Research Software Engineer
B.Psych.Sci (Hons), PhD (Statistics)
Dr. Nick Tierney completed his undergrad and honours in Psychological Science, then took an unconventional turn into a PhD in Statistics. He now works as a research software engineer with Dr. Nick Golding here at the Telethon Kids Institute. He is currently working on improving and maintaining the greta (https://greta-stats.org/) R package for statistical modelling. He is also interested in implementing workflows to automate data analysis.
Previously Dr. Tierney was at Monash University (2017-2020), working as a research fellow, then lecturer. He worked with Professor Di Cook, creating exploratory data analysis techniques. He also taught ETC1010, introduction to Data Analysis (https://dmac.netlify.org/)
Dr. Tierney’s research interests are broad, but centred around improving data analysis. This includes exploratory data analysis, statistical modelling, diagnostics, and understanding how colour choice can impact decision making. Dr. Tierney is a strong believer in free and open source software, and has written several popular R packages to improve data analysis, which can be seen on his software page: http://njtierney.com/software.
Dr. Tierney is a keen outdoors person, in particular hiking, rock climbing, and kayaking. He recently hiked 300K of the Australian Alpine Walking Track, a very rugged adventure, especially when hiking solo. Nick is also interested in coffee, music, and photography, especially analog film photography.
Expanding Tidy Data Principles to Facilitate Missing Data Exploration, Visualization and Assessment of Imputations
Despite the large body of research on missing value distributions and imputation, there is comparatively little literature with a focus on how to make it easy to handle, explore, and impute missing values in data. This paper addresses this gap. The new methodology builds upon tidy data principles, with the goal of integrating missing value handling as a key part of data analysis workflows.Published research Infectious Disease Ecology and ModellingJuly 2022
A Journey from Wild to Textbook Data to Reproducibly Refresh the Wages Data from the National Longitudinal Survey of Youth Database
Textbook data is essential for teaching statistics and data science methods because it is clean, allowing the instructor to focus on methodology. Ideally textbook datasets are refreshed regularly, especially when they are subsets taken from an ongoing data collection.Published research Geospatial Health and DevelopmentMay 2022
Missing data: current practice in football research and recommendations for improvement
A survey of 136 articles published in 2019 (sampled at random) was conducted to determine whether a statement about missing data was included.Published research Infectious Disease Ecology and Modelling
Education and Qualifications