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Figure 11 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 11. Variation in Shannon diversity (H'), species richness (d), and evenness (J') of copepods in different depth strata in the upper 500 m of the central (a) and western (b) Bay of Bengal.
Figure 1 in Population structure and spatial distribution of the tiger (Panthera tigris, Felidae, Carnivora) in Southwestern Primorye (Russian Far East)
Figure 1. Census routes and places of encounters of tiger tracks in study area: blue dots, during the expedition surveys; red dots, during monitoring of the model site.
Figure 4 in Population structure and spatial distribution of the tiger (Panthera tigris, Felidae, Carnivora) in Southwestern Primorye (Russian Far East)
Figure 4. Distribution of tigers in the Amba, Barabashevka, and Narva River watersheds according to photoidentification results.
Fig. 1 in Stable isotope analysis spills the beans about spatial variance in trophic structure in a fish host - parasite system from the Vaal River System, South Africa
Fig. 1. Map of the Vaal River showing the position of sampling sites (I: below Grootdraai Dam; II: Vaal Dam; III: below Vaal River Barrage; IV: Bloemhof Dam; V: below Vaalharts Weir; VI: Douglas Weir) along the Vaal River. The block (B) indicates the position of the Vaal River within South Africa and insert A indicates the position of South Africa shaded on the African continent.
Bauder Et Al. - Landscape features fail to explain spatial genetic structure
<p> RMarkdown script and data in an Excel sheet to evaluate spatial genetic structure in white-tailed deer across Ohio and compare the support for isolation by distance (IBD) and isolation by landscape resistance (IBR) models in explaining this structure. We used genetic data from 619 individual deer from 24 counties across Ohio tested at 11 microsatellites and haplotypes from a 547-bp fragment of the mitochondrial DNA control region. We used spatial and non-spatial genetic clustering tests to evaluate genetic structure in both types of genetic data and empirically optimized landscape resistance surfaces to compare IBD and IBR using microsatellite data.</p> <p>v2 (BauderEtAl_Files_for_archiving2.zip) includes additional and updated files not in v1. </p>
Clusters of cause specific neonatal mortality and its association with per capita gross domestic product: a structured spatial analytical approach
<p>Database used for the analysis of the manuscript entitled: Clusters of cause specific neonatal mortality and its association with per capita gross domestic product: a structured spatial analytical approach. The aim of the study was to investigate the cluster areas of asphyxia-associated neonatal mortality and to explore the per capita gross domestic product (GDP) as an associated risk factor in São Paulo State (SP), Brazil.</p>
Figure 3 in Factors influencing spatial and temporal structure of frog assemblages at ponds in southeastern Brazil
Figure 3. Distribution of nine tadpole species within ponds at Santuário do Caraça, southeastern Brazil, according to variables used to describe microhabitats used by them and period of occurrence, in the first three axes of discriminant space. In the first discriminant axis, smaller values represent larger association to the bottom. In the second axis, the largest values indicate use of deeper microhabitats by tadpoles. In the third axis, larger values indicate species that used both microhabitats with and without aquatic vegetation, and lower values indicate species that used only microhabitats with aquatic vegetation. Centroids for each species are shown on the right.
Figure 1 in Factors influencing spatial and temporal structure of frog assemblages at ponds in southeastern Brazil
Figure 1. Mean monthly temperatures and monthly rainfall at the study site between September 2003 and December 2004.
Figure 2 in Factors influencing spatial and temporal structure of frog assemblages at ponds in southeastern Brazil
Figure 2. Distribution of adult individuals of 22 anuran species at Santuário do Caraça, southeastern Brazil, according to variables used to describe microhabitat use and activity periods, in the first three axes of the discriminant function. Centroids for each species are shown on the right.
Fig. 4 in Spatial genetic structure in the vulnerable smooth-coated otter (Lutrogale perspicillata, Mustelidae): towards an adaptive conservation management of the species
Fig. 4. Multidimensional Scaling (MDS) plot (stress: 0.0045) performed using average pairwise TN93 (Tamura & Nei, 1993) distances among investigated Lutrogale perspicillata groups created according to the country of origin of samples (modern + museum DNA and GenBank entries).
Fig. 3. A in Spatial genetic structure in the vulnerable smooth-coated otter (Lutrogale perspicillata, Mustelidae): towards an adaptive conservation management of the species
Fig. 3. A, Lutrogale perspicillata network computed using haplotypes (h) from the 305 bp-long sequence alignment (modern + museum DNA and GenBank entries). A scale to infer the number of sequences for each pie (i.e., haplotype) was provided together with a length bar to compute the number of mutational changes. The colour of each country and the number of each haplotype are indicated. See Table S1 for more details. B, Mismatch Distributions (MD) of the mtDNA pairwise differences (dotted: observed; line: expected) calculated for South East Asia haplogroup (Fig. 3A). Estimates of FS and R2 statistics (with related P values), r (raggedness index) and the outcome of SSD and SSD* test under a model (H0) of sudden demographic and spatial population expansion, respectively, are provided.
