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3,206 results for “property (T)”
Effects of gravel size and content on the mechanical properties of conglomerate
<p>Stress-strain curves and fracture distribution characteristics indicate that gravel content influences the mechanical properties of rocks. In this study, uniaxial compression tests were conducted on the conglomerate containing gravels having diameters between 2–26 mm. In these tests, we found that many micro fractures were generated around evenly distributed gravels. Additionally, we found that as the gravel content increased, the uniaxial compressive strength and elastic modulus of conglomerate decreased; however, the plasticity characteristics of conglomerate increased. The results of our analysis imply that when the gravel content is less than <span><span></span></span> (14.61–31.72 %), the macro mechanical properties are mainly influenced by the cementing material, and between <span><span></span></span> and <span><span></span></span> (78.50 %), failure is determined by the local Orowan additional stress, which is related to the mechanical properties of the cement and the cementing strength,and higher than <i>f</i><sub><i>ch</i></sub><span><span></span></span>, failure is determined by the mutual Hertz stress among the gravels.</p>
Datapack for 'Nanocluster evolution and mechanical properties of ion irradiated T91 ferritic-martensitic steel' paper
<p>Data pack for the paper 'Nanocluster evolution and mechanical properties of ion irradiated T91 ferritic-martensitic steel', Journal of Nuclear Materials, 2021</p>
Data from: Can differential nutrient extraction explain property variations in a predatory trap?
Predators exhibit flexible foraging to facilitate taking prey that offer important nutrients. Because trap-building predators have limited control over the prey they encounter, differential nutrient extraction and trap architectural flexibility may be used as a means of prey selection. Here, we tested whether differential nutrient extraction induces flexibility in architecture and stickiness of a spider's web by feeding Nephila pilipes live crickets (CC), live flies (FF), dead crickets with the web stimulated by flies (CD) or dead flies with the web stimulated by crickets (FD). Spiders in the CD group consumed less protein per mass of lipid or carbohydrate, and spiders in the FF group consumed less carbohydrates per mass of protein. Spiders from the CD group built stickier webs that used less silk, whereas spiders in the FF group built webs with more radii, greater catching areas and more silk, compared with other treatments. Our results suggest that differential nutrient extraction is a likely explanation for prey-induced spider web architecture and stickiness variations.
Data from: Web building and silk properties functionally covary among species of wolf spider
While phylogenetic studies have shown covariation between the properties of spider major ampullate (MA) silk and web building, both spider webs and silks are highly plastic so we cannot be sure whether these traits functionally co-vary or just vary across environments that the spiders occupy. Since MaSp2-like proteins provide MA silk with greater extensibility, their presence is considered necessary for spider webs to effectively capture prey. Wolf spiders (Lycosidae) are predominantly non-web building, but a select few species build webs. We accordingly collected MA silk from two web building and six non-web building species found in semi-rural ecosystems in Uruguay to test whether the presence of MaSp2-like proteins (indicated by amino acid composition), silk mechanical properties, and silk nanostructures, were associated with web building across the group. The web building and non-web building species were from disparate subfamilies so we estimated a genetic phylogeny to perform appropriate comparisons. For all of the properties measured we found differences between web building and non-web building species. A phylogenetic regression model confirmed that web building and not phylogenetic inertia influences silk properties. Our study definitively showed an ecological influence over spider silk properties. We expect that the presence of the MaSp2-like proteins and the subsequent nanostructures improves the mechanical performance of silks within the webs. Our study furthers our understanding of spider web and silk co-evolution and the ecological implications of spider silk properties.
Rheological and thermal degradation properties of hyperbranched polyisoprene prepared by anionic polymerisation
<div class="WordSection1"> <p class="RSCB01ARTAbstract"><span><a name="_Hlk6757244">Hyperbranched polyisoprene was prepared by anionic copolymerisation under high vacuum condition. Size exclusion chromatography (<i>SEC</i>) was used to characterise the molecular weight and branching nature of these polymers. The characterisation by differential scanning calorimetry (<i>DSC</i>) and melt rheology indicated lower <i>T<sub>g</sub></i> and complex viscosity in the branched polymers as compared to the linear polymer. Degradation kinetics of these polymers was explored using thermogravimetric analysis (TGA) via non-isothermal techniques. The polymers were heated under nitrogen from ambient temperature to 600 °C using heating rates from 2, to 15 °C min<sup>-1</sup>. Three kinetics methods namely Friedman, Flynn–Wall–Ozawa (FWO) and Kissinger–Akahira–Sunose (KAS) were used to evaluate the dependence of activation energy (<i>E<sub>a</sub></i>) on conversion (<i>α</i>). The hyperbranched polyisoprene decomposed via multistep mechanism as manifested by the nonlinear relationship between <i>α</i> and <i>E<sub>a</sub></i> while the linear polymer exhibited a decline in <i>E<sub>a</sub></i> at higher conversions. The average <i>E<sub>a</sub></i> values range from 258 to 330 kJmol<sup>-1</sup> for the linear, and 260 to 320 kJmol<sup>-1</sup> for the branched polymers. The thermal degradation of the polymers studied involved one-dimensional diffusion mechanism as determined by Coats–Redfern method. This study may help in understanding the effect of branching on the rheological and decomposition kinetics of polyisoprene</a>.</span></p> </div> <p> </p>
Data from: Model-assisted analysis of sugar metabolism throughout tomato fruit development reveals enzyme and carrier properties in relation to vacuole expansion
A kinetic model combining enzyme activity measurements and subcellular compartmentation was parameterized to fit the sucrose, hexose, and glucose-6-P contents of pericarp throughout tomato (Solanum lycopersicum) fruit development. The model was further validated using independent data obtained from domesticated and wild tomato species and on transgenic lines. A hierarchical clustering analysis of the calculated fluxes and enzyme capacities together revealed stage-dependent features. Cell division was characterized by a high sucrolytic activity of the vacuole, whereas sucrose cleavage during expansion was sustained by both sucrose synthase and neutral invertase, associated with minimal futile cycling. Most importantly, a tight correlation between flux rate and enzyme capacity was found for fructokinase and PPi-dependent phosphofructokinase during cell division and for sucrose synthase, UDP-glucopyrophosphorylase, and phosphoglucomutase during expansion, thus suggesting an adaptation of enzyme abundance to metabolic needs. In contrast, for most enzymes, flux rates varied irrespectively of enzyme capacities, and most enzymes functioned at <5% of their maximal catalytic capacity. One of the major findings with the model was the high accumulation of soluble sugars within the vacuole together with organic acids, thus enabling the osmotic-driven vacuole expansion that was found during cell division.
Data from: Interspecific variation in the structural properties of flight feathers in birds indicates adaptation to flight requirements and habitat
1. The functional significance of intra- and interspecific structural variations in the flight feathers of birds is poorly understood. Here, a phylogenetic comparative analysis of four structural features (rachis width, barb and barbule density and porosity) of proximal and distal primary feathers of 137 European bird species was conducted. 2. Flight type (flapping and soaring, flapping and gliding, continuous flapping or passerine type), habitat (terrestrial, riparian or aquatic), wing characteristics (wing area, S and aspect ratio, AR) and moult strategy were all found to affect feather structure to some extent. Species characterized by low wing-beat frequency flight (soaring and gliding) have broader feather rachises (shafts) and feather vanes with lower barb density than birds associated with more active flapping modes of flight. However, the effect of flying mode on rachis width disappeared after controlling for S and AR, suggesting that rachis width is primarily determined by wing morphology. 3. Rachis width and feather vane density are likely related to differences in force distribution across the wingspan during different flight modes. An increase in shaft diameter, barb density and porosity from the proximal to distal wing feathers was found and was highest in species with flapping flight indicating that aerodynamic forces are more biased towards the distal feathers in flapping flyers than in soarers and gliders. 4. Habitat affected barb and barbule density, which was greatest in aquatic species, and within this group, barb density was greater in divers than non-divers, suggesting that the need for water repellency and resistance to water penetration may influence feather structure. However, we found little support for the importance of porosity in water repellency and water penetration, because porosity was similar in aquatic, riparian and terrestrial species and among the aquatic birds (divers and non-divers). We also found that barb density was affected by moult pattern. 5. Our results have broad implications for the understanding of the selection pressures driving flight feather functional morphology. Specifically, the large sample size relative to any previous studies has emphasized that the morphology of flight feathers is the result of a suite of selection pressures. As well as routine flight needs, constraints during moulting, habitat (particularly aquatic) and migratory requirements also affect flight feather morphology. Identifying the exact nature of these trade-offs will perhaps inform the reconstruction of the flying modes of extinct birds.
