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.
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.
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.
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.
The aim of this study was to develop a realistic network model to predict qualitatively the relationship between lockdown duration and coverage in controlling the progression of the incidence curve of an epidemic with the characteristics of COVID-19 in a closed and non-immune population.
Effects of lockdown time and rate on the progression of an epidemic incidence curve in a virtual closed population of 10 thousand subjects. Predictor variables were R0 values established in the most recent literature (2.7 and 5.7), without lockdown and with coverages of 25%, 50%, and 90% for 21, 35, 70, and 140 days in 13 different scenarios for each R0, where individuals remained infected and transmitters for 14 days. We estimated model validity by applying an exponential model on the incidence curve with no lockdown, with growth rate coefficient observed in realistic scenarios. Pairwise comparisons were performed using Wilcoxon test with Bonferroni correction between peak amplitude, peak latency, and total number of cases for each R0 used.
For R0=5.7, the flattening of the curve occurs only with long lockdown periods (70 and 140 days) with a 90% coverage. For R0=2.7, coverages of 25 and 50% also result in curve flattening and reduction of total cases, provided they occur for a long period (70 days or more). Short and soft lockdowns had no relevant effect on incidence or casuistry.
These data corroborate the importance of lockdown duration regardless of virus transmission.
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.
This case represents part of the European high voltage transmission network. It contains 1,354 buses, 260 generators, 1,991 branches and it operates at 380kV and 220kV. The data stems from the Pan European Grid Advanced Simulation and State Estimation (PEGASE) project. The PEGASE original data was obtained from Matpower (Which is also included here). The case has been modified to incorporate an HVDC Link and a MTDC grid. Two files contain the modified data, one in excel and one in AIMMS.dat
Here are the data and the codes used in Hiramoto&Cline (2020). Imaris image data, Matlab code, and data in mat or fig (with metadata) files are provided. These Matlab codes require a statistic toolbox.