Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
613
datasets available to search
ShareScore release 0.9.0
Dataset results
613 results for “stress analysis”
Early-life food stress hits females harder than males in insects: a meta-analysis of sex differences in environmental sensitivity
<p><span>Fitness consequences of early-life environmental conditions are often sex-specific, but corresponding evidence for invertebrates remains inconclusive. Here we use meta-analysis to evaluate sex-specific sensitivity to early-life nutritional conditions in insects. Using literature-derived data for 85 species with broad phylogenetic and ecological coverage, we show that </span><span>females are generally more sensitive to food stress than males. Stressful nutritional conditions during development typically lead to female-biased mortality and thus increasingly male-biased sex ratios of emerging adults. We further demonstrate that the general trend of higher sensitivity to food stress in females can primarily be attributed to their typically larger body size in insects and hence higher energy needs during development. By contrast, there is no consistent evidence of sex-biased sensitivity in sexually size-monomorphic species. Drawing conclusions regarding sex-biased sensitivity in species with male-biased size dimorphism remains to wait for the accumulation of relevant data. Our results suggest that environmental conditions leading to elevated juvenile mortality may potentially affect the performance of insect populations further by reducing the proportion of females among individuals reaching reproductive age. Accounting for sex-biased mortality is therefore essential to understanding the dynamics and demography of insect populations, not least importantly in the context of ongoing insect declines.</span></p>
GWAS analysis for tolerance to heat stress and milk production, in subtropical Egyptian goats
<p>The conservation of local Egyptian goat genotypes has been a national program since 2009. Our study investigated different subtropical Egyptian goat populations (367 does) from different harsh ecological zones(hot Upper Egypt, Coastal Zone of Western Desert, and Wahati Desert Oasis) to identify genes associated with heat stress. We examined the physiological response of animals that were exposed to simulated summer grazing conditions, and physiological parameters including respiration rate, gas volume, rectal temperature, and skin temperature. Temperature Humidity Index ranged from 98.6 to 109.3, indicating that the animals were under severe heat stress. Results showed significant differences between the populations in their tolerance to heat stress, with Saidi goats being the most adapted to hot-dry conditions. Respiration rate was found to be the most reliable physiological trait for differentiating between animals in their tolerance to heat stress. The GWAS analysis involved 157 genotypes and 54,032 marker-SNPs, revealing 90 SNPs associated with heat stress and 70 SNPs associated with milk production. Chromosome 1 had the highest number of SNPs associated with heat-stress traits, while chromosome 4 exhibited the largest number of significant SNPs associated with milk production. Several genes were associated with heat stress response and tolerance in goats. These genes were categorized into three main groups: heat stress response, stress response, and reproduction, and feed intake. The first group includes USP54, KDM6A, and ETNPPL, which have multiple functions, such as steroid biosynthesis, metabolism, and stress response, and are also involved in key biological processes such as reproduction, immune response, and metabolism. The second group, which is mostly associated with immune response, includes GLTSCR2 and NAALADL2. Finally, a group of genes that may control animal feed intakes, including TRPM3 and ZBTB8A. Additionally, several genes, such as FHIT, GALNT18, RAPGEF5, and RBFOX1, linked to milk production, are known to be linked fertility, and fatty acid composition. These findings provide insights into the potential roles of these genes in heat stress response and tolerance in goats, and the genetic basis for improved milk production and animal welfare and can be used as selection markers in the ongoing breeding programs.</p>
Host-pathogen interactions under pressure: a review and meta-analysis of stress-mediated effects on disease dynamics
<p>Human activities have increased the intensity and frequency of natural stressors and created novel stressors, altering host-pathogen interactions, and changing the risk of emerging infectious diseases. Despite the ubiquity of such anthropogenic impacts, predicting the directionality of outcomes has proven challenging. Here, we conduct a review and meta-analysis to determine the primary mechanisms through which stressors affect host-pathogen interactions and to evaluate the impacts stress has on host fitness (survival and fecundity) and pathogen infectivity (prevalence and intensity). We assessed 891 effect sizes from 71 host species (representing seven taxonomic groups) and 78 parasite taxa from 98 studies. We found that infected and uninfected hosts had similar sensitivity to stressors and that responses varied according to stressor type. Specifically, limited resources compromised host fecundity and decreased pathogen intensity, while abiotic environmental stressors (e.g., temperature and salinity) decreased host survivorship and increased pathogen intensity, and pollution increased mortality but decreased pathogen prevalence. We then used our meta-analysis results to develop Susceptible-Infected theoretical models to illustrate scenarios where infection rates are expected to increase or decrease in response to resource limitation or environmental stress gradients. Our results carry implications for conservation and disease emergence and reveal areas for future work. </p>
The raw microarray data and the differential expression analysis results from "Manipulating the growth environment through co-culture to enhance stress tolerance and viability of probiotic strains in the gastrointestinal tract".
