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Interdisciplinary research is fundamental when it comes to tackling complex problems in our highly interlinked world, and is on the rise globally. Yet, it is unclear why--in an increasingly competitive academic environment--one should pursue an interdisciplinary career given its recent negative press. Several studies have indeed shown that interdisciplinary research often achieves lower impact compared to more specialized work, and is less likely to attract funding. We seek to reconcile such evidence by analyzing a dataset of 44,419 research grants awarded between 2006 and 2018 from the seven national research councils in the UK. We compared the research performance of researchers with an interdisciplinary funding track record with those who have a specialized profile. We found that the former dominates the network of academic collaborations, both in terms of centrality and knowledge brokerage; but such a competitive advantage does not immediately translate into impact. Indeed, by means of a matched pair experimental design, we found that researchers who transcend between disciplines on average achieve lower impacts in their publications than the subject specialists in the short run, but eventually outperform them in funding performance, both in terms of volume and value. Our results suggest that launching an interdisciplinary career may require more time and persistence to overcome extra challenges, but can pave the way for a more successful endeavour.
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The characteristics of the $h$-index in the field of condensed matter physics are studied using high-quality data from ResearcherID. The results are examined in terms of theoretical descriptions of the $h$-index overall dependence on a researchers total number of published papers, and total number of citations. In particular, the models by Hirsch, Egghe and Rousseau, as well as by Glanzel and Schubert are examined. Special emphasis is placed on the deviations from such statistical descriptions, and it is argued that the deviation of a particular researchers $h$ value from the Egghe-Rouseau models prediction can be used as a supplementary measure of impact. A corresponding analysis with similar results is performed using the multi-author $h_m$-index.
Chinas scientific output has risen precipitously over the past decade; it is now the worlds second-largest producer of scientific papers, behind only the United States. The quality of Chinas research is also on the rise (Van Noorden, 2016). The online visibility and impact of Chinas research are also important issues worth exploring. In this study, we investigate the altmetric performance of publications in the field of Biotechnology and Applied Microbiology and published by authors from Chinese affiliations. We find that papers published by those authors from Chinese affiliations have much lower visibility on the social web than articles from other countries, when there is no significant difference for the citations. Fewer of Chinas publications get tweeted, and those tweeted publications attract less social attention. A geographical analysis of tweeters shows that scholarly articles get most of their social attention from the authors home countries, a finding that is also confirmed by correlation and regression analysis. This situation, which is unfavorable for researchers from Chinese affiliations, is caused, in part, by the inaccessibility of mainstream social networking platforms in mainland China.
Bibliometrics provides accurate, cheap and simple descriptions of research systems and should lay the foundations for research policy. However, disconnections between bibliometric knowledge and research policy frequently misguide the research policy in many countries. A way of correcting these disconnections might come from the use of simple indicators of research performance. One such simple indicator is the number of highly cited researchers, which can be used under the assumption that a research system that produces and employs many highly cited researchers will be more successful than others with fewer of them. Here, we validate the use of the number of highly cited researchers (Ioannidis et al. 2020; PLoS Biol 18(10): e3000918) for research assessment at the country level and determine a country ranking of research success. We also demonstrate that the number of highly cited researchers reported by Clarivate Analytics is also an indicator of the research success of countries. The formal difference between the numbers of highly cited researchers according to Ionannidis et al. and Clarivate Analytics is that evaluations based on these two lists of highly cited researchers are approximately equivalent to evaluations based on the top 5% and 0.05% of highly cited papers, respectively. Moreover, the Clarivate Analytics indicator is flawed in some countries.
Knowledge of how science is consumed in public domains is essential for a deeper understanding of the role of science in human society. While science is heavily supported by public funding, common depictions suggest that scientific research remains an isolated or ivory tower activity, with weak connectivity to public use, little relationship between the quality of research and its public use, and little correspondence between the funding of science and its public use. This paper introduces a measurement framework to examine public good features of science, allowing us to study public uses of science, the public funding of science, and how use and funding relate. Specifically, we integrate five large-scale datasets that link scientific publications from all scientific fields to their upstream funding support and downstream public uses across three public domains - government documents, the news media, and marketplace invention. We find that the public uses of science are extremely diverse, with different public domains drawing distinctively across scientific fields. Yet amidst these differences, we find key forms of alignment in the interface between science and society. First, despite concerns that the public does not engage high-quality science, we find universal alignment, in each scientific field and public domain, between what the public consumes and what is highly impactful within science. Second, despite myriad factors underpinning the public funding of science, the resulting allocation across fields presents a striking alignment with the fields collective public use. Overall, public uses of science present a rich landscape of specialized consumption, yet collectively science and society interface with remarkable, quantifiable alignment between scientific use, public use, and funding.
We show how faceted search using a combination of traditional classification systems and mixed-membership topic models can go beyond keyword search to inform resource discovery, hypothesis formulation, and argument extraction for interdisciplinary research. Our test domain is the history and philosophy of scientific work on animal mind and cognition. The methods can be generalized to other research areas and ultimately support a system for semi-automatic identification of argument structures. We provide a case study for the application of the methods to the problem of identifying and extracting arguments about anthropomorphism during a critical period in the development of comparative psychology. We show how a combination of classification systems and mixed-membership models trained over large digital libraries can inform resource discovery in this domain. Through a novel approach of drill-down topic modeling---simultaneously reducing both the size of the corpus and the unit of analysis---we are able to reduce a large collection of fulltext volumes to a much smaller set of pages within six focal volumes containing arguments of interest to historians and philosophers of comparative psychology. The volumes identified in this way did not appear among the first ten results of the keyword search in the HathiTrust digital library and the pages bear the kind of close reading needed to generate original interpretations that is the heart of scholarly work in the humanities. Zooming back out, we provide a way to place the books onto a map of science originally constructed from very different data and for different purposes. The multilevel approach advances understanding of the intellectual and societal contexts in which writings are interpreted.
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