In the datasets, the scale of organizational culture based on eleven items of primary cultural values (PCV) and nine items of secondary cultural values (SCV). Work motivation variables in the datasets used four items of motive motivation (MM), seven items of expectation motivation (ME), and with nine items of incentive motivation (IM). Interpersonal communication variables were seven items of social sensitivity (SS), nine items of social insight (SI), and four items of social communication skills (SCM). The items, including labels and ratings, will be explained later, with more comprehensive details as supplementary material (see "Annotated Questionnaire" in supplementary material). This was because each item had different rankings and choices. To measure teacher performance used three scales from the book of effective teacher performance. The teacher performance scale used three variables of thirteen items of the learning process (LL), four items of scientific work (SW), and three items of service (S).
The data set contains Hadamard matrices of order 28 in machine-readable form, convenient for use in programs.
Data taken from the site
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.
Contributors:Kerem Özkap, Ertan Peksen, Ismail Kaplanvural, Deniz Çaka
This data and code are associated with the article "3D Scanner Technology Implementation to Numerical Modeling of GPR" by the same authors. The 3D scanner data and Matlab code used in the article are provided with other necessary files. The Readme file comprises detailed descriptions of the data files and formats.
Please see the publication for more information about this data set.
This data was collected during a study on the perceptions of RAS-produced fish in Germany.
In the discussion of expanding sustainable aquaculture production in the EU, recirculating aquaculture systems (RAS) adopt an increasingly important role. While capable of mitigating certain environmental externalities commonly found with other forms of aquaculture and enabling the intensive production of safe and healthy aquatic products, RAS are also characterized by higher investment and operating costs which require higher sales prices. Incorporating principles from the theory of planned behavior, multi-attribute attitude models and a taste test, this research investigates consumers’ and fine dining chefs’ perception of RAS-produced fish using the example of pikeperch (Sander lucioperca). While perception and stated purchase intention of RAS-produced pikeperch is generally positive, results reveal a comparably small portion of consumers willing to pay above average prices. Healthiness related aspects are of greatest relevance to both investigated groups and regression analysis shows that personal norms and moral obligation are significant positive predictors of consumers’ purchase intention.
In this research, we developed a high throughput method to systematically map functional connections from the dorsal cortex to the thalamus in awake mice by combing optogenetic inactivation with multichannel recording.
Here, we provide:
1. Raw data of multi-units and single-units obtained in this study.
2. MATLAB codes for data analysis.
Contributors:Bence Bakos, Balázs Szili, Boglarka Szabo, Peter Horvath, Gyöngyi Kirschner, János Kósa, Erzsebet Toldy, Péter Lakatos, István Takács
Background: Vitamin D metabolism and obesity have been linked by several studies, however the reason for this association is unclear. Our objective was to investigate potential correlations between genetic variants in key enzymes of vitamin D metabolism and body mass index on a representative sample of the Hungarian adult population. Methods: Altogether 462 severely vitamin D deficient individuals were studied at the end of winter to minimize environmental and maximize any relevant genetic effect. Furthermore, participants with lifestyle factors known to affect vitamin D metabolism were also excluded. We selected 23 target SNPs in five genes that encode key proteins of vitamin D metabolism (NADSYN1, GC, CYP24A1, CYP2R1, VDR). Results: Variants in 2 genetic polymorphisms; rs2853564 (VDR) and rs11023374 (CYP2R1) showed a significant association with participants‘ BMI levels . These associations survived further adjustment for total-, free-, or bioactive-25(OH) vitamin D levels, although the variance explained by these 2 SNPS in BMI heterogeneity was only 3.2%. Conclusion: Our results show two novel examples of the relationship between genetics of vitamin D and BMI, highlighting the potential role of vitamin D metabolism in the physiology of obesity.
Locations, trips, travel times, and travel distances for the simulation of an integrated item-sharing and crowdshipping platform in Atlanta, Georgia, US. Further information is provided in Description_of_files.html.
The data is test stress-strain data for cadaveric human supraspinatus tendon obtained in a uni-axial tensile test on MTS EM Tension testing equipment at University of South Africa's Biomechanics Research Laboratory. The equipment was operated in displacement mode on all the three tests at 0.1 s-1 of strain rate. The samples were not preconditioned. All columns are appropriately labelled to enable researchers to understand what each column represents. The test equipment did not have an environmental chamber at the time of the test so the samples were inevitably exposed to varying conditions before and during the test procedure.
This is a main processing MATLAB program file. It calculates the stress using nonlinear least squares curve fitting routine.
The function is called by the above file to fetch test data from the Excel file 'TestData.xlsx' and extracts only the section within the elastic region. There are three tests in the file and each test data is different in terms of the upper limit of the elastic region. The function calls each test data as specified in its argument.
This MATLAB function implements the Yeoh material model.
This MATLAB function implements the standard nonlinear solid model using the sigmoidal kernel function.