<p>The signal data for each spot were subsequently quantified by using Feature Extraction software (Agilent Technologies).M1.txt to M5.txt: monoculture; C1.txt to C5.txt: co-culture; P1.txt to P5.txt: pH-controlled monoculture. The differential expression analysis results were obtained by using limma.</p>
Early-life food stress hits females harder than males in insects: a meta-analysis of sex differences in environmental sensitivity
Open the record for dataset details and reuse information.
Data and analyses from: Context matters: A meta-analysis of the variable impacts of transgenerational and developmental plasticity on responses to stress
Open the record for dataset details and reuse information.
Host-pathogen interactions under pressure: a review and meta-analysis of stress-mediated effects on disease dynamics
Open the record for dataset details and reuse information.
Multi-omics analysis reveals the glycolipid metabolism response mechanism in the liver of Genetically Improved Farmed Tilapia (GIFT, Oreochromis niloticus) under hypoxia stress
<p><span><b>Background: </b>Dissolved oxygen (DO) in the water is a vital abiotic factor in aquatic animal farming. A hypoxic environment affects the growth, metabolism, and immune system of fish. Glycolipid metabolism is a vital energy pathway under acute hypoxic stress, and it plays a significant role in the adaptation of fish to stressful environments. In this study, we used multi-omics integrative analyses to explore the mechanisms of hypoxia adaptation in Genetically Improved Farmed Tilapia (GIFT, <i>Oreochromis niloticus</i>). </span></p> <p><span><b>Results:</b><b> </b>The 96 h median lethal hypoxia (96h-LH50) for GIFT was determined by linear interpolation. We established control (DO: 5 mg/L) groups (CG) and hypoxic stress (96h-LH50) groups (HG) and extracted liver tissues for high-throughput transcriptome and metabolome sequencing. A total of 581 differentially expressed (DE) genes and 1250 DE metabolites were detected between CG and HG, and were annotated using tools at the KEGG database. We verified the transcript levels of eight DE genes by quantitative real-time PCR.</span></p> <p><span><b>Conclusions: </b>Analyses of essential glycolipid metabolism pathways of GIFT under hypoxia stress showed that, after 96 h of hypoxia stress, lipid metabolism became the primary metabolic pathway in GIFT. Our findings reveal the changes in metabolites and gene expression that occur under hypoxia stress, and shed light on the regulatory pathways that function under such conditions. Ultimately, this information will be useful to devise strategies to decrease the damage caused by hypoxia stress in farmed fish.</span></p>
Vocalization Patterns in Laying Hens - An Analysis of Stress-Induced Audio Responses
<p>This repository houses a comprehensive collection of data and resources from the study "Vocalization Patterns in Laying Hens - An Analysis of Stress-Induced Audio Responses." Led by Dr. Suresh Neethirajan at Mooanalytica, Department of Agriculture & Aquaculture, Faculty of Agriculture & Computer Science, Dalhousie University, this research represents a significant foray into the field of poultry ethology and welfare monitoring using advanced machine learning techniques.</p> <p><strong>Key Components of the Repository</strong></p> <ol> <li> <p><strong>Experimental Audio Data</strong></p> <ul> <li><strong>Control and Treatment Vocalizations</strong>: Audio recordings of laying hens under two different stress conditions – sudden umbrella opening (Treatment 1) and simulated dog barking sounds (Treatment 2), along with control groups. The processed dataset is approximately 460 MB for the control group experimental data and about 2 GB for the 2 treatment group experimental data, capturing the nuanced responses of hens to these stressors.</li> <li><strong>Original Raw Data</strong>: The original, unprocessed audio data is around 9 GB in size. Though not included in the repository, it can be made available upon reasonable request.</li> </ul> </li> <li> <p><strong>Algorithm and Code Files</strong></p> <ul> <li><strong>CNN Feature Extraction and Classification Algorithms</strong> Python scripts used for the extraction of features from the audio data using Convolutional Neural Networks (CNN) and subsequent classification.</li> <li><strong>Supplementary Algorithms</strong> Additional code files that support the processing and analysis of the audio data.