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1,473 results for “Geographic distribution”
Data from: A geographical distribution database of the genus Dysdera in the Canary Islands (Araneae, Dysderidae)
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Data from: Do genetic drift and gene flow affect the geographic distribution of female plants in gynodioecious Lobelia siphilitica?
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Data from: Tackling intraspecific genetic structure in distribution models better reflects species geographical range
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Diversity of cellular slime molds (dictyostelids) in the Fanjing Mountain Nature Reserve and geographical distribution comparisons with other representative nature reserves in different climate zones of China
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Connecting species’ geographical distributions to environmental variables: range maps versus observed points of occurrence
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Fig. 3 in Geographic variation of Moenkhausia bonita (Characiformes: Characidae) in the rio de la Plata basin, with distributional comments on M. intermedia
Fig. 3. Most discriminant axes obtained from the multivariate analyses of the square-root transformed meristic data of Moenkhausia bonita. a. principal component analysis; b. discriminant canonical analysis.
Fig. 4 in Geographic variation of Moenkhausia bonita (Characiformes: Characidae) in the rio de la Plata basin, with distributional comments on M. intermedia
Fig. 4. Tukey box plots showing intraspecific variation of Moenkhausia bonita in the most informative counts found through the multivariate analyses. a. circumpeduncular scales; b. maxillary teeth; c. scales below the lateral line; d. number of gill rakers on each limb (upper vs. lower, in the bottom and top parts, respectively). Number of specimens in brackets.
Fig. 2 in Geographic variation of Moenkhausia bonita (Characiformes: Characidae) in the rio de la Plata basin, with distributional comments on M. intermedia
Fig. 2. Most discriminant axes obtained from the size-free multivariate analyses using the adjusted morphometric data of Moenkhausia bonita. a. principal component analysis; b. discriminant canonical analysis.
Fig. 1 in Geographic variation of Moenkhausia bonita (Characiformes: Characidae) in the rio de la Plata basin, with distributional comments on M. intermedia
Fig. 1. Map showing the distributional range of Moenkhausia bonita across the rio de la Plata basin (based on the examined specimens). Black arrow indicates type locality.
Fig. 6 in Geographic variation of Moenkhausia bonita (Characiformes: Characidae) in the rio de la Plata basin, with distributional comments on M. intermedia
Fig. 6. Coloration pattern in alcohol-preserved specimens of Moenkhausia bonita. a. MLP 11244, 31.9 mm SL, Uruguay basin; b. MLP 11241, 33.1 mm SL, Paraná basin.
Fig. 5 in Geographic variation of Moenkhausia bonita (Characiformes: Characidae) in the rio de la Plata basin, with distributional comments on M. intermedia
Fig. 5. Coloration pattern in live of Moenkhausia bonita from different geographic basins. a. CI-FML 7272, unnamed stream between Blanco and Bermejo rivers, Salta, Argentina (by Felipe Alonso); b. CI-FML 5351, El Oculto stream, Bermejo basin, Orán, Salta, Argentina (by Felipe Alonso); c. uncatalogued specimen, Lagoon or Bahia in BR362 next to rio Paraguay in Corumbá, Brazil; d. uncatalogued specimen, Mburucuyá National Park, Paraná basin, Corrientes, Argentina (by Liliana Ciotek); e. uncatalogued specimen, Lagoon at rio Riachuelo, Paraná basin, San Luis del Palmar, Argentina (by Miguel Albarenga).
Geographical Distribution of Mammal Images (Flickr-Mammal dataset)
<p>This dataset is created based on the geographical distribution of common mammal images on Flickr.</p> <p>In this dataset, we provide the metadata of all images in this dataset, including their geotags, license status, URL to the images, and their corresponding countries and geographical regions (based on <a href="https://unstats.un.org/unsd/methodology/m49/">UN M49 Standard</a>).</p> <p>We also provide easy-to-use scripts for the user to download the images from their URLs.</p> <p>If you use this dataset in your research, we would appreciate a reference to the following paper:</p> <p>Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip B Gibbons. "<a href="https://proceedings.icml.cc/static/paper_files/icml/2020/3152-Paper.pdf">The Non-IID Data Quagmire of Decentralized Machine Learning</a>." <em>Proceedings of the 37th International Conference on Machine Learning (ICML 2020)</em>.</p> <p>Bibtex entry</p> <pre><code>@incollection{icml2020_3152, author = {Hsieh, Kevin and Phanishayee, Amar and Mutlu, Onur and Gibbons, Phillip}, booktitle = {International Conference on Machine Learning ({ICML})}, pages = {5819--5830}, title = {The Non-{IID} Data Quagmire of Decentralized Machine Learning}, year = {2020} }</code></pre> <p> </p>
FIGURE 38 in Haasea Verhoeff, 1895-a genus of tumultuous history and chaotic recordsredefinition, revision of taxonomy and geographic distributions, with descriptions of two new species from Austria and Serbia (Diplopoda, Chordeumatida, Haaseidae)
FIGURE 38. Distribution of the genus Haasea.
FIGURE 10 in Haasea Verhoeff, 1895-a genus of tumultuous history and chaotic recordsredefinition, revision of taxonomy and geographic distributions, with descriptions of two new species from Austria and Serbia (Diplopoda, Chordeumatida, Haaseidae)
FIGURE 10. Attems personal collections catalogue in NHMW (photo NHMW).
Figure 4 from: Zaragoza-Tapia F, Pulido-Flores G, Gardner SL, Monks S (2020) Host relationships and geographic distribution of species of Acanthobothrium Blanchard, 1848 (Onchoproteocephalidea, Onchobothriidae) in elasmobranchs: a metadata analysis. ZooKeys 940: 1-49. https://doi.org/10.3897/zookeys.940.46352
Figure 4 Number of species of Acanthobothrium reported from elasmobranchs in each biogeographic region (Last et al. 2016b).
Figure 1 from: Zaragoza-Tapia F, Pulido-Flores G, Gardner SL, Monks S (2020) Host relationships and geographic distribution of species of Acanthobothrium Blanchard, 1848 (Onchoproteocephalidea, Onchobothriidae) in elasmobranchs: a metadata analysis. ZooKeys 940: 1-49. https://doi.org/10.3897/zookeys.940.46352
Figure 1 Type localities of species of Acanthobothrium reported worldwide and the biogeographic regions (Last et al. 2016b) of the geographic distribution of their hosts (see Table 1).
Figure 3 from: Zaragoza-Tapia F, Pulido-Flores G, Gardner SL, Monks S (2020) Host relationships and geographic distribution of species of Acanthobothrium Blanchard, 1848 (Onchoproteocephalidea, Onchobothriidae) in elasmobranchs: a metadata analysis. ZooKeys 940: 1-49. https://doi.org/10.3897/zookeys.940.46352
Figure 3 Families of rays: A number of species of rays per family B number of species of rays parasitized by species of Acanthobothrium. Note: The first number within parentheses corresponds to the number of species of ray that have been reported as hosts of Acanthobothrium and the second is the number of species that have been described from that Family C percentage of species of rays reported to be parasitized within the total number of families of rays- note: Red color = parasitized; Blue color = not parasitized.
Figure 2 from: Zaragoza-Tapia F, Pulido-Flores G, Gardner SL, Monks S (2020) Host relationships and geographic distribution of species of Acanthobothrium Blanchard, 1848 (Onchoproteocephalidea, Onchobothriidae) in elasmobranchs: a metadata analysis. ZooKeys 940: 1-49. https://doi.org/10.3897/zookeys.940.46352
Figure 2 Families of sharks: A number of species of sharks per family B number of species of sharks parasitized by species of Acanthobothrium. Note: The first number within parentheses corresponds to the number of species of shark that have been reported as hosts of Acanthobothrium and the second is the number of species that have been described from that Family C percentage of species of shark reported to be parasitized within the total number of families of sharks- note: Red color = parasitized; Blue color = not parasitized.
Fig. 3 in Gallancyra gen. nov. (Phthiraptera: Ischnocera), with an overview of the geographical distribution of chewing lice parasitizing chicken
Fig. 3. Geographical distribution of four species of menoponid chewing lice parasitizing wild and domestic chicken (Gallus spp.). Each circle is divided into four sectors, representing the four louse species: upper left = Menacanthus cornutus (Schömmer, 1913); upper right = Menacanthus stramineus (Nitzsch, 1818); lower left = Menacanthus pallidulus (Neumann, 1912); lower right = Menopon gallinae (Linnaeus, 1758). Black sectors indicate that this louse species is known from this country, whereas hollow sectors indicate that we have found no published records of this species in this country. The presence of the four species of chewing lice in a country is based on the reports summarized in Table 1. Note that the menoponid species Amyrsidea powelli (Bedford, 1920) appears to be established on chicken in Nigeria (Fabiyi 1986, 1996), and that Menacanthus longiscleritus Naz & Rizvi, 2016, has been described from chicken in Pakistan. These are not shown on the map.
Figs 7–8 in Gallancyra gen. nov. (Phthiraptera: Ischnocera), with an overview of the geographical distribution of chewing lice parasitizing chicken
Figs 7–8. Gallancyra dentata (Sugimoto, 1934) gen. et comb. nov. ex Gallus gallus (Linnaeus, 1758) (NHMUK010682393). 7. Male subgenital plate and terminal end of abdomen, ventral view. 8. Female subgenital plate and terminal end of abdomen, ventral view. Abbreviations: vms = vulval marginal setae; vss = vulval submarginal setae.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.