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1,989 results for “Fires”
Figure 4 in The History of Little Fire Ant Wasmannia auropunctata Roger in the Hawaiian Islands: Spread, Control, and Local Eradication
Figure 4. Map of Kauai showing location infested by Wasmannia auropuntata (2012). Currently this site is putatively ant free.
Figure 2 in The History of Little Fire Ant Wasmannia auropunctata Roger in the Hawaiian Islands: Spread, Control, and Local Eradication
Figure 2. Location of properties infested with Wasmannia auropunctata in January 2007 prepared by Hawaii Department of Agriculture.
Figure 6 in The History of Little Fire Ant Wasmannia auropunctata Roger in the Hawaiian Islands: Spread, Control, and Local Eradication
Figure 6. Locations of known sites on Oahu infested with Wasmannia auropunctata. (currently the infestation in Mililani and the original infestation in Waimanalo are putatively ant-free)
Figure 1 in Little Fire Ant, Wasmannia auropunctata (Roger) (Hymenoptera: Formicidae), Established at Several Locations on Guam
Figure 1. Map collection sites (after Burdick 2006) of W. auropunctata on Guam: (a) Primo Northern Wasteland, Yigo (N 13.5411, E 13.5411); (b) Nimitz Hill, Piti (N 13.4612, E 144.7080); (c) Pigua, Merizo (N 13.2648, E 144.6719);(d) Santa Rita (N 13.3919, E 144.6671); (e) Going to Veteran's Park, Umatac (N 13.3037, E 144.6742); (f) Matgue River, Piti (N 13.4685, E 144.7078); (g) Nimitz Hill, Piti (N 13.4641, E 144.7039).
Figure 1 in About the nutrition of Cleroclytus semirufus Kraatz, 1884 (Coleoptera, Cerambycidae) with the exudate of the Fire blight of fruit crops
Figure 1.Cleroclytus semirufus: A - habitus, dorsal view; B - nutrition on the flowers of Spiraea; C, D - feeding on exudate of the bacterium Erwinia amylovora on an apple tree.
Fig. 23. Forest Fires. The scene after a in The herpetofauna of Coahuila, Mexico: composition, distribution, and conservation status
Fig. 23. Forest Fires. The scene after a forest fire in the vicinity of Arteaga, in the municipality of Arteaga. Photo by Manuel Nevárez de los Reyes.
Fig. 1 in Red imported fire ant, Solenopsis invicta (Burden) (Hymenoptera: Formicidae), abundance and arthropod community diversity affected by pasture management
Fig. 1. Mean ± SE Solenopsis invicta mound abundance (A) and mound area (B) in adaptive multi-paddock and conventionally grazed (CG) pastures (n = 6). Statistical analysis was conducted using 1-way analysis of variance (ANOVA), *α = 0.05.
Fig. 1 in Actions of the fire ant Solenopsis saevissima (Smith) (Hymenoptera: Formicidae) on a big-eared opossum carcass
Fig. 1. Carcass of big-eared opossum (Didelphis aurita) colonized by fire ants Solenopsis saevissima (Hymenoptera: Formicidae) and other insects on 12 Nov 2017, Juiz de Fora, Minas Gerais, Brazil. (A) Fire ants initially monopolized the carcass, constructing a dirt mound on the muzzle, thus displaying the burying behavior of the ants. (B) Circled are ants beginning construction of a soil mound near the tail. (C) Diptera depositing eggs on the carcass. Afer the carcass was moved to the periphery of the roadway, the ants lost their dominance to other necrophagous insects. (D) Skeletonized carcass; ants and other necrophagous insects remained until the end of the decomposition process.
Fig. 7. Forest fires. A in The herpetofauna of Hidalgo, Mexico: composition, distribution, and conservation status
Fig. 7. Forest fires. A forest fire for land use conversion in the vicinity of El Naranjal, in the municipality of Pisaflores. Photo by Christian Berriozabal-Islas.
Fig. 3 in Insect herbivory following fire on Lyonia fruticosa, an ericaceous shrub of Florida scrub
Fig. 3. Lyonia fruticosa traits with time-since-fire: (A) height, (B) number of stems, (C) proportion of plants flowering, and (D) leaf area. For all panels, points show the mean ± 1 SE of plants within a management unit. Generalized additive models were fitted using the mean values for each management unit to avoid pseudoreplication. Solid regression lines and the shaded areas show the predicted values with 1 SE.
Fig. 2 in Insect herbivory following fire on Lyonia fruticosa, an ericaceous shrub of Florida scrub
Fig. 2. Time-since-fire and herbivore damage by type across whole plants. Points show the mean ± 1 SE of plants within a management unit. Generalized additive models were fitted using the mean values for each management unit to avoid pseudoreplication. When significant, solid regression lines and the shaded areas show the predicted values with 1 SE.
Fig. 1 in Insect herbivory following fire on Lyonia fruticosa, an ericaceous shrub of Florida scrub
Fig. 1. Time-since-fire and herbivory across whole plants. (A) Percent herbivory with time-since-fire; (B) proportion of leaves damaged with time-since-fire. For both panels, points show the mean ± 1 SE of plants within a management unit. Generalized additive models were fitted using the mean values (see text for details). Solid regression lines and the shaded areas show the predicted values with 1 SE.
FireSafetyNet: An Image-Based Dataset with Pretrained Weights for Machine Learning-Driven Fire Safety Inspection
<p>This dataset offers a diverse collection of images curated to support the development of computer vision models for detecting and inspecting Fire Safety Equipment (FSE) and related components. Images were collected from a variety of public buildings in Germany, including university buildings, student dormitories, and shopping malls. The dataset consists of self-captured images using mobile cameras, providing a broad range of real-world scenarios for FSE detection.</p> <p>In the journal paper associated with these image datasets, the open-source dataset FireNet (Boehm et al. 2019) was additionally utilized for training. However, to comply with licensing and distribution regulations, images from <a href="https://www.firenet.xyz/">FireNet</a> have been excluded from this dataset. Interested users can visit the FireNet repository directly to access and download those images if additional data is required. The provided weights (.pt), however, are trained on the provided self-made images and FireNet using YOLOv8.</p> <p>The dataset is organized into six sub-datasets, each corresponding to a specific FSE-related machine learning service:</p> <ol> <li> <p><strong>Service 1: FSE Detection</strong> - This sub-dataset provides the foundation for FSE inspection, focusing on the detection of primary FSE components like fire blankets, fire extinguishers, manual call points, and smoke detectors.</p> </li> <li> <p><strong>Service 2: FSE Marking Detection</strong> - Building on the first service, this sub-dataset includes images and annotations for detecting FSE marking signs.</p> </li> <li> <p><strong>Service 3: Condition Check - Modal</strong> - This sub-dataset addresses the inspection of FSE condition in a modal manner, focusing on instances where fire extinguishers might be blocked or otherwise non-compliant. This dataset includes semantic segmentation annotations of fire extinguishers. For upload reasons, this set is split into <em>3_1_FSE Condition Check_modal_train_data (containing training images and annotations) </em>and <em>3_1_FSE Condition Check_modal_val_data_and_weights (containing validation images, annotations </em>and<em> the best weights).</em></p> </li> <li> <p><strong>Service 4: Condition Check - Amodal</strong> - Extending the modal condition check, this sub-dataset involves amodal detection to identify and infer the state of FSE components even when they are partially obscured. This dataset includes semantic segmentation annotations of fire extinguishers. This dataset includes semantic segmentation annotations of fire extinguishers. For upload reasons, this set is split into <em>4_1_FSE Condition Check_amodal_train_data (containing training images and annotations) </em>and <em>4_1_FSE Condition Check_amodal_val_data_and_weights (containing validation images, annotations </em>and<em> the best weights).</em></p> </li> <li> <p><strong>Service 5: Details Extraction - Inspection Tags</strong> - This sub-dataset provides a detailed examination of the inspection tags on fire extinguishers. It includes annotations for extracting semantic information such as the next maintenance date, contributing to a thorough evaluation of FSE maintenance practices.</p> </li> <li> <p><strong>Service 6: Details Extraction - Fire Classes Symbols</strong> - The final sub-dataset focuses on identifying fire class symbols on fire extinguishers.</p> </li> </ol> <p>This dataset is intended for researchers and practitioners in the field of computer vision, particularly those engaged in building safety and compliance initiatives.</p>
SeasFire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth System
<p>The <strong>SeasFire Cube</strong> is a scientific datacube for seasonal fire forecasting around the <strong>globe</strong>. Apart from seasonal fire forecasting, which is the aim of the SeasFire project, the datacube can be used for several other tasks. For example, it can be used to model teleconnections and memory effects in the earth system. Additionally, it can be used to model emissions from wildfires and the evolution of wildfire regimes.<br> <br> It has been created in the context of the <a href="https://seasfire.hua.gr/">SeasFire project</a>, which deals with "<em>Earth System Deep Learning for Seasonal Fire Forecasting</em>" and <strong>is funded by the European Space Agency (ESA) </strong> in the context of ESA Future EO-1 Science for Society Call.<br> <br> It contains <strong>21 years</strong> of data (2001-2021) in an <strong>8-days</strong> time resolution and <strong>0.25 degrees grid</strong> resolution. It has a diverse range of seasonal fire drivers. It expands from atmospheric and climatological ones to vegetation variables, socioeconomic and the target variables related to wildfires such as burned areas, fire radiative power, and wildfire-related CO2 emissions.</p> Datacube properties <table><tbody><tr> <th> <p><strong>Feature</strong></p> </th> <th> <p><strong>Value</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>Spatial Coverage</p> </td> <td> <p>Global</p> </td> </tr> <tr> <td> <p>Temporal Coverage</p> </td> <td> <p>2001 to 2021</p> </td> </tr> <tr> <td> <p>Spatial Resolution</p> </td> <td> <p>0.25 deg x 0.25 deg</p> </td> </tr> <tr> <td> <p>Temporal Resolution</p> </td> <td> <p>8 days</p> </td> </tr> <tr> <td> <p>Number of Variables</p> </td> <td> <p>54</p> </td> </tr> <tr> <td> <p>Tutorial Link </p> </td> <td> <p><a href="https://github.com/SeasFire/seasfire-datacube">https://github.com/SeasFire/seasfire-datacube</a></p> </td> </tr> </tbody> </table> <table> <tbody><tr> <th>Full name</th> <th>DataArray name</th> <th>Unit</th> <th>Contact *</th> </tr> </tbody><tbody> <tr> <th>Dataset: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview">ERA5 Meteo Reanalysis Data</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Mean sea level pressure</th> <td>mslp</td> <td>Pa</td> <td>NOA</td> </tr> <tr> <th>Total precipitation</th> <td>tp</td> <td>m</td> <td>MPI</td> </tr> <tr> <th>Relative humidity</th> <td>rel_hum</td> <td>%</td> <td>MPI</td> </tr> <tr> <th>Vapor Pressure Deficit</th> <td>vpd</td> <td>hPa</td> <td>MPI</td> </tr> <tr> <th>Sea Surface Temperature</th> <td>sst</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Skin temperature</th> <td>skt</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Wind speed at 10 meters</th> <td>ws10</td> <td>m*s-2</td> <td>MPI</td> </tr> <tr> <th>Temperature at 2 meters - Mean</th> <td>t2m_mean</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Temperature at 2 meters - Min</th> <td>t2m_min</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Temperature at 2 meters - Max</th> <td>t2m_max</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Surface net solar radiation</th> <td>ssr</td> <td>MJ m-2</td> <td>MPI</td> </tr> <tr> <th>Surface solar radiation downwards</th> <td>ssrd</td> <td>MJ m-2</td> <td>MPI</td> </tr> <tr> <th>Volumetric soil water level 1</th> <td>swvl1</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th> <table> <tbody> <tr> <th>Volumetric soil water level 2</th> </tr> </tbody> </table> </th> <td>swvl2</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th>Volumetric soil water level 3</th> <td>swvl3</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th>Volumetric soil water level 4</th> <td>swvl4</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th>Land-Sea mask</th> <td>lsm</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th>Dataset: Copernicus <p><a href="http://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview">CEMS</a></p> </th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Drought Code Maximum</th> <td>drought_code_max</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Drought Code Average</th> <td>drought_code_mean</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Fire Weather Index Maximum</th> <td>fwi_max</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Fire Weather Index Average</th> <td>fwi_mean</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="http://confluence.ecmwf.int/display/CKB/CAMS%3A+Global+Fire+Assimilation+System+%28GFAS%29+data+documentation">CAMS: Global Fire Assimilation System (GFAS)</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Carbon dioxide emissions from wildfires</th> <td>cams_co2fire</td> <td>kg/m²</td> <td>NOA</td> </tr> <tr> <th>Fire radiative power</th> <td>cams_frpfire</td> <td>W/m²</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://climate.esa.int/en/projects/fire/data/">FireCCI - European Space Agency’s Climate Change Initiative</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Burned Areas from Fire Climate Change Initiative (FCCI)</th> <td>fcci_ba</td> <td>ha</td> <td>NOA</td> </tr> <tr> <th>Valid mask of FCCI burned areas</th> <td>fcci_ba_valid_mask</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th><br> Fraction of burnable area</th> <td>fcci_fraction_of_burnable_area</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Number of