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2,214 results for “Strength”
IODP Expedition 372A Vane shear strength (AVS)
<p>Shear strength was measured on section halves using a GEISA automated vane shear (AVS) frame and device controller. The device is suitable for sediment not affected by cementation (i.e., saturated, clay-rich, soft sediment). Shear strength measurements should be considered only approximate, particularly because the influence of pore pressure changes during the undrained experiment cannot be estimated. Report includes vane shear strength at the sample's failure point, maximum torque angle, penetration direction, and rate of vane rotation.</p>
IODP Expedition 374 Vane shear strength (Torvane)
<p>Sediment strength was measured on section halves using a Humbolt Manufacturing Torvane handheld instrument. The Torvane is suitable for sediment not affected by cementation (i.e., saturated, clay-rich, soft sediment).</p>
Strength of a Woman
<p>A song by Finity "Strength of a Woman" had its premiere during the concert "From Men To Women" at DUOS Festival "Women Power" in Kilifi, September 4, 2021, within the project "Transcultural Perspectives in Art Education".</p> <p>Finity belongs to collective AFiNiTY AFRiCA at the Pwani University in Kilifi (Kenya), which a music brand, an affiliate of REIVO MUSIC KENYA RECORDS. It aims at being one of the biggest brands in Africa that embraces real talents and also empowers the society with its channels of charity.</p>
Data for empirical example in: An effect size for comparing the strength of morphological integration across studies
<p>Understanding how and why phenotypic traits covary is a major interest in evolutionary biology. Biologists have long sought to characterize the extent of morphological integration in organisms, but comparing levels of integration for a set of traits across taxa has been hampered by the lack of a reliable summary measure and testing procedure. Here we propose a standardized effect size for this purpose, calculated from the relative eigenvalue variance, Vrel. First we evaluate several eigenvalue dispersion indices under various conditions, and show that only Vrel remains stable across samples size and the number of variables. We then demonstrate that Vrel accurately characterizes input patterns of covariation, so long as redundant dimensions are excluded from the calculations. However, we also show that the variance of the sampling distribution of Vrel depends on input levels of trait covariation, making Vrel unsuitable for direct comparisons. As a solution, we propose transforming Vrel to a standardized effect size (Z-score) for representing the magnitude of integration for a set of traits. We also propose a two-sample test for comparing the strength of integration between taxa, and show that this test displays appropriate statistical properties. We provide software for implementing the procedure, and an empirical example illustrates its use.</p>
Vesicular release probability sets the strength of individual Schaffer collateral synapses
<p>This repository contains the data used to generate the figures of the manuscript entitled 'Vesicular release probability sets the strength of individual Schaffer collateral synapses' and all the data used for the quantal analysis.</p>
IODP Expedition 351 Vane shear strength (AVS)
<p>Shear strength was measured on section halves using a GEISA automated vane shear (AVS) frame and device controller. The device is suitable for sediment not affected by cementation (i.e., saturated, clay-rich, soft sediment). Shear strength measurements should be considered only approximate, particularly because the influence of pore pressure changes during the undrained experiment cannot be estimated. Report includes vane shear strength at the sample's failure point, maximum torque angle, penetration direction, and rate of vane rotation.</p>
Dataset: First Trust Growth Strength ETF (FTGS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: First Trust Capital Strength ETF (FTCS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: First Trust SMID Capital Strength ETF (FSCS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: First Trust Dividend Strength ETF (FTDS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Interactive Strength Inc. (TRNR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Extracting Ridge and Valley Lines in Mountainous Areas from Airborne Lidar Data by Utilizing Line Feature Strength
