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# **README** This project accompanies the workshop on "Reproducibility of Linguists" to be held in Cologne in the 5th and 6th of February 2019. services/ "" This readme file was generated on 2019/01/24 by Timo B. Roettger Contact: ------------------- ### **GENERAL INFORMATION** #### **Title of project** Reproducibility Workshop Cologne     #### **Author Information** ##### **Contact Information**     **Name**: Timo B. Roettger     **Institution**: Northwestern University, Department of Linguistics     **Address**: 2016 Sheridan Rd, Evanston, IL 60208     **Email**:     ------------------- ### **SHARING/ACCESS INFORMATION** #### **Licenses/restrictions placed on the data**: CC0 1.0 Universal (CC0 1.0): Public Domain Dedication     #### **Recommended citation for the data**: Roettger, T. B. (2019, January 25). Reproducibility Workshop Cologne. Retrieved from   ------------------- ### **DATA & FILE OVERVIEW** #### **Project components** **Slides**: contains the slides of the workshop in `.pdf` and a list of full references cited on slides. * **01_ReplicationCrisis**: Do we have a replication crisis? What about linguistics? Reasons for it and a road map for the workshop. * **02_Stats101**: What is inferential statistics? What is null hypothesis significance testing? What does a *p*-value mean? Type-I, Type-II, Type-S, Type-M errors, power, *p*-hacking, HARKing, researcher degrees of freedom and more. * **03_Preregistration**: What is publication bias? What is preregistration and what are Registered Reports? How can they make science more credible? What is in it for us? * **04_Reproducibility**: What is reproducibility? Why should we share data and code? Are there reasons not to? Where and when should we share? How do we share? Projects on OSF; RStudio projects linked to GitHub and more. * **05_Incentives**: What behavior is being rewarded in science? What are the consequences? How can we change it? * **06_TowardsARevolution**: Is a revolution of scientific practices on its way? Do we share more openly? Do we preregister more? Outlook to a brighter future. **Exercises**: contains hands-on exercises that are part of the workshop. 1. `MakeOSFProject` (day 2): create a OSF project, including adding collaborators, adding structure and components, make a wiki, etc. 2. `MakeRStudioProject` (day 2): create a RStudio project and link it to a GitHub repository. 3. `P-hacking` (day 1): gain an intuition about what it means to p- hack using the p-hacker shiny app retrieved from" 4. `ReproduceThat` (day 2): try to reproduce a project and figure out why it fails. 5. `UnluckySampling` (day 1): gain an intuition about sampling error. **Demonstrations** (workshop exclusive): contains hands-on exercises that are part of the workshop. 1. ``: Demonstration of the main components of an project. Explains how to create a project, add collaborators, add structure and content, add licenses, etc. 2. ``: Demonstration of setting up projects in RStudio. Explains how to set up a RStuido project, how to add structure and content, how to version control your project with GitHub and how you link your GitHub projet to your osf project. **Additional Information**: contains additional documents that are also given to participants as hand outs, including: 1. `PrelimSchedule.pdf`: Updated preliminary schedule as a rough overview of topics. 2. `Reproducibilitylexicon.pdf`: List of explanation for relevant terminology. 3. `ReadMe_template.rtf`: Template containing relevant meta data categories. Open in text editor, copy content into osf wiki. 4. `InstallPackages.R` makes sure all relevant libraries are installed on your machine. Open in R or RStudio and run. 5. `Bayesian statistics` contains teaching materials from the Birmingham Statistics for Linguists Summer School: slides and `.rmd` files to dive into Bayesian inference with the beautiful `brms` package.   6. `FurtherResources.pdf` list of reading recommendations for selected topics and online resources. ------------------- ### **SOFTWARE REQUIREMENTS** In order to do the exercises you need to install following software 1. R: 2. RStudio: 3. Git:
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