Filter Results
9954915 results
  • Using all stocks listed in the Australian Securities Exchange and macroeconomic data for Australia, the dataset comprises the following series: 1. Monthly returns for 20 size-price to cash flow portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database) 2. Monthly returns for 25 size-book to market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database) 3. Monthly returns for 41 industry portfolios. (Raw data source: Datastream database) 4. Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for Australia. (Raw data source: OECD) 5. Fama and French (1993) factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database) 6. Fama and French (2015) factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology. (Raw data source: Datastream database) 7. Three-month interest rate of the Treasury Bill for Australia. (Raw data source: OECD) We have produced all return series using the following data from Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) price-to-cash flow ratio (PC series), (v) primary SIC codes, and (vi) tax rate (WC08346 series). We use the rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data. REFERENCES: Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277.
    Data Types:
    • Dataset
    • Text
  • Materials used to produce figures in the manuscript entitled "Photonuclear Reactions in Lightning II: Comparison between Observation and Simulation Model" (Y. Wada et al., submitted to Journal of Geophysical Research - Atmospheres) are included.
    Data Types:
    • Other
    • Tabular Data
    • Dataset
    • Text
  • Mendeley Supplemental Tables and Figures for Paller A, et al. Efficacy and Patient-Reported Outcomes from a Phase IIb, Randomized Clinical Trial of Tapinarof Cream for the Treatment of Adolescents and Adults with Atopic Dermatitis. J Am Acad Dermatol. 2020
    Data Types:
    • Dataset
    • Document
  • Filename: TestData.xlsx 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. Filename: Hyperelastic_vs_Viscoelastic_Nonlin_Modelling.m This is a main processing MATLAB program file. It calculates the stress using nonlinear least squares curve fitting routine. Filename: funTestData_ShoulderTendon.m 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. Filename: funyehmod.m This MATLAB function implements the Yeoh material model. Filename: funstdnonlinsolid.m This MATLAB function implements the standard nonlinear solid model using the sigmoidal kernel function.
    Data Types:
    • Software/Code
    • Tabular Data
    • Dataset
  • This a data about the corona virus COVID-19. It contains the actual reported data. Also, it includes the predicted COVID-19 data in the future based on a model developed to predict in the future. The model used will be published in one of the journals later and will be found on my profile with title "Optimistic Prediction Model For the COVID-19 Coronavirus Pandemic based on the Reported Data Analysis". The daily folder contains the daily data. The predicted folder contains the predicted data for each country. The total cases folder contains the total cases for each country. he section folder contains a latex code for plotting the figures for each country. Also the source file from European Centre for Disease Prevention and Control is included. More updated files available in the website of European Centre for Disease Prevention and Control.
    Data Types:
    • Software/Code
    • Tabular Data
    • Dataset
    • Document
    • Text
  • The digital entrepreneurship research & publication dataset, which was indexed by Scopus from 1993 to 2019. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, Correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.
    Data Types:
    • Tabular Data
    • Dataset
  • CMIP6 and observational data for trend comparisons in the lower- and mid-troposphere
    Data Types:
    • Tabular Data
    • Dataset
  • Supplemental material: 1. Additional File 1 (pdf) Figure S1. Overview of study design. Figure S2. Complete workflow followed in the present study. Figure S3. Differential association between arachidonic acid and BMI across CS and control group. Figure S4. PCA and PLS-DA models of metabolomic signature. 2. Additional File 2 (xslx) Table S1. Chromatographic separation and mass spectrometric detection conditions. Table S2. Raw concentration and biochemical data of the identified metabolites. Table S3. Descriptive statistics of metabolite concentrations. Table S4. Descriptive statistics of total concentrations from metabolic classes. Table S5. Concentration changes of serum metabolic classes in Cushing syndrome compared with control group. Table S6. Spearman correlations between the concentrations of metabolites from the same metabolic class grouped by CS. Table S7. Classification performance and selection of the PLS components. Table S8. Metabolomic signature performance based on sPLSDA model. Table S9. Pairwise correlations between the 374 metabolites assessed. Table S10. Differential correlations across groups between metabolites of the same metabolic class. Table S11. Cushing syndrome differential network correlations. Table S12. Centrality measures of differential network analysis. Table S13. Altered biochemical canonical pathways during CS.
    Data Types:
    • Tabular Data
    • Dataset
    • Document
  • Source data for figures in the related paper, including raw data underlying graphs and uncropped versions of gels or blots presented in the figures
    Data Types:
    • Image
    • Sequencing Data
    • Tabular Data
    • Dataset
  • The dataset linked with the data in brief article on biogas potential of cattle manure and olive cake.
    Data Types:
    • Tabular Data
    • Dataset
7