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11,837 results for “Stress;”
Data presented in Stress regimes and dynamic implications of the southeastern margin of the Tibetan Plateau: Insights from a refined focal mechanism catalog
<p>The dataset includes thewaveform data required for source mechanism inversion presented in the paper Stress regimes and dynamic implications of the southeastern margin of the Tibetan Plateau: Insights from a refined focal mechanism catalog</p>
Data files for "Genomic-to-space measurements reveal global ocean nutrient stress"
<p>Data files to be used with the following code: https://github.com/ljustick/genomic_to_space_nut_stress</p>
Pelt biting as a practical indicator of social and environment stress in farmed red deer
<p>Data sets used in the analyses of paper title "Pelt biting as a practical indicator of social and environment stress in farmed red deer"</p>
Ruffed grouse stress-scape dataset
<p>Context</p> <p>Variability in temperature and snow cover are characteristics of high-latitude environments that impose significant pressures on overwintering species. To cope with increased energetic demands and decreased resources, species occupying seasonal environments often seek out refugia that buffer them from inclement conditions. Ruffed grouse (<i>Bonasa umbellus</i>) roosting in the thermally stable microhabitat beneath deep snow are buffered from negative effects of cold temperatures on physiological stress (glucocorticoid hormone levels).</p> <p>Objective</p> <p>Despite physiological advantages of accessing warmer refugia during winter, it is unknown how land cover and winter climate promote the occurrence of such refugia over space and time. Analogous to the landscape of fear, which mediates how prey navigate spatial variation in predation risk, mapping a landscape of stress, or stress-scape, may identify hotspots where metabolic challenges persist.</p> <p>Methods</p> <p>We assayed droppings for fecal corticosterone metabolites (FCMs) collected from radio-tagged ruffed grouse over three winters and developed a spatial model of FCM concentrations across the extent of our study area, thus quantifying a stress-scape.</p> <p>Results</p> <p>FCMs increased with shallower snow depths, less dense snow, colder ambient temperatures, and more open habitat. However, despite considerable spatiotemporal variation in snow depth, snow density, and temperature, the regions across the landscape where grouse had elevated FCM levels were consistent and predictable across years.</p> <p>Conclusions</p> <p>Stress-scapes offer a new tool for understanding and quantifying indirect effects of stressors and can identify areas of the landscape where there may be consistent hotspots of stress that are the result of multiple ecological and environmental challenges. </p>
Distinct and essential roles of bZIP transcription factors in stress response and pathogenesis in Alternaria alternata
<p>The ability to cope with environmental abiotic stress and biotic stress is crucial for the survival of plants and microorganisms, which enable them to occupy multiple niches in the environment. Previous studies have shown that transcription factors play crucial roles in regulating various biological processes including multiple stress tolerance and response in eukaryotes. This work identified multiple critical transcription factor genes, metabolic pathways and gene ontology (GO) terms related to abiotic stress response were broadly activated by analyzing the transcriptome of phytopathogenic fungus Alternaria alternata un- der metal ions stresses, oxidative stress, salt stresses, and host-pathogen interaction. We determined the biological functions and regulatory roles of the bZIP transcriptional factor (TF) genes in the phytopathogenic fungus A. alternata by analyzing targeted gene deletion mutants. Morphological analysis provides evidence that bZIPs including Gcn4, MeaB, Atf1, Hac1 and Ada1 are required for morphogenesis as the colony morphology of these gene deletion mutants was significantly different from that of the wild-type. In addition, bZIPs are involved in the resistance to multiple stresses such as oxidative stress (Ada1, Yap1, MetR) and virulence (Hac1, MetR, Yap1, Ada1) at varying degrees. Transcriptome data demonstrated that the inactivation of bZIPs (Hac1, Atf1, Ada1 and Yap1) significantly affected many genes in multiple critical metabolism pathways and gene ontology (GO) terms. Moreover, the ΔHac1 mutants displayed reduced aerial hypha and are hypersensitivity to endoplasmic reticulum disruptors such as tunicamycin and dithiothreitol. Transcriptome analysis showed that inactivation of Hac1 significantly affected the proteasome process and its downstream unfolded protein binding, indicating that Hac1 participates in the endoplasmic reticulum stress response through the conserved unfolded protein response. Taken together, our findings identified many crucial transcription factor genes and pathways related to cell development, abiotic stress response and pathogenesis, and expand our understanding of how microbial pathogens utilize these genes to deal with environmental stresses and achieve successful infection in the host plant.</p>
