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563 results for “genetic differentiation”
Fig. 4 in Temporal And Spatial Pattern Of Genetic Differentiation In Isophya Kraussi (Orthoptera: Tettigonoidea) In Ne Hungary
Fig. 4. Results of the PCA analysis for the two samples collected in the Mogyoróskuti meadows in 1999 and in 2001; i.e. temporalvariation within a population. The points represent the genotypic
Fig. 3 in Temporal And Spatial Pattern Of Genetic Differentiation In Isophya Kraussi (Orthoptera: Tettigonoidea) In Ne Hungary
Fig. 3. Results of the PCA analysis for the three distinct populations; i.e. spatial variation (Karst region: Mogyoróskuti meadows, Haragistya; Zemplén Mts.: Gyertyánkúti meadows). The points represent
Fig. 1 in Temporal And Spatial Pattern Of Genetic Differentiation In Isophya Kraussi (Orthoptera: Tettigonoidea) In Ne Hungary
Fig. 1. Sample sites. Aggtelek Karst region: Haragistya near Aggtelek (1), Mogyoróskuti meadows near Jósvafő (2); Zemplén Mts.: Gyertyánkúti meadows near Telkibánya (3)
Fig. 2 in Genetic Differentiation And Linkage Disequilibrium In A Spatially Fragmented Population Of Cheilosia Vernalis (Diptera: Syrphidae) From The Balkan Peninsula
Fig. 2. Standardized variance of allelic frequencies FST (open symbols) and genetic distance D (NEI 1978) (filled symbols) plotted against corresponding geographic distance between subpopulation pairs of Cheilosia vernalis: Durmitor-Morinj (75 km), Fruška Gora- Durmitor (240 km), and Fruška Gora-Morinj (306 km). Pearson correlation coefficients between geographic distance and FST and D
Fig. 1 in Genetic Differentiation And Linkage Disequilibrium In A Spatially Fragmented Population Of Cheilosia Vernalis (Diptera: Syrphidae) From The Balkan Peninsula
Fig. 1. Map of Serbia and Montenegro showing sampling sites for the studied subpopulations of Chelosia vernalis, and genotype distribution at the Pgm locus. The Pgm locus was the most variable locus in the surveyed subpopulations, and along with differences of allele frequency variances at the
Fig. 4 in MORPHOLOGICALAND GENETIC DIFFERENTIATION OF SAXIFRAGA HIRCULUS L. (SAXIFRAGACEAE) POPULATIONS IN LITHUANIA Edita Meškauskaitė, Donatas Naugžemys, Donatas Žvingila, Jonas Remigijus
Fig. 4. UPGMA dendrogram of genetic relationships among 20 S. hirculus individuals from Merkinė population
FIGURE 5 in Genetic differentiation through dispersal and isolation in two freshwater fish species from coastal basins of Northeastern Brazil
FIGURE 5 | Time-calibrated phylogeny for samples of Prochilodus lacustris from Maranhão coastal basins and Tocantins basin. Time is in thousands of years. The color bars in the tree correspond to the populations presented in Fig. 3A.
FIGURE 4 in Genetic differentiation through dispersal and isolation in two freshwater fish species from coastal basins of Northeastern Brazil
FIGURE 4 | Time-calibrated phylogeny for samples of Schizodon dissimilis from coastal basins of northeastern Brazil. Time is in thousands of years. The color bars in the tree correspond to the populations presented in Fig. 2A.
FIGURE 3 in Genetic differentiation through dispersal and isolation in two freshwater fish species from coastal basins of Northeastern Brazil
FIGURE 3 | Distribution and haplotype structure of Prochilodus lacustris. A. Paleodrainage reconstruction of coastal basins from northeastern Brazil and geographic distribution of groups defined by SAMOVA. B. Estimate of the probable groups of populations produced by the BAPS. C. Haplotypes networks of mtDNA control region. All analysis recovered a total of six groups in this area, but the SAMOVA do not identify the same groups that other analysis. The colors used to highlight areas in the network correspond to populations in map.
Fig. 2 in Morphological and genetic data suggest a complex pattern of inter-island colonisation and differentiation for mining bees (Hymenoptera: Anthophila: Andrena) on the Macaronesian Islands
Fig. 2 Median Joining network of Andrena species (Micrandrena) of the Canary Islands and the Madeira Archipelago. Circle size is relative to number of haplotype copies present in dataset. A branch represents a single nucleotide change (mutation); bars on branches represent inferred missing haplotypes (single nucleotide changes). LG La Gomera, LP La Palma. The colours correspond to those used to represent the location of species on islands in Fig. 1
Fig. 3 in Morphological and genetic data suggest a complex pattern of inter-island colonisation and differentiation for mining bees (Hymenoptera: Anthophila: Andrena) on the Macaronesian Islands
Fig. 3 Dated species tree demonstrating the phylogenetic relationships of the different island populations calculated with *BEAST compared to the outgroup species Andrena enslinella, A. subopaca, A. minutuloides, and A. semilaevis (all Micrandrena); dating is based
Fig. 6 in Morphological and genetic data suggest a complex pattern of inter-island colonisation and differentiation for mining bees (Hymenoptera: Anthophila: Andrena) on the Macaronesian Islands
Fig. 6 Correlation of genetic distance (ΦST) and A squared Mahalanobis distance of morphometric data (r2= 0.18) and B Euclidian distance of qualitative morphological data (r2= 0.12)
Fig. 1 in Morphological and genetic data suggest a complex pattern of inter-island colonisation and differentiation for mining bees (Hymenoptera: Anthophila: Andrena) on the Macaronesian Islands
Fig. 1 Location of the Azores, the Archipelago of Madeira, the Selvagens Islands, the Canary Islands, and Cape Verde (a). Close view to the islands of the Madeira Archipelago (b) and the Western Canary Islands (below, right) (c). Tenerife is characterised by the regions of Anaga, Teno, Las Cañadas/Teide, and Dorsal Rift. The taxa of the A. wollastoni group (Kratochwil, 2020) and the centres of their distri-
Fig. 2 in Genetic and morphological differentiation among populations of the narrowly endemic and karst forest-adapted Pilea pteridophylla (Urticaceae)
Fig. 2 Morphological variation among individuals of Pilea pteridophylla sampled along its distribution range in the tropical karst forest of southern Mexico. Plot of individual scores for the first two components of the principal component analysis using morphological data. Coloured symbols represent the two populations recognized for the species: red circles, Tabasco; and blue circles, Chiapas. Ellipses correspond to the 95% confidence intervals estimated for each population. The lines represent the dispersion of the individuals within each population
Fig. 3 in Genetic and morphological differentiation among populations of the narrowly endemic and karst forest-adapted Pilea pteridophylla (Urticaceae)
Fig. 3 Statistical parsimony networks of rps16-trnQ, trnL-trnF and rps16-trnQ + trnL-trnF dataset using the gaps as missing data. Coloured symbols represent the two populations recognized for the species: red circles, Tabasco; and blue circles, Chiapas. Open-white circles represent the number of mutational steps between haplotypes. The size of the circles is proportional to the frequency of each haplo-
Fig. 1 in Genetic and morphological differentiation among populations of the narrowly endemic and karst forest-adapted Pilea pteridophylla (Urticaceae)
Fig. 1 Mountain karst forests of Mexico and the studied species Pilea pteridophylla A. K. Monro (Urticaceae). A Geographic distribution of the Mountain karst forests of Mexico. B Individual from the Chiapas population. C Individual from the Tabasco population
Figure 2. A in Genetic differentiation of the Meriones tristrami (Mammalia: Rodentia) subpopulations in Turkey - inferring allozyme variations
Figure 2. A dendrogram summarizing the genetic relationships of M. tristrami subpopulations (NTSYSpc options: Coefficient: SM (SimQual), clustering method: UPGMA) (see Nei, 1978) (Mt1 = Gaziantep, Adana, Mt2 = Central Anatolia (Cihanbeyli/Konya, Sivrihisar/Eskişehir), Mt3 = Denizli, Mt4 = Şanlıurfa, Mt5 = Iğdır, Mt6 = Tosya/Kastamonu, Mt7 = Karadağ/Karaman, Mt8 = Turgutlu/Manisa).
Figure 1 in Genetic differentiation of the Meriones tristrami (Mammalia: Rodentia) subpopulations in Turkey - inferring allozyme variations
Figure 1. Map of the locations of the M. tristrami specimens in Turkey (Gaziantep, Adana: Mt1; Central Anatolia (Cihanbeyli/Konya, Sivrihisar/Eskişehir): Mt2; Denizli: Mt3; Şanlıurfa: Mt4; Iğdır: Mt5; Tosya/Kastamonu: Mt6; Karadağ/Karaman: Mt7; Turgutlu/ Manisa: Mt8).
Figure 2 in A genetic method to differentiate Petromyzon marinus ammocoetes from those of the paired species Lampetra fluviatilis and L. planeri
Figure 2. - PCR-RFLP results of the 560 bp Cytb fragment using HinfI restriction enzyme. Pistes 1-2: L. fluviatilis; 3: Ladder 100 bp; 4 to 6: L. planeri; 7 to 11: P. marinus; 12: Ladder Bacteriophage lambda digested by EcoR1- HindIII.
Figure 1. - Study stream locations where lamprey were collected. 1 in A genetic method to differentiate Petromyzon marinus ammocoetes from those of the paired species Lampetra fluviatilis and L. planeri
Figure 1. - Study stream locations where lamprey were collected. 1: Gironde basin; 1a: Livenne River; 1b: Dronne River; 1c: Dordogne River; 1d: Cere River; 2: Loire basin; 3: Meuse basin.
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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)
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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.