<p><strong><span>Background</span></strong><strong><span>:</span></strong><span> </span><span>DEMs (digital elevation models) are very important in many fields, such as in Geomatics and in water conservation of mountainous areas etc. Geomorphic feature lines are necessary data for the topography interpolation and computation from DEMs.</span></p> <p><strong><span>Methods</span></strong><strong><span>:</span></strong><span> </span><span>Instead of the parameter space, we propose a novel automatic extraction of Geomorphic feature lines in the feature space from discrete airborne LiDAR (Light detection and ranging) data by TVM (tensor voting method) developed originally for image data in this article. A tensor field for discrete airborne LiDAR points is first established and then utilizing the TVM, a new geometric feature metric of data, the line feature strength, was captured. A practical line growing method based on the local maximum line feature strength is proposed in the article.</span></p> <p><strong><span>Results</span></strong><strong><span>:</span></strong><span> </span><span>Compared with the general line growing that is based on a certain threshold, our line growing method is quite effective, in particular for the extraction of primary and minor ridge and valley lines in mountainous areas.</span></p> <p><strong><span>Conclusions</span><span>:</span></strong><span> </span><span>The method presented in this paper is fast and automated and can furnish operators with a wealth of detailed information about minor line features. This will enable the extraction of ridge and valley lines tailored to specific requirements. It is no doubt that the method developed here can be generalized to a large amount of Lidar data.</span></p>
Brain Journal-The Status of Positive Psychology Strengths within the Romanian School in the Digital Society -Figure 1. Pie chart vitalitysuccess
<p>The Pie charts above illustrate the distribution of responses for the "Vitality" strength. It can<br> be noticed how the lower values are much more frequent than the high ones, in terms of promoting<br> this strength in school. Thus, the participants at this study consider that school does not attach<br> enough importance to this strength, even though it has proven to be so mandatory in assuring the<br> students' well-being and personal success.</p>
BRAIN Journal-The Ambivalence of Strengths and Weaknesses of E-Learning Educational Services-Figure 2. Frequency of principal strong indices related to 102 'strong articles'
<p>We ordered the obtained data for the identified strengths and weaknesses. This was done by giving a point for each presence of an index, or not giving it for its absence. Thus, we quantified a total of 63 strengths and 91 weaknesses. In addition to finding a great diversity of views captured in the selected texts, the primary data analysis allowed us to set the parameters with the highest frequency. So, the most important for strengths is flexibility representing 29.7% of the total of studied bibliography and an occurrence frequency of 13.6% from the total of identified strengths (Figure 2). </p>
BRAIN Journal-The Ambivalence of Strengths and Weaknesses of E-Learning Educational Services-Figure 1. Frequencies of strengths and weaknesses through a survey of literature from 2000 to 2012
<p>We identified 192 specific studies which analyze, directly or indirectly, the subject of the strengths and weaknesses of e-learning educational services, respectively those containing the idea of some of their strengths and weaknesses ambivalence. The frequencies of strengths and weaknesses reported to intervals corresponding to the years when they were published are shown in Figure1. </p>
BRAIN Journal-The Ambivalence of Strengths and Weaknesses of E-Learning Educational Services-Figure 3.Frequency of principal week indices related to the 101 'week articles'
<p>Reduced social interaction is the most important for weaknesses, representing 9.4% of the total of studied bibliography and an occurrence frequency of 5.5% (Figure3). A significant frequency difference of the weaknesses indices compared to the strengths indices was observed. If the top 5 strengths have frequencies ranging between 13.6% and 7%, none of the weaknesses has an occurrence frequency over 6%, all barely ranging between 5.5% and 3.4%, relative to the middle of strengths frequency range. For the practice of e-learning educational services, this may show either a still insufficient detection or theoretical analysis of weaknesses, or, indeed, the superiority of these services.</p>
BRAIN Journal-The Ambivalence of Strengths and Weaknesses of E-Learning Educational Services-Figure 5. Frequency of common indices related to 102 'common articles'
<p>An additional and very interesting perspective is provided by the analysis of common indices (ambivalent), based on the 102 works-common articles type, and their frequencies (Figure 5). </p> <p>The data reading indicates 13 ambivalent indices as percentages in descending order: 1. flexibility, 19%; 2. interactivity, 15.4%; 3. cost, 13%; 4. accessibility, 12%; 5. time, 7%; 6. anxiety/reduce social impact, 7%; 7. usability, 6%; 8. connection, 4%; 9. develop skills, 4%; 10. responsibility, 4%; 11. quality, 4%; 12. diversity, 3%; 13. delivery, 1% (Figure 5).</p>
