.
FIGURE 1 in Revision of the type series of Empis (Planempis) mandarina Frey, the type species of Planempis (Diptera: Empididae)
FIGURE 1. Empis (Planempis) spp., male; A–C: habitus, lateral aspect; D-F: anterior part of body, dorsal aspect. A, D: E. (P.) mandarina Frey, lectotype male; B, E: E. (P.) lindebergi sp. nov., holotype male; C, F: E. (P.) janssoni sp. nov.
FIGURE 4 in Revision of the type series of Empis (Planempis) mandarina Frey, the type species of Planempis (Diptera: Empididae)
FIGURE 4. Empis (Planempis) lindebergi sp. nov., holotype male; A; genitalia, lateral aspect; B: cerci, dorsal aspect; C: ditto, posterior aspect; D: phallus (excluding part in coelom), lateral aspect; E. ditto, ventral aspect.
FIGURE 2 in Revision of the type series of Empis (Planempis) mandarina Frey, the type species of Planempis (Diptera: Empididae)
FIGURE 2. Empis (Planempis) mandarina Frey, lectotype male; A; genitalia, lateral aspect; B: cerci, dorsal aspect; C: ditto, posterior aspect; D: phallus (excluding part in coelom), lateral aspect; E. ditto, ventral aspect.
FIGURE 22 in Systematic revision of the trilobite genera Laudonia and Lochmanolenellus (Olenelloidea) from the lower Dyeran (Cambrian Series 2) of western Laurentia
FIGURE 22. Ontogenetic development of the cephalon of Lochmanolenellus trapezoidalis n. sp. 1, 2, Cephala in phase 4 of development. 1, GSC 137512, x15. 2, GSC 137513, x12. 3–6, Cephala in early phase 5 of development. 3, GSC 137514, x12. 4, GSC 137515, x12. 5, GSC 137516, x9. 6, GSC 137517, x9. All in dorsal view. 1 and 3–7 from GSC locality 95191; 2 from GSC locality 95192; all from Unit 4 of Rosella Formation, Cassiar Mountains, northern British Columbia.
FIGURE 18. 1–6 in Systematic revision of the trilobite genera Laudonia and Lochmanolenellus (Olenelloidea) from the lower Dyeran (Cambrian Series 2) of western Laurentia
FIGURE 18. 1–6, Bivariate scatterplots depicting intraspecific variation in various aspects of cephalic morphology of Lochmanolenellus subquadratus n. sp. Symbols represent phases of cephalic development. 1, Proportional length of LA. 2, Proportional length of the glabella. 3, Divergence of the ocular lobe from the exsagittal axis. 4, Proportional anterior advancement of the genal spine bases. 5, Orientation of the distal portion of the posterior cephalic margin (distal PCM). 6, Proportional cephalic width, measured as the ratio of the transverse distance between the genal spine bases to glabella length. 7, 8, Bivariate scatterplots depicting intraspecific variation and interspecific disparity in various aspects of cephalic morphology among morphologically mature Lochmanolenellus species. Symbols represent species. 7, Proportional length of the ocular lobes. 8, Cephalic shape, measured as the ratio of the transverse distance between the intergenal spine bases to the transverse distance between the genal spine bases.
FIGURE 15 in Systematic revision of the trilobite genera Laudonia and Lochmanolenellus (Olenelloidea) from the lower Dyeran (Cambrian Series 2) of western Laurentia
FIGURE 15. Bivariate scatterplots depicting intraspecific variation in various aspects of cephalic morphology of Lochmanolenellus pentagonalis n. sp. Symbols represent phases of cephalic development. 1, Proportional cephalic width, measured as the ratio of the transverse distance between the genal spine bases to glabella length. 2, Orientation of the distal portion of the posterior cephalic margin (distal PCM), quantified as the inner angle between it and a transverse line. 3, Cephalic shape, measured as the ratio of the transverse distance between the intergenal spine bases to the transverse distance between the genal spine bases. 4, Degree of lateral flaring of the genal spine, quantified as the angle between the inner margin of the proximal portion of the genal spine and the distal portion of the posterior cephalic margin (distal PCM).
FIGURE 10. 1–3 in Systematic revision of the trilobite genera Laudonia and Lochmanolenellus (Olenelloidea) from the lower Dyeran (Cambrian Series 2) of western Laurentia
FIGURE 10. 1–3, Cephala of Lochmanolenellus cf. primus 1 from upper portion of Puerto Blanco Formation at USNM locality 801c, Cerros de la Proveedora, Caborca area, Sonora, Mexico. 1, USNM 115684, x1.5. 2, USNM 115683, x1.5. 3, Enlargement of 2 showing traces of polygonal prosopon in anterior cephalic border furrow, x4. 4–7, Cephala of Lochmanolenellus cf. primus 2 from Unit 4 of Rosella Formation, Cassiar Mountains, northern British Columbia. 4, Cephalon in phase 3 of development; GSC 137488, from GSC locality 95190, x22. 5, Cephalon in early phase 5 of development; GSC 137489, from GSC locality 95190, x15. 6, Cephalon in phase 5 of development; GSC 137490, from GSC locality 95190, x7. 7, Cephalon in phase 5 of development; GSC 137491, from GSC locality 95189, x6. All specimens in dorsal view.
FIGURE 7 in Systematic revision of the trilobite genera Laudonia and Lochmanolenellus (Olenelloidea) from the lower Dyeran (Cambrian Series 2) of western Laurentia
FIGURE 7. Bivariate scatterplots depicting intraspecific variation and interspecific disparity in various aspects of cephalic morphology among morphologically mature Laudonia bispinata and La. amputata. 1, Proportional length of the ocular lobes. 2, Divergence of the ocular lobe from the exsagittal axis. 3, Proportional anterior advancement of the genal spine bases, measured as the ratio of the exsagittal distance between the posterior limit of the sagittal axis and the base of the genal spine (genal spine advancement) to sagittal glabella length. 4, Degree of lateral flaring of the genal spine, quantified as the angle between the inner margin of the proximal portion of the genal spine and the distal portion of the posterior cephalic margin (distal PCM). 5, Orientation of the distal portion of the posterior cephalic margin (distal PCM). 6, Proportional width of the anterior cephalic border (measured anterior to the contact of the ocular lobes with LA). 7, Proportional cephalic width, measured as the ratio of the transverse distance between the genal spine bases to glabella length. 8, Proportional cephalic width, measured as the ratio of the transverse distance between the intergenal spine bases to glabella length.
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