Demagnetization energy and internal stress in magnetite from temperature dependent hysteresis measurements - temperature hysteresis data
<p><strong>Data set of hysteresis data files for the <em>Geophysical Research Letters</em> article <em>"Demagnetization energy and internal stress in magnetite from temperature dependent hysteresis measurements"</em></strong></p> <p>MicroMag 2900/3900 Data Files (Series 0016.002) - The file name and description in the file provide the specimen information. Each file contains information on the instrument, settings, measurement, script, and the measured data: field, moment, and temperature.</p>
Molecular dynamics of solids at constant pressure and stress using anisotropic stochastic cell rescaling - dataset
<p>Supporting data related to manuscript "Molecular dynamics at constant pressure and stress using anisotropic stochastic cell rescaling"</p>
Trier Social Stress Test Protocol
<p>Components/procedure of Trier Social Stress Test. </p>
Underling data for "The Battery Life Estimation of a Battery under Different Stress Conditions"
<p>Each file reports the capacity trend as a function of the number of cycles, for NMC cells aged according to different protocols, as reported in the following table:</p> <table> <tbody> <tr> <td> <p><strong>Test Number</strong></p> </td> <td> <p><strong>Discharge Current (C-Rate)</strong></p> </td> <td> <p><strong>ΔSOC = SOCin − SOCfin</strong></p> </td> <td> <p><strong>Cycle between Control Tests</strong></p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>1 C</p> </td> <td> <p>80−20</p> </td> <td> <p>200</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>2 C</p> </td> <td> <p>80−20</p> </td> <td> <p>200</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>3 C</p> </td> <td> <p>80−20</p> </td> <td> <p>100 <sup>1</sup></p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>5 C</p> </td> <td> <p>80−20</p> </td> <td> <p>100</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>1 C</p> </td> <td> <p>90−10</p> </td> <td> <p>160</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>1 C</p> </td> <td> <p>70−30</p> </td> <td> <p>320</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>5 C</p> </td> <td> <p>70−30</p> </td> <td> <p>320</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>3 C</p> </td> <td> <p>90−10</p> </td> <td> <p>160 </p> </td> </tr> <tr> <td> <p>9</p> </td> <td> <p>8 × 3C@80%DOD + 10 × 2C@40%DOD</p> </td> <td> <p>90−10, 70−30</p> </td> <td> <p>90</p> </td> </tr> </tbody> </table> <p>The number of the file corrrespond to the test number (e.g. B1 referers to results for a cell undergone test 1). Cells tested where 20 Ah EIG NMC-graphite.</p> <p>In order to compare the results for cells subjected to life tests with different ΔSOCs, it is necessary to appropriately quantize the cumulative charge, to obtain a suitably defined equivalent cycle. To this end, we consider the greatest common factor (GCF) among ΔSOCs. We then define the number of equivalent cycles (ECs) of each life test as the ratio between the ΔSOC value and GCF. In our case, we considered 3 ΔSOC: 40%, 60%, and 80%. The GCF is therefore 20%. Hence, the number of EC equals 2, 3, and 4 for each cycle at ΔSOC 40, 60, and 80%, respectively. Obviously, this is not the only quantization possible, but a different choice does not affect the validity of the model, as long as the transformation is linear, although it would lead to different results for the parameters.</p> <p>The capacity has been evaluated with a standard charge-discharge cycle at 0.5C rate, after a given number of test cycles, as reported in the files. All files are structured as follows:</p> <table> <tbody> <tr> <td>Column number</td> <td>1</td> <td>2</td> <td>3</td> </tr> <tr> <td>Field description</td> <td>Number of cycles</td> <td>Number of equivalrent cycles</td> <td>Relative capacity</td> </tr> </tbody> </table> <p>Relative capacity: ratio between the capacity value after N cycle and the capacity value at the beginning of life.</p>
GWAS on salt stress responses in root system architecture parameters of wild tomato (S. pimpinellifolium)