Fig. 2 in Spatial genetic structure in the vulnerable smooth-coated otter (Lutrogale perspicillata, Mustelidae): towards an adaptive conservation management of the species
Fig. 2. Photos of MNHN-ZM-MO-2001-350, L. p. perspicillata holotype resident in the mammal collection of the National Museum of Natural History of Paris, France. A, right side, lateral view (bar length = 20 cm); B, left forelimb, lateral view; C, basement, in French "Lutra perspicillata = Lutra leptonix Horsf., loutre de Java par m Diard, mai 1821, la tête est au lab d'anatomie", which can be translated into and interpreted as: "Lutra perspicillata = Lutra leptonix (Horsfield, 1824), Java otter from M. Diard, May 1821, skull is in the lab of anatomy" (see also Material and Methods). Photos courtesy and copyright: © MNHN - RECOLNAT - Laura Flamme - 2014.
Fig. 1 in Spatial genetic structure in the vulnerable smooth-coated otter (Lutrogale perspicillata, Mustelidae): towards an adaptive conservation management of the species
Fig. 1. Lutrogale perspicillata distribution (in yellow; see insets for Iraq and Pakistan) including sampling localities of modern (white circles) and museum (green squares) individuals. As far as the latter are concerned, we reported only sites for which samples were successfully investigated (see Table S1 for the entire sample size of this study; symbol "?" stands for unknown locality). The white stars indicate, in Iraq, the locality (TaqTaq, Kurdistan) where the sample of Omer et al. (2012) was collected, in Cambodia/Thailand and Malaysia, the country/ies of origin of EF472348 and KY117557 GenBank sequence, respectively. In Iraq, Pakistan, and supposedly Java, Indonesia, the green squares indicate localities (when known) of L. p. maxwelli, L. p. sindica, and L. p. perspicillata museum holotypes, respectively. Finally, Naga Hills at the border between Myanmar and India as well as Bahoo-Kalat River Basin between Iran and Pakistan are indicated (see text for more details). The species' geographic range was adapted from IUCN (International Union for Conservation of Nature) 2015. Lutrogale perspicillata. The IUCN Red List of Threatened Species 2019-3 was modified using CorelDraw!12 (2003). Digital images (insets) were obtained from Google Earth 7.1.5.1557 (2015 Google Inc.) and Google Earth map data (Data SIO, NOAA, U.S. Navy, NGA, GEBCO - Image Landsat). Please note that thick dotted lines mark out new borders for L. p. sindica and L. p. perspicillata subspecies as established in this study (see text for more details).
Fig. 3 in Spatial and temporal structure of fish assemblages in a hyperhaline coastal system: Ría Lagartos, Mexico
Fig. 3. Non-metric multi-dimensional scaling ordination plot derived from a Bray-Curtis similarity matrix constructed from the fish abundance data (log X + 1 transformation) in different types of habitat (a) and climatic seasons (b) in the Ría Lagartos system between October 2004 and December 2008. hyperhaline: H, rocky: R, seagrass: S, channel: C, marine: M. %: Rainy, % Northerlies, + Dry.
Fig. 4 in Spatial and temporal structure of fish assemblages in a hyperhaline coastal system: Ría Lagartos, Mexico
Fig. 4. Canonical Correspondence Analysis bi-plot. Length and direction of arrows indicate the relative importance and force of change in the environmental variables. CCA axis 1 and CCA axis 2 had eigen values of 0.629 and 0.214 respectively. The habitats and seasons are indicated. Only the species approximately 90% of IVI are shown. Species abbreviations are the first letter of the genus name and first four letters of the species name. DO: dissolved oxygen.
Fig. 2 in Spatial and temporal structure of fish assemblages in a hyperhaline coastal system: Ría Lagartos, Mexico
Fig. 2. Spatial (type of habitat) and seasonal variations in temperature, salinity, and dissolved oxygen (±1 STD).
Fig. 1 in Spatial and temporal structure of fish assemblages in a hyperhaline coastal system: Ría Lagartos, Mexico
Fig. 1. Study area showing the Ría Lagartos Lagoon system, Mexico and sampling stations. Letters show different types of habitats and numbers are replicas in the same habitat. (hyperhaline: H, rocky: R, seagrass: S, channel: C, and marine: M).
Fig. 7. a in Ecomorphological patterns of the fish assemblage in a tropical floodplain: effects of trophic, spatial and phylogenetic structures
Fig. 7. a) Diagram of Canonical Discriminant Analysis for the ecomorphological indices of the fish assemblage grouping in habitat types in the upper Paraná River floodplain (rivers, channels, connected and disconnected lagoons). b) Histograms with the scores of the habitat types for Canonical axis 1.
Fig. 6 in Ecomorphological patterns of the fish assemblage in a tropical floodplain: effects of trophic, spatial and phylogenetic structures
Fig. 6. Diagram of Canonical Discriminant Analysis for the ecomorphological indices of the fish assemblage grouping in trophic guilds in the upper Paraná River floodplain (detritivores, insectivores, piscivores, invertivores, omnivores and herbivores).
Fig. 4 in Ecomorphological patterns of the fish assemblage in a tropical floodplain: effects of trophic, spatial and phylogenetic structures
Fig. 4. Distribution of scores centroids of the 35 species on the first two axes of the Principal Components Analysis (PC 1 and PC 2), applied to the correlation matrix (Pearson) formed by 22 ecomorphological indices. Each polygon defines the morphological space occupied by the species that compose the corresponding trophic guild.
ScienceDex guides
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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.