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Results of reliability assessment of punching shear resistance models for flat slabs without shear reinforcement through three different reliability analysis techniques: Mean Value First Order Second Moment Method (MVFOSM), First Order Second Method (FOSM) and a Monte-Carlo Simulation with Importance Sampling (MC-IS).
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Band gaps and Total Density of States for calculated lead-free perovskites
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The research question for this dataset was: How will climate change impact the growth of spring wheat in Fairbanks, Alaska? The DSSAT CERES-Wheat crop simulation model was used to answer this research question. Data consists of DSSAT V4.7.0.0 Files and Field Data. Data in the Field Data files were collected at the University of Alaska Fairbanks small grains variety trial plot in Fairbanks, AK. This field data was input into DSSAT. DSSAT Files were used to calibrate, validate, and apply the DSSAT CERES-Wheat crop simulation model for simulating spring wheat growth (cultivar Ingal) in projected climate change scenarios. These DSSAT files are ready for a modeler to use.
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This data set contains the entry, descent and landing parameters of Mars lander with approximate estimation reference area, ballistic coefficient and drag force. These parameters have been gathered from research articles and reliable online resources
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The dataset contains the data collected in a user study carried out to evaluate the impact of using domain knowledge, ontologies, in the creation of global post-hoc explanations of black-box models. The research hypothesis was that the use of ontologies could enhance the understandability of explanations by humans. To validate this research hypothesis we ran a user study where participants were asked to carry out several tasks. In each task, the answers, time of response, and user understandability and confidence were collected and measured. The data analysis revealed that the use of ontologies do enhance the understandability of explanations of black-box models by human users, in particular, in the form of decision trees explaining artificial neural networks.
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GIS data associated with Neugarten RA et al. 2020. Trends in protected area representation of biodiversity and ecosystem services in five tropical countries. Ecosystem Services 42:101078. Includes data from Cambodia, Guyana, Liberia, Madagascar, and Suriname Datasets included: country boundaries, protected areas in 2003 and 2017, biodiversity priority areas, forest cover in 2003 and 2015, forest carbon stocks, non-timber forest products, and freshwater ecosystem services
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The dataset provides a chaos game representation (CGR) of SARS-CoV-2 virus nucleotide sequences. The dataset is composed of 100 virus instances of SARS-CoV-2. In addition, the dataset also provides a CGR representation of 11540 viruses from the Virus-Host DB dataset and the other three Riboviria viruses from NCBI.
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The models of doing for school mathematics varies and mainly based on etymological sense of meaning according to words or phrases in a sentence. The students positioned their thinking based on word understanding of a proof problem. They also relate between presented information and formula in doing proof. When facing visual representations, the students tend to prove by algebra. The visual representations to be imagined merely as a tool for arranging a formal proof, i.e. when so many algebra symbols in proving, including formula and the procedures. For algebraic or analytic or symbolic-illustrative representation, the mathematics proof is just symbolic manipulation than the meaning. In doing proof, a meaning of word or the combination construct a pattern of proof changed to mathematics statement. For example, a word 'to test' has multi-example pattern. While, 'to judge' shows logical pattern. The etymological sense is a learning understanding of doing proof. There s an intersection of the etymological and the understanding. Data presented in Etymological-Understanding axis. The intersections moved from information (or data) toward pattern in proof problem. There are two other intersection, i.e. meaning and experience. That is an easy way to prove much of mathematics problems. When facing to the difficult proof problem, a set of the intersection have an action role in doing proof to an intervention of the etymological sense of meaning. That is a real learning activities, specifically in doing proof. . There is also an intersection of doing proof between word meaning and logic. That is a new culture in doing mathematics proof, a belief of the learning. The data embedded in a four quadrant of Monophonic-Context axis and Velocity-Viscosity axes (Fig. 2). The students' experience also appear in thinking, drawing (a step to a proof), testing, and developing their performances. The etymological sense of word or phrase meaning brings the students to a broader or more rational (not common sense) ways to prove. The proof trajectories growth cross mathematics content knowledge. That is psychological context addressed by students' belief and determined using the empirical proof. The fitted of the empirical data mapping its model as an entropy value indicated 'creative proof', i.e. in illustrating, describing mathematics representations, making a rational (or consistence) relation, and generating a proof. The models of proof show the differences representation. When the students ask to 'try', then they elaborate a proof by cases. . But, to 'determine' made more algebra thinking. Finally, there are performance levels in doing proof etymologically. Nine performances aroused as variables of etymological sense of meaning. That is a measure of teaching and learning of doing proof. That is an exploring of the etymological toward more proof representation. That is way to assess doing proof through meaning of word. .
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This dataset contains records of an experimental research project on energetic expenditure during walking and carrying burdens in a sample of humans, of both sexes, aged from 23 to 50 years old. The dataset also contains some anthropometrical and body composition data of the subjects. Metabolic rates were also recorded during the different tests, performed at the BioEnergy and Motion Lab (LabBioEM) of National Research Center on Human Evolution (CENIEH, Spain) in 2015. Main outcomes and findings of the experimental project were published in several works (Prado-Nóvoa et al., 2019; Vidal-Cordasco et al., 2017; Zorrilla-Revilla et al., 2017). The list of references and the description of recorded variables are included in the data and document files.
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