<p>The population of +/2 200 accessions of wild tomato was screened with the protocol described <a href="https://www.protocols.io/view/studying-root-system-architecture-changes-in-tomat-2mqgc5w">here</a> with the only exception that the plants were transferred 4 days after germination (rather than 3 - described in the protocol). The images were analyzed using the <a href="https://smartroot.github.io/">SmartRoot</a> for days 0, 1, 2, 3, and 4 after transfer to treatment plates (0 or 100 mM NaCl, 1/4 MS, 0.5% sucrose, 0.1% MES, 1% Dashin agar). The data analysis was performed as described <a href="https://rpubs.com/mjulkowska/BIGpimp_RSA_salt">here</a>, while the pareto front calculations were done according to Chandrasekhar & Julkowska paper (<a href="https://www.biorxiv.org/content/10.1101/2021.08.12.456185v1">preprint here</a>). The GWAS was performed using the ASReml script similar to <a href="https://onlinelibrary.wiley.com/doi/10.1111/tpj.15310">Awlia et al. (2021)</a>. </p>
Resilience of seagrass populations to thermal stress does not reflect regional differences in ocean climate
<p>1. The prevalence of local adaptation and phenotypic plasticity among populations is critical to accurately predicting when and where climate change impacts will occur. Currently, comparisons of thermal performance between populations are untested for most marine species or overlooked by models predicting the thermal sensitivity of species to extirpation.</p> <p>2. Here we compared the ecological response and recovery of seagrass populations (<i>Posidonia oceanica</i>) to thermal stress throughout a year-long translocation experiment across a 2800 km gradient in ocean climate. Transplants in central and warm-edge locations experienced temperatures >29 ºC, representing thermal anomalies >5ºC above long-term maxima for cool-edge populations, 1.5ºC for central and <1ºC for warm-edge populations.</p> <p>3. Cool, central and warm-edge populations differed in thermal performance when grown under common conditions, but patterns contrasted with expectations based on thermal geography. Cool-edge populations did not differ from warm-edge populations under common conditions and performed significantly better than central populations in growth and survival.</p> <p>4. Our findings reveal that thermal performance does not necessarily reflect the thermal geography of a species. We demonstrate that warm-edge populations can be less sensitive to thermal stress than cooler, central populations suggesting that Mediterranean seagrasses have greater resilience to warming than current paradigms suggest.</p>
Triggering and protective factors against stress among healthcare staff during the Covid-19 pandemic. A hospital management study.
<p>Data set to carry out the study "Triggering and protective factors against stress among healthcare staff during the Covid-19 pandemic. A hospital management study".</p>
Data from : Limited influence of irrigation on pre-monsoon heat stress in the Indo-Gangetic Plain
<p>The dataset contains WRF-CLM4 simulation post-processed output for three experiments named CTL, AGR, and MOD for pre-monsoon (April-May) from 2004-2016. Here, CTL represents WRF-CLM4 simulation with no irrigation, AGR represents WRF-CLM4 simulation with agricultural census-based irrigation data and MOD represents WRF-CLM4 simulation with model-estimated irrigation data. Here, C1, C2, and C3 represent different parameterization scheme combinations.</p> <ul> <li>C1:Mellor-Yamada Nakanishi and Niino Level 3 - WRF Double-Moment 6-class scheme - Tiedtke scheme (MYNN3-WDM6-Tiedtke)</li> <li>C2:Mellor-Yamada Nakanishi and Niino Level 3 - WRF Single-Moment 6-classscheme - KF scheme (MYNN3-WSM6-KF)</li> <li>C3:Mellor-Yamada Nakanishi and Niino Level 3 - WRF Single-Moment 6-class scheme - Grell3D (MYNN3-WSM6 Grell3D)</li> </ul> <p>The post-processed output contains the following variables:</p> <ol> <li>Land Surface Temperature</li> <li>Mean Air Temperature</li> <li>Maximum Air Temperature</li> <li>Wet-bulb Temperature</li> <li>Specific Humidity</li> <li>Latent Heat Flux</li> <li>Sensible Heat Flux</li> <li>Planet Boundary Layer Height</li> <li>Surface Pressure</li> <li>Relative Humidity</li> </ol> <p>The dataset also contains a spreadsheet that contains the FAO monthly calendar of the percentage of crop irrigation and pre-monsoon season crop calendar based on crop production data and annual reports “Agricultural Statistics At a Glance” from the Government of India. In addition, the file contains raw pre-monsoon data (area under the crop and crop irrigated area) from 2004 to 2016 for five crops (rice, maize, gram, sugarcane, and sunflower) over Indo-Gangetic Plain (Bihar, Uttar Pradesh, Haryana, Punjab, and Rajasthan). The pre-monsoon irrigation files for WRF contain irrigation data input for the WRF-CLM4 model.</p>
Data repository for "Climate change increases the severity and duration of soil water stress in the temperate forest of eastern North America"
<p>Dataset provided for publication in Frontiers in Forests and Global Change : "Climate change increases the severity and duration of soil water stress in the temperate forest of eastern North America".</p>
AWASH Water Stress Metrics
<p>This data repository contains county-level water stress results from the analysis performed in "The importance of infrastructure and national demand to represent constraints on water supply in the United States".</p> <p>The largemaps.pdf file contains the figures from the paper in a larger format.</p> <p>summary.csv shows for each county the median water stress (across years) as well as the range of water stress, for each of the four scenarios (local runoff, river network only, with canals, and with canals and reservoirs).</p> <p>results.zip provides the raw results. The zipped file contains a folder 'results', which includes the spatiotemporal optimization outcomes either for surface water demands or total demands (`-alldemand` files). The `byfips-...` files then show metrics for the excess stress from all demands across counties, and the `fipstime-...` files show these excess stress results across counties and months.</p>
Source models for "Across-slab propagation and low stress drops of deep earthquakes in the Kuril subduction zone"
<p>This repository is for the model results for eight deep earthquakes in the Kuril subduction zone modelled using a second-degree moments method in csv format.</p> <p><a href="https://zenodo.org/api/files/ee2b378e-c4a5-4ef0-b07b-e2b2513b3236/Turner_et_al_2022_model_results_subvertical.csv">Turner_et_al_2022_model_results_subvertical.csv</a> - Source models with fixed Amin > 5 km, assuming the sub-vertical fault plane reported in the GCMT catalogue. Event is the GCMT event code. Aspect ratio is the ratio (Amin/Amax). Duration is the rupture duration; Amax is the maximum characteristic fault dimension; Amin is the minimum characteristic fault dimension; Phi is the angle between Amax and the strike; v0 is the centroid velocity; Theta is the angle between the centroid velocity and the strike; and mft is the misfit between the data and the higher-order synthetics calculated for the best-fitting source model obtained from the Monte Carlo inversions.</p> <p> </p> <p><a href="https://zenodo.org/api/files/ee2b378e-c4a5-4ef0-b07b-e2b2513b3236/Turner_et_al_2022_model_results_subvertical.csv">Turner_et_al_2022_model_results_subhorizontal.csv</a> - Source models with fixed Amin > 5 km, assuming the sub-vertical fault plane reported in the GCMT catalogue. Column headers are the same as in <a href="https://zenodo.org/api/files/ee2b378e-c4a5-4ef0-b07b-e2b2513b3236/Turner_et_al_2022_model_results_subvertical.csv">Turner_et_al_2022_model_results_subvertical.csv</a>.</p>
Pulsed stress hypothesis revisited – A case study of Metopolophium dirhodum and spring wheat
<p>Life table data for the rose-grain aphid, Metopolophium dirhodum, reared on the spring wheat, Triticum sativum, under four regimes of water supply: 40C - continuous drought (40 SWC), 70C - well watered (70 SWC), 40-0, 40-1, 40-2 and 40-3 - pulsed stress timed for one week before aphids were established (40-0), during nymphal development (40-1), and during first (40-2) and second week of reproduction (40-3). </p>
Supplementary Table S5 - Article: Transcriptome Analysis Provides Novel Insights into Salinity Stress Response in two Egyptian Rice Varieties with Different Tolerance Levels
<p><strong>Table S5.</strong> Repository data showing genes identified by MapMan in Giza 178 in different pathways. </p> <p>A, Cell wall modifications.</p> <p>B, Hemicellulose synthesis.</p> <p>C, Cellulose synthesis.</p> <p>D, Mannan-xylose-arabinose-fucose. </p> <p>E, cell wall peroxidase.</p> <p>F, TF MYB. </p> <p>G, bZIP. </p> <p>H, Histone.</p>
Supplementary Table S4 - Article: Transcriptome Analysis Provides Novel Insights into Salinity Stress Response in two Egyptian Rice Varieties with Different Tolerance Levels
<p><strong>Table S4.</strong> Repository data showing genes identified by MapMan in Giza 177 in different pathways. </p> <p>A, Cell wall modifications.</p> <p>B, Hemicellulose synthesis.</p> <p>C, Cellulose synthesis.</p> <p>D, Mannan-xylose-arabinose-fucose.</p> <p>E, cell wall peroxidase.</p> <p>F, TF MYB. </p> <p>G, bZIP. </p> <p>H, Histone.</p>
Supplementary Table S2 - Article:Transcriptome Analysis Provides Novel Insights into Salinity Stress Response in two Egyptian Rice Varieties with Different Tolerance Levels
<p><strong>Table S2.</strong> Repository data for the global analysis produced for cv Giza 177. </p> <p>A, Up regulated genes observed when comparing salt stressed plants vs unstressed controls. </p> <p>B, Down regulated genes in Giza 177 observed when comparing salt stressed plants vs unstressed controls.</p> <p>C, Gene Ontology enrichment analysis (GOEA) results for Giza 177 up regulated genes. </p> <p>D, GOEA results for Giza 177 down regulated genes. </p>
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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.