During his last visit Down Under, Martin Jucker delivered a workshop on a data visualisation tool known as ParaView. As any good Australian host would do, we took him out for a beer afterwards and got chatting about the options researchers have for publishing their code. Off the back of that discussion (yep, we’re taking all the credit), Martin decided to publish his custom ParaView software package with the Journal of Open Research Software. We caught up with him in this video to chat about the experience. (It’s a long video, so feel free to jump to the following sections:)
- What does ParaView help researchers do by producing scientific visualizations?
- What is an example of the scientific visualisation which Martin produces through Paraview?
- What is “The Art of Science”?
- Where can researchers (who can code) publish their software as “scholarly publications”?
- What does a researcher get out of publishing their software as a research publication?
- What is a Digital Object Identifier (DOI) for Publishing Research?
- What is the value of a DOI for a researcher who is publishing their software?
- What is the impact factor and what does it mean for a researcher?
- What is the h-index and what does it mean to a researcher?
- Why don’t researchers who write research software get cited for the code that other researchers use in their publications?
- How should researchers who write code get credit (citation) for the research code they are writing?
- What new coding skills should early career researchers be looking to learn so they can do cutting edge research?
- Are technical research skills as important (if not more important) than the research knowledge itself?
- What are the advantages of publishing your code in the open for other researchers to use and reuse (to assure reproducible research)?
- Why should researchers look to use websites like GitHub to engage the community in the research through code and open source software?
- How can a researcher get started with understanding code to do their science?
- Are “Computer Programming Languages” the new lingua franca of science, or do they change too quickly?
- What are some of the programming languages which the next generation of researchers are looking to use? e.g. Rstat, iPython, Matlab, Fortran, etc.
- How should an early career researcher go about picking which programming language they should learn first?
- As a researcher who can already code what new programming languages would you start to learn today?
