In science there is only physics, all the rest is stamp collecting.
—Attributed to Lord Rutherford (1871–1937)
A clear distinction between information and the understanding built upon it has existed in science since at least the era of Copernicus and Newton, and probably long before. Much of science is now based upon acquiring and utilizing data. In the high throughput era – be that astronomical, metrological or post-genomic–data generation is no longer the principal and overwhelming bottleneck; instead it is the ability to interpret data usefully that limits us. After a century of empirical research, immunology and vaccinology, amongst a plethora of other disciplines, are poised to reinvent themselves as genome-based, high-throughput sciences. Immunology, and indeed all biological disciplines in the post-genomic era, must address a pressing challenge: how best to capitalize on a vast, and potentially overwhelming, inundation of new data; data which is both dazzlingly complex and delivered on a hitherto inconscionable scale. Though many might disagree, I feel that immunology can only do so by embracing fully computation and computational science.
However, to be useable and useful, data that we wish to model, understand and predict must be properly accumulated and archived. Ideally, this accumulated and archived data should be easily accessible, and transparently so. Once this would have meant card indexes and an immeasurable proliferation ...