Sixth Exploratory Round Table Conference in Shanghai on Big Data in the Natural Sciences and Humanities
The Sixth Exploratory Round Table Conference or ERTC, on the topic of "Big Data in the Natural Sciences and Humanities", takes place in Shanghai from 19 to 21 November 2015 under the auspices of the Shanghai Institute for Advanced Studies. The event is the sixth of a series of annual conferences which are intended to provide a joint platform for scientists of both Max Planck Society (MPG) and Chinese Academy of Sciences (CAS) together with international leading scientists to discuss and evaluate newly emerging and rapidly evolving fields of research. The two supporting organisations thus create visions and seeds towards the establishments of topical areas at the leading edge of science. As such, the ERTC adds a novel instrument to ongoing processes of priority-setting in the further development of the research portfolio of both organisations.
Big Data has become a ubiquitous notion in recent years. Technological developments, in particular in informatics and high-throughput approaches, have revolutionized data generation in all fields of science. As a consequence, researchers in almost all areas of science face new and unforeseen challenges: Gathering data is so easy and quick that it exceeds by far the capacity to validate, analyze, visualize, store, and curate all the information. Tackling this challenge will without doubt lead to unprecedented data-driven scientific discoveries.
In the field of biomedicine, the dramatic advances in technologies that can be summed up as omics, such as high throughput DNA-sequencing, lead to vast amounts of data at dramatically plummeting costs. This revolution in biomedical research raises high expectations as to the increase of knowledge, understanding of health and disease, and eventually the development of powerful therapies to treat thus far incurable diseases such as cancer or depression, in a personalized and precise fashion.
Such data-driven methods are revolutionizing not only drug design and drug discovery. Regarding chemistry and materials science, Big Data techniques in combination with computational modeling facilitate analyzing the vast space of yet unexplored compounds and materials - thus complementing and in several cases even replacing experiments. This high-throughput screening needs to be combined with novel big-data analytics tools, which then enables the identification of new scientific phenomena, advances materials science and engineering, and predicts materials with technologically relevant properties and functions.
Last but not least, the analysis of large volumes of data opens up new avenues of research in the field of the humanities and social sciences. The wealth of data which is already born digital as well as mass digitizing existing analog data allow to answer complex questions that were previously unanswerable. Analyzing the development and diffusion of knowledge, modeling cultural evolution, and predicting human behavior are just some of the challenges that lie ahead.
Big Data requires innovative technologies to efficiently process large quantities of data within tolerable elapsed times. Machine learning is one of today’s most rapidly growing technical fields lying at the core of data science and artificial intelligence, indispensable for analyzing and classifying data. This development also provides challenges for theory-building. Whereas data mostly exhibit correlations and statistical dependencies, theory provides causal relationships. The interplay between data mining and theory building is an important issue, as Big Data continues to pervade scientific and private life. Furthermore, data capture both in the health segment and in daily life can pose a severe threat to privacy, as individuals are increasingly divulging data relating to individual behavior and performance.
This ERTC aims at elaborating a critical review of the presently existing ideas, strategies and aspirations of Big Data science. The results of the ERTC will serve as a basis for further consideration by CAS and MPG regarding research in this field.