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613 results for “stress analysis”
Stress analysis and Q-factor of free-standing (La,Sr)MnO3 oxide resonators (Dataset)
<p>Datafiles of the article "Stress analysis and Q-factor of free-standing (La,Sr)MnO3 oxide resonators"</p>
Data archive for Pepper, Bateson and Nettle, 'Telomeres as integrative markers of exposure to stress and adversity: A systematic review and meta-analysis'
<p>Data archive for the paper 'Telomeres as integrative markers of exposure to stress and adversity: A systematic review and meta-analysis' by Gillian Pepper, Melissa Bateson and Daniel Nettle. This version was uploaded in July 2018 after peer-review in the journal Royal Society Open Science. Compared to earlier version, it incorporates some minor error correction to the dataset, and reflects the revised analyses we performed after peer review. </p> <p>Our protocol and recording guide, which were preregistered on the Open Science Framework in 2016, are also included here, as is our PRISMA diagram.</p> <p>The data file 'unprocessed data' contains the data as extracted from the literature, with associations shown both as provided in the original papers, and converted to correlation coefficients. The algorithms for converting all the different associations to correlation coefficients are described in the flowchart and implemented in the R script 'effect conversion algorithms.r'.</p> <p>The data file 'processed data.csv' is the dataset analysed in the paper. Compared to 'unprocessed data.csv', it excludes: associations from studies of non-human animals; duplicate associations; a small number of associations from studies of medical treatments; and associations considered subparts or subscales of other associations. These exclusions are outlined in Methods section of the paper. In addition, in the processed data file, all correlations are aligned in direction so as to make them comparable (variable 'ValencedEffect'); and all associations are assigned to broad and fine categories.The script 'unprocessed to processed.r' makes the processed data file from the unprocessed one, or you can simply work from the processed one directly. </p> <p>The R script 'telomere metanalysis script RSOS REVISED.r' reproduces the analyses found in the paper.</p> <p>This version of the archive (July 17 2018) contains one small correction in the data files compared to all earlier versions. </p>
Stress-Strain Analysis of Polycrystalline Copper with Goss Texture Using Crystal Plasticity FEM
<pre>Stress-strain analysis of single-phase polycrystalline copper with a Goss texture using a cubic representative volume element (RVE) and periodic boundary conditions, performed with Abaqus through the crystal plasticity finite element method.</pre>
Supplementary data: Computational analysis of mechanical stress in colonic diverticulosis
<p>The data set contains code, source data, and derivatives data for the results presented in our research paper titled "Computational analysis of mechanical stress in colonic diverticulosis".</p> <p>The "code" contains Abaqus (SIMULIA, Providence, RI) files for the simulations presented in the paper (tested with Abaqus version 6.13) and jupyter notebooks developed to analyze simulation results.</p> <p>The "sourcedata" folder contains Excel files with parameters used for the simulations as well as data directly extracted from the simulation results.</p> <p>The "derivatives" folder contains secondary data calculated based on the files from "sourcedata".</p> <p>The README document included in the dataset contains a more detailed description of the files and folders.</p>
Arctic/Antarctic Ocean-Surface Stress Analysis, 2011-2021/2013-2021
<p>This record contains data related to article "Constructing Satellite-based Ocean-surface Stress and Ekman Circulation in the Arctic and Antarctic Oceans". It offers a high-resolution, daily analysis of ocean-surface stress and Ekman circulation over the Arctic and Southern Ocean, derived from multiplatform satellite observations.</p> <p>All data are projected onto a 25 km EASE2 grid with daily resolution. The dataset (netcdf) contains the following variables:</p> <p>- zonal components of ocean-surface stress (TAUx, N/m2)</p> <p>- meridional components of ocean-surface stress (TAUy, N/m2)</p> <p>- magnitude of ocean-surface stress (TAU, N/m2)</p> <p>- uncertainty estimates for TAUx (N/m2)</p> <p>- uncertainty estimates for TAUy (N/m2)</p> <p>- Ekman Pumping Rate (m/s)</p> <p>- Land mask</p> <p>- Longitude</p> <p>- Latitude</p> <p>L.Yu acknowledges the support of the NASA Vector Wind Science Team program for this research.</p> <p> </p>
Impact of heat stress on the fitness outcomes of symbiotic infection in aphids: a meta-analysis
<p>This is the dataset for the article "<em><strong>Impact of heat stress on the fitness outcomes of symbiotic infection in aphids: a meta-analysis</strong></em>". </p> <p>Beneficial symbiosis shape their host eco-evolutionary responses. Here we show how the responses of insect-microbe associations may be modulated by rising temperatures. The outcome of the symbiotic association is therefore temperature-dependent, but also trait-dependent. We show the importance of better understanding the cost-benefits balance of insect-microbe associations faced with climate change.</p>
