Codes to produce the data in the figures in the main paper. These codes utilise the theory established in the methods and Supplemental material. Codes are written in Python (Jupyter) and Wolfram Mathematica notebooks.
Julia code used to simulate the data presented in "Surface Temperature Estimation in Determined Multi-Wavelength Pyrometry Systems"
This datasets contains the model output at 8 assimilation cycles, only mean background or mean analysis in both assimilation Experiments (Exp_all, Exp_b) are uploaded.
Exp_all/analysis: the mean analysis of Exp_all
Exp_all/background: the mean background of Exp_all
Exp_b/analysis: the mean analysis of Exp_b
Exp_b/background: the mean background of Exp_b
Contributors:So-Young Chang, Seyoung Mun, Kyudong Han, Min Young Lee
RNA sequencing data to Identify DEGs in control EBs and EBs irradiated at 630 nm.
RNA-seq raw data
1. Control EBs - read1 & read2 (fq.gz)
2. EBs irradiated at 630 nm - read1 & read2 (fq.gz)
RNA-seq raw data of this study are available at other drive (https://drive.google.com/open?id=1JJP4eRvW0r4ZviSvGQJOJKD2eb4_J6os).
Contributors:Alexandre Beluco, Frederico A. During Filho, Lúcia M. R. Silva, Jones Souza da Silva, Luís E. Teixeira, Gabriel Vasco, Fausto A. Canales, Elton G. Rossini, José Souza, Giuliano Daronco, Alfonso Risso
Homer Legacy software is a well-known software for simulation of small hybrid energy systems that can be used for both design and research. This dataset is a set of files generated by Homer Legacy bringing the simulation results of hybrid energy systems over the last seven years, as a consequence of the research work led by Dr. Alexandre Beluco, Federal University of Rio Grande do Sul, in southern Brazil. This dataset is being published in conjunction with a paper in Data Science Journal, which presents further explanations about the hybrid energy systems that were simulated and the papers that publish and discuss the results. The readme.pdf file included in this dataset and the associated article provide more details. These files are made available both for their educational nature, as case studies, and for the possibilities of research that can always be opened from the dissemination of research data. The next steps of this research point to the study of the influence of energetic complementarity on the performance of hybrid systems and to the study of hybrid systems equipped with hybrid storage,
Contributors:Martin Bitomský, Pavla Mládková, Robin Pakeman, Martin Duchoslav
Data from: Bitomský M., Mládková P., Pakeman RJ, & Duchoslav M. (2020). Clade composition of a plant community indicates its phylogenetic diversity. Ecology and Evolution. doi: 10.1002/ece3.6170
Data summarises results from the case studies and simulations presented in our paper. In addition, we provide an R script for calculation of proposed phylogenetic diversity metrics (the clade indices).
Brief description of each file:
1. Grasslands_DNA_markers_info.xls - Accession numbers of all DNA markers used for phylogeny inference in grasslands
2. Grasslands_DNA_alignment_BEFORE_GBlocks.fasta - DNA alignment matrix before utilisation of the GBlocks tool
3. Grasslands_DNA_alignment_AFTER_GBlocks.fasta - DNA alignment matrix after utilisation of the GBlocks tool
4. Grasslands_BEAST_file.xml - BEAST .xml file submitted to the CIPRES portal (www.phylo.org)
5. Grasslands_tree.txt - Dated MCC tree, grasslands (newick format)
6. Grasslands_tree.nex - Dated MCC tree, grasslands (nexus format)
7. Phyto-database_pruned_tree.txt - Pruned dated tree from the super tree of European flora (Durka & Michalski 2012, Ecology), phytosociological database (newick format)
8. Plot_data.slx - plot data of all case studies + species lists
9. Simulation_results.txt - Summary of R2 values (phylogeny-based metric ~ the clade index) for simulated phylogenies and community matrices (manipulated: phylogenetic scale, species pool size and species richness range)
10. Bitomsky2020EE_R_script_indices.R - An R script for computation of the clade indices (with notes and examples)
Contributors:Eefje Poppelaars, Johannes Klackl, Belinda Pletzer, Eva Jonas
Open data and R analysis scripts for the paper as submitted for publication: "Poppelaars, E. S., Klackl, J., Pletzer, B., & Jonas, E. (2020). Delta-beta cross-frequency coupling as an index of stress regulation during social-evaluative threat."
