All the modern gadgets have a name starting with i… or e… – and these syntactic patterns convey meaning when considered in the correct context; the use of such terms expose the development of a culture, in some sense. Perhaps that is why e-science is called e-science! The subject matter of the term then is that of traditional science, modified in light of that particular factor dominating of our age – which is Information, or Data. But is this modified nomenclature just a nod to a passing fad, or a hint of a longer term paradigmatic shift?

Of course, the evolution of naming conventions are not the important factor. The real focus in answering the question must be to show what added value the application of eScientific principles could impart, and to explain why an individual would want to study it.

Many of our national and cultural hopes for growth are founded in our investment in the knowledge economy – the “creation, advancement and dissemination of knowledge” is the mission statement of our particular community, and the latest products are data handling tools, and methods for or implementations of data abstractions.

The traditional and well known Scientific Method can be described thus :

• Form a hypothesis
• Design an experiment to test it
• Conduct experiment
• Decide if hypothesis has been falsified
• If yes – form new hypothesis
• If no – design new experiment
• Repeat

This method has been implemented to great effect for many years. However, development in technology and computational power has led to scientific experiments whose procedural requirements and result outputs are vast, where the scope of new discoveries can exceed the bounds of the initial insights. An experiment into one type of particle or reaction may yield the desired results for that experiment, but can also provide a large dataset applicable to other problems in the field.

Two issues arise from this; firstly, that achieving results in a modern experiment can require a level of resources that are simply unavailable even to the largest of discrete communities; and secondly, that the large output sets of such experiments could perhaps be exploited in some way to solve other problems in similar or even unrelated fields, reducing duplication of effort and expense.

It is in tackling these two issues over years of collaborative research that e-science has developed. It encompasses the hardware and software requirements for long term storage of data, for providing easy, timely and appropriate access to such data, and also the development and deployment of suitable tools for interacting with such large collections, between and across diverse objects and networks.

e-science, then, is a methodology for coping with and benefiting from the increasing scale of experimentation and data output across all types of academic or research endeavours. It can also provide an extension to the scientific method, in enabling deduction of new hypotheses from examination of large scale data sets – for example in spotting trends previously unforeseen in more limited data sweeps.

It should hence be clear that there is ample reason to study e-science – as long as scientific research itself is of value then e-science, as a development of that method, is also a worthwhile endeavour, and one that should see considerable growth and success in future. In addition there is much to be gained as an individual student. As e-science methods can and are or will be applied to a large range of subject areas, there is a great deal of opportunity to learn about and participate in many different and interesting topics. This enables a person involved in e-science to gain a knowledge of developments at the forefront of varying disciplines and to have an insight into the future direction and development of modern thought.

My background as a student of philosophy, perhaps, nurtured my interest in the methods by which new concepts and paradigms develop and propagate. In this age of technology, data and information, studying e-science offers an opportunity to learn about data and its relation to knowledge, and the further relation of knowledge to mind. I think it is telling that many e-science projects involve large scale examination of data that can be linked with the concept of what it is to be a person or a mind, from human genome studies to distributed networks acting as learning machines. Similarly, there are reasons in many other subject areas to consider the benefits that the applications of e-science tools and methodologies could bring.

And this is why e-science should be an enduring, useful and interesting topic of study and research in years to come.