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326 results for “biogeographical regionalization”
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>
Biogeographic history of a large clade of ectomycorrhizal fungi, the Russulaceae, in the Neotropics and adjacent regions
<p>## Metadata</p> <p>backbone_accessions.tsv - GenBank/INSDC accession numbers for LSU, rpb1 and rpb2 accessions used for the Russulaceae backbone tree including 472 taxa.</p> <p>ITS_sequences_OTUs.tsv - Metadata for all 34,624 ITS sequences used in the study. Columns: "accession": accession ID in analysis – GenBank/INSDC or UNITE accession number for compiled data, lab ID for newly generated data; "specimen": specimen/voucher number, for newly generated sequences; "INSDC_accession": INSDC/GenBank accession for new newly generated data; "taxon": specimen identification; "New": whether ITS sequences was generated in this study (*); "OTU": name of cluster/OTU, if not the sequence accession itself (*); "In_tree": whether sequence is represented in the Russulaceae supertree after filtering steps (*), "lb" long-branch accession removed during tree estimation, "ol" outlier removed during tree estimation; "area": biogeographic area assigned.</p> <p> </p> <p>## Sequences and alignments</p> <p>backbone_concat.fasta - Concatenated LSU-rpb1-rpb2 alignment for 372 backbone taxa.</p> <p>backbone_concat_part.txt - Gene partitions and substitution models applied to the backbone alignment.</p> <p>einsi_clade1_Russula_trimmed.fasta - Alignment of 2,279 representative ITS sequences in the Russula clade; alignment end columns with >90% missing data/gaps were trimmed.</p> <p>einsi_clade2_LactariusMultifurca_trimmed.fasta - Alignment of 621 representative ITS sequences in the Lactarius-Multifurca clade; alignment end columns with >90% missing data/gaps were trimmed.</p> <p>einsi_clade3_Lactifluus_trimmed.fasta - Alignment of 482 representative ITS sequences in the Lactifluus clade; alignment end columns with >90% missing data/gaps were trimmed.</p> <p> </p> <p>## Phylogenetic trees</p> <p>12_make_supertree.R - R script for grafting clade trees onto the backbone tree to produce a supertree.</p> <p>backbone_calibrated.nwk - Time-calibrated Russulaceae backbone phylogeny.</p> <p>backbone_TBE.raxml.support - Russulaceae backbone phylogeny annotated with transfer bootstrap expectation support values.</p> <p>clade1_Russula_TBE.raxml.support - Russula subclade ITS phylogeny (2,279 tips), annotated with transfer bootstrap expectation support values.</p> <p>clade2_LactariusMultifurca_TBE.raxml.support - Lactarius-Multifurca subclade ITS phylogeny (621 tips), annotated with transfer bootstrap expectation support values.</p> <p>clade3_Lactifluus_TBE.raxml.support - Lactifluus subclade ITS phylogeny (482 tips), annotated with transfer bootstrap expectation support values.</p> <p>supertree_calibrated.nwk - Combined Russulaceae supertree, time-calibrated (root age = 1).</p> <p>tree_calibrated_clade1_Russula.nwk - Russula subclade ITS backbone phylogeny, time-calibrated (root age = 1).</p> <p>tree_calibrated_clade2_LactariusMultifurca.nwk - Lactarius-Multifurca subclade ITS backbone phylogeny, time-calibrated (root age = 1).</p> <p>tree_calibrated_clade3_Lactifluus.nwk - Lactifluus subclade ITS backbone phylogeny, time-calibrated (root age = 1).</p> <p> </p> <p>## Biogeographic analysis</p> <p>3_disp_counts.R - R script to count dispersal events between biogeographic areas, based on stochastic mapping output.</p> <p>9_disp_count_time.R - R script to count dispersal events to and from each area through time, based on stochastic mapping output.</p> <p>area_codes.tab - Area letter coding and colours used for biogeographic analysis and plotting.</p> <p>area_shapes.zip - Shapefiles for the nine biogeographic areas defined, based on merged areas from Dinerstein et al. 2017 (https://doi.org/10.1093/biosci/bix014) and Löwenberg-Neto (2014: https://doi.org/10.11646/zootaxa.3802.2.12; 2015: https://doi.org/10.11646/10.11646/zootaxa.3985.4.9).</p> <p>areas_manually_zenodo.csv - Manual assignment of 800 ITS sequences to biogeographic areas based on associated literature records or metadata.