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In the last two decades there have been studies claiming that science is becoming ever more interdisciplinary. However, the evidence has been anecdotal or partial. Here we investigate how the degree of interdisciplinarity has changed between 1975 and 2005 over six research domains. To do so, we compute well-established bibliometric indicators alongside a new index of interdisciplinarity (Integration score, aka Rao-Stirling diversity) and a science mapping visualization method. The results attest to notable changes in research practices over this 30 year period, namely major increases in number of cited disciplines and references per article (both show about 50% growth), and co-authors per article (about 75% growth). However, the new index of interdisciplinarity only shows a modest increase (mostly around 5% growth). Science maps hint that this is because the distribution of citations of an article remains mainly within neighboring disciplinary areas. These findings suggest that science is indeed becoming more interdisciplinary, but in small steps — drawing mainly from neighboring fields and only modestly increasing the connections to distant cognitive areas. The combination of metrics and overlay science maps provides general benchmarks for future studies of interdisciplinary research characteristics.

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.

Encouraging knowledge flow between mutually relevant disciplines is a worthy aim of research policy makers. Yet, it is less clear what types of research promote cross-disciplinary knowledge flow and whether such research generates particularly influential knowledge. Empirical questions remain as to how to identify knowledge-flow mediating research and how to provide support for this research. This study contributes to addressing these gaps by proposing a new way to identify knowledge-flow mediating research at the individual research article level, instead of at more aggregated levels. We identify journal articles that link two mutually relevant disciplines in three ways-aggregating, bridging, and diffusing. We then examine the likelihood that these papers receive subsequent citations or have funding acknowledgments. Our case study of cognitive science and educational research knowledge flow suggests that articles that aggregate knowledge from multiple disciplines are cited significantly more often than are those whose references are drawn primarily from a single discipline. Interestingly, the articles that meet the criteria for being considered knowledge-flow mediators are less likely to reflect funding, based on reported acknowledgements, than were those that did not meet these criteria. Based on these findings, we draw implications for research policymakers.

Bibliometric analysis of publication metadata is an important tool for investigating emerging fields of technology. However, the application of field definitions to define an emerging technology is complicated by ongoing and at times rapid change in the underlying technology itself. There is limited prior work on adapting the bibliometric definitions of emerging technologies as these technologies change over time. The paper addresses this gap. We draw on the example of the modular keyword nanotechnology search strategy developed at Georgia Institute of Technology in 2006. This search approach has seen extensive use in analyzing emerging trends in nanotechnology research and innovation. Yet with the growth of the nanotechnology field, novel materials, particles, technologies, and tools have appeared. We report on the process and results of reviewing and updating this nanotechnology search strategy. By employing structured text-mining software to profile keyword terms, and by soliciting input from domain experts, we identify new nanotechnology-related keywords. We retroactively apply the revised evolutionary lexical query to 20 years of publication data and analyze the results. Our findings indicate that the updated search approach offers an incremental improvement over the original strategy in terms of recall and precision. Additionally, the updated strategy reveals the importance for nanotechnology of several emerging cited-subject categories, particularly in the biomedical sciences, suggesting a further extension of the nanotechnology knowledge domain. The implications of the work for applying bibliometric definitions to emerging technologies are discussed.

Interest in cross-disciplinary research knowledge interchange runs high. Review processes at funding agencies, such as the U.S. National Science Foundation, consider plans to disseminate research across disciplinary bounds. Publication in the leading multidisciplinary journals, Nature and Science, may signify the epitome of successful interdisciplinary integration of research knowledge and cross-disciplinary dissemination of findings. But how interdisciplinary are they? The journals are multidisciplinary, but do the individual articles themselves draw upon multiple fields of knowledge and does their influence span disciplines? This research compares articles in three fields (Cell Biology, Physical Chemistry, and Cognitive Science) published in a leading disciplinary journal in each field to those published in Nature and Science. We find comparable degrees of interdisciplinary integration and only modest differences in cross-disciplinary diffusion. That said, though the rate of out-of-field diffusion might be comparable, the sheer reach of Nature and Science, indicated by their potent Journal Impact Factors, means that the diffusion of knowledge therein can far exceed that of leading disciplinary journals in some fields (such as Physical Chemistry and Cognitive Science in our samples).

