Infrastructure never gets adequately funded because it cuts across disciplinary boundaries, it doesn't benefit particular groups. Infrastructure is a prerequisite to great leaps forward and is thus never captured within disciplinary funding, or normal governmental operations. We need to revise radically our conception of cyberinfrastructure. It isn't just a set of tubes through which bytes flow, it is a set of structures that network different areas of knowledge...and that is software and social engineering, not fiber optic cable. The superhighways of the biological information age should not be understood as simply physical data roads, long ropes of fiber and glass. They need to be structures of knowledge. The Eisenhower Freeways of Biological Knowledge are yet to be built. But that doesn't mean the task isn't worth starting.
- James Boyle, William Neal Reynolds Professor of Law, Duke University Law School
Knowledge sharing is at the root of scholarship and science. A hypothesis is formulated, research performed, experimental materials designed or acquired, tests run, data obtained and analyzed, and finally a publication. The scholar writes a document outlining the work for dissemination in a scholarly journal.
If it passes the litmus test of peer review, the research enters the canon of the discipline. Over time, it may become a classic with hundreds of citations. Or, more likely, it will join the vast majority of research, with less than two citations over its lifetime, its asserted contributions to the canon increasingly difficult to find – because, in our current world, citations are the best measure of relevance-based search available.
But no matter the fate of an individual publication, the system of publishing is a system of sharing knowledge. We publish as scholars and scientists to share our discoveries with the world (and, of course, to be credited with those discoveries through additional research funding, tenure, and more). And this system has served science extraordinarily well over the more than three hundred years since scholarly journals were birthed in France and England.
Into this old and venerable system has come the earthquake of modern information and communication technologies. The Internet and the Web have made publication cheap and sharing easy – from a technical perspective. The cost of moving, copying, forwarding, and storing the bits in a single scientific publication approach zero.
These technologies have created both enormous efficiency gains in traditional industries (think about how Wal-Mart uses the network to optimize its supply chains) and radical reformulation of industry (Amazon.com in books, or iTunes in music). Yet the promise of enormous increases in efficiency and radical reformulations have to date failed to make similar shattering changes to the rate of meaningful discovery in many scientific disciplines.
For the purposes of this article, I focus on the life sciences in particular. The problems I articulate affect all the scientific disciplines to one extent or another – but the life sciences represent an ideal discussion case. The life sciences are endlessly complex and the problems of global health and pharmaceutical productivity such an enormous burden that the pain of a missed connection is personal. Climate change represents a problem of similar complexity and import to the world, and this article should be contemplated as bearing on research there as well, but my topic is in the application of cyberinfrastructure to the life sciences, and there I’ll try to remain.
Despite new technology after new technology, the cost of discovering a drug keeps increasing, and the return on investment in life sciences (as measured by new drugs hitting the market for new diseases) keeps dropping. While the Web and email pervade pharmaceutical companies, the elusive goal remains “knowledge management:” finding some way to bring sanity to the sprawling mass of figures, emails, data sets, databases, slide shows, spreadsheets, and sequences that underpin advanced life sciences research. Bioinformatics, combinatorial drug discovery, systems biology, and an innumerable number of words ending with “-omics” have yet to relieve the skyrocketing costs and increase the percentage of success in clinical trials for new drug compounds.
The reasons for this are many. First and foremost, drug discovery is hard – really, really hard. And much of the low-hanging fruit has been picked. There are other reasons having to do with regulatory requirements, scientific competition, distortions in funding, and more. But there is one reason that stands out as both a significant drag on discovery and as a treatable problem, one that actually can be solved in the short term: we aren’t sharing knowledge as efficiently as we could be.
