Jimmy Oh wins Open Source Research Prize
Jimmy Oh wins the Catalyst IT/Dragonfly Open Source Research Prize
Jimmy Oh has been awarded the Catalyst IT/Dragonfly Open Research Prize for his work on the TableToLongForm package.
Jimmy is a PhD student at the University of Auckland in the Department of Statistics. His research examines methods to make open data more accessible to a wider audience, ultimately culminating in a tool (or tools) to make the process of obtaining, processing and using open data easy.
Jimmy presented his work on the TableToLongForm package at the 2013 “Analytics for a Changing World: From Data to Decisions” conference jointly held by the New Zealand Statistical Association and the Operations Research Society of New Zealand. The conference was held between 24-27 November 2013, at the University of Waikato in Hamilton.
The $500 award is sponsored by Catalyst IT and Dragonfly Science. Catalyst is a specialist in free and open source technologies. Catalyst's mission is to make open source the preferred technology choice of New Zealand (see www.catalyst.net.nz). Dragonfly Science carries out independent statistical modelling and analysis (see www.dragonfly.co.nz).
To be eligible for the Open Source Research Prize, each stage of the research process had to be carried out with open source tools. After careful consideration of all the talks at this conference, the judges decided to award the Open Source Research Prize to Jimmy Oh. Jimmy's TableToLongForm package made it easier to access open data sources, which furthered the goals of open source research and open data.
Catalyst were delighted to hear that Open Source Research Prize encouraged Jimmy to continue developing TableToLongForm, whose initial plans were to shelve it for a few months to focus on other projects.
TableToLongForm is an R package that automatically converts hierarchical Tables intended for a human reader into a simple LongForm Dataframe that is machine readable, hence enabling much greater utilisation of the data. It does this by recognising positional cues present in the hierarchical Table (which would normally be interpreted visually by the human brain) to decompose, then reconstruct the data into a LongForm Dataframe.
Further information on TableToLongForm package can be found at: https://www.stat.auckland.ac.nz/~joh024/Research/TableToLongForm/