</p> <p>corHMM_ER.Rdata - R data archive with input data and results for the corHMM/Mv biogeographic area reconstruction.<br> <br> corHMM_ER_stoch_maps.Rdata - R data archive with results from the corHMM/Mv biogeographic stochastic mapping.</p> <p>disp_counts_focal.tab - Dispersal counts to and from each focal area through time, based on BioGeoBEARS stochastic mapping output.</p> <p>disp_counts_sam_afr.tab - Dispersal counts between Afrotopics and lowland tropical S. America through time, based on BioGeoBEARS stochastic mapping output.</p> <p>disp_matrix_025.txt - Dispersal rates between biogeographic areas (2.5% quantiles), based on stochastic mapping output.</p> <p>disp_matrix_975.txt - Dispersal rates between biogeographic areas (97.5% quantiles), based on stochastic mapping output.</p> <p>disp_matrix_median.txt - Dispersal rates between biogeographic areas (median values), based on stochastic mapping output.</p> <p> </p> <p>## Diversification analysis</p> <p>5_rates_per_area.R - R script to partition diversification rates by biogeographic area, both overall and through time, based on BAMM diversification rates and area stochastic mapping.</p> <p>event_data.txt - Posterior samples of diversification rate regimes estimated with BAMM.</p> <p>div_rates_area_overall.txt - Overall diversification rates per biogeographic area, based on BAMM diversification rates and area stochastic mapping.</p> <p>div_rates_per_area_025.tsv - Diversification rates through time (2.5% quantiles) partitioned by biogeographic area, based on BAMM diversification rates and area stochastic mapping.</p> <p>div_rates_per_area_975.tsv - Diversification rates through time (97.5% quantiles) partitioned by biogeographic area, based on BAMM diversification rates and area stochastic mapping.</p> <p>div_rates_per_area_median.tsv - Diversification rates through time (means) partitioned by biogeographic area, based on BAMM diversification rates and area stochastic mapping.</p> <p>mcmc_out.txt - BAMM posterior sample characteristics.</p>
Appendix S3 from: Droissart V, Dauby G, Hardy OJ, Deblauwe V, Harris DJ, Janssens S, Mackinder BA, Blach-Overgaard A, Sonké B, Sosef MSM, Stévart T, Svenning J-C, Wieringa JJ, Couvreur TLP (2018) Beyond trees: biogeographical regionalization of tropical Africa. Journal of Biogeography. DOI:10.1111/jbi.13190
<p>This dataset corresponds to GIS file that were generated in the study published by Droissart, Dauby et al. in <em>Journal of Biogeography</em>:</p> <p>Droissart V, Dauby G, Hardy OJ, Deblauwe V, Harris DJ, Janssens S, Mackinder BA, Blach-Overgaard A, Sonké B, Sosef MSM, Stévart T, Svenning J-C, Wieringa JJ, Couvreur TLP (2018) Beyond trees: biogeographical regionalization of tropical Africa. <em>Journal of Biogeography. </em>DOI:10.1111/jbi.13190</p> <p><em>Please cite the aforementioned article and the dataset herein, when using of any of these files in this dataset.</em></p> <p> </p> <p>The GIS file is referred in the paper as <strong>Appendix S3</strong> and correspond to the map presented in Figure 1. Each polygons of the shapefile correspond to the main floristic bioregions and transition zones of tropical Africa delimited using bipartite network clustering analysis of 24,719 plant species.</p> <p>The coordinate system of the ESRI shapefile is GCS_WGS_1984. Field descriptions for the associate table are:</p> <ul> <li><strong>bionames</strong>: name of the bioregions as given in Table S1.1.</li> <li><strong>bioreg_ID</strong>: identifier of the bioregions as given in Table S1.1 and Fig. 1. T= Transition zones</li> <li><strong>cluster_ID</strong>: identifier of clusters delimited using bipartite network clustering on the 24,719 plant species of the RAINBIO database, as given in Table S1.1 and Fig. S2.1.</li> </ul>
Fig. 2 in Two new species ofCaciaNewman (Coleoptera, Cerambycidae, Lamiinae) from the Mindoro Biogeographic Region of the Philippines
Fig. 2. Genitalia ofCacia (Ipocregyes) katrinaesp. nov.: A-C. Whole system of genitalia, A. Lateral aspect, B. Ventral aspect, C. Dorsal aspect. D-F. Aedeagus, D. Dorsal aspect, E. Ventral aspect, F. Lateral aspect. G. Tegmen.
