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**Background preparation - Laura Fortunato** Students should install the Git-it Desktop App, following the installation instructions here: (including installing Git via GitHub Desktop and ensuring they have a suitable text editor available). **Registered Reports workshop - Chris Chambers** On the Monday of the course (9.00-10.00am), Chris Chambers will introduce attendees to the concept of Registered Reports — a type of empirical article offered by academic journals that is designed to reward transparency and openness in research (for details see In contrast to conventional research articles, Registered Reports are peer reviewed before results of a research study are known to the authors, and usually before the results even exist. Research proposals that meet high standards of theoretical or applied value and methodological rigour are then accepted in advance, with final acceptance guaranteed regardless of the outcomes provided researchers adhere to their approved protocol and conduct the research to a high standard. This model of publishing eliminates bias against negative (or null) results, and also makes clear the distinction between confirmatory hypothesis testing and exploratory post hoc analysis. The Monday seminar will include an overview to the Registered Reports format, how it solves various problems in research and publishing, the benefits it brings to authors in terms of efficiency and article impact, and anticipated answers to frequently asked questions. Later on Monday (5.15-6.30pm), attendees will perform a practical exercise in small groups to develop their own preregistered protocol using a template Registered Report included in the workshop pack (and downloadable from Attendees are invited to complete this template in advance of the workshop using an example from their own research (a planned study or hypothetical study). Attendees who send their templates to Chris Chambers ( by 3 January will receive personalised feedback on their completed templates in advance of the workshop, increasing the value of the practical exercise. **Getting to grips with R Markdown – Mike Smith** Two R packages are required for this session which can be found on the Bioconductor website. The following commands can be inserted into the R console and should get everything installed: install.packages('BiocManager') BiocManager::install(c('BiocWorkflowTools', 'BiocStyle', 'rticles', 'knitr', 'rmarkdown')) **The Open Science Framework - Courtney Soderberg** Course attendants should aim to create an OSF account before the workshop and to set their international storage settings [if they want their data stored not in the US]. Instructions on how to setup an account can be found here ( ) for signing up and here for how to set global storage ( ) settings if they run into issues. **Diagnosing publication bias and other anomalies - Daniel Lakens** Please install R and install R studio before the workshop. IMPORTANT: Please install these R packages ( If needed, see this help file on how to run R code. **Power and data simulation - Dorothy Bishop** The workshop will illustrate data simulation in Excel and R. In a large lecture theatre it won’t be possible to work interactively with the audience, and it is fine if you prefer just to listen and learn and then try out some of the exercises which will be available online later. However, if you would like to bring a laptop with you, you are welcome to try to work along as examples are illustrated. The initial examples will not require anything other than a laptop with Excel. Later examples will illustrate the benefits of using the R programming language. Don’t worry if you are not familiar with R: the exercises will give you a taster and they do not assume prior experience. There are some suggestions for familiarising yourself with the basics below, but you should be able to benefit from the lecture, even if you are a total novice. People who are not familiar with R, who would like to try to work along with the exercises will need to do some preparation in advance, by installing R and R studio (instructions below). Those already familiar with R should install the packages listed below. 1. Install R (free software) • Open an internet browser and go to • Click the "download R" link in the middle of the page under "Getting Started." • Click on the link for a CRAN location close to you Mac users: • Click on the "Download R for (Mac) OS X" link at the top of the page. • Click on the file containing the latest version of R under "Files." • Save the .pkg file, double-click it to open, and follow the installation instructions. • Windows users: • Click on the "Download R for Windows" link at the top of the page. • Click on the "install R for the first time" link at the top of the page. • Click "Download R for Windows" and save the executable file somewhere on your computer. • Run the .exe file and follow the installation instructions. 2. install R studio – a friendly interface for R • Go to and click on the "Download RStudio" button. • Click on "Download RStudio Desktop." Mac users: • Click on the version recommended for your system, or the latest Mac version, save the .dmg file on your computer, double-click it to open, and then drag and drop it to your applications folder. Windows users: • Click on the version recommended for your system, or the latest Windows version, and save the executable file. Run the .exe file and follow the installation instructions. Once R studio is installed, you need not open the original R software: instead, you access R by opening the R studio application 3. Install packages • Open R studio. • On the menu bar at the top, select Tools|Install Packages For this workshop, we use three packages. You can select these by typing the name in the box that pops up, and then selecting Install after selecting the one you want. The packages are MASS, HMisc and yarrr (Note that yarrr has three Rs!) MASS is used for generating multivariate normal random numbers (among other things), HMisc is useful for computing correlations, and yarrr is used to create a nice kind of plot called a pirate plot. For each one you install, you will see text flashing by on your console as it is loaded. Just last week, a nice package was published for doing and visualising correlations, called corrr (again 3 Rs!). If you are an R expert and want a challenge beyond the elementary content of the exercises, you could install that as well and see what it can do. Upload some introductory scripts From the Open Science Framework site,, upload these scripts and save on a folder that you will use for work in R: 1. Simulation_ex1_intro.R 2. Simulation_ex1_multioutput.R 3. Simulation_ex2_correlations.R R for total novices • A very friendly and basic interface for learning R is here: • There are various online courses on R from Data Carpentry and Software Carpentry that you can find by Googling: these are geared to different disciplines, and you may find it helpful to browse to find one most suited to your field. This one, although designated for Social Sciences, is a good introduction to the ‘tidyverse’ – a set of functions in R that make data manipulation relatively easy: It also has a good first lesson to just help you find your way around R studio. I’m always interested to hear of other free online resources that people find helpful, so let me know if you have any recommendations. **Simulation / other methods for power calculations – Natasha Karp** Steps: 1. Visit website: 2. Scroll down the page to the "Download the software " section and select the text “click this link to the file pifface.jar” 3. This will return a dialog window which asks whether you want to open or save. 4. Select save and this will save an executable jar file in your download folder. If you then click Open folder you can see the jar file and you can copy it to a memorable location for the course. 5. Please check the software opens. When you click on the executable then the following dialog box will open If you click OK then the following software window will appear Note: To run the software you will to have the Java Runtime Environment (JRE) or the Java Development Kit (JDK) installed on your system. You may already have it; but if not, the JRE may be downloaded from Oracle at **Using R for data cleaning and data wrangling - Paul Thompson** Hopefully you have successfully installed R and RStudio by this point, if not pleasedo so before you continue. You will need to download the R script called "script for cumberland lodge_thompson.R" and "simulated_TEF_data.csv" files from the "Thompson" folder found on OSF here. You will also need to run the following R code snippet to download required R packages: list_of_packages = c("assertr","tidyverse","MASS") new.packages = list_of_packages[!(list_of_packages %in% installed.packages()[,"Package"])] if(length(new.packages))install.packages(new.packages) library(assertr) library(tidyverse) library(MASS) Copy the code into R command line, and then press **Return**. Hopefully, this should run without errors. **Why use Bayesian analysis? - Alex Etz** Students will do some exercises to learn JASP, so will need to download a copy from the website here: