I’m not sure when I first heard the term “E-Science”, but it wasn’t that long ago. My first impression was that it sounds like one of those words that should be unsucked (i.e., jargon). Now that I know more about it, I’m inclined to think that jargon is in the ear of the beholder. Here’s why:
The most commonly used definition for E-Science is that it is type of scientific research that uses large-scale computing infrastructure to process very large datasets (i.e., “Big Science“, which generates “Big Data“). However many (most?) often I hear E-Science used as an umbrella term that describes any size of science that involves digital data and/or analysis. These days, that pretty much covers all science. I therefore contend that E-Science as a phrase is redundant – it was describing what used to be a subset of science, but is now more correctly describing all science. So why is there an “E” at all?
There are journals, websites, and meetings focused on E-Science (I blogged about attending the Microsoft eScience Workshop just a few months ago). In fact, I’m currently participating in an E-Science Institute, sponsodred by the Association of Research Libraries, the Digital Library Federation, and DuraSpace. The goal of the Institute is to provide opportunities for “academic and research libraries to boost institutional support of e-research and the management and preservation of our scientific and scholarly record.” Libraries are facing the new digital frontier head-on: they are interested in providing services that meet researchers’ needs, and these services have changed dramatically in the last few decades.
The argument for keeping the “E”: Although science researchers have no need for the distinction between Science and E-Science, it is a helpful distinction for groups that provide services to academia at large. Not all disciplines are as digital as the sciences: think about art history, studies of ancient texts, or observations of other cultures. Those groups that provide services or assistance for the broader academic community should, therefore, continue to consider E-Science.
Some readings, recommended by the E-Science Institute organizers (and me!):
- Jim Gray on e-Science, A Transformed Scientific Method. from The Fourth Paradigm: Data-Intensive Scientific Discovery, Tony Hey et al. Microsoft Research, 2009 . Link
- E-Science and the Life Cycle of Research, Charles Humphrey. June, 2008. Link
- Special Online Collection: Dealing with Data, Science Magazine, AAAS. February 11, 2011. Link (free registration available)
This work, unless otherwise expressly stated, is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License.