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42 results for “compositional variability”
Ectomycorrhizal community composition associated with Nothofagus pumilio seedlings harvested from Variable Retention treatments at Los Cerros Ranch, Tierra del Fuego, Argentina.
This dataset contains data on Nothofagus pumilio seedlings sampled from a Variable Retention (VR) managed forest in Tierra del Fuego, Argentina seven years after harvesting. We evaluated the effects of a VR timber management system on the EMF community associated with N. pumilio seedlings. We quantified the abundance, composition, and diversity of EMF across aggregate (AR) and dispersed retention (DR) sites within a VR managed area and compared them to primary forest (PF) stands. EMF assemblage and taxonomic identities were determined by ITS-rDNA sequencing of individual root tips sampled from 280 seedlings across three landscape replicates of each VR treatment. To better understand seedling performance, we tested the relationships between fungal colonization, fungal taxonomic composition, seedling biomass, and VR treatment across our study sites. This data was collected as a comparative component to a larger project understanding the effect of mycorrizhae on seedling success after various disturbances such as logging and fire that was ongoing at the Bonanza Creek LTER and other arctic locations.
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable.
Fig. 4 in Variability Of The Phenotypic Composition Of Cepaea Hortensis (Gastropoda, Helicidae) In Western Ukraine: In Space And Time
Fig. 4. Theoretical ratio of genotype frequencies among carriers of the recessive allele (presence of bands) depending on the percentage of banded individuals at the site.
Fig. 2 in Variability Of The Phenotypic Composition Of Cepaea Hortensis (Gastropoda, Helicidae) In Western Ukraine: In Space And Time
Fig. 2. The number of studied sites in Lviv with different frequencies of banded, yellow and white unbanded shells. The numerals on the abscissa indicate the frequency intervals: 0 — absent, 1 — up to 10 %, 2 — from 11 to 20 %, etc.
Fig. 1 in Variability Of The Phenotypic Composition Of Cepaea Hortensis (Gastropoda, Helicidae) In Western Ukraine: In Space And Time
Fig. 1. Location of the sites in Lviv used for the quantitative study of the shell coloration variability of C. hortensis in 1999–2004 (A) and in 2015–2021 (B).
Figure 8 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 8. Relationships between values of distribution coefficients (Kd Shell/Water Zn (A), Kd Shell/Water Sr (B) and Kd Shell/Water Al (C)) and principal component 1, revealed from the shell shape analysis of Beringiana beringiana. For the numbers of localities see caption for Figure 7.
Figure 6. Normed PCA factorial graph F1 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 6. Normed PCA factorial graph F1 × F2 of two revealed geographical groups of Beringiana beringiana samples (blue circles indicate samples from Primorsky Krai, Kunashir, Sakhalin, and Iturup islands; red circles indicate samples from Kamchatka Peninsula). Ellipses show 95 % confidence interval.
Figure 5 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 5. Relationships between Kd Shell/Sediment Al and Kd Shell/Water Al: (1) Peschanoye Lake, Kunashir Island, (2) Bolshoye Vavayskoye Lake, Sakhalin Island, (3) Vaskovskoye Lake, Primorsky Krai, (4) Lebedinoe Lake, Iturup Island, (5) Kurazhechnoye Lake, Kamchatka Peninsula, (6) Khalaktyrskoye Lake, Kamchatka Peninsula, (7) Chernoye Lake, Sakhalin Island.
Figure 4 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 4. Median joining networks of Beringiana beringiana (N = 50) based on the COI gene fragment. The circle size is proportional to the number of available sequences belonging to the given haplotype (the smallest = 1 sequence). The red numbers near branches indicate the number of nucleotide substitutions between haplotypes.
Figure 2 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 2. Shells of Beringiana beringiana specimens, collected from studied waterbodies: A – Kurazhechnoye Lake, Kamchatka Peninsula (voucher number RMBH biv1212/2), B – Vaskovskoye Lake, Primorsky Krai (RMBH biv1181/1), C – Lebedinoye Lake, Iturup Island (RMBH biv1254/2), D – Bolshoye Vavayskoye Lake, Sakhalin Island (RMBH biv1250/1), E - Peschanoye Lake, Kunashir Island (RMBH biv1251/1), F – Khalaktyrskoye Lake, Kamchatka Peninsula (RMBH biv1213/3). Scale bar = 50 mm.
Figure 1 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 1. Map of the studied region. Lakes: (1) Kurazhechnoye, Kamchatka Peninsula (n = 5), (2) Khalaktyrskoye, Kamchatka Peninsula (n = 5), (3) Lebedinoye, Iturup Island (n = 3), (4) Peschanoye, Kunashir Island (n = 3), (5) Chernoye, Sakhalin Island (n = 3), (6) Bolshoye Vavayskoye, Sakhalin Island (n = 3), (7) Vaskovskoye, Primorsky Krai (n = 3). n reveals the number of collected shells of Beringiana beringiana.
Figure 3. Principal components 1 and 2 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 3. Principal components 1 and 2 visualized by drawing synthetic outlines of extreme (±2SD) and mean shell shapes of Beringiana beringiana. The umbo position marked by star.
Figure 11 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 11. Relationships between values of KAl and longitudinal cross-sectional area of shell (mm2), d Shell/Sediment revealed from the shell shape analysis of Beringiana beringiana. For the numbers of localities see caption for Figure 7.
Figure 7 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 7. Relationships between Ca-normalized concentration of zinc in shells and principal component 1, (A), copper in shells and principal component 1 (B), revealed from the shell shape analysis of Beringiana beringiana: (1) Peschanoye Lake, Kunashir Island, (2) Lebedinoe Lake, Iturup Island, (3) Bolshoye Vavayskoye Lake, Sakhalin Island, (4) Kurazhechnoye Lake, Kamchatka Peninsula, (5) Vaskovskoye Lake, Primorsky Krai, (6) Khalaktyrskoye Lake, Kamchatka Peninsula.
Figure 10 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 10. Relationships between values of Kd Shell/Water Al (A), Kd Shell/Water P (B), Kd Shell/Water Fe (C) and longitudinal cross-sectional area of shell (mm2), revealed from the shell shape analysis of Beringiana beringiana. For the numbers of localities see caption for Figure 7.
Figure 9 in Impact of aquatic habitat environment on the elemental composition and shell shape variability of the Beringian freshwater mussel Beringiana beringiana (Bivalvia, Unionidae)
Figure 9. Relationships between Zn: Ca – ratio in Beringiana beringiana shells and longitudinal cross-sectional area of shell (mm2), revealed from shell shape analysis of Beringiana beringiana. For the numbers of localities see caption for Figure 7.
Raw data used in the manuscript titled "Metabolomic Analysis of Histological Composition Variability of High-Grade Serous Ovarian Cancer Using 1H HR MAS NMR Spectroscopy "
<p>The folder contains raw data used in the manuscript titled "Metabolomic Analysis of Histological Composition Variability of High-Grade Serous Ovarian Cancer Using <sup>1</sup>H HR MAS NMR Spectroscopy ".</p> <p> </p> <p> Raw data measured on Bruker Avance III 400 MHz NMR spectrometer:</p> <p>- 1D <sup>1</sup>H HR MAS NMR spectra (path: <em>Patient_code – Sample_code/500/fid</em>)</p> <p>- 2D <sup>1</sup>H-<sup>1</sup>H J-resolved HR MAS NMR spectra (path: <em>Patient_code – Sample_code/600/ser</em>).</p> <p> </p> <p>Metadata is included in <em>Metadata.xlsx</em> file.</p> <p>Each sample is described with the following parameters:</p> <p>- patient code (after anonymization),</p> <p>- sample code (the label <em>l</em> or <em>r</em> denotes the <em>left</em> or <em>right</em> ovary in patients from whom samples were obtained bilaterally),</p> <p>- sample weight,</p> <p>- clinic-pathological parameters (such as: age, BMI, menopausal status, diagnosis, FIGO stage),</p> <p>- percentage tissue content obtained from histopathological analysis after HR MAS NMR studies (cancer cells, epithelial compartment within benign tumors, necrosis, inflammation, fibrosis, calcification, normal ovary, vessels, fatty tissue).</p> <p> </p> <p>Some samples were considered representative of particular tissue components:</p> <p>- cancer (HGSOC) compartment,</p> <p>- fibrotic stroma within malignant (HGSOC) tumors,</p> <p>- fibrotic stroma within benign tumors,</p> <p>- normal ovary tissue (the samples collected from the control group),</p> <p>- normal ovary tissue (the samples collected from the cancer patients),</p> <p>- necrosis,</p> <p>- non-tumoral fibrous tissue / fibrous tumor capsule (obtained from the patients with benign non-neoplastic lesions)</p> <p>- corpus albicans</p> <p>The assignment of the samples to these categories is indicated in the column <em>Tissue components.</em></p> <p><em> </em></p> <p>The samples classified as outliers in PCA model 1 are indicated in the column <em>Outliers</em>.</p> <p>The samples included in multivariate models are indicted in the columns: <em>PCA 2, PCA 3, PCA 4, PCA 5, PCA 5a, PCA 6, OPLS-DA 1, OPLS-DA 2, OPLS-DA 3, OPLS-DA 4, OPLS-DA 5, OPLS-DA 6 and OPLSR.</em></p> <p><em> </em></p>
Social vulnerability to flooding in Ecuador : input variables, PCA vs Expert composite indices
<p><strong>Social vulnerability indices are used to better understand and predict the consequences of disasters, and support the development of improved disaster management policies. This research specifically supports the Ecuadorian Red Cross in generating a flood-specific social vulnerability index to inform flash flood early action protocol.</strong></p> <p>The dataset presents the results from the analysis of the social vulnerability to flooding in Ecuador, from individual input variables to the composite indices outputs. The results are available at the Parroquia level in Ecuador (admin level 3), for 1032 Parroquia excluding the Galapagos Islands.</p> <ul> <li>The dataset comprises, for each Parroquia, the estimation of <strong>15 variables characterizing the social vulnerability to flooding specific to Ecuador context</strong>. The variables are selected from literature review and consultation with Ecuadorian Red Cross disaster practitioners : <em>Disability, Poverty incidence, Gini Index, Agricultural labor share, Vectorborne disease incidence, Waterborne disease incidence, Social Security affiliation, Education level, Sanitation, Driking water access, Power access, Road travel time, Wall structure, Mobile access and Internet access.</em> All variables are normalized from 0 to 1, directed toward increasing vulnerability, and renamed accordingly.</li> <li>In addition, the <strong>Administrative level names, PCODE, calculated Area, population density,</strong> as well as related <strong>sub-regions</strong> are also referenced.</li> <li>Individual variables are integrated into <strong>composite vulnerability indices</strong>, using two different approaches: i) the Principal Component Analysis approach, using the first component <strong>PCA(n=1) </strong>and the first 5 components <strong>PCA(n=5)</strong> separately ; ii) the <strong>expert judgement weighting</strong> of the variables. The output composite indices, normalized from 0 to 1 are presented in 3 separated columns.</li> </ul> <p> </p>
Supporting Information from The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)
<p>Supporting Information (Open Data) from The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)</p>
Data from: Resource partitioning among pelagic predators remains stable despite annual variability in diet composition
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.