Electrochemical and thermodynamic properties of 1-phenyl-3-(phenylamino)propan -1-one with Na2WO4 on N80 Steel
<p>The corrosion inhibition effect and adsorption behavior of 1-phenyl-3-(phenylamino)propan-1-one(PPAPO) on N80 steel in hydrochloric solution have been investigated by Fourier transform infrared (FTIR), electrochemical method and scanning electron microscopy (SEM). The corrosion inhibition mechanism of PPAPO with Na<sub>2</sub>WO<sub>4</sub> was interpreted from the point of view of the thermodynamic. The results indicated that PPAPO with Na<sub>2</sub>WO<sub>4</sub> acted as a mixed type inhibitor. The inhibition film formed on N80 steel surface can increase the charge transfer resistance and prevent the occurrence of corrosion reaction, thereby reducing the corrosion rate of metal surface. The inhibition efficiency was up to 99.65%; and the inhibitor PPAPO with Na<sub>2</sub>WO<sub>4</sub> showed good synergistic effect on N80 corrosion behavior in HCl solution. The adsorption behavior of inhibitors on N80 steel surface was in accordance with the Langmuir adsorption model, and mainly belonged to chemisorption. The adsorption process of PPAPO on N80 surface was spontaneous and irreversible endothermic reaction.</p>
Data from: Genes, geology, and germs: gut microbiota across a primate hybrid zone are explained by site soil properties, not host species
Gut microbiota in geographically isolated host populations are often distinct. These differences have been attributed to between-population differences in host behaviors, environments, genetics, and geographic distance. However, which factors are most important remains unknown. Here we fill this gap for baboons by leveraging information on 13 environmental variables from 14 baboon populations spanning a natural hybrid zone. Sampling across a hybrid zone allowed us to additionally test whether phylosymbiosis (codiversification between hosts and their microbiota) is detectable in admixed, closely related primates. We found little evidence of genetic effects: neither host genetic ancestry, host genetic relatedness, nor genetic distance between host populations were strong predictors of baboon gut microbiota. Instead, gut microbiota were best explained by the baboons' environments, especially the soil's geologic history and exchangeable sodium. Indeed, soil effects were 15 times stronger than those of host-population FST, perhaps because soil predicts which foods are present, or because baboons are terrestrial and consume soil microbes incidentally with their food. Our results support an emerging picture in which environmental variation is the dominant predictor of host-associated microbiomes. We are the first to show that such effects overshadow host species identity among members of the same primate genus.
Data from: Analysis of statistical correlations between properties of adaptive walks in fitness landscapes
The fitness landscape metaphor has been central in our way of thinking about adaptation. In this scenario, adaptive walks are an idealized dynamics that mimics the uphill movement of an evolving population towards a fitness peak of the landscape. Recent works in experimental evolution have demonstrated that the constraints imposed by epistasis are responsible for reducing the number of accessible mutational pathways towards fitness peaks. Here we exhaustively analyze the statistical properties of adaptive walks for two empirical fitness landscapes and for theoretical NK landscapes. Some general scenario can be drawn from our simulation study. Regardless the dynamics, we observe that the shortest paths are more regularly used. Although the accessibility of a given fitness peak is reasonably correlated to the number of monotonic pathways towards it, the two quantities are not exactly proportional. A negative correlation predictability and mean path divergence is established, and so with the decrease of the number of effective mutational pathways ensues the convergence of the attraction basin of fitness peaks. On the other hand, other features are not conserved among fitness landscapes, such as the relationship between accessibility and predictability.
Data from: Wall structure and material properties cause viscous damping of swimbladder sounds in the oyster toadfish Opsanus tau
Despite rapid damping, fish swimbladders have been modelled as underwater resonant bubbles. Recent data suggest that swimbladders of sound-producing fishes use a forced rather than a resonant response to produce sound. The reason for this discrepancy has not been formally addressed, and we demonstrate, for the first time, that the structure of the swimbladder wall will affect vibratory behaviour. Using the oyster toadfish Opsanus tau, we find regional differences in bladder thickness, directionality of collagen layers (anisotropic bladder wall structure), material properties that differ between circular and longitudinal directions (stress, strain and Young's modulus), high water content (80%) of the bladder wall and a 300-fold increase in the modulus of dried tissue. Therefore, the swimbladder wall is a viscoelastic structure that serves to damp vibrations and impart directionality, preventing the expression of resonance.
Global Star-formation Properties Extracted from Synthetic Star-forming Regions | Appendix C
<p>We provide in this online-material <span class="math-tex">\(\sim 5800\)</span> realistic synthetic observations (FITS files) of a synthetic star-forming region described in detail in Chapter 4 of the PhD thesis. </p> <p>Please cite the following papers:</p> <p>http://adsabs.harvard.edu/abs/2017ApJ...849….3K<br> http://adsabs.harvard.edu/abs/2017ApJS..233....1K</p>
Global Star-formation Properties Extracted from Synthetic Star-forming Regions | Appendix D
<p>We provide in this online-material measured dust surface density maps, dust temperature maps and corresponding <span class="math-tex">\(\chi^2\)</span> maps of a synthetic star-forming region described in detail in Chapter 5 of the PhD thesis.</p> <p>Please cite the following papers:</p> <p>http://adsabs.harvard.edu/abs/2017ApJ...849….3K<br> http://adsabs.harvard.edu/abs/2017ApJS..233....1K<br> http://adsabs.harvard.edu/abs/2017ApJ...849….1K</p>
Electrical properties of Bi-implanted amorphous chalcogenide films
<p>Y. G. Fedorenko<sup>1a</sup>, M. A. Hughes<sup>1</sup>, J. L. Colaux<sup>1</sup>, C. Jeynes<sup>1</sup>, R. M. Gwilliam<sup>1</sup>, K. Homewood<sup>1</sup>, B. Gholipour<sup>2</sup>, J. Yao<sup>2</sup>, D. W. Hewak<sup>2</sup>, T.-H. Lee<sup>3</sup>, S. R. Elliott<sup>3</sup> and R. J. Curry<sup>1</sup></p> <p><sup>1</sup>Advanced Technology Institute, Department of Electronic Engineering, University of Surrey, Guildford, GU2 7XH, United Kingdom</p> <p><sup>2</sup>Optoelectronics Research Centre, University of Southampton, Southampton SO17 1BJ, United Kingdom</p> <p><sup>3</sup>Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom</p> <p> </p> <p><sup>a</sup>Present address: Department of Physics, University of Liverpool, The Oliver Lodge Laboratory, Liverpool L69 7ZE, United Kingdom</p>
[Dataset] enhanced functional connectivity properties of human brains during in-situ nature experience
<p>Raw data</p> <p>Artifact-free EEG raw data recorded by Emotiv Epoc (sample rate 128/s, 14 channels).</p>
Supplementary information for: "A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling".
<p>This is supplementary data for F1000Research article: A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling.</p> <p>Here are files for modeling archaerhodopsin-3 with I-TASSER, Medeller and RosettaCM algorithms, structure postprocessing and spectra calculations.</p> <p>Please, refer to the readme.txt for the description.</p>
Appendix for "Properties and Styles of Software Technology Tutorials"
<p>This artifact contains details of the resource collection, analysis scripts, and data analyzed in the paper "Properties and Styles of Software Technology Tutorials" by Deeksha M. Arya, Jin L.C. Guo, and Martin P. Robillard.</p>
Calgary Historical Property Assessements, 2019 (Residential Properties)
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SoilCompDB: Global soil compressive properties database. Version 1.0
<p><strong>Data collection and processing</strong></p><p>Our data collection comprised published journal articles sourced from Web of Science and Scopus databases, using search terms such as 'soil precompression stress,' 'soil compression index,' 'soil compaction index,' 'soil recompression index,' 'soil swelling index,' 'soil precompaction stress,' and 'preconsolidation pressure' for articles published up to February 2022. A total of 1235 publications were found. Duplicate records were eliminated using the Endnote Web citation management application. The remaining references were exported to Rayyan software for title and abstract screening based on predefined criteria for full-text selection. After a careful review, we identified 128 papers where the data on soil compressive properties (precompression stress, compression index, and swelling index) were reported in numerical format or legible graphical format and considered suitable for inclusion in the database. We employed the WebPlotDigitizer software to extract data from figures within the original publications. For each chosen study, we systematically recorded data concerning soil compressive properties and collected information on soil properties, soil conditions, site characteristics, and experimental settings. We compiled 4,743 individual data entries.</p><p><strong>Time and place</strong></p><p>The database includes data from 128 independent studies published between 1992 and 2021. Each study reported between 1 and 360 measurements, with a study median of 14 measurements and a mean of 38 measurements, totalling 4743 database entries. Our database includes data from 20 countries, with a significant concentration of the data originating from Brazil, followed by Germany, Switzerland, Sweden, and Denmark. The majority of the data came from arable soils, representing approximately 72% of data entries. </p><p><strong>Instruments</strong></p><p>The soil compressive properties included in the database were based on soil compressive tests performed in the laboratory by uniaxial method. The procedure used for stress application on soil samples was mainly the stepwise stress application method, while the constant strain rate method was applied in few studies (less than 2% of the data). The component of the compressive curve related to the soil packing state was represented by soil bulk density, void ratio, and strain. The stress component of the curve was represented in a logarithmic form in the entirety of the database. The database also comprised eight different methods for calculating precompresion stress: Casagrande (1936), Dias Junior and Pierce (1995), Lamandé et al. (2017), Sullivan and Robertson (1996), Casini (2012), Culley and Larson (1987), Pacheco Silva (1990), Gregory et al. (2006).</p><p><strong>Resources</strong></p><p>Web of Science, Scopus – literature search</p><p>Endnote Web – removal of duplicates</p><p>Rayyan software – initial paper selection based on title and abstract</p><p>WebPlotDigitizer – data extraction from figures</p><p>Microsoft Access – database platform</p><p><strong>Description of the collected data (column, unit, and description)</strong></p><p>Sample ID- A unique identification number assigned to each individual sample within the database </p><p>Study ID- Identification number assigned to each research study in the database</p><p>Reference - Research paper reference</p><p>Year - Year of research paper publication </p><p>Language - Language of the research paper </p><p>Soil classification (SiBCS) - Soil Classification according to the Brazilian System (SiBCS), as described in portuguese-language papers</p><p>Soil classification (original in paper) - Soil classification described in research paper </p><p>Soil classification (convertion to Soil Taxonomy orders) - Soil classification aligned with the Soil Taxonomy system developed by the United States Department of Agriculture (USDA) </p><p>Location - Study location country </p><p>Texture classification (USDA) - Soil textural classification according USDA</p><p>Texture classification USDA (letter code) - Letter code for soil textural classification according USDA: S=sand; LS=loamy sand; SL=sandy loam; SiL=silt loam; Si=silt; L=loam; SCL= Sandy clay loam; SiCL=Silty clay loam; CL=clay loam; SC=Sandy clay; SiC=Silty clay; C=clay</p><p>Clay (USDA) - % - Soil clay content (weight based) - (<0.002 mm) </p><p>Silt (USDA) - % - Soil silt content (weight based) - (0.002 < x < 0.05 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006) </p><p>Sand (USDA) - % - Soil sand content (weight based) - (0.05 < x < 2 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006)</p><p>USDA PSD interpolated - =0 if the data was NOT interpolated; =1 if the data was interpolated</p><p>Published texture class - Texture classification provided in the source publication when the values for clay, silt and sand were not available</p><p>Clay - g kg-1 - Soil clay content - original in the paper</p><p>Clay class upper boundary - µm - The clay class upper boundary informed in source publication</p><p>Silt - g kg-1 - Silt clay content - original in the paper</p><p>Silt class upper boundary - µm - The silt class upper boundary informed in source publication</p><p>Sand - Soil sand content - original in the paper</p><p>Sand class upper boundary - µm - The sand class upper boundary informed in source publication</p><p>Particle size data flag - =0 if no issues; =1 if there are issues (summing)</p><p>Sum particle size- g kg-1 - Sum of clay, silt, and sand content</p><p>Soil depth FROM – cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the minimum depth at which soil samples were collected </p><p>Soil depth TO – cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the maximum depth at which soil samples were collected </p><p>Depth – cm -Specific depth value as presented in paper, or when soil depth is showed as a range (e.g., 0-10cm), it indicates the average depth at which soil samples were collected (e.g 5cm) </p><p>SOC - g kg-1 - Soil organic carbon content informed in research paper or soil organic carbon content calculate from soil organic matter content by multiplying by 0,58 </p><p>SOC converted from SOM - 1= yes for soil organic carbon derived from soil organic matter content calculations</p><p>Particle density - Mg m-3 - Soil particle density </p><p>Initial matric potential – hPa - Soil water matric potential before loading</p><p>log Initial matric potential - Soil water matric potential expressed by log </p><p>Wetness (based on initial matric potential) - 1=if initial matric potential (MP)<100 hPa; 2= if 100<=initial MP<1000 hPa; 3= initial MP>=1000 hPa</p><p>Initial gravimetric water content - g g-1 - Gravimetric soil water content before loading provided by source publication, or calculated by volumetric water content divided by soil bulk density</p><p>Initial volumetric water content - m3 m-3 - Volumetric soil water content before loading, when the soil bulk density was not reported</p><p>Initial water content data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>Matric potential type - Compressive tests performed on soil samples under different conditions: 1= equilibrated at matric potential; 2= field matric potential; 3= air-dried samples </p><p>Initial bulk density - Mg m-3 - Soil bulk density before loading </p><p>Initial BD data source - Graph or table from where the data was collected, or explanation on calculation used </p><p>Initial volumetric water content calculated - m3 m-3 - Soil volumetric water content calculated by multiplying soil gravimetric water content by soil bulk density</p><p>Precompression stress – kPa - Precompression stress </p><p>Precompression stress (SD) – kPa - Standard deviation for precompression stress values reported in paper </p><p>Precompression stress data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>Compression index - Compression index </p><p>Compression index (SD) - Standard deviation of compression index values reported in paper </p><p>Compression index data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>Swelling index - Swelling index </p><p>Swelling index (SD) - Standard deviation of swelling index values reported in paper </p><p>Swelling index data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>N - Number of replicates used for calculating precompression stress, compression index, and swelling index when mean values are reported</p><p>Land use (paper) - Land use described in the research paper</p><p>Land use (categories) - Land use categorized</p><p>Land use standardized - Land use classified as: arable, forest, grassland, and native vegetation. The latter includes forest, grassland, and savanna</p><p>Land use (number code) - Number code for land use: 1=Arable, 2= forest, 3= grassland, and 4= native vegetation</p><p>Tillage system - Tillage system</p><p>Tillage system (arable soils) - Tillage system for arable soils classified as "conventional" and "conservation"</p><p>Coordinates - Geographical coordinates of study location</p><p>Climate - Climatic region classification: temperate, tropical, subtropical</p><p>Climatecod - Code number assigned to each climatic region: 1=temperate, 2=tropical, 3=subtropical</p><p>Sampling position (paper) - Field position where soil samples were collected with details described in the paper</p><p>Sampling position - Field position where soil samples were collected standardized</p><p>Treatment - Experimental treatment type where the soil samples were collected</p><p>Stress rate - kPa - Stress applied in compressive tests </p><p>Minimum stress – kPa - Minimum stress applied in compressive tests</p><p>Maximum stress – kPa - Maximum stress applied in compressive tests</p><p>Number of stress rate steps - Number of steps in stepwise stress application procedure</p><p>Stess application type - 1=Stepwise stress 2=one sample per stress 3=Strain controlled</p><p>Stess application type – min - Time for stress application in each step in stepwise stress application procedure</p><p>Degree of deformation at the end of loading - % - Degree of deformation at the end of compressive test</p><p>Sample diameter – cm - Diameter of the soil samples</p><p>Sample height – cm - Height of the soil samples</p><p>Ratio sample diameter and height - Ratio between diameter and height of the soil samples</p><p>Sample volume - cm3 -Sample volume when the sample diameter and height are nor presented</p><p>Precompression stress calculation method - Calculation method of precompression stress</p><p>Precompression stress calculation method (number code) - Number code for calculation method PC:1=Casagrande (1936); 2=Dias Junior and Pierce (1995); 3= Lamandé et al. (2017); 4=O`Sullivan and Robertson (1996); 5=Casini (2012); 6=Culley and Larson (1987);7=ABNT (1990); 8=Gregory et al. (2006)</p><p>Description of precompression stress calculation - Brief explanation of precompression stress calculation</p><p>Soil compressive curve components - Component of the soil compression curve related to the soil packing state: soil bulk density, void ratio, and strain. </p><p>Soil compressive curve components (number code) - Number code for component of the soil compressive curve related to the soil packing state: 1= soil bulk density; 2= strain; 3= void ratio</p><p>Curve components source - Source of the component of the soil compressive curve related to the soil packing state: 1= showed in the paper, 2= according to original method for precompression stress calculation, 3= described in method, but not clear in the paper</p><p>Compressive curve available - Original soil compressive curve available in the paper: 1= No 2=Yes</p><p>Comments - Brief comments on the paper</p><p><strong>Issues and remarks</strong></p><p>We sought out important information not included in the paper by directly communicating with the authors whenever possible. In cases where multiple papers covered the same experiment, we prioritized the one offering more comprehensive details. If two papers complemented each other, we included both. When analyzing studies comparing various methods for calculating soil precompression stress, we exclusively gathered data calculated using the widely accepted Casagrande (1936) method. To ensure comparability across studies, we standardized the collected data by converting it to the same unit. The standardization process involved: i) assuming that 58% of soil organic matter (SOM) was soil organic carbon (SOC) when only SOM was reported, ii) calculating soil bulk density using a soil particle density of 2.65 Mg m-3 when only total porosity data were provided, and iii) harmonizing all texture data to the USDA classification system, which defines the silt/sand boundary as 50 μm, utilizing the k-nearest neighbor approach (referred to as "similarity method" by Nemes et al. (1999). </p><p><strong>Reference</strong></p><p>Associação Brasileira de Normas Técnicas - ABNT. NBR 12007: Ensaio de adensamento unidimensional. Rio de Janeiro: 1990.</p><p>Casagrande, A., 1936. Determination of the preconsolidation load and its practical significance. In: Proceedings of the International Conference on Soil Mechanics and Foundation Engineering, vol. III, Harvard University, Cambridge, MA, pp. 60–64.Casini, F. 2012. Deformation induced by wetting: A simple model. Can. Geotech. J. 49:954–960 10.1139/T2012-054. doi:10.1139/t2012-054</p><p>Culley, J.L.B., Larson, W.E., 1987. Susceptibility to compression of a clay loam Haplaquoll. Soil Sci. Soc. Am. J. 51, 562–567.</p><p>Dias Junior, M.S., Pierce, F.J., 1995. A simple procedure for estimating preconsolidation pressure from soil compression curves. Soil Technology 8, 139–151. doi:10.1016/0933-3630(95)00015-8</p><p>Gregory, A.S., Whalley, W.R., Watts, C.W., Bird, N.R.A., Hallett, P.D., Whitmore, A.P., 2006. Calculation of the compression index and pre-compression stress from soil compression test data. Soil Till Res. 89:45-57. doi:10.1016/j.still.2005.06.012</p><p>Lamandé, M., Schjønning, P., Labouriau, R., 2017. A novel method for estimating soil precompression stress from uniaxial confined compression tests. Soil Sci. Soc. Am. J. 81 https://doi.org/10.2136/sssaj2016.09.0274.</p><p>Nemes, A., Wösten, J.H.M., Lilly, A., Oude Voshaar, J.H., 1999. Evaluation of different procedures to interpolate the cumulative particle-size distribution to achieve compatibility within a soil database. Geoderma 90: 187-202. 129 </p><p>O'Sullivan, M.F., Robertson, E.A.G., 1996. Critical state parameters from intact samples of two agricultural topsoils. Soil Tillage Res 39(3 – 4):161 – 173.</p>
Data of "Effects of bacteria-embedded polylactic acid (PLA) capsules on fracture properties of strain hardening cementitious composite (SHCC)"
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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)
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.