patches</th> <td>fcci_number_of_patches</td> <td>N</td> <td>NOA</td> </tr> <tr> <th>Fraction of observed area</th> <td>fcci_fraction_of_observed_area</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Dataset: Nasa MODIS <a href="https://lpdaac.usgs.gov/products/mod11c1v006/">MOD11C1</a>, <a href="https://lpdaac.usgs.gov/products/mod13c1v006/">MOD13C1</a>, <a href="https://lpdaac.usgs.gov/products/mcd15a2hv006/">MCD15A2</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Land Surface temperature at day</th> <td>lst_day</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Leaf Area Index</th> <td>lai</td> <td>m²/m²</td> <td>MPI</td> </tr> <tr> <th>Normalized Difference Vegetation Index</th> <td>ndvi</td> <td>unitless</td> <td>MPI</td> </tr> <tr> <th>Dataset: Nasa SEDAC <a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-adjusted-to-2015-unwpp-country-totals-rev11">Gridded Population of the World (GPW), v4</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Population density</th> <td>pop_dens</td> <td>persons per square kilometers</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="http://www.globalfiredata.org/data.html">Global Fire Emissions Database (GFED)</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Burned Areas from GFED (large fires only)</th> <td>gfed_ba</td> <td>hectares (ha)</td> <td>MPI</td> </tr> <tr> <th>Valid mask of GFED burned areas</th> <td>gfed_ba_valid_mask</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th>GFED basis regions</th> <td>gfed_region</td> <td>N</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="http://gwis.jrc.ec.europa.eu/apps/country.profile/downloads">Global Wildfire Information System (GWIS)</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Burned Areas from GWIS</th> <td>gwis_ba</td> <td>ha</td> <td>NOA</td> </tr> <tr> <th>Valid mask of GWIS burned areas</th> <td>gwis_ba_valid_mask</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://psl.noaa.gov/data/climateindices/list/">NOAA Climate Indices</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Arctic Oscillation Index</th> <td>oci_ao</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Western Pacific Index</th> <td>oci_wp</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Pacific North American Index</th> <td>oci_pna</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>North Atlantic Oscillation</th> <td>oci_nao</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Southern Oscillation Index</th> <td>oci_soi</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Global Mean Land/Ocean Temperature</th> <td>oci_gmsst</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Pacific Decadal Oscillation</th> <td>oci_pdo</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Eastern Asia/Western Russia</th> <td>oci_ea</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>East Pacific/North Pacific Oscillation</th> <td>oci_epo</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Nino 3.4 Anomaly</th> <td>oci_nino_34_anom</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Bivariate ENSO Timeseries</th> <td>oci_censo</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://www.esa-landcover-cci.org/">ESA CCI</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Land Cover Class 0 - No data</th> <td>lccs_class_0</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 1 - Agriculture</th> <td>lccs_class_1</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 2 - Forest</th> <td>lccs_class_2</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 3 - Grassland</th> <td>lccs_class_3</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 4 - Wetlands</th> <td>lccs_class_4</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 5 - Settlement</th> <td>lccs_class_5</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 6 - Shrubland</th> <td>lccs_class_6</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 7 - Sparse vegetation, bare areas, permanent snow and ice</th> <td>lccs_class_7</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 8 - Water Bodies</th> <td>lccs_class_8</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://ecoregions.appspot.com/">Biomes</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Dataset: Calculated</th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Grid Area in square meters</th> <td>area</td> <td>m²</td> <td>NOA</td> </tr> </tbody> </table> <p>*The datacube specifications (temporal, spatial resolution, chunk size) have been set up by the Max Planck Institut (MPI) team. For the variables that the contact is MPI, Lazaro Alonso (lalonso <at> bgc-jena.mpg.de) has led the efforts to collect and process them. For the variables that the contact is NOA, Ilektra Karasante (ile.karasante <at> noa.gr) has led the efforts to collect and process them.</p>
Character Networks of 'A Song of Ice and Fire' Adaptations Across Media
