Some thoughts on Open Data

Posted by Cheng Soon Ong on May 15, 2008

At the end of last year, Science Commons announced the Protocol for Implementing Open Access Data which concerns the interoperability of scientific data. bbgm has summarized this into 10 points, of which I would like to focus on the first. Quoting bbgm:

Given the amount of legacy data, it is unlikely 
that a single license will work for scientific data. 
Therefore, the memo focuses principles for 
open access data and a protocol for 
implementing those principles.

Is licensing appropriate for scientific data?

The first knee jerk reaction is to say "Of course! It will protect different people's interests.". However, as pointed out by John Wilbanks, data available in the public domain cannot be made "more free" by licensing, only less. Quoting him:

The public domain is not an “unlicensed commons”. 
The public domain does not equal the BSD. 
It is not a licensing option.
It is the natural legal state of data.

There are several other opinions here and here, but at the end of the day, it is clear that open data is highly important for scientific research, and possibly even more important than open source. My personal view is that for machine learning, public domain seems to be the best for our data.

Taking this idea of "public domain" to the area of software, one can ask the question whether all academic software should be open source. I had the pleasure of spending a few days last week talking to Neil Lawrence and Carl Rasmussen. Neil seems to have software available for each paper that he has recently submitted available on a group webpage. Carl is one of the many people who has contributed to the Gaussian Processes website. The listed projects would be considered (I guess) public domain, or "freely available for academic use". Does it matter that these really useful pieces of software do not have explicit licensing? Should they be considering some form of license?