Between March 22nd and 23rd of 2016, many researchers and leaders in the field of science of science will be meeting in the Library of Congress in Washington, DC. The organizing committee and the invited speakers have all been instrumental in starting an analytics approach to study science. The deadline for submissions just passed (Feb 22nd, 2016), but they seem to be still accepting submissions. Here is the abstract of the conference (emphases are mine).
Modern scientific research has grown exponentially over the past several decades. Growth in research articles, patents, preprints, white papers and informal content on the web (e.g., company product descriptions) has become “big data”, an information overload for scientists, engineers and also the government agencies, foundations and companies that support them. More than simply a change in scale, science increasingly constitutes a complex system: apparently complicated, involving strong interaction between components, and predisposed to emergent or unexpected collective outcomes. The growing number of global scientists and research teams, increasingly connected via multiple channels—international conferences, online publications, e-mail, and science blogs—have increased and multiplied this complexity. Moreover, intensifying specialization in science and engineering disciplines has made it more difficult for scientists to communicate and collaborate, and for evaluators to judge the promise and progress of research investments in those areas.
All of these changes in this 400 Billion dollar enterprise suggest the importance of quantitative and automated approaches to science that take into account the deluge of information that no individual scientist can master. The increasing availability of large-scale datasets that capture major activities in science has created an unprecedented opportunity to explore the patterns of scientific production and reward with rich mathematical and computational models. In contrast with standard bibliometric studies, the recent surge in science of science studies is characterized by distinct flavors: (i) They typically rely on large-scale datasets, ranging from hundreds of thousands to millions of authors, papers and their citations; (ii) Instead of evaluating metrics, they use models to more deeply probe the mechanisms driving science, from knowledge production to scientific impact, systematically distinguishing predictable from random patterns; (iii) Ambition and a diversity of purposes, ranging from work that partners with scientists involved to help drive discovery, the reformulation of science and innovation policy, and use of science as an observatory to probe phenomena that are more universal and widely applicable than the institutions of science themselves. As such, the tools and perspectives vary, involving social scientists, information and computer scientists, economists, physicists and mathematicians, with results published in venues with non-overlapping readership.
The goal of this conference is to bring together leading researchers from various disciplines and form discussions on this proliferating subject. We specifically look for contributions that satisfy one or more of the aforementioned flavors.
Amazing. Exciting times to be in big data analytics of science!