Genome-wide analysis identified candidate variants and genes associated with heat stress adaptation in Egyptian sheep breeds
<p>The current study was conducted from 2009 to 2019 in three hot and dry agroecological zones in Egypt: Western Desert coastal zone, New Valley desert oasis, and hot-dry Upper Egypt. Within these zones, three local sheep breeds were studied: Barki (83 ewes), Wahati (55 ewes) and Saidi (68 ewes). During the study period, the animals exercised under natural heat stress (simulating summer grazing on poor pasture). Meteorological and physiological parameters were measured and recorded. The heat tolerance index of the animals was calculated to identify animals with high and low heat tolerance based on the animals' response to the five main physiological parameters (scale from 0 to 5). DNA samples were extracted for genomic analysis. The genetic diversity measurements showed a significant influence of breed and location on the populations. The influence of breed is more significant than that of location. The inbreeding analysis shows that the desert breeds (Wahati and Barki) have lower values than the urban breed (Saidi). The high rate of sub-clustering indicates the process of sub-population through inbreeding pressure. Wahati and Barki are very distinct breeds with strong identification, while Saidi breed has crosses with other breeds. The most significant SNPs associated with heat tolerance were found in MYO5A, PRKG1, GSTCD, and RTN1 genes (P < 0.0001). MYO5A had an effect of 0.74 on the trait heat tolerance in the studied population. It produces a protein that is widely distributed in the melanin-producing neural crest of the skin. Genetic association between genetic and phenotypic variations showed that OAR1 18300122.1, located in ST3GAL3, had the greatest positive effect on heat tolerance. GWAS analysis identified SNPs associated with heat tolerance in the PLCB1, STEAP3, KSR2, UNC13C , PEBP4, and GPAT2 genes.</p>
Determination of traits responding to iron toxicity stress at different stages and genome-wide association analysis for iron toxicity tolerance in rice (Oryza sativa L.)
<p>This vcf file constitute underlying raw data material for the manuscript "Determination of traits responding to iron toxicity stress at different stages and genome-wide association analysis for iron toxicity tolerance in rice (Oryza sativa L.)". <br> The SNP genotype data came from a whole-genome resequencing and were called using the Nipponbare IRGSP 1.0 rice reference genome. SNPs with a miss rate greater than 30% and minor allele frequency (MAF) less than 5% were removed. Heterozygous alleles were also excluded. Finally, 160,498 SNPs were selected and used in the GWAS analysis. </p>
Figure 7 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis
Figure 7. Results from the sensitivity analysis depicting variations in the overall effect size estimates (mean ± 95% confidence intervals [CIs]) of water-stress effects on (A) weed germination/emergence, (B) seedling radicle/root length, (C) plant height, and (D) leaf area when a particular study is omitted from the analysis. The vertical black solid and dashed lines represent overall effect sizes (mean ± 95% CIs) with all studies included.
Figure 3 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis
Figure 3. Overall water-stress effects on germination/emergence of grass and broadleaf weeds (top) and six weed families—Asteraceae, Fabaceae, Convolvulaceae, Amaranthaceae, Rubiaceae, and Poaceae (bottom). The vertical black dashed line represents zero effect. The black dots are overall mean effect sizes, and the black lines are 99% confidence intervals (CIs).The values in parentheses are the number of observations followed by the number of studies for each pair-wise comparison. The mean effect sizes were considered significantly different when their 99% CIs did not include zero.
Figure 4 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis
Figure 4. The log response ratio for germination and seedling radicle length of broadleaf (green dots/line) and grass (red dots/line) weed species as a function of water-stress intensity. Water stress increased as solution osmotic potential (ψsolution) decreased and vice versa.The subgroups for germination are 0 to −0.2, −0.2 to −0.4, −0.4 to −0.6, −0.6 to −0.8, −0.8 to −1.0, −1.0 to −1.4, and <−1.4 MPa, while the subgroups for radicle length are 0 to −0.2, −0.2 to −0.4, −0.4 to −0.6, −0.6 to −1.0, and <−1.0 MPa. Only ψsolution-based studies were used in this analysis. For each subgroup, the solid dots and lines represent mean effect sizes and their corresponding 99% confidence intervals (CIs).The mean effect sizes were considered significantly different when their 99% CIs did not include zero. Similarly, the water-stress effects were significantly different for each subgroup and among weed types only when their 99% CIs did not overlap with one another. The fitted lines represent a four-parameter logistic regression model, and the coefficients of the models are presented in Table 2.
