Recently there have been questions from various parties about the future of a hackathon platform I have been working on called Dribdat, originally announced and mentioned on this forum. I wrote a précis of what I think makes Dribdat relevant to the open data community, with some question marks about the future of the project.
From a School of Data standpoint, hackathons are an excellent testing ground for data sources and way to apply learning hands-on in a challenging-yet-supportive environment. Dribdat helps by connecting participants and visitors to new projects, and helping to spread data literacy through well documented/ cross-referenced/ sustainable development.
Some of the possibilities being considered may also be of interest to other communities and users. The central question is that I am trying to work out here, is how best to use limited capacity to optimally “hack” on this project - and at the same time, open up to wider collaboration. I am writing this in hopes of finding additional contributors to support the next development sprints, or at least getting some feedback on whether/how to continue the project.
You can help by:
- checking out past hackathons, e.g. http://hack.opendata.ch
- joining a Dribdat-powered hackathon, i.e. http://make.opendata.ch
- installing and using the software yourself, see http://github.com/loleg/dribdat
- reviewing the architecture, comparing this to similar platforms
- reviewing open issues, proposing other improvements
- contributing to the Python code base
Further thoughts posted on GitHub - please feel free to respond here or there: