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Data for "The Role of Mood and Stress in Physical Activity Engagement: Insights from Ecological Momentary Assessment
<p>Data collected in the SmartPA study in a joint effort from the Department of Psychology at the University of Salzburg (Jens Blechert) and the Ludwig Boltzmann Institute for Digital Health and Prevention Salzburg.</p>
Growing up with nutritional stress leads to peripheral social network positions, independent of 'personality'
<p>Variables:</p> <p>BirdID: unique identifier of individuals</p> <p>Day: numerical, day of testing</p> <p>Room: physical Location of aviary, arbitrary numbering</p> <p>Group: numerical. Each group consists of one sex. </p> <p>Degree, Strength, EC, CV: social network variables. When precluded by a "D"; calculated using duration-based networks. When precluded by "F"; calculated using frequency-based networks. </p> <p>Sex: 1 = female, 2 = male</p> <p>DT: developmental treatment. Easy or Hard </p> <p>GenDad & GenMom: unique identifier. Genetic father or mother. </p> <p>DS, GP, PC1NO, PC1exp, Immobility: personality scores from Gerritsma et al., 2023. When precluded by "s", they are standardized ((x - mean) / sd). </p> <p>Broodsize: numerical, natal broodsize. </p> <p>nestID: unique identifier for nest, arbitrary numbering. </p> <p>Aviary: aviary in which individual was born. Arbitrary numbering. </p> <p>Groupday: combination of day and group. First day of first group would be 11. Tenth day of first group would be 101. </p> <p>Sexmc: mean-centered sex (sex-avg(sex)). </p> <p>Broodsizemc: mean-centered brood size. </p> <p>logTI: log10(immobility)</p> <p>DT3: numerical representation of DT. 1 = hard, 0 = easy. </p>
Figure 3 in High-temperature stress induces bacteria-specific adverse and reversible effects on Ulva (Chlorophyta) growth and its chemosphere in a reductionist model system
Figure 3: Temperature shift experiment from 18 °C to 30 °C. (A) After the temperature shift, the longitudinal growth of the propagules of equal length in three different tripartite communities was measured with ImageJ software and compared with the established model system for
Figure 2 in High-temperature stress induces bacteria-specific adverse and reversible effects on Ulva (Chlorophyta) growth and its chemosphere in a reductionist model system
Figure 2: Bioassay for morphogenetic activity performed at 18 °C. Using a tripartite community with Ulva mutabilis, the morphogenetic activity of the thallusin-releasing bacteria Maribacter sp. was complemented by one out of the four strains isolated from the surface of Ulva ohnoi. Under standard conditions, axenic gametes (A) were cultivated in the tissue culture flask with the tested bacteria alone (B–E), in the presence of Roseovarius sp. (G–J) or with Maribacter sp. MS6 (L–O) in comparison to the controls (F and K). Arrows with closed heads indicate protrusion formation due to the lack of thallusin released by Maribacter sp. Arrows with open heads indicate rhizoid formation in the presence of Maribacter sp. Magnification bar = 100 µm.
Figure 1 in High-temperature stress induces bacteria-specific adverse and reversible effects on Ulva (Chlorophyta) growth and its chemosphere in a reductionist model system
Figure 1: Workflow. Selected bacteria IH2, IH18, IH25, and G8 were collected from the surface of Ulva ohnoi, phenocopying the activity of Roseovarius sp. MS2 and forming a tripartite community with Maribacter sp. MS6 and the gametophyte of Ulva mutabilis (morphotype "slender"; strain FSU-UM5-1). Ulva mutabilis (25 mg dry weight) was cultivated with the two bacterial strains (OD620 = 0.001) under standard conditions (Wichard and Oertel 2010). Propagules of equal length were stressed by a temperature shift from 18 °C to 30 °C using continuous light (80 µmol photon m−2 s−1) to avoid chronobiological effects. Axenic cultures and tripartite communities were prepared according to Spoerner et al. (2012). exo-Metabolomics and multivariate analysis of the metabolite profiling of the supernatant (150 mL) of four tripartite communities were performed according to Alsufyani et al. (2017) and Ghaderiardakani et al. (2022). Drawings of Ulva were taken from Wichard (2023) under the terms of CC BY 4.0. Created with BioRender.com.
Figure 3 in Multiple metals and agricultural use affects oxidative stress biomarkers in freshwater Aegla crabs
Figure 3. Biomarkers grouped by hydrographic basin (Suzana River basin, Ligeirinho-Leãozinho River basin, Dourado River basin). Different letters indicate significant differences (p <0.05), as compared by one-way ANOVA plus Tukey post-test (between basins).
Figure 2 in Multiple metals and agricultural use affects oxidative stress biomarkers in freshwater Aegla crabs
Figure 2. Biplot of PCA ordination for the metal concentration in sediment of the three studied basins. Suzana River basin (S); Ligeirinho-Leãozinho River basin (L); Dourado River basin (D).
Figure 1 in Multiple metals and agricultural use affects oxidative stress biomarkers in freshwater Aegla crabs
Figure 1. Map of the sampling sites. (A) Suzana River basin (between 27°35'38" and 27°36'16"S; 52°11'11" and 52°13'41"W); (B) Ligeirinho-Leãozinho River basin (between 27°40'15" and 27°36'16"S; 52°16'03" and 52°13'41"W); (C) Dourado River basin (between 27°33'59" and 27°37'13"S; 52°17'46" and 52°19'36"W). The circles (•) indicate the sampled streams. The numbered boxes indicate the areas of land uses analysis.
Datasets for the Results of Scratch Tests of Green Wood and Results of Scratch Tests of Timber Components (D2.1) and Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains and Stresses and Crack Risk (D2.2) of 5G-TIMBER EU Project
<p>7 June 2024: added D2.2_data_statistics.zip and D2.2_analysis_results.zip, which are the datasets for D2.2 "<span>Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains and Stresses and Crack Risk" of the Horizon Europe Innovation Action project "5G-TIMBER: Secure 5G-Enabled Twin Transition for Europe's TIMBER Industry Sector" (project reference: 101058505).</span></p> <p>D2.2 presents the Hygro-Thermo-Mechanical (HTM) models and the finite element (FE) analyses of selected wooden components that use the material properties of wood presented in deliverable D2.1 "Input database for selected wood parts and timber components: material properties, representative environmental conditions,<br>and loads" (see below). </p> <p>------</p> <p>Figures_22_23_24_25.xlsx : Results of Scratch Tests of Green Wood</p> <p>corrected_Figures_26_27_28_29_30.xlsx : Results of results of Scratch Tests of Timber Components (new version, uploaded on 26 October 2023)</p> <p>This dataset consists of 2 Excel files that correspond to the scratch test results reported in the deliverable D2.1 "Input Database for Selected Wood Parts and Timber Components: Material Properties, Representative Environmental Conditions and Loads" of the Horizon Europe Innovation Action project "5G-TIMBER: Secure 5G-Enabled Twin Transition for Europe's TIMBER Industry Sector" (project reference: 101058505).</p> <p>The purpose of D2.1, to which this dataset is related, is to present the input data needed for the Hygro-Thermo-Mechanical (HTM) models and the related finite element (FE) analyses planned for a follow-up deliverable, i.e., the D2.2. (Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains And Stresses And Crack Risk). The data include the material properties for green wood and selected wooden components, as well as the plans to collect environmental conditions and loads to be considered in the analyses for prediction of the crack risk of timber components under moisture variations. In additions, new results of scratch tests of wood and wooden components, supported by computed tomography (CT) investigations, are collected to define a model for shear failure risk to be added to the HTM computational models.</p> <p>In D2.1, scratch tests carried out at VTT are described and their results are collected to provide information about the moisture effects of wood logs during cutting operations in sawmills, as well as on relevant fracture and shear properties for wooden components in sawing centres before using them to produce wooden elements of modular buildings in the production. The scratch tests are supported by CT tomography investigations and these results are also reported in the deliverable.</p> <p>D2.1 is available here: <a title="Deliverable D2.1 "Input Database for Selected Wood Parts and Timber Components: Material Properties, Representative Environmental Conditions and Loads" " href="../records/10577505" target="_blank" rel="noopener">https://zenodo.org/records/10577505</a> </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.
Fig. 6 in Effect of Nutrient, Light Intensity and Temperature on the Growth Rates and Metabolism of a Stress-Resistant Bacillariophyta Species Entomoneis sp. - in Izmir Bay (Aegean Sea) Abstract
Fig. 6: Maxiumum growth rate determination of all temperatures, light intensities and nutrient concentrations.
Fig. 5 in Effect of Nutrient, Light Intensity and Temperature on the Growth Rates and Metabolism of a Stress-Resistant Bacillariophyta Species Entomoneis sp. - in Izmir Bay (Aegean Sea) Abstract
Fig. 5: Entomoneis sp biomass (Chl a, µg /L) under different N/P ratios and light intensities (a) representing growth under T1°C (b) T2°C (c) and T3°C.
Fig. 2 in Effect of Nutrient, Light Intensity and Temperature on the Growth Rates and Metabolism of a Stress-Resistant Bacillariophyta Species Entomoneis sp. - in Izmir Bay (Aegean Sea) Abstract
Fig. 2: 3D response surface plot and contour line of Box– Behnken Design showing the mutual effect of temperature and light intensity on chlorophyll a concentration (µg/L) of Entomoneis sp. using an N/P ratio of 11.
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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.