Assay 3 Competitive Trade-offs readme.txt file was generated on 2002-08-25 by Patrick J. Chen GENERAL INFORMATION 1. Title of Dataset: Assay 3 Competitive Trade-offs 2. Author Information A. Principal Investigator Contact Information Name: Rees Kassen Institution: University of Ottawa Email: rees.kassen@uottawa.ca B. Associate or Co-investigator Contact Information Name: Patrick Chen Institution: University of Ottawa Email: pchen041@uottawa.ca 3. Date of data collection (single date, range, approximate date): 2016-2018 4. Geographic location of data collection: Ottawa, Ontario, Canada 5. Information about funding sources that supported the collection of the data: NSERC Discovery Grant 6. Recommended citation for this dataset: Chen, Patrick; Kassen, Rees (2020), The evolution and fate of diversity under hard and soft selection - all data and code, v6, Dryad, Dataset, https://doi.org/10.5061/dryad.hx3ffbgbm DATA & FILE OVERVIEW 1. File List: Assay 3 Competitive Trade offs.csv – raw data file Assay 3 Competitive Trade offs.r – code file 2. Relationship between files, if important: Assay 3 Competitive Tradeoffs.csv required for R code file to run. 3. Additional related data collected that was not included in the current data package: None. 4. Are there multiple versions of the dataset? no METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: Methods can be found in associated publication 2. Methods for processing the data: Data that was included for this analysis was time point (not series) data on experimental populations of bacteria undergoing categorical forms of population regulation. Processing of raw data was done with R. Data was imported and cleaned to remove blank data, as well as contaminated lines to be excluded for analysis. Analysis was done using linear mixed models, modelling relative fitness to ancestral (continuous) against treatment (categorical), the class of the individual colony (categorical) and time points (categorical). We followed up with comparative tests to estimate means and confidence intervals between treatments, timepoints and class using emmeans(). Fig 4 was generated from this data set using ggplot. 3. Instrument- or software-specific information needed to interpret the data: R is necessary to run and interpret data Structure of R file Analysis Import of necessary libraries Data cleaning of raw dataset and subsetting Analysis of data using a lmer model, followed by emmeans() Plotting Fig 4 Import of necessary data files Data cleaning and labelling ggplot object for Fig 4 and output to tiff file