Bigdata 2016 - Scholarly Big Data: AI Perspectives, Challenges, and Ideas Workshop

Academics and researchers worldwide continue to produce large numbers of scholarly documents including papers, books, technical reports, etc. and associated data such as tutorials, proposals, and course materials. For example, PubMed has over 20 million documents, 10 million unique names and 70 million name mentions. Google Scholar has many millions more, it is believed. These rapidly-growing online documents offer several benefits for discovery, learning, and staying informed. However, data mining applications are now faced with the challenge of efficiently processing more documents in less time.
Understanding how at scale research topics emerge, evolve, or disappear, what is a good measure of quality of published works, what are the most promising areas of research, how authors connect and influence each other, who are the experts in a field, and who funds a particular research topic are some of the major foci of the rapidly emerging field of Scholarly Big Data. More recently, social web style metrics for measuring the impact of scholarly work for example, Altametrics were formulated. Academic social networks such as academia.edu and researchgate are also being developed to systematically enable collaboration and discovery on the scientific web. In addition to citation and co-authorship networks based on scholarly manuscripts, newer forms of scholarly communications are now possible via interfaces such as Twitter and Facebook groups. How useful are these novel networking abilities for scholarly communications? Can we detect topical flows, influential ideas, and citation and collaboration patterns based on interactions on these platforms? Can activities on these platforms serve as substitutes for assessing scholarly impact before citations are accumulated? These novel questions form an important theme in our proposed workshop.