National Consortium for Data Science Names Erjia Yan as 2015 Data Fellow

The National Consortium for Data Science (NCDS), a public-private partnership to advance data science and address the challenges and opportunities of big data, named Drexel University College of Computing & Informatics Assistant Professor Erjia Yan, PhD as an NCDS Data Fellow for the 2015 calendar year.

Each Data Fellow receives $50,000 to support work that addresses data science research issues in novel and innovative ways. Their work will be expected to advance the mission and vision of the NCDS, which formed in early 2013. Yan, who was one of three faculty members at three different universities to receive this honor, will be supported by the NCDS fellowship for his project titled “Assessing the Impact of Data and Software on Science Using Hybrid Metrics” (see abstract below).

Data Fellow positions are open to faculty members at NCDS member institutions, which includes universities in the University of North Carolina system, Duke University, Texas A & M University and Drexel University. A wide range of researchers from six different member universities applied for the Fellowships. Their research proposals addressed many of the hot topics in data science, from cybersecurity to applying the techniques used by online music databases to develop more precise search algorithms and interest students in data science.

“This is the second year we’ve provided Data Fellows awards and we believe the program is a great way to bring together talented faculty researchers and our industry members who are interested in the practical applications of their work,” said Stan Ahalt, chair of the NCDS steering committee and director of UNC Chapel Hill’s Renaissance Computing Institute (RENCI), one of the founding members of the consortium, in a recent press release. “We had applications from across our membership and the quality was outstanding. I know our members look forward to learning more about our new Fellows and to understanding how their research will advance data science and help organizations in business, government and academia address their data challenges.”

Dr. Yan’s research interests lie in network science, informetrics, and scholarly data analysis, in the area of using scholarly networks to study scholarly communication, with a focus on its methods (e.g., ranking, clustering, topic modeling, and path finding) as well as its applications (e.g., evaluating research impact, studying scientific collaboration, addressing issues related to disciplinarity and interdisciplinarity, and exploring knowledge flow and transfer patterns). He earned his doctorate in information science and master of information science degree from Indiana University Bloomington, and a bachelor of science degree from Nanjing University.

Abstract, “Assessing the Impact of Data and Software on Science Using Hybrid Metrics:”

In the age of data, the critical components of scientific and industrial research increasingly are data and software. These products can have significant impacts on future scientific discoveries and business innovation. Yet, they can be difficult to discover and assess because new knowledge is still catalogued in the form of published research papers. This project will address the problem of discovering and assessing the impact of data sets and software by identifying referencing patterns and designing hybrid metrics to assess the full impact of data and software. Unlike current data repository indexing, the project aims to provide context-driven, full text data analytics for data and software in order to account for the unsystematic ways in which these products are cited in scientific literature, including hyperlinks to web pages, footnotes, endnotes, and digital object identifiers. Ultimately, the project seeks to develop a system that will comprehensively capture the impact of data and software on knowledge production and discovery.

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