Alan L. Porter

Is science becoming more interdisciplinary? Measuring and mapping six research fields over time

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.

Measuring researcher interdisciplinarity

We offer two metrics that together help gauge how interdisciplinary a body of research is. Both draw upon Web of Knowledge Subject Categories (SCs) as key units of analysis. We have assembled two substantial Web of Knowledge samples from which to determine how closely individual SCs relate to each other. “Integration” measures the extent to which a research article cites diverse SCs. “Specialization” considers the spread of SCs in which the body of research (e.g., the work of a given author in a specified time period) is published. Pilot results for a sample of researchers show a surprising degree of interdisciplinarity.

Technology opportunities analysis

Abstract We present an approach to efficiently generate effective intelligence on emerging technologies. This approach draws on monitoring and bibliometrics to mine the wealth of information available in major public electronic databases. The approach uses new software to expedite secondary analyses of database searches on topics of interest. We illustrate the range of information profiles possible by examining research and development (R&D) publications and patents pertaining to electronics assembly and, more specifically, to multichip module development.

Automated extraction and visualization of information for technological intelligence and forecasting

Abstract Empirical technology forecasting (TF) is not well utilized in technology management. