Biography

Lisa DeBruine is a professor in the School of Psychology and Neuroscience and (starting March 1st) Academic Lead for Good Research Practice at the University of Glasgow. They are a founding member and current Associate Director of the Psychological Science Accelerator, founder of ManyFaces, chair of the APA Open Science and Methodology Expert Panel, former local network lead and upcoming institutional lead for the UK Reproducibility Network, and a member of the psyTeachR team for reproducible research educational resources.

Interests
Education

  • PhD in Psychology, 2004

    McMaster University

  • MSc in Biology, 2000

    University of Michigan

  • Graduate Certificate in Women's Studies, 2000

    University of Michigan

  • BSc in Biological Psychology and Anthropological Zoology, 1998

    University of Michigan

Projects

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All simulation dataviz teaching R shiny team science
Data Simulation Workshops
Workshop materials for data simulation and power analysis.
Tutorials
Data Simulation Workshops
Glossary
Create glossaries for R Markdown and Quarto documents
Tutorials
Glossary
Intro to R Packages
Making an R package develops generic coding skills and gives you valuable insight to how R works. We’ll cover setting up a package project, creating functions, documenting them with roxygen, creating vignettes, unit testing, package testing, version control with git, and distribution with github.
Book
Intro to R Packages
Code Check
This project aims to make code review easier and more common in research. The first project is to create a guide for research code review aimed at novice to intermediate coders.
GitHub
Code Check
ManyFaces
Broadly, the aim of ManyFaces is to improve, diversify, and crowdsource key aspects of face research, including perception and recognition. This involves, for example, the collection and use of face stimuli; sharing existing stimulus sets; standardising stimulus collection procedures; and organising stimulus collection across multiple labs to obtain larger and more diverse face stimulus sets. ManyFaces also aims to crowdsource data collection across our members’ labs to test key research questions in face perception and recognition, enabling larger-scale designs and more diverse participant samples and generalisable findings. Finally, we aim to organise training workshops for key methods (e.g., morphing) and analyses (e.g., mixed effects models) used in face research.
Website @ManyFaces@fosstodon.org @ManyFacesTeam Brainstorming Doc
ManyFaces
30 Day Chart Challenge
30-Day Chart Challenge 2022
Book Code
30 Day Chart Challenge
Shiny
Shiny lets you make web applications that do anything you can code in R. For example, you can share your data analysis in a dynamic way with people who don’t use R, collect and visualize data, or even make data aRt.
Book Demo Apps
Shiny
Faux
R functions for simulating factorial datasets.
CRAN Tutorials DOI
Faux
WebMorph
An open source, online tool for morphing and transforming faces.
R Package DOI
WebMorph
Experimentum
An open-source, online platform for psychology studies.
Demo Manual Code DOI
Experimentum
Psychological Science Accelerator
A globally distributed network of psychological science laboratories that coordinates data collection for democratically selected studies.
PSA PSA001 Paper
Psychological Science Accelerator
psyTeachR
A community that aims to rebuild the psychology methods curriculum upon a foundation of openness and reproducibility.
Books Resources Code
psyTeachR

Recent Posts

Recoding with case_match and case_when
Loops
Data from Images
Testing for normality
Experimentum Data Wrangling Demo
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Recent & Upcoming Talks

Data simulation with {faux} and data simulation for mixed designs
Being able to simulate data allows you to prep analysis scripts for pre-registration, calculate power and sensitivity for analyses that don’t have empirical methods, create reproducible examples when your data are too big or confidential to share, enhance your understanding of statistical concepts, and create demo data for teaching and tutorials.
2024-02-01 09:30 — 2024-02-02 16:30 Bern, Switzerland
Code
Data simulation with {faux} and data simulation for mixed designs
Research software engineering with Julia: basics, visualization, and statistics
We will address the most essential paradigms of software development, which support the design of efficient, user-friendly, and sustainable software (with a focus on the scientific programming language Julia).
2023-10-09 — 2023-10-13 Stuttgart, Germany
Research software engineering with Julia: basics, visualization, and statistics
Ethics and Open Research
I will discuss open research in the context of ethics committees, with a focus on data sharing.
2023-10-06 — University of Glasgow
Slides
Ethics and Open Research
Replicability and Generalisability in Face Research
In this talk, I will discuss several initiatives to increase the replicability and generalisability of research on faces.
2023-09-21 15:00 — 16:00 Amsterdam, NL
Slides
Replicability and Generalisability in Face Research
Plenary: Everything is cool when you’re part of a team
How large-scale collaborations such as the Psychological Science Accelerator can improve both replicability and generalisability.
2023-07-23 00:00 — 00:00 České Budějovice, Czechia
Slides
Plenary: Everything is cool when you’re part of a team
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Publications

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Applied Data Skills
This book provides an overview of the basic skills needed to turn raw data into informative summaries and visualisations presented in professional reports and presentations.
Cite Project Book Code DOI
Applied Data Skills
Open-source tutorials benefit the field
Open research is increasingly required by journals and funders but involves many new skills. Creating open-source tutorials is useful to the field and personally rewarding, but these efforts must be credited accordingly.
Cite Project Supplemental Materials Code DOI
Data Skills for Reproducible Research
This book provides an overview of skills needed for reproducible research and open science using the statistical programming language R and tidyverse packages. It covers data visualisation, data tidying and wrangling, archiving, iteration and functions, probability and data simulations, general linear models, and reproducible workflows.
Cite Project Book Code DOI
Data Skills for Reproducible Research
Data visualisation using R, for researchers who don't use R
In this tutorial, we provide a practical introduction to data visualization using R specifically aimed at researchers who have little to no prior experience of using R.
Cite Project Book Code Preprint DOI
Data visualisation using R, for researchers who don't use R
Improving Transparency, Falsifiability, and Rigor by Making Hypothesis Tests Machine-Readable
We examine what a machine-readable hypothesis test should look like and demonstrate the feasibility of machine-readable hypothesis tests in a real-life example using the fully operational prototype R package scienceverse.
Preprint R Package Shiny App DOI
Improving Transparency, Falsifiability, and Rigor by Making Hypothesis Tests Machine-Readable
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Popular Topics

alpha categorical coding cronbach data data skills dataviz faces faux lme4 lmer methods mixed effects PSA psyTeachR R simulation teaching team science tidyverse