“eScience is a research method that involves the collection, processing and utilization of scientific information in the form of data. Increasingly efficient methods of collecting, generating, digitalizing and subsequently storing data are opening up completely new opportunities for conducting research in all professional and academic areas. The processing of huge volumes of data available, including computer calculations, models, and visualizations, allows the solution and comprehension of complex problem issues that were not previously accessible. Traditionally, eScience has primarily been used in the natural science disciplines such as particle physics and bioinformatics. The application of eScience in other contexts is on the rise, however, and the potential for obtaining new research results is huge.” Source: Dansk Roadmap for Forskningsinfrastruktur 2011 (Danish Roadmap for Research Infrastructure, 2011), The Danish Agency for Science, Technology and Innovation.
e-Infrastructure for eScience
- Network infrastructure: a network specially designed for research purposes and featuring high-capacity links to international partners and resources worldwide
- High Performance Computing (HPC/supercomputing): calculation resources for research data
- Storage, data management: data storage, architecture and security
- Applications and services that support and supplement other areas of e-infrastructure
e-Infrastructure is thus the infrastructure that makes eScience possible.
What are researchers saying about eScience?
Niels Brügger, Professor with Special Responsibilities, School of Communication and Culture – Media Studies, Aarhus University:
‘Digital Humanities’ refers to using a digital computer for humanistic analysis and doing much more than simply writing a text in MS Word. The driving force behind digital humanities is that growth in analogue data has ceased. More-or-less all new data in the world are generated in digital format. So it is no longer a question of whether we humanities researchers should work digitally, but how we should do so.
Jakob Grove, Associate Professor, Biomedicine, Aarhus University:
As I see it, eScience is a concept that transcends the classical academic terms in the sciences, uniting disciplines that share a common trait: namely that researchers working in their chosen fields make use of high-performance computer systems in their research. This includes ‘Big data’, but is not limited to it; quite the reverse, there may be data-intensive or calculation-intensive projects—or projects that feature elements of both. That is not to say that eScience covers the ordinary use of online archive systems such as article servers, because that would result in the concept losing its meaning, along with its initial ‘e’. Only if the process involves automated data collection from the archive systems and text mining can it be termed ‘eScience’.
Naturally, I have looked these things up previously and read what other people think. There is plenty of variation in what different people understand ‘eScience’ to mean. Some definitions are quite narrow, while others are broader. Some people are also keen to include aspects such as a requirement for partnership and/or the use of open data, but personally, I find it difficult see this as a distinction that gives rise to a useable definition.
Professor Ole Sigmund, Mechanical Engineer, Technical University of Denmark:
eScience spans a broad scope, but in my own area (mechanical and multi-physical calculation and optimization), it has do to with using large computers to run calculations on constructions and processes that cannot be handled by standard computers. With the development of software and access to the biggest national and international calculation clusters, we can calculate and optimize entire flights rather than individual wing spars—with the inherent potential to achieve weight reductions and to cut fuel consumption.
Professor Brian Vinter, Niels Bohr Institute, University of Copenhagen:
eScience is science powered by computer capacity, in that the computer plays a key role in research and progress in a field that is often limited by computer performance. Approaches to using computers can generally be divided into two groups: simulations and data processing. Some sciences can work with pure ab initio simulations, and are limited only by the calculation power of the computer, while others are based purely on data analysis, and are limited by computer storage capacity as well as the speed at which data can be read and written. In most cases, however, both effective calculations and effective IO (Input/Output) are required.