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Nanoindentation data of W and W-Ta thin films
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We focused on learning that improves skills while using students’ physical senses and developed a simulation using augmented reality to teach body positioning for radiography. This simulation allows students to simulate angle placement repeatedly by using both their hands without positioning aids and angle gauges. The simulation was used in radiography practical training and a questionnaire survey was conducted after the class (Layout_&_Rawdata.xlsx). The responses ‘not confident’ and ‘not very confident’ accounted for 71% (χ2 = 7.05, p = 0.008) of a question regarding the students’ perception of their confidence using computer technology (ChisquaredTest.html). All students responded positively to question 1 and questions 3–6 that were regarding learning using the simulation. However, there were a certain number of ‘neither agree nor disagree’ (n = 12, 29%) or ‘disagree’ (n = 2, 5%) responses for question 2. There was no significant correlation between the responses to question 2 and those of students’ computer confidence levels (ρ = −0.03, p = 0.13) (Polychoric_correlation_coefficient.html).
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The clinical, radiomics, adn so on.
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The data were originally collected for the paper "Olfactory Response as a Marker for Alzheimer’s Disease: Evidence from Perception and Frontal Oscillation Coherence Deficit" in Ziaeian Hospital, Tehran, Iran. The study was conducted on mild AD and normal participants. This data includes EEG from 4 channels (Fp1-Fz-Cz-Pz) with A1 earlobe as reference. The sampling frequency is 200 Hz. It contains EEG segments during olfaction of two odors (Lemon and Rosewater). Each segment contains 1 second before and 2 seconds after the stimulus onset. Segments corresponding to Lemon odor are indicated by 0 and Rosewater segments are denoted by 1 in a vector inside the dataset. In addition, noisy epoch numbers are included in the dataset. The order of the channels in the dataset is as follows: Fp1 - Fz - Cz - Pz. The data is preprocessed and eye-blink artifact was removed using FastICA algorithm. Data on Iran-SIT score as well as participants' MMSE scores can be found in .xlsx file. If you used this data and found it helpful, please cite our paper.
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This dataset support our methods publication recently accepted in Ecosystems Manuscript highlights • Stable isotope analysis suggests δ13C-CH4 oxidation and fractionation occurs during transport • Substantial fine-scale vertical and radial heterogeneity identified in tree stem CH4 emissions • Novel smartphone 3D photogrammetry can more accurately estimate the tree stem surface area compared to traditional methods • Fine-scale sampling method shows 86-89% of methane flux emanates from the lower 30cm of wetland forest tree stems   Manuscript Abstract: Tree stem methane emissions are gaining increasing attention as an overlooked atmospheric source pathway. Existing methods for measuring tree stem greenhouse gas fluxes and isotopes may provide robust integrated emission estimates, but due to their coarse resolution, the capacity to derive insights into fine-scale dynamics of tree stem emissions are limited. We demonstrate and field-test an alternative method that is Small, Nimble, In situ and allows for Fine-scale Flux (‘SNIFF’) measurements, on complex and contrasting stem surfaces. It is light weight and therefore suitable to remote field locations enabling real time data observations allowing for field-based data driven sampling regimes. This method facilitated novel results capturing fine-scale vertical and radial methane flux measurements (5cm increments) and revealed: (1) 86-89% of methane emissions emanated from the lower 30cm of sampled wetland tree species; (2) uncovered clear vertical and horizontal trends in δ13C-CH4 possibly due to fractionation associated with oxidation and mass-dependant fractionation during diffusive transport; and (3) demonstrated how substantial radial heterogeneity can occur. We also compared a variety of upscaling approaches to estimate methane flux per tree when using this method, including novel smartphone 3D photogrammetry, that resulted in a substantially higher stem surface area estimation (>16 to 36%) than traditional empirical methods. Utilising small chambers with high radial and vertical resolution capabilities may therefore facilitate future assessments into the drivers, pathways, oxidation sinks and magnitude of various tree stem greenhouse gas emissions, and compliment previous broad-scale sampling techniques.
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The structral after preprocessing with SUIT in normal controls, amnestic mild cognitive impairment and Alzheimer's disease. The functional data could can be obtained by contacting the author, dantao.peng@outlook.com
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Complete source code, dataset, images, and calibration code.
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This is a survey data on 164 professionals that includes university faculty, senior administrative, technical, and managerial staff of an institution f higher learning. The question is to what extent do these professionals know about and used GIS/RS in their routine and research work? The hypotheses are (1) those who were aware and know GIS/RS were more likely to use the tool and this varies by background factors, (2) respondents who know about the tools were more likely to use them. Variables examined include background factors (including residence, age, marital status), education, and years of work experience. Other key variables examined were awareness, knowledge, attitudes and application and the tools. Findings showed that awareness about GIS/RS was significantly related to residence, age, marital status, education, type of carrier or profession. Knowledge was significantly associated with sources heard about GIS/RS. Also, obstacles to use was significantly related to residence, age, type of carrier or profession, and sources heard about GIS/RS. Finding of this data will be useful in driving increased knowledge and use of GIS/RS for planning and research all over the world.
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The data is reported in the article entitled "Estimation of the Critical Infiltration Rate for Air Compression During Infiltration" written by Yunbo Wei, Kouping Chen and Jichun Wu. The air pressure data can be found in the file "Figure3 - air pressure.xlsx". The air saturation data of Figure 4 is named in the form of "grade_rate.txt". The matlab programs that draw Figure 7 and 8 is can be found in the file "Figure7.m" and "Fiugre8.m". The data in other figures, as well as the calculation process, can be found in the corresponding files.
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The data set contains Hadamard matrices of order 28 in machine-readable form, convenient for use in programs. Data taken from the site http://www.maths.gla.ac.uk/~es/hadamard/hadamard.php This data set complements following ones Ukhalov, Alexey; Nevskii, Mikhail (2018), “Functions for checking necessary conditions for maximality of 0/1-determinant and example”, Mendeley Data, v1 http://dx.doi.org/10.17632/sm3x4xrb42.1 Ukhalov, Alexey (2019), “Matrices having the largest known determinant in machine-readable form”, Mendeley Data, v1 http://dx.doi.org/10.17632/hzf94h43c5.1 Data is presented in three formats: Wolfram Mathematica Notebook, PDF, and Plain Text.
Data Types:
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