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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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There is a detailed Readme.pdf in the files for the informations about the dataset. The main purpose is providing a dataset for the vibration behavior of a robot manipulator system under the control input of model-associative vibration control (MAVC) prodecure. Velocity profile is shown as [∗,𝑡𝑐𝑜𝑛,𝑡𝑑𝑒𝑐,𝑡𝑚] in study. In the case studies for both simulations and experiments, the parameters are varied as follows; 𝑡𝑐𝑜𝑛 can be valued as 0, 𝑡1ℎ or 2𝑡1ℎ, 𝑡𝑑𝑒𝑐 can be valued as 𝑡1ℎ,2𝑡1ℎ,3𝑡1ℎ,4𝑡1ℎ or 5𝑡1ℎ and 𝑡𝑚 can be valued as 1 or 1.5 seconds for corresponded 90 or 135 angular displacements. Thus thirty different velocity profiles are produced with aim to performed on system. Cases are invastigated with and without performing the MAVC procedure. Than the robot manipulator is examined for both unloaded and loaded cases, therefore total one hundred twenty cases are occured. More details can be found in related study.
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The data supporting all analyses presented in the results of each experiment for manuscript "Differences in time-based task characteristics help to explain the age-prospective memory paradox" (Accepted for publication in Cognition on 31st March, 2020) are provided here as supplementary online material. For each experiment, we provide each participant’s demographic data; proportion correct score for event, time-of-day, and time-interval cued tasks for each setting (i.e., Virtual Week and MEMO); and contextual data (MEMO). The contextual data for Experiment 1 includes reported location and activity; for Experiment 2 and 3: location, activity, and retrospective memory test (recognition of quiz type). Note: odd quiz numbers (e.g., “quiz 1.1”; day 1 quiz 1] are always time-of-day quizzes; and even quiz numbers (e.g., “quiz 1.2”; day 1 quiz 2) are always time-interval quizzes.
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Primary and raw data from the manuscript "CACNA1S haploinsufficiency confers resistance to New World arenavirus infection"
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Identifying Factors Affecting E-customer Loyalty in Gamified Trusted Store Platforms: A Case Study Analysis in Iran
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Supplementary Data. Includes Excel data tables for ages and shapefiles for ages, geomorphology and ice-sheet reconstruction.
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Non-marginal (average) AWARE CFs and WSI CFs: We provide a shapefile, CSV file and KML file of the average AWARE characterization factors (CFs) based on the marginal AWARE CFs from Boulay et al. (2018). We also provide it together with average WSI factors from Pfister and Bayer (2014), since based on the UNEP SETAC recommendation, AWARE should be used together with an alternative scarcity method to test sensitivities (Jolliet et al. 2018). The XLS version of the average AWARE CFs is available from the original publication: Pfister S, Scherer L, Buxmann K (2020) Water scarcity footprint of hydropower based on a seasonal approach - Global assessment with sensitivities of model assumptions tested on specific cases. Science of The Total Environment. https://doi.org/10.1016/j.scitotenv.2020.138188 DATA structure: The CSV files lists CFs for each month (01 to 12) and each methods: AWARE_01 stands for original marginal AWARE CFs of January, AWARE_a_01 represents the newly calculated average AWARE CFs for January, WSI_01 are the marginal WSI CFs for January and WSI_AVG_01 the average WSI CFs for January. The CSV file can be linked to WaterGAP watersheds based on the "BAS34S_ID" . The WaterGAP shapefile is e.g. available at http://www.wulca-waterlca.org/aware.html. The Shapefile and KML file follows the same order but are already linked to the watershed shapefile.
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