Fig. 4 in Two new species ofCaciaNewman (Coleoptera, Cerambycidae, Lamiinae) from the Mindoro Biogeographic Region of the Philippines
Fig. 4.Cacia (Cacia) aeschyae sp. nov.: A. Whole system of genitalia, ventral aspect. B. Whole system of genitalia, dorsal aspect. C. Aedeagus, ventral aspect. D. Aedeagus, lateral aspect. E. Tegmen, lateral aspect. F. Tegman, ventral aspect. G. 9th tergite. H. 7th abdominal tergite.
Fig. 14 in Late Miocene large mammals from Yulafli, Thrace region, Turkey, and their biogeographic implications
Fig. 14. Map (slightly modified from Vasiliev et al. 2004) of the Tethys (dark grey) and Paratethys (light grey) region in early late Miocene times, showing the main mammalian localities with and without Dorcatherium, and the tentative extent of the provinces discussed in the text. 1, North−Western Province; 2, North−Dacian Province; 3, Balkano−Iranian (= Sub−Paratethyan) Province; 4, Greek Macedonian Province; 5, Eastern Aegean Province; 6, Anatolian Province.
Fig. 13 in Late Miocene large mammals from Yulafli, Thrace region, Turkey, and their biogeographic implications
Fig. 13. Plot of length vs. distal width of the metacarpal of some large late Miocene Giraffidae. Black symbols are for "Palaeogiraffa", others are for Samotherium.
Fig. 9 in Late Miocene large mammals from Yulafli, Thrace region, Turkey, and their biogeographic implications
Fig. 9. Measurements of the cross−section of the lower i2s in various Proboscideans. From Tassy (1986: fig. 14) and y = Yulafll.
Fig. 2 in Late Miocene large mammals from Yulafli, Thrace region, Turkey, and their biogeographic implications
Fig. 2. Indarctos arctoides, TTMEU−CY−46, Yulafli, Turkey, Vallesian, late Miocene. Left mandibular ramus, lateral (A) and occlusal (B) views of p2–m2 (stereo).
Linked collectors and determiners for: A NEW SPECIES OF BEGONIA FROM THE CHOCÓ BIOGEOGRAPHICAL REGION OF COLOMBIA.
Natural history specimen data linked to collectors and determiners held within, "A NEW SPECIES OF BEGONIA FROM THE CHOCÓ BIOGEOGRAPHICAL REGION OF COLOMBIA". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/53e7fcaf-b16a-4c9c-8589-338c0e60eedf">https://bionomia.net/dataset/53e7fcaf-b16a-4c9c-8589-338c0e60eedf</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/53e7fcaf-b16a-4c9c-8589-338c0e60eedf">https://gbif.org/dataset/53e7fcaf-b16a-4c9c-8589-338c0e60eedf</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Tagalis graziae, a new species of Saicinae from the Chocó biogeographic region in Colombia (Hemiptera: Reduviidae).
Natural history specimen data linked to collectors and determiners held within, "Tagalis graziae, a new species of Saicinae from the Chocó biogeographic region in Colombia (Hemiptera: Reduviidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/db889bab-85e8-4560-b26d-6460669e59f9">https://bionomia.net/dataset/db889bab-85e8-4560-b26d-6460669e59f9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/db889bab-85e8-4560-b26d-6460669e59f9">https://gbif.org/dataset/db889bab-85e8-4560-b26d-6460669e59f9</a>. Formatted as a Frictionless Data package.
Figures 203-209 in A revision of the tribe Coelidiini of the Oriental, Palearctic and Australian biogeographical regions (Hemiptera: Cicadellidae: Coelidiinae)
Figures 203-209. Genitalia, Olidiana lata, sp. nov. 203) Male pygofer, lateral view. 204) Aedeagus and dorsal connective, lateral view. 205) Aedeagus and dorsal connective, dorsal view. 206) Style, lateral view. 207) Style, dorsal view. 208) Connective, caudal view. 209) Subgenital plate, ventral view.
