Day 1 | Inauguration | January 27, 2021: 3 pm to 5:30 pm

Science cannot afford to lag behind the real-time data world in its ability to infer meaning from data and take action based on that meaning, especially when it is possible to build systems that help turn individual data/observations/records that can be captured, turn them into organized datasets that can be analyzed, modeled, and visualized. In the 21st century, much of the vast volume of scientific data is captured by new instruments on a 24/7 basis, along with information generated in the world of computer models. Building the necessary capabilities and the cyberinfrastructure for this new research paradigm of science⎯data-intensive and computationally heavy is becoming increasingly critical.

eScience is a term created by John Taylor was used to describe a large funding initiative starting in November 2000. According to the IEEE eScience conference series (started in 2005) “eScience studies, enacts, and improves the ongoing process of innovation in computationally-intensive or data-intensive research methods; typically this is carried out collaboratively, often using the distributed infrastructure. eScience encompasses all fields of research and addresses all stages of the research lifecycle, from the formulation of the research questions, through large scale simulations and data analytics, scientific discovery, up to long-term sharing, reusing, and reapplying of the results, data as well as the relevant tools, processes and knowledge.”

Cyberinfrastructure⎯term used by National Science Foundation, US and other funders in the US, is a technological and sociological solution to the problem of efficiently connecting laboratories, data, computers, and people to enable derivation of novel scientific theories and knowledge. eScience is where Data Sciences meet science. Almost everything about science is changing because of the impact of information technology. The ‘fourth paradigm’ a term coined and championed by Jim Gray is the new paradigm in science. According to Jim Gray ‘Experimental, theoretical, and computational science are all being affected by the data deluge, and a fourth, “data-intensive” science paradigm is emerging.’

The Fourth Paradigm as a concept focuses on how science can be advanced by sharing data. It is based on the idea that computational science constitutes a new set of methods beyond empiricism, theory, and simulation. It is concerned with data discovery in the sense that researchers and scientists require tools, technologies, and platforms that seamlessly integrate into standard scientific methodologies and processes. It allows for the integration of these tools and technologies for research. Finally, the framework is designed to provide new opportunities for researchers and scientists to share their data to encourage new scientific discovery.

Tycho Brahe’s assistant Johannes Kepler took Brahe’s catalogue of systematic astronomical observations and discovered the laws of planetary motion. This forgotten reality of scientific research highlights the division between the mining and analysis of captured and carefully archived experimental data and the creation of theories. This division is one aspect of the Fourth Paradigm. Building the Cyberinfrastructure to bridge this division is the vision of eScience. Digital Libraries are one of the tools/systems that constitute the cyberinfrastructure.

The generic infrastructure for data/information management, the data repositories, the tools of analysis, and the technical and legal issues of data stewardship are some of the common areas of interests between the digital libraries communities and the eScience group.

Prof. K. Vijay Raghavan, Principal Scientific Advisor, Government of India
India, will inaugurate the LTC 2021 on January 27,2021 at 3 pm.

Professor Tony Hey, who led UK’s eScience Programme during 2001-2005, will give the LTC 2021 Conclave Keynote. His talk is entitled:

‘AI for Science: The Fourth Paradigm, eScience and Open Science’

Abstract:

The talk will begin with a short overview of the dramatic advances in AI made by Deep Neural Networks and review some applications of these technologies to the huge scientific datasets now being produced by the large-scale experimental facilities at the UK’s Rutherford Appleton Laboratory. The focus of the talk will then turn to progress towards open science and include a discussion of the FAIR data initiative and the role of research libraries. The talk will conclude with some thoughts on how these developments can transform much of scientific research.

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