Tanmoy Chakraborty, IIIT-Delhi, India (http://faculty.iiitd.ac.in/~tanmoy/)
Sumit Bhatia, IBM Research AI, India (http://sumitbhatia.net/)
Cornelia Caragea, University of Illinois at Chicago, USA (https://www.cs.uic.edu/~cornelia/)
Past two decades have witnessed the rapid growth of scientific publications in all areas of research. Easier access to published literature (open access, arxiv preprints, etc.) coupled with the advances in computational technologies, has provided data scientists a fertile ground to explore, study and analyze vast amounts of scholarly data available. Scholarly data mining has thus made it possible to do “research about research!” It plays a vital role in scientometrics, bibliometrics, webometrics, and altmetrics, that require applying sophisticated algorithms to curate and derive useful insights from scholarly data. Moreover, the knowledge extracted from the scientific data can help in several decision making processes such as policy making for fund disbursement, identifying research gap in a department and recruiting faculties to fill up the gap, speculating upcoming research areas, etc. On the other hand, the increasing popularity and use of these metrics as a measure of quality of research output, for determining university rankings, and in decision making (tenure and recruitment decisions), has also given rise to objectionable practices to artificially boost these measures (self citations, citation-cliques, etc.). Given that, is it always right to consider these metrics as a reliable proxy of research quality? How should decision and policy makers use these metrics to account for such malpractices.
This special issue aims to bring together the latest groundbreaking research on issues related to
knowledge extraction and deriving insights from scientific data. Of special interest is the role these
metrics play in policy and decision making – both positives and negatives. We welcome both theoretical
and empirical research, and case studies that lead to the development of novel algorithms, tool,
techniques, metrics, decisions and measurements related to scholarly data. The papers submitted to this
special issue should provide insights, analysis, and understanding about the “scientific research” that
would otherwise not have been possible by traditional methods and offer recommendations for the
decision and policymakers about use of such metrics.
We invite submission of high-quality manuscripts reporting relevant research in the area of collecting, managing, mining, and understanding scientific data. Topics of interest include, but are not limited to:
Manuscripts must be submitted through the Expert Systems electronic submission system at
https://mc.manuscriptcentral.com/exsy (select “Special Issue on mining knowledge from Scientific Data”
as the manuscript type). Submissions shall adhere to the Wiley’s instructions and guidelines for authors
available at the journal web site: https://onlinelibrary.wiley.com/journal/14680394. Papers will be
evaluated for their originality, contribution significance, soundness, clarity, and overall quality. The
interest of contributions will be assessed in terms of technical and scientific findings, contribution to the
knowledge and understanding of the problem, methodological advancements, and/or applicative value.
Paper submission due: Aug 15, 2019
Initial review Feedback: October 15, 2019
Revision Due: December 15, 2019
Final review decision: January 15, 2020