Michael Nielson provides a quick, powerful treatise of the coming open science revolution in, “Reinventing Discovery.”
The book is a great read, pouring through recent examples of citizen science projects such as Galaxy Zoo and Fold.it and providing an optimistically sober perspective toward open science with partially open data and/or data delay for big scientific projects. This shift will be reliant on the incentive systems we have for scientists (current a “publish or perish” model), requirement levied by the funding organizations, and the desires of the sub communities. The last three chapters are a must read for those interested in designing tools and systems to create a reputation economy and break the academic race to write papers valued by the published scientific community, and instead, bring about an open data and open science approach for 21st century scientists.
“[T]ools can be used to amplify our collective intelligence, in much the way that manual tools have been used for millennia to amplify our physical strength.”
In Chapert 4, Patterns of Online Collaboration, Nielson provides a great framework of design pronciples open science projects could follow learning from open source communities:
- Modularity – find clever ways of splitting up the overall tasks into smaller subtasks. The less monolitic and more modular, this makes a greater range of expertise available to the collaboration resulting in greater cognitive diversity.
- Small Contributions – encourage small contributions to reduce barriers to entry – you can always have one more shot to make a small contribution. Microcontribution helps build a vibrant community, a sense that something is afoot, that progress is being made; small contributions help the collaboration rapidly explore a much broader range of ideas than would otherwise be the case.
- Reuse – allow easy reuse of earlier work by other people. “Good programmers code; great programmers reuse other people’s code.”
- Gamification – use signaling mechanisms such as scores to help people decide were to direct their attention. Automated scoring is important because the scores help participants focus their attention where it will do the most good. The better the architecture of attention in a system, the more collective intelligence is amplified