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2020-01-17

Week 1: Tuesday, January 14, 2020

Topics

  • Introduction to the course
  • Needs/interests assessment, and goal setting
  • Discussion: Why trust science?
  • Setting up computational environments
    • On your local machine
    • In the cloud

Homework

Due by the start of class on 2020-01-21.

  1. In a paragraph or two, discuss whether you think researchers in your field do and should embrace “Mertonian norms.” Why or why not?
  2. In a paragraph or two, describe your current knowledge of computer programming languages, and at least three learning goals you have for building upon that base.
  3. Create a GitHub account, send me your account name in an email, and see if you can create your own copy (fork or clone) of the course repository. If you succeed, take a screen shot of the repository in your local GitHub account.
  4. Write these items up in a Word (.docx) file, and email it to me. Make sure to use a sensible file name, e.g., psy525-YOUR_LAST_NAME-2020-01-21.docx.

Week 2: Tuesday, January 21, 2020

Topics

  • The values of science
  • Cases of scientific misconduct
  • What is reproducibility? Are we in a crisis?

Readings/webinars

Homework

Due by the start of class on 2020-01-28.

  1. Choose one of the verification bias examples from Flawed Science. In a paragraph or two, propose ways you might avoid this sort of bias in your own research.
  2. Choose one of the TOP guideline categories where either your own research practices have room to improve or you are doing rather well. In a paragraph or two, explain your reasoning.

Week 3: Tuesday, January 28, 2020

Topics

Readings/webinars

Homework

Due by the start of class on 2020-01-28.

1 Pick one of the recommended elements from Table 1 in Munafò, et al. (2017). Evaluate the recommendation. Do you agree that it would mitigate one or more threats to reproducibility. Why or why not? Do you agree with the assessment about the degree to which stakeholders have adopted the recommended practice? 2. Edit/create a text-based workflow for an active project you are working on. + Annote the workflow to indicate where it could be made more reproducible, transparent. + If you are feeling super-ambitious, you may want to try creating a graph-based workflow.

Week 4: Tuesday, February 4, 2020

Topics

  • Pregistration and registered reports
  • Introduction to RStudio and R Markdown

Readings/webinars

Homework

Due by the start of class on 2020-02-11.

  1. Find a preregistration document for a study relevant to your research interests on aspredicted.org or osf.io. In a few paragraphs, comment on what was and what was not included. Would the preregistration provide researchers sufficient structure to carry out the research without ‘HARKing’? What downsides do you see to preregistration?
  2. Create a template for a reproducible research report in R Markdown.
  3. Convert your workflow from last week’s assignment into R Markdown.

Week 5: Tuesday, February 11, 2020

Topics

  • Version control
  • git
    • GitHub
    • GitHub pages

Readings/webinars

Homework

Due by the start of class on 2020-02-18.

  1. Create GitHub repo for the project you completed last week
    • Open an issue flagging @rogilmore so I know to look at your repo and document.
  2. Create a repo for your final course project
    • Create a Markdown document where you start to outline the possible directions that your final project might take.
    • Open an issue so I can take a look.
  3. Clone a repo; fix/change something; make a pull request.

Week 6: Tuesday, February 18, 2020

Topics

  • Simulation as a tool for reproducible and transparent science
  • Visualization tools in R

Homework

Due by the start of class on 2020-02-25.

  1. Create your own simulated data set for a real or proposed study.
    • You may adapt or build upon the examples used in class.
    • Put the results in an R Markdown (.Rmd) file.
    • Commit the .Rmd file to your private repo on GitHub and either raise an Issue on GitHub or submit a pull request.
  2. Plot the results of your simulation using ggplot2 commands.
  3. Make sure that your simulation has the following sub-sections:
    • Introduction/Motivation
      • What are you simulating and why? Where do the parameter estimates come from? The literature or your best guess?
    • Plots
    • Statistical Analyses
    • Discussion/Conclusions
      • What did you discover or demonstrate?
  • Please label the r chunks in your R Markdown files

Week 7: Tuesday, February 25, 2020

Topics

  • Doing other useful things with R and R Markdown

Readings/webinars

Homework

Due by the start of class on 2020-03-03.

  1. Create a set of HTML talk slides using R Markdown.
  2. Try rendering the slides as a Word document, PowerPoint document, or PDF.
  3. Create a new repo and generate a simple website using R Markdown.

Week 8: Tuesday, March 3, 2020

Topics

Homework

Due by the start of class on 2020-03-17.

  1. Complete a 1-2 page write-up describing your plans for your final project.

Spring Break March 9 - 12, 2020

NO CLASS

Week 9: Tuesday, March 17, 2020

Topics

  • Python, the other language of data science
    • Intro to Jupyter
    • Jupyter for research, teaching, talks

Homework

Due by the start of class on 2020-03-24.

TBD

Week 10: Tuesday, March 24, 2020

Topics

Readings/webinars

  • de Leeuw, J.R. (2015). jsPsych: A JavaScript library for creating behavioral experiments in a Web browser. Behavior Research Methods, 47(1), 1-12. doi:10.3758/s13428-014-0458-y

Homework

Due by the start of class on 2020-03-31.

  1. Open a PsychoPy demo program and save it with a new name. Add additional documentation to the demo program that explains what’s happening. Change one or more parameters to make the program do something slightly different, and explain what parameters you changed.

Week 11: Tuesday, March 31, 2020

Topics

  • Using APIs
    • U.S. Census
    • Google Drive
    • Box
    • Wikidata
    • Databrary
    • OSF

Homework

Due by the start of class on 2020-04-07.

  1. Create a Jupyter notebook where you document your exploration of the U.S. Census, Box, or Google Drive APIs.

Week 12: Tuesday, April 7, 2020

Topics

  • Where to share?
  • Publishing data
  • Challenges to sharing
  • Your open science portfolio
  • Funder policies

Readings/webinars

  • Meyer, M. N. (2018). Practical Tips for Ethical Data Sharing. Advances in Methods and Practices in Psychological Science, 2515245917747656. SAGE Publications Inc. Retrieved from https://doi.org/10.1177/2515245917747656
  • Gilmore, R.O. et al. (2020).

Homework

Due by the start of class on 2020-04-07.

  1. Choose two outlets for sharing research data or materials and compare and contrast their strengths and weaknesses.

Week 13: Tuesday, April 14, 2020

Topics

  • Catch-up week

Week 14: Tuesday, April 21, 2020

Topics

  • Preparation for student projects

Week 15: Tuesday, April 28, 2020

Topics

  • Student project presentations