Authors : Ying Huang, Yi Zhang, Jan Youtie, Alan L. Porter, Xuefeng Wang
How do funding agencies ramp-up their capabilities to support research in a rapidly emerging area?
This paper addresses this question through a comparison of research proposals awarded by the US National Science Foundation (NSF) and the National Natural Science Foundation of China (NSFC) in the field of Big Data.
Big data is characterized by its size and difficulties in capturing, curating, managing and processing it in reasonable periods of time. Although Big Data has its legacy in longstanding information technology research, the field grew very rapidly over a short period.
We find that the extent of interdisciplinarity is a key aspect in how these funding agencies address the rise of Big Data. Our results show that both agencies have been able to marshal funding to support Big Data research in multiple areas, but the NSF relies to a greater extent on multi-program funding from different fields.
We discuss how these interdisciplinary approaches reflect the research hot-spots and innovation pathways in these two countries.
URL : How Does National Scientific Funding Support Emerging Interdisciplinary Research: A Comparison Study of Big Data Research in the US and China
DOI : 10.1371/journal.pone.0154509
Fractional counting of citations in research evaluation: An option for cross- and interdisciplinary assessments :
“In the case of the scientometric evaluation of multi- or interdisciplinary units one risks to compare apples with oranges: each paper has to assessed in comparison to an appropriate reference set. We suggest that the set of citing papers first can be considered as the relevant representation of the field of impact. In order to normalize for differences in citation behavior among fields, citations can be fractionally counted proportionately to the length of the reference lists in the citing papers. This new method enables us to compare among units with different disciplinary affiliations at the paper level and also to assess the statistical significance of differences among sets. Twenty-seven departments of the Tsinghua University in Beijing are thus compared. Among them, the Department of Chinese Language and Linguistics is upgraded from the 19th to the second position in the ranking. The overall impact of 19 of the 27 departments is not significantly different at the 5% level when thus normalized for different citation potentials”.
URL : http://arxiv.org/abs/1012.0359