I grew up reading very enthusiastic accounts of the work of very idealistic scientists, who mostly believed that they were working on a shared enterprise which by right belonged to all of humanity. The cool thing is that there have actually been a lot of scientists who believed that, and who lived that way.

There have been great strides in the hard sciences and many of the natural sciences (geography, biostatistics, bioinformatics) thanks chiefly to the intuition of the researchers and the availability of the technology capable of carrying out the tasks required.

From what I've seen, technology in the sciences is still by and large captive tech. Most large instrumentation requires software using undocumented interface specs. Sure, it may use a standard DB9 RS232 connector, but good luck finding the command set documented anywhere without an NDA or a substantial amount of cash (or both). Unfortunately, even among the scientists I know, one in particular insists on keeping his computer model private; this precludes peer review and slows down development. Others will typically release binaries for Win32 (most ecologists seem to be Win32-dependant) but none of the source.

There have been some tremendous exceptions (examples here and elsewhere), in and out of ecology - which is great. As long as practicing scientists work within the spirit of open source, I think we may just see ecology explode just as the computing world did as more and more people embraced open source.

The younger crowd of grad students seem more inclined to use Linux and free software, so there is hope. Unfortunately, the general technical literacy level is low enough to worry me. We go to schools, colleges, universities to learn to think critically, to analyse, to delve and write. Courses in methodologies, critical thought, and the like, are offered. As important as these are, they don't seem to go far enough; but in most biology departments I've been to, computer courses appear to be too far 'away' from the actual science at hand. And hell, I don't want to take a course on learning how to use Word, thanks.

Perhaps the largest impediment to 'free science' is communication; most scientific journals are increasingly expensive. At up to $3000 per institutional subscription, it doesn't take long before many libraries carry fewer journals. As these costs increase, the next logical step is to start an "open source, peer review online journal"; indeed, this has already started. Unfortunately, they're not all that print friendly (granted, they're easier for a braille terminal to read, compared to a pdf). I think it will take numerous far-sighted individuals to pull off an SGML-based, open source journal; but I also think it will happen soon enough.

Back to analysing those pesky data.