</li> </ul> </li> <li> <p><strong>MFCC Feature Dataset</strong></p> <ul> <li>An Excel file containing the 40 Mel Frequency Cepstral Coefficients (MFCC) features extracted from the vocalization data. This dataset provides a detailed spectral analysis of the hen's vocalizations, crucial for understanding their response to stress.</li> </ul> </li> </ol> <p><strong>Study Overview</strong></p> <p>This study aimed to classify and analyze the vocalization patterns of laying hens subjected to different stressors. Using a CNN model, the research identified distinct vocal patterns between control and treated groups, indicating unique vocal responses to different types of stressors. This study is pivotal in understanding the impact of environmental stressors on poultry welfare and behavior. The age of the chickens and the timing of stressor application were also critical factors influencing vocalization patterns.</p> <p><strong>Implications and Applications:</strong></p> <p>The findings from this study have significant implications for poultry welfare monitoring and management. By providing a non-invasive method to assess the well-being of chickens, this research contributes valuable insights into enhancing poultry management practices and welfare standards.</p> <p>The resources in this repository are intended for researchers, academicians, and professionals in animal behavior, veterinary science, and poultry management. We encourage the use of these data and tools for further research and practical applications in the field of precision (Digital) livestock farming and animal welfare.</p> <p>For any queries or requests related to the raw dataset, please contact Dr. Suresh Neethirajan.</p>
Carmichael_etal_Data_Analysis_All_Stress_Responses
<p>Data for the submission "Reconciling variability in multiple stressor effects using environmental performance curves". This includes growth rate data for 12 bacterial taxa exposed to gradients of temperature, pH and salinity. </p> <p>The R script for the data analysis pipeline is included, as well as a README file describing the data layout.</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>
Supplementary Table S3 - Article: Transcriptome Analysis Provides Novel Insights into Salinity Stress Response in two Egyptian Rice Varieties with Different Tolerance Levels
<p><strong>Table S3.</strong> Repository data for the global analysis produced for cv Giza 178. </p> <p>A, Up regulated genes observed when comparing salt stressed plants vs unstressed controls. </p> <p>B, Down regulated genes in Giza 178 observed when comparing salt stressed plants vs unstressed controls.</p> <p>C, Gene Ontology enrichment analysis (GOEA) results for Giza 178 up regulated genes. </p> <p>D, GOEA results for Giza 178 down regulated genes. </p>
Network analysis reveals that acute stress exacerbates gene regulatory responses of the gill to seawater in Atlantic salmon
<p>The transition from freshwater to seawater represents a physiological challenge for Atlantic salmon smolts preparing for downstream migration. Stressors occurring during downstream migration to the ocean impair the ability of smolts to maintain osmotic/ionic homeostasis in seawater. The molecular mechanisms underlying this interaction are not fully understood, especially at the organ level. We combined RNA-Seq with measures of whole-animal homeostasis to examine gene expression dynamics in the gills of smolts associated with impaired seawater tolerance after an aquaculture-related stressor. Smolts were given a 24 h seawater tolerance test before and after exposure to an acute handling/confinement stress. RNA-Seq followed by differential expression and weighted gene correlation network analysis (WGCNA) was used to quantify the transcriptional response of the gill to handling/confinement stress, seawater and their interaction. Exposure to acute stress was associated with a general stress response and impaired osmotic/ionic homeostasis in seawater. We identified gene networks in the gill exhibiting response to acute stress alone, seawater alone, and others exhibiting combined effects of both stress and seawater. Our findings indicate that acute handling/ confinement stress increases the intensity of seawater-related gene expression and suggest that increased investment in mechanisms related to ion transport may be part of a compensatory response to impaired seawater tolerance in smolts.</p>
Surrogate-modelling & machine learning dataset : finite element stress analysis of biaxial specimen with random elastic properties - 1000 samples
<p>Dataset finite element stress analysis of biaxial specimen with random elastic properties</p> <p>Unzip and execute dataset.py to visualise data samples. PyVista is needed.</p>
Surrogate-modelling & machine learning dataset : finite element stress analysis of biaxial specimen with random elastic properties - 100 samples
<p>Dataset finite element stress analysis of biaxial specimen with random elastic properties</p> <p>Unzip and execute dataset.py to visualise data samples. PyVista is needed</p>
Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 1000 samples
<p>Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 1000 samples</p> <p>unzip, run data_set.py to see how to interact with the dataset using PyTorch Geometric toolbox</p>
Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 6000 samples
<p>Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 6000 samples</p> <p>unzip, run data_set.py to see how to interact with the dataset using PyTorch Geometric toolbox</p>
Elasto-plastic residual stress analysis of selective laser sintered porous materials based on 3D-multilayer thermo-structural phase-field simulations
<p>The supporting data and utilities from the publication "Elasto-plastic residual stress analysis of selective laser sintered porous materials based on 3D-multilayer thermo-structural phase-field simulations" are recorded in this dataset. </p> <p>Non-isothermal phase-field simulations of SLS process on SS316L material and subsequent elasto-plastic calculations were performed to analyze the development of plastic deformation and residual stress in SLS produced components during the processing. The dependence of the fusion zone, residual stress and plastic strain on the processing parameters namely, Beam power (Unit: Watts) and Scan speed (Unit: mm/s) were investigated. </p> <p>To promote FAIR research data principles, the processed simulation data from the thermo-elasto-plastic calculations for all the process parameter sets (hereby refered as P-v sets) are curated in this dataset. The raw temporal data obtained from the processing simulations and the elasto-plastic could not be included in this dataset due to its high volume. However, the corresponding raw data can be requested by contacting the creators of this dataset (Yangyiwei Yang: <a href="mailto:yangyiwei.yang@mfm.tu-darmstadt.de">yangyiwei.yang@mfm.tu-darmstadt.de</a> and Somnath Bharech: <a href="mailto:somnath.bharech@tu-darmstadt.de">somnath.bharech@tu-darmstadt.de</a>).</p> <p>This dataset includes: </p> <ul> <li><code>average_value.csv</code>: Contains average values of mechanical properties (such as residual stress, plastic strain) for the powder bed and the fused strut of all the process parameter sets.</li> <li><code>mesostructures_tep_sls.zip</code> : Contains resampled mesostructures obtained at the last time step of the SLS processing simulations with thermo-elasto-plastic calculations for the P-v sets reported in the aforementioned investigation. Nomenclature of the sub-directories indicating the P-v sets follows: <code>tep_<beam power>-<scan speed></code>. Each of these sub-directories contain the mesostructures from last time step of the thermo-elasto-plastic analysis of each of the four layer scans and is named as: <code>TP_layer{1..4}_output_final.e</code>. These files can be opened using Paraview v.5.8.1 or higher. The nodal values are explained as follows:</li> </ul> <table> <tbody> <tr> <td><strong>Nodal value name</strong></td> <td><strong>Symbol</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> </tr> <tr> <td>T</td> <td>\(T\)</td> <td>Temperature field normalized by \(T_M\)</td> <td>-</td> </tr> <tr> <td>c</td> <td>\(\rho\)</td> <td>Substance order parameter</td> <td>-</td> </tr> <tr> <td>eps_ij </td> <td>\(\varepsilon\)</td> <td>Strain</td> <td>-</td> </tr> <tr> <td>epsp_ij</td> <td>\(\varepsilon^\text{pl}\)</td> <td>Plastic strain</td> <td>-</td> </tr> <tr> <td>peeq</td> <td>\(p_\text{e}\)</td> <td>Accumulated plastic strain</td> <td>-</td> </tr> <tr> <td>sigma_ij </td> <td>\(\sigma\)</td> <td>Stress</td> <td>MPa</td> </tr> <tr> <td>vonmises</td> <td>\(\sigma_\text{e}\)</td> <td>von Mises stress </td> <td>MPa</td> </tr> <tr> <td>u</td> <td>\(\mathbf{u}\)</td> <td>Displacement</td> <td>µm</td> </tr> </tbody> </table> <p> </p>
ScienceDex guides
Understand access before you commit
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.