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  • A crack data set for object detection based on deep learning.
    Data Types:
    • Image
    • Dataset
    • Document
  • Supplementary data for Original article
    Data Types:
    • Image
    • Dataset
    • Document
  • Cavitation inside fuel injection nozzles affects the atomization process of injected liquids. It is necessary to understand and model the process for realizing an appropriate injection strategy for an efficient combustion. As the nature of the fuel injector, it has contraction, divergent and bending parts from small to large scale. These geometrical characteristics of the nozzle have an effect on the cavitation phenomena even if it is kind of a small manufacturing variation. However, a simultaneous contained database for the transient cavitation structure especially inside the real-scale nozzle and the nozzle geometry has not been established well. Therefore, parametric investigations have been done on our manufactured transparent nozzles. And the results will be shared step by step for the cavitation model evaluations and developments. In this database, the results in below on each nozzle are uploaded. 1. High speed imaging of the transient cavitation structure. 2. Nozzle geometry which modified as close as the measured shape. 3. Samples of mesh files and simulation results (PDF). The purpose of this database is to provide the data to someone who intends to understand and model the cavitation phenomena. This research work is financially supported by German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) within the project DI 591/29-1. First of all, please read "About_this_database.pdf".
    Data Types:
    • Other
    • Geospatial Data
    • Image
    • Dataset
    • Document
  • 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.
    Data Types:
    • Software/Code
    • Dataset
    • Text
  • 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
  • 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
  • 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
  • This dataset includes four bioinformatic pipelines to analyze data generated through 3' RACE-seq or TAIL-seq experiments in Arabidopsis thaliana or in Nicotiana benthamiana. These pipelines allow measuring the mRNA poly(A) tail length and detecting other 3’ untemplated nucleotides. This dataset contains all scripts and files that are required for the analyses, including all homemade python and bash scripts.
    Data Types:
    • Software/Code
    • Tabular Data
    • Dataset
    • Text
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