<p><strong>Description. </strong>This work aims at comparing three crossmedia adaptations of the same story: George R. R. Martin’s <em>A Song of Ice an Fire</em> original novels, the comics directly adapted from this series, and the <em>Game of Throne</em> TV show. To perform our analysis, we used two representations of these stories: textual vs. graph-based. The textual one corresponds to summaries, whereas the graphs are character networks, representing the interactions between characters. We performed a descriptive analysis of the three adaptations, and tackle a task consisting in automatically aligning the three stories.</p> <p>This dataset contains the input files used by our scripts (various versions of the character networks) as well as the files produced during the processing.</p> <ul> <li>The archives starting with `nets_` correspond to the network files used as input during processing.</li> <li>Those starting with `out_` are the files (mainly stats and plots) produced by our scripts.</li> </ul> <p>The networks are available in several versions, using the `graphml` format:</p> <ul> <li>Folder `cumul`: cumulative networks, i.e. dynamic networks that grow from the beginning to the end of the story. The very last network corresponds to the <em>static</em> network, i.e. the network representing the whole timeline.</li> <li>Folder `instant`: also a dynamic network, but this time each instant is not considered as an increment. Each `graphml` file only focuses on a specific temporal subdivision of the story.</li> </ul> <p>The temporal subdivisions depend on the considered medium:</p> <ul> <li>Novels: only <em>chapter</em>.</li> <li>Comics: <em>chapter</em> or <em>scene</em>.</li> <li>TV show: <em>episode</em>, <em>block</em> or <em>scene</em>.</li> </ul> <p>TV show blocks were defined <em>ad hoc</em> to get a subdivision larger than a scene but smaller than a whole episode. We experimented with two versions: blocs based on the location persistence vs. character similarity.</p> <p><strong>Software. </strong>The scripts are publicly available online: <a href="https://github.com/CompNet/Sachan">https://github.com/CompNet/Sachan</a></p> <p><strong>References. </strong>This work was published in the following article:</p> <ul> <li> <div> <div>A. Amalvy, M. Janickyj, S. Mannion, P. MacCarron, and V. Labatut, “Interconnected Kingdoms: Comparing ‘A Song of Ice and Fire ́Crossmedia Adaptations Using Complex Networks,” <em>Social Network Analysis and Mining</em>, vol. 14, p. 199, 2024. DOI: <a href="https://doi.org/10.1007/s13278-024-01365-z">10.1007/s13278-024-01365-z</a> ⟨<a href="https://hal.science/hal-04722579">hal-04722579</a>⟩</div> </div> </li> </ul> <p><strong>Citation. </strong>If you use these scripts, please cite the above article:</p> <pre><code>@Article{Amalvy2024c, author = {Amalvy, Arthur and Janickyj, Madeleine and Mannion, Shane and MacCarron, Pádraig and Labatut, Vincent}, title = {Interconnected Kingdoms: Comparing `A Song of Ice and Fire' Crossmedia Adaptations Using Complex Networks}, journal = {Social Network Analysis and Mining}, year = {2024}, volume = {14}, pages = {199}, doi = {10.1007/s13278-024-01365-z}, }</code><br><br></pre>
Linked collectors and determiners for: Review of the Spirobolida on Madagascar, with descriptions of twelve new genera, including three genera of ' fire millipedes' (Diplopoda).
Natural history specimen data linked to collectors and determiners held within, "Review of the Spirobolida on Madagascar, with descriptions of twelve new genera, including three genera of ' fire millipedes' (Diplopoda)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/e6ea8eea-7156-4785-8313-f4bd88dadad1">https://bionomia.net/dataset/e6ea8eea-7156-4785-8313-f4bd88dadad1</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/e6ea8eea-7156-4785-8313-f4bd88dadad1">https://gbif.org/dataset/e6ea8eea-7156-4785-8313-f4bd88dadad1</a>. Formatted as a Frictionless Data package.
Figures 3–4 in Pyroghatsiana: A new genus of fire-colored beetles (Coleoptera: Pyrochroidae: Pyrochroinae) from the Southern Ghats, India
Figures 3–4. Pyroghatsiana madurensis (Pic), adult female. 3) pronotum-scutellum-elytral bases, dorsal view. 4) habitus, left lateroventral view.
Figures 1–2 in Pyroghatsiana: A new genus of fire-colored beetles (Coleoptera: Pyrochroidae: Pyrochroinae) from the Southern Ghats, India
Figures 1–2. Pyroghatsiana madurensis (Pic), adult female. 1) habitus, dorsal view. 2) head, dorsal view.
Figures 5–7. Dendroides spp. 5–6 in Pyroghatsiana: A new genus of fire-colored beetles (Coleoptera: Pyrochroidae: Pyrochroinae) from the Southern Ghats, India
Figures 5–7. Dendroides spp. 5–6) Dendroides canadensis Latreille, adult female. 5) head, dorsal view. 6) pronotumscutellum-elytral bases, dorsal view. 7) Dendroides ussuriensis L. N. Medvedev, adult female. head, dorsal view.
Figures 4–6 in Sundapyrochroa: A new genus of Fire-Colored Beetles (Coleoptera: Pyrochroidae: Pyrochroinae) from the Sunda Shelf, with a key to the three species
Figures 4–6. Sundapyrochroa nigripennis (Pic). 4) Habitus, adult male, dorsal. 5) Habitus, melanic adult male, dorsal. 6) Habitus, adult female, dorsal.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.