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Fig. 6 in Oviposition of AedeS japoNiCUS japoNiCUS (Diptera: Culicidae) and associated native species in relation to season, temperature and land use in western Germany
Fig. 6 Logistic regression of probability proportion of adults hatched from larvae of Cx. pipiens s.l. vs. Ae. japonicus japonicus collected at the specified temperatures. Data from 2017 and 2018 (23 May to 25 September) for ovitraps located along the forest–settlement transect. Function jitter was used for taxa proportion data to improve visibility [85]
Fig. 2 a in Circulating dengue virus serotypes and vertical transmission in AEdES larvae during outbreak and inter-outbreak seasons in a high dengue risk area of Sri Lanka
Fig. 2 a Distribution of dengue cases in the Kegalle District and Mawanella MOH area, Sri Lanka from December 2015 to March 2017. b Distribution of DENV serotypes in patients and distribution of Aedes mosquito larvae in and around the residences of dengue patients in Mawanella from December 2015 to March 2017. Abbreviations: DENV1, -2, -3, -4, DENV serotypes 1, 2, 3, 4
Figure 1 in Seasonal mite population distribution on Caryocar brasiliense trees in the Cerrado domain
Figure 1. Number of Acaridae, Agistemus sp., Eutetranychus sp., Histiostoma sp., Proctolaelaps sp., and Tetranychus sp.1 and 2 on leaf (per cm 2), and number of Proctolaelaps sp. and Histiostoma sp. per fruit on Caryocar brasiliense. Montes Claros, Minas Gerais State, Brazil.
Figure 2 in Seasonal mite population distribution on Caryocar brasiliense trees in the Cerrado domain
Figure 2. Temperature (°C), rainfall (mm), relative humidity of air (%), sunlight (h), and velocity of wind (m/sec). Montes Claros, Minas Gerais State, Brazil.
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>
Luka Dončić Rookie Year Statistics (2018-2019 NBA Season)
<p>This dataset contains key performance statistics for Luka Dončić during his rookie season with the Dallas Mavericks in the 2018-2019 NBA season. The dataset includes per-game statistics such as points per game (PPG), assists per game (APG), rebounds per game (RPG), and shooting percentages. The data is focused on analyzing Dončić's impactful first season in the NBA.</p>
Linked collectors and determiners for: Dasymutilla Ashmead (Hymenoptera, Mutillidae) in Panama: new species, sex associations and seasonal flight activity.
Natural history specimen data linked to collectors and determiners held within, "Dasymutilla Ashmead (Hymenoptera, Mutillidae) in Panama: new species, sex associations and seasonal flight activity". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/30786276-ef8a-4cf1-b6c7-1cf0c992b30a">https://bionomia.net/dataset/30786276-ef8a-4cf1-b6c7-1cf0c992b30a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/30786276-ef8a-4cf1-b6c7-1cf0c992b30a">https://gbif.org/dataset/30786276-ef8a-4cf1-b6c7-1cf0c992b30a</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Taxonomic revision of the seasonal South American killifish genus Simpsonichthys (Teleostei: Cyprinodontiformes: Aplocheiloidei: Rivulidae)..
Natural history specimen data linked to collectors and determiners held within, "Taxonomic revision of the seasonal South American killifish genus Simpsonichthys (Teleostei: Cyprinodontiformes: Aplocheiloidei: Rivulidae).". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/09a4f2d6-bc81-4c03-908d-85cbaf0fc5b6">https://bionomia.net/dataset/09a4f2d6-bc81-4c03-908d-85cbaf0fc5b6</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/09a4f2d6-bc81-4c03-908d-85cbaf0fc5b6">https://gbif.org/dataset/09a4f2d6-bc81-4c03-908d-85cbaf0fc5b6</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Croton sertanejus, a new species from Seasonally Dry Tropical Forest in Brazil, and redescription of C. echioides (Euphorbiaceae).
Natural history specimen data linked to collectors and determiners held within, "Croton sertanejus, a new species from Seasonally Dry Tropical Forest in Brazil, and redescription of C. echioides (Euphorbiaceae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/95293339-8775-4667-aa17-7639808b7a7d">https://bionomia.net/dataset/95293339-8775-4667-aa17-7639808b7a7d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/95293339-8775-4667-aa17-7639808b7a7d">https://gbif.org/dataset/95293339-8775-4667-aa17-7639808b7a7d</a>. Formatted as a Frictionless Data package.