PLATE 5. A-I in A revision of the tribe Coelidiini of the Oriental, Palearctic and Australian biogeographical regions (Hemiptera: Cicadellidae: Coelidiinae)
PLATE 5. A-I. Dorsal habitus. (A) Singillatus gracilis, sp. nov.; (B) Singillatus ventrospinatus, sp. nov.; (C) Taharana caverna, sp. nov.; (D) Taharana sublamina, sp. nov.; (E) Taharana abstrusa, sp. nov.; (F) Taharana biunca, sp. nov.; (G) Taharana lacertosa, sp. nov.; (H) Taharana biavicula, sp. nov.; (I) Taharana brevicutata, sp. nov.
PLATE 4. A-I in A revision of the tribe Coelidiini of the Oriental, Palearctic and Australian biogeographical regions (Hemiptera: Cicadellidae: Coelidiinae)
PLATE 4. A-I. Dorsal habitus. (A) Olidiana parafringa, sp. nov.; (B) Olidiana lata, sp. nov.; (C) Olidiana vincula, sp. nov.; (D) Olidiana inaequabilia, sp. nov.; (E) Olidiana tonkinensis, sp. nov.; (F) Olidiana bispiculata, sp. nov.; (G) Olidiana implicata, sp. nov.; (H) Olidiana pennata, sp. nov.; (I) Olidiana filiata, sp. nov.
Figures 451-457 in A revision of the tribe Coelidiini of the Oriental, Palearctic and Australian biogeographical regions (Hemiptera: Cicadellidae: Coelidiinae)
Figures 451-457. Genitalia, Webbolidia kristenseni, sp. nov. 451) Male pygofer, lateral view. 452) Aedeagus and dorsal connective, lateral view. 453) Aedeagus and dorsal connective, dorsal view. 454) Style, lateral view. 455) Style, dorsal view. 456) Connective, caudal view. 457) Subgenital plate, ventral view.
Figures 431-436 in A revision of the tribe Coelidiini of the Oriental, Palearctic and Australian biogeographical regions (Hemiptera: Cicadellidae: Coelidiinae)
Figures 431-436. Genitalia, Trinoridia calcaris, sp. nov. 431) Male pygofer, lateral view. 432) Aedeagus and dorsal connective, lateral view. 433) Aedeagus and dorsal connective, dorsal view. 434) Style, lateral view. 435) Connective and style, caudal view. 436) Subgenital plate, ventral view.
Figures 424-430 in A revision of the tribe Coelidiini of the Oriental, Palearctic and Australian biogeographical regions (Hemiptera: Cicadellidae: Coelidiinae)
Figures 424-430. Genitalia, Trinoridia trifida, sp. nov. 424) Male pygofer, lateral view. 425) Aedeagus and dorsal connective, lateral view. 426) Aedeagus and dorsal connective, dorsal view. 427) Style, lateral view. 428) Style, dorsal view. 429) Connective, caudal view. 430) Subgenital plate, ventral view.
Figures 444-450 in A revision of the tribe Coelidiini of the Oriental, Palearctic and Australian biogeographical regions (Hemiptera: Cicadellidae: Coelidiinae)
Figures 444-450. Genitalia, Webbolidia magna, sp. nov. 444) Male pygofer, lateral view. 445) Aedeagus and dorsal connective, lateral view 446) Aedeagus and dorsal connective, dorsal view. 447) Style, lateral view. 448) Style, dorsal view. 449) Connective, caudal view. 450) Subgenital plate, ventral view.
Figures 417-423 in A revision of the tribe Coelidiini of the Oriental, Palearctic and Australian biogeographical regions (Hemiptera: Cicadellidae: Coelidiinae)
Figures 417-423. Genitalia, Taharana oblongiserrata, sp. nov. 417) Male pygofer, lateral view. 418) Pygofer caudodorsal processes, dorsal view. 419) Aedeagus and dorsal connective, lateral view. 420) Aedeagus, dorsal view. 421) Style, lateral view. 422) Connective, caudal view. 423) Subgenital plate, ventral view.
Figures 375-381 in A revision of the tribe Coelidiini of the Oriental, Palearctic and Australian biogeographical regions (Hemiptera: Cicadellidae: Coelidiinae)
Figures 375-381. Genitalia, Taharana protriangulata, sp. nov. 375) Male pygofer, lateral view. 376) Pygofer caudodorsal processes, lateral view. 377) Aedeagus and dorsal connective, lateral view. 378) Aedeagus, dorsal view. 379) Style, lateral view. 380) Connective, caudal view. 381) Subgenital plate, ventral view.
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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)
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DANDI Archive for NWB datasets
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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.