The availability of ‘Big Data’ – at scale and at pace – together with the technology and techniques required to harness the information such data contain, seem certain to transform both science and society in dramatic and unprecedented ways.
Jim Gray – the Turing Award-winning computer scientist – proposed that Data Science might constitute a ‘fourth scientific paradigm’ that radically extends our ability to interrogate, understand and manipulate physical and social worlds beyond what is possible using our three established (empirical, theoretical and computational) paradigms.
Data Science transforms not only how science is done, but also what science means; and the novel insights generated using data-driven analyses (including those delivered through Artificial Intelligence and Machine/Deep Learning) have much to offer every aspect of pure and applied research – from seemingly abstract studies of expression, representation and meaning through to prosaic (though equally complex) practical questions in disciplines more familiar with quantitative techniques (such as engineering, ecology, economics and psychology). Thus, while Data Science plays a central role in predictive analyses of climate change, and its use in evidence-based practice for public health is growing; its novel techniques and insights remain under-utilised in cultural studies and have yet to transform global Higher Education.
For these reasons, the WUN-DSN aims to build on WUN’s reach and impact of successful WUN RDF-facilitated research networks – many of which (with some exceptions) currently do not explicitly exploit novel Data Science techniques – using the evidence, outputs, and opportunities generated by the WUN-DSN to directly benefit all four of the WUN Global Challenges and their associated priority areas by:
1. strengthening understanding of how Data Science might transform research practice, and how organisational structures, networking facilities, training resources and professional guidance might enhance the uptake of Data Science techniques;
2. delivering outputs designed to support Data Science networking, skills development and professional practice;
3. generating opportunities for integrating collaborations with data scientists, and Data Science techniques, within existing and future WUN RDF-supported research.
Associate Professor Kelvin Tsoi, Chinese University of Hong Kong
Assistant Professor Catherine Mooney, University College Dublin
Associate Professor Simon Poon, University of Sydney
Professor Thea de Wet, University of Johannesburg