Identifying nanoscience and nanotechnology (nano) publications in global databases is essential to profile and track research in this ever-changing field. Crafting an effective search query to retrieve as many nano-related publications as feasible, while excluding irrelevant publications, is challenging. This paper reports on a major update to a well-established nano-search routine. We offer a nine-module search that significantly augments retrieval from the Web of Science (WoS) compared with use of the WoS Nanoscience & Nanotechnology Category (Nano-WC). We compare search results, showing modular overlaps with a “Nano*” search, and among the modules. Analyzing the resulting set of 2.2 million nano-publication abstract records affirms the tremendous multidisciplinary reach of nano-related research and the continued strong growth of the field. By country, China has enhanced its role as the world’s leading producer of nano-publications, with slower growth for the USA. Relatively high nano-publication growth is also evident for India, Iran, and several other emerging economies. Analyses of nano-publications for 2013–2017 identify more than 20 emerging topics, primarily in the energy and two-dimensional material domains that are apt to be actively researched in the coming few years.

Tech Mining makes exploitation of text databases meaningful to those who can gain from derived knowledge about emerging technologies. It begins with the premise that we have the information, the tools to exploit it, and the need for the resulting knowledge. The information provided puts new capabilities at the hands of technology managers. Using the material present, these managers can identify and access the most valuable technology information resources (publications, patents, etc.); search, retrieve, and clean the information on topics of interest; and lower the costs and enhance the benefits of competitive technological intelligence operations.

The purpose of this study is to modernize previous work on science overlay maps by updating the underlying citation matrix, generating new clusters of scientific disciplines, enhancing visualizations, and providing more accessible means for analysts to generate their own maps.We use the combined set of 2015 Journal Citation Reports for the Science Citation Index (n of journals = 8,778) and the Social Sciences Citation Index (n = 3,212) for a total of 11,365 journals. The set of Web of Science Categories in the Science Citation Index and the Social Sciences Citation Index increased from 224 in 2010 to 227 in 2015. Using dedicated software, a matrix of 227 × 227 cells is generated on the basis of whole-number citation counting. We normalize this matrix using the cosine function. We first develop the citing-side, cosine-normalized map using 2015 data and VOSviewer visualization with default parameter values. A routine for making overlays on the basis of the map (“wc15.exe”) is available at http://www.leydesdorff.net/wc15/index.htm.Findings appear in the form of visuals throughout the manuscript. In Figures 1–9 we provide basemaps of science and science overlay maps for a number of companies, universities, and technologies.As Web of Science Categories change and/or are updated so is the need to update the routine we provide. Also, to apply the routine we provide users need access to the Web of Science.Visualization of science overlay maps is now more accurate and true to the 2015 Journal Citation Reports than was the case with the previous version of the routine advanced in our paper.The routine we advance allows users to visualize science overlay maps in VOSviewer using data from more recent Journal Citation Reports.

The US National Science Foundation Research Coordination Network (RCN) program broke new ground in funding the development of new research communities of practice. This assessment of RCN supports the conclusion that networking activity was increased for a sample set of projects relative to a comparison group. Journal articles resulting from RCN support are scored as highly interdisciplinary. Moreover, those articles appear as notably influential, being published in high-impact journals and being highly cited. The RCN program does indeed seem to be fostering new biological science research networks.

Technology emergence has become a hot topic in R&D policy and management communities. Various methods of measuring technology emergence have been developed. However, there is little literature discussing how to evaluate the results identified by different methods. This research sharpens a promising Technology Emergence Indicator (TEI) set by assessing alternative formulations on three distinct datasets: Dye-Sensitized Solar Cells, Non-Linear Programming, and Nano-Enabled Drug Delivery. Our TEIs derive from a conceptual foundation including three attributes of emergence: persistence, community, and growth that we systematically address through a 3-dimensional evaluation framework. Comparing TEI behavior through sensitivity analyses shows good robustness for the measures. The TEI serve to distinguish emerging R&D topics in the field under study. They can further be used to identify highly active players publishing on those topics. Importantly, results show that identified emerging terms and topics persist to a strong degree; thus, they serve to predict highly active R&D foci within the technical domain under study.