Hypotheses and analyses were preregistered: Poppelaars, E. S., Klackl, J., Pletzer, B., & Jonas, E. (2018). Delta-beta cross-frequency coupling as an index of stress regulation during social-evaluative threat. Open Science Framework. https://osf.io/8gchf/register/565fb3678c5e4a66b5582f67.
Description of the dataset:
A dataset of 37 men and 30 women (tested in the luteal phase of their menstrual cycle) participated in a public speaking task to induce social-evaluative threat. Responses of multiple stress systems were measured (sympathetic and parasympathetic nervous system activity, self-reported affect, and hypothalamic–pituitary–adrenal axis activity), as well as personality traits (e.g. trait social anxiety), and EEG delta-beta cross-frequency coupling (e.g., frontal and parietal amplitude-amplitude correlation and phase-amplitude coupling).
Description of analyses files:
- File 'README.txt' contains the description of the files (metadata).
- File 'SET_CFC_MatlabOutput.xlsx' contains the delta-beta coupling data, calculated using MATLAB scripts from https://github.com/ESPoppelaars/Cross-frequency-coupling.
- File 'SETData.sav' contains the raw stress and personality data, taken from https://doi.org/10.17632/7vj8r76s6f.
- Files 'SET_CFC.outl.del.RData' contains the complete dataset with missing values and outliers deleted.
- File 'Codebook_SET_CFC.outl.del.csv' contains a description of all variables in the 'SET_CFC.outl.del.RData' file (metadata).
- Files 'SET_CFC.outl.del.imp.RData' and 'SET_CFC.outl.del.imp.extra.RData' contain multiple imputed datasets (without missing values) that can be used to reproduce results from the paper.
- File 'LSA_HSA_brief.RData' contains data to use as informed priors for the Bayesian analyses, calculated from data published at https://doi.org/10.3758/s13415-018-0603-7.
- File 'Codebook_LSA_HSA_brief.csv' contains a description of all variables in the 'LSA_HSA_brief.RData' file (metadata).
- File '01_CalculationOfData.R' is an R analysis script that imports the raw data, calculates new variables, and imputes missing data via multiple imputation using the 'predictorMatrixAdj.xlsx' file.
- File '02_AnalysisOfImputedData.R' is an R analysis script that calculates descriptive statistics, creates plots, and tests hypotheses using t-tests, Bayesian statistics, and multiple lineair regressions. Also uses the custom functions: 'BF.evidence.R', 'cohen.d.magnitude.R' and 'p.value.sig.R', as well as the 'BF_t.R' file as taken from https://doi.org/10.17045/sthlmuni.4981154.v3.
Contributors:Mattia Biesuz, Gian Domenico Sorarù, Michele Tomasi, Emanuele Zera, Ovidiu Ersen, André Lindemann
Raw data of the paper "Polymer-Derived Si3N4 Nanofelts for Flexible, High Temperature, Lightweight and Easy-Manufacturable Super-Thermal Insulators".
Contributors:Robert Killins, Haiwei Chen
Data for Paper: The Impact of the Yield Curve on the Equity Returns of Insurance Companies
This study uses monthly data for all insurance companies listed on the major U.S. and Canadian public equity markets (NYSE, NASDAQ, and TSX) over the period between January 2000 and June 2019. This provides a sample of ninety-five U.S. insures and eight Canadian insurers. The monthly returns for both the U.S. and Canadian insurers are obtained through DataStream. The Fama-French factors, which include the market, size, and value factors, are obtained via AQR for the U.S. and Canada, respectively. The reasoning for obtaining these factors from AQR as opposed to Kenneth French’s website is because AQR has specific factors for Canada, while the Kenneth French website only has North American or Global factors to apply to the Canadian data. The interest rate data for the U.S. is obtained via the U.S. Federal Reserve Economic Database (FRED) and for Canada through Statistics Canada (Table 10-10-0122-01). Various interest rates are obtained to measure the various section of the term structure in both countries. These include the 3-month treasury, the two-, five- ten- and twenty-year notes and bonds.