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Register a new userMeasuring national capability over big sciences multidisciplinarity: A case study of nuclear fusion research
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In the era of big science, countries allocate big research and development budgets to large scientific facilities that boost collaboration and research capability. A nuclear fusion device called the tokamak is a source of great interest for many countries because it ideally generates sustainable energy expected to solve the energy crisis in the future. Here, to explore the scientific effects of tokamaks, we map a countrys research capability in nuclear fusion research with normalized revealed comparative advantage on five topical clusters -- material, plasma, device, diagnostics, and simulation -- detected through a dynamic topic model. Our approach captures not only the growth of China, India, and the Republic of Korea but also the decline of Canada, Japan, Sweden, and the Netherlands. Time points of their rise and fall are related to tokamak operation, highlighting the importance of large facilities in big science. The gravity model points out that two countries collaborate less in device, diagnostics, and plasma research if they have comparative advantages in different topics. This relation is a unique feature of nuclear fusion compared to other science fields. Our results can be used and extended when building national policies for big science.
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As a part of science of science (SciSci) research, the evolution of scientific disciplines has been attracting a great deal of attention recently. This kind of discipline level analysis not only give insights of one particular field but also shed light on general principles of scientific enterprise. In this paper we focus on graphene research, a fast growing field covers both theoretical and applied study. Using co-clustering method, we split graphene literature into two groups and confirm that one group is about theoretical research (T) and another corresponds to applied research (A). We analyze the proportion of T/A and found applied research becomes more and more popular after 2007. Geographical analysis demonstrated that countries have different preference in terms of T/A and they reacted differently to research trend. The interaction between two groups has been analyzed and shows that T extremely relies on T and A heavily relies on A, however the situation is very stable for T but changed markedly for A. No geographic difference is found for the interaction dynamics. Our results give a comprehensive picture of graphene research evolution and also provide a general framework which is able to analyze other disciplines.
Scientific knowledge cannot be seen as a set of isolated fields, but as a highly connected network. Understanding how research areas are connected is of paramount importance for adequately allocating funding and human resources (e.g., assembling teams to tackle multidisciplinary problems). The relationship between disciplines can be drawn from data on the trajectory of individual scientists, as researchers often make contributions in a small set of interrelated areas. Two recent works propose methods for creating research maps from scientists publication records: by using a frequentist approach to create a transition probability matrix; and by learning embeddings (vector representations). Surprisingly, these models were evaluated on different datasets and have never been compared in the literature. In this work, we compare both models in a systematic way, using a large dataset of publication records from Brazilian researchers. We evaluate these models ability to predict whether a given entity (scientist, institution or region) will enter a new field w.r.t. the area under the ROC curve. Moreover, we analyze how sensitive each method is to the number of publications and the number of fields associated to one entity. Last, we conduct a case study to showcase how these models can be used to characterize science dynamics in the context of Brazil.
In the social sciences, researchers search for information on the Web, but this is most often distributed on different websites, search portals, digital libraries, data archives, and databases. In this work, we present an integrated search system for social science information that allows finding information around research data in a single digital library. Users can search for research data sets, publications, survey variables, questions from questionnaires, survey instruments, and tools. Information items are linked to each other so that users can see, for example, which publications contain data citations to research data. The integration and linking of different kinds of information increase their visibility so that it is easier for researchers to find information for re-use. In a log-based usage study, we found that users search across different information types, that search sessions contain a high rate of positive signals and that link information is often explored.
The Open Research Knowledge Graph (ORKG) provides machine-actionable access to scholarly literature that habitually is written in prose. Following the FAIR principles, the ORKG makes traditional, human-coded knowledge findable, accessible, interoperable, and reusable in a structured manner in accordance with the Linked Open Data paradigm. At the moment, in ORKG papers are described manually, but in the long run the semantic depth of the literature at scale needs automation. Operational Research is a suitable test case for this vision because the mathematical field and, hence, its publication habits are highly structured: A mundane problem is formulated as a mathematical model, solved or approximated numerically, and evaluated systematically. We study the existing literature with respect to the Assembly Line Balancing Problem and derive a semantic description in accordance with the ORKG. Eventually, selected papers are ingested to test the semantic description and refine it further.
Modern science is dominated by scientific productions from teams. A recent finding shows that teams with both large and small sizes are essential in research, prompting us to analyze the extent to which a countrys scientific work is carried out by big/small teams. Here, using over 26 million publications from Web of Science, we find that Chinas research output is more dominated by big teams than the rest of the world, which is particularly the case in fields of natural science. Despite the global trend that more papers are done by big teams, Chinas drop in small team output is much steeper. As teams in China shift from small to large size, the team diversity that is essential for innovative works does not increase as much as that in other countries. Using the national average as the baseline, we find that the National Natural Science Foundation of China (NSFC) supports fewer small team works than the National Science Foundation of U.S. (NSF) does, implying that big teams are more preferred by grant agencies in China. Our finding provides new insights into the concern of originality and innovation in China, which urges a need to balance small and big teams.
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