Figure 3 in Seasonal abundance of Tetranychus urticae and Amblyseius swirskii (Acari: Tetranychidae and Phytoseiidae) on four strawberry cultivars
Figure 3. Overall mean numbers of Tetranychus urticae and Amblyseius swirskii on four strawberry cultivars during (a) 2017/2018 and (b) 2018/2019 seasons.
Figure 1 in Seasonal abundance of Tetranychus urticae and Amblyseius swirskii (Acari: Tetranychidae and Phytoseiidae) on four strawberry cultivars
Figure 1. Mean numbers of Tetranychus urticae and Amblyseius swirskii populations on four strawberry cultivars during 2017/2018 season.
Figure 2 in Seasonal abundance of Tetranychus urticae and Amblyseius swirskii (Acari: Tetranychidae and Phytoseiidae) on four strawberry cultivars
Figure 2. Mean numbers of Tetranychus urticae and Amblyseius swirskii populations on four strawberry cultivars during 2018/2019 season.
FIG. 8. — Auksiivik 174X Feature 567, a in To freeze or to dry: Seasonal variability in caribou processing and storage in the barrenlands of Northern Canada
FIG. 8. — Auksiivik 174X Feature 567, a marrow cracking area. Note anvil and hammer stones near centre of photo.
Figure 9. Leptolebias itanhaensis, UFRJ 6323 in Monophyly and taxonomy of the Neotropical seasonal killifish genus Leptolebias (Teleostei: Aplocheiloidei: Rivulidae), with the description of a new genus
Figure 9. Leptolebias itanhaensis, UFRJ 6323, female, paratype, 16.2-mm standard length (some hours after collection); Brazil, Estado de São Paulo, Itanhaém (photo by W. J. E. M. Costa).
Figure 6. Leptolebias aureoguttatus, UFRJ 6331 in Monophyly and taxonomy of the Neotropical seasonal killifish genus Leptolebias (Teleostei: Aplocheiloidei: Rivulidae), with the description of a new genus
Figure 6. Leptolebias aureoguttatus, UFRJ 6331, male, 22.3-mm standard length (some hours after collection); Brazil, Estado do Paraná, Praia de Leste (photo by W. J. E. M. Costa).
Figure 2 in Climatic and cultivar effects on phytoseiid species establishment and seasonal abundance on citrus
Figure 2 Abundances (number of individuals per beating sample) of phytoseiid mite species on seedlings in August. A – mean Amblyseius swirskii abundance with and without pollen provisioning. B – The relationship betweenTyphlodromus athiasae andA. swirskii abundances on different cultivars. The order of cultivars appearing in the legend corresponds to the magnitudes of their fitted intercepts (Pomello> Volka> …> Shamouti). Error bars are ± 1 SE
Figure 1 in Climatic and cultivar effects on phytoseiid species establishment and seasonal abundance on citrus
Figure 1 Phytoseiid species abundances (number of individuals per beating sample) on different cul- tivars in April, 5 weeks post release, on seedlings where Euseius stipulatus was released, with pollen provisioning (white bars), on seedlings where Euseius scutalis was released, with pollen provision- ing (gray bars), and on seedlings where no predator was released, without pollen provisioning (black bars). A – Euseius stipulatus abundances. B –Iphiseius degeneransabundances. C –Amblyseius swirskii abundances. Error bars are ± 1 SE.
Figure 3 in Climatic and cultivar effects on phytoseiid species establishment and seasonal abundance on citrus
Figure 3 Mean daily reproductive output per female (panels A and B) and survival rate (of both sexes, panels C and D), ofA. swirskii and E. stipulatus on Pomelo and Shamouti leaf discs in climate-controlled chambers. Panels A and C – Temperature regime 1 (simulating spring temperatures). Panels B and D – Temperature regime 2 (simulating summer temperatures). See Table 2 for the daily temperature schedule of each regime. Note the different scales of reproductive output between the two temperature regimes. Error bars are ± 1 SE.
Elucidating the present-day chemical composition, seasonality and source regions of climate-relevant aerosols across the Arctic land surface
<p>Data presented in the figures of the journal article "Elucidating the present-day chemical composition, seasonality and source regions of climate-relevant aerosols across the Arctic land surface" by Moschos et al.</p>
Figure 2 in Breeding season of the hermit crab Dardanus deformis H. Milne Edwards, 1836 (Anomura, Diogenidae) in Maputo Bay, southern Mozambique
Figure 2. Monthly values of temperature (A) and rainfall (B) at Costa do Sol during the sampling period.
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.