Date(s) - 02/14/2020
10:00 am - 11:00 am


3910 15th Ave NE
Seattle WA

Please join us for a guest seminar by Nick Tierney (Monash University) titled “A Realistic Guide to Making Data Available Alongside Code to Improve Reproducibility.”

The seminar will take place on Friday, February 14th from 10:00-11:00 AM in the Seminar Room of the WRF Data Science Studio, 6th floor Physics/Astronomy Tower. NOTE: Time of seminar changed from 2 PM to 10 AM.

Related image

Data makes science possible. Sharing data improves visibility, and makes the research process transparent. This increases trust in the work, and allows for independent reproduction of results. However, a large proportion of data from published research is often only available to the original authors. Despite the obvious benefits of sharing data, and scientists’ advocating for the importance of sharing data, most advice on sharing data discusses its broader benefits, rather than the practical considerations of sharing. This paper provides practical, actionable advice on how to actually share data alongside research. The key message is sharing data falls on a continuum, and entering it should come with minimal barriers.

Born and bred in Brisbane, Queensland, Nick first completed honors in Psychological Science at the University of Queensland in 2012. After graduating, Nick wanted to study more statistics, and help bridge the gap between policy and statistics, and so began a PhD in Statistics under Kerrie Mengersen in 2013, submitting just recently in 2017. This PhD research focused on statistical applications in complex health data. Throughout the course of his PhD, Nick further realized a passion for keeping science open, and making it easy for others to do complex tasks, like explore missing data, or extract data from databases with complicated interfaces. To this end he joined the rOpenSci collective, organising the first Australian rOpenSci unconference, and was recently invited to the USA rOpenSci Unconference in LA in May.

Currently Nick’s research is focused on improving the analysis of missing data, focusing on data visualization, and also methods to statistically explore missing data patterns.