BRAIN Journal-The Ambivalence of Strengths and Weaknesses of E-Learning Educational Services-Figure 4. Frequency of strong/weak indices related to the 102 'common articles'
<p>Based on the data presented in Figure 2 and Figure 3 and carrying out a comparative and cumulative analysis to identify ambivalent strengths and weaknesses, the 102 ambivalent strong/weak indices related to the 'common articles' in Figure 4 were highlighted. </p> <p>Their analysis shows that: 1. There are at least 13 ambivalent indicators identified in the studied literature (Table 1); 2. The strengths weight is 62% while the weight of weaknesses is 38.9%, resulting in a pretty big difference in favor of underlining and supporting the strengths, 23.1% more than in favor of weaknesses. These data indicate a significantly higher perception and approach in favor of appreciating the strengths of e-learning educational services, even in the case of their ambivalence. 3. In this context, the data illustrate the following three cases: 3.1. a huge gap between the perception and the interpretation of an index as strength and as weakness (e.g., flexibility is regarded 7.5 times more a strength rather than a weakness). It can be seen that this category of indices definitely belongs to strengths, acknowledged and validated by a large number of studies. In relation to these, efforts will be made for the development, improvement, elevation and obtaining superior parameters. 3.2. a relative correspondence between the perception and the interpretation of an index as strength and as weakness (e.g., the time required to design and implement educational services is considered a strength at a rate of 3.2% and a weakness at a rate of 3.3%). It results that this category of indices has to be studied thoroughly and watched in experimental studies, to replace the uncertainty area in their analysis, to determine which their area of predominance is, to what extent their identified limits and shortcomings have been reduced to allow their conversion into strengths or not; 3.3. a very large gap between the perception and interpretation of an index as a weakness and as a strength (e.g., lack of instructional delivery is considered as being a weakness 20 times more than a strength).This shows that this category of indices comes into focus as weaknesses which need to be analyzed, studied and experimented in order to reduce their negative impact. </p>
Compressive Strength of Model Ice Measurements
<p>This data package contain a detailed measurement report and the measurements data from the mechanical property measurements conducted in HSVA Large Ice Tank.<br>This work is part of HYDRALAB+ Adaptation for Climate Change – project that was supported by the European Community's Horizon 2020 Programme through the <br>grant to the budget of the Integrated Infrastructure Initiative HYDRALAB+, Contract no. 654110. The measurements are a part of the deliverable D9.5 Collection of Benchmark data sets of Joint Research Activities (JRA) 2 – COMPLEX (Cross disciplinary Observations of Morphodynamics and Protective structures, Linked to Ecology and eXtreme events) within Hydralab+ project.</p> <p>The focus of the tests was to provide compressive strength measurement data when the target flexural strength and aspect ratio (length to width ratio) is varied. The Young's modulus, flexural strength, salinity, and density are reported along previous lines. Additionally, the measurement data, raw and filtered, from the compressive strength measurements is provided. The provided measurement data was saved in ASCII format in .txt files. The files are named according to the number of the test, 001.txt etc. The first column in the files is the time stamp in seconds. The second column is the raw unfiltered data in Newtons. The third column is the filtered measurement data in Newtons.</p>
Classification of small strength-2 covering arrays
<p>The files contain strength-2 covering arrays classified in the study "J. I. Kokkala, K. Meagher, R. Naserasr, K. J. Nurmela, P. R. J. Östergård, B. Stevens, On the Structure of Small Strength-2 Covering Arrays".</p> <p>Each line in a file corresponds to a <span class="math-tex">\(\mathrm{CA}(N;2,k,v)\)</span>, rows of the covering array are separated by space. The file name format is <em>ca_N_2_k_v.txt</em> if all covering arrays were classified. Whenever only uniform covering arrays are classified, the file name is <em>uca_N_2_k_v.txt</em>. An additional suffix <em>_delta_δ</em> in the file name implies that only covering arrays with that <span class="math-tex">\(\delta\)</span> parameter (see the publication) were classified. In some cases, the number of inequivalent covering arrays was too large for the arrays to be stored; those cases are omitted here.</p>
ScienceDex guides
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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.