Figure 1 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis
Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; Page and McKenzie 2021) flow diagram highlighting the selection procedure of 86 scientific published papers included in the meta-analysis.
Figure 8 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis
Figure 8. Results from the sensitivity analysis depicting variations in the overall effect size estimates (mean ± 95% confidence intervals [CIs]) of water-stress effects on (A) branches/tillers per plant, (B) leaves per plant, (C) inflorescences per plant, (D) seeds per plant, (E) total biomass, (F) root biomass, (G) shoot biomass, and (H) root:shoot ratio, when a particular study is omitted from the analysis. The vertical black solid and dashed lines represent overall effect sizes (mean ± 95% CIs) with all studies included.
Figure 6 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis
Figure 6. Density plots depicting the distribution of the individual effect sizes for all 12 response variables considered in this meta-analysis: (A) weed seed germination/emergence; (B) radicle/root length, plant height, and leaf area; (C) branches/tillers per plant, leaves per plant, inflorescences per plant, and seeds per plant; and (D) total biomass, root biomass, shoot biomass, and root:shoot ratio.
Figure 2 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis
Figure 2. Overall water-stress effects on weed germination/emergence, growth characteristics, and seed production. The vertical black dashed line represents zero effect. The black dots are overall mean effect sizes, and the black lines are 95% confidence intervals (CIs). The values in parentheses are the number of observations followed by the number of studies for each pair-wise comparison. The mean effect sizes were considered significantly different when their 95% CIs did not include zero.
Figure 5 in Effect of water stress on weed germination, growth characteristics, and seed production: a global meta-analysis
Figure 5. The log response ratio for weed growth characteristics (plant height, leaf area, branches/tillers per plant, leaves per plant,root biomass, shoot biomass, and root:shoot ratio) and seed production (inflorescences per plant and seeds per plant) as a function of water-stress intensity. Water stress increased as soil moisture (% field capacity) decreased and vice versa. The green and red dots represent broadleaf and grass weed species, respectively. The solid black points and the lines represent mean effect sizes and their 99% confidence intervals (CIs) for low (>60%), moderate (30%–60%), and severe (<30% field capacity) water-stress subgroups. The mean effect sizes were considered significantly different when their 99% CIs did not include zero. Similarly, the water-stress effects were significantly different for each subgroup and among weed types only when their 99% CIs did not overlap with one another.
MADFORWATER: WP1: Water and water-related vulnerabilities in Egypt, Morocco and Tunisia: Task1.2: Analysis and mapping of water stress, water vulnerability and potential for water reuse in Egypt, Morocco and Tunisia: Subtask1.2.b: Data collection on water stress and vulnerability: Souss-Massa Region Subset
<p>This folder contains the dataset that I used to write my conference paper "Groundwater Resources Scarcity in Souss-Massa Region and Alternative Solutions for Sustainable Agricultural Development"</p>
Physiological Data Collected from smartwatch: EDA, Pulse Rate, and Skin Temperature for Stress and Fatigue Analysis
<p>The dataset contains multiple columns capturing both <strong>physiological and demographic data</strong>.<strong> Physiological data</strong>, collected using the <strong>Empatica EmbracePlus smartwatch,</strong> includes electrodermal activity (EDA), pulse rate, and skin temperature. These metrics provide insights into participants' stress and fatigue levels. Empatica's proprietary algorithms preprocess the raw data, extracting digital biomarkers and metrics that reflect the wearer's physiological and behavioral states. <strong>The processed data is aggregated on a per-minute basis.</strong></p> <p>Demographic information, such as age, gender, fitness level, and sleep duration from the previous night, is also included. Additionally, participants rated their perceived physical fatigue on the Borg scale (ranging from 6 to 20), offering a subjective measure of exertion during or after physical tasks.</p> <p>The dataset was collected during controlled simulations of industrial tasks in a fitness environment. These simulations involved repetitive activities, including weightlifting, resistance band exercises, and isometric tasks, designed to mimic the physical demands of industrial work. This approach allowed for the safe and effective study of physical fatigue. The resulting data provides valuable insights into the physiological responses associated with repetitive physical labor.</p>
Haplotype analysis of GWAS candidates identified for root:shoot ratio changes under salt stress in Arabidopsis
<p>The haplotype analysis was performed on 7 loci identified through GWAS by Magdalena Julkowska, while she was a PostDoc at KAUST, Saudi Arabia, workin in the lab of Dr. Mark Tester.</p>
"Analysis of near-field stresses in an analogue strike-slip fault model" Dataset
<p>This dataset contains both raw data and preliminary processed data. Overall, the dataset is divided into three parts: loading device data, internal strain brick data and VIC-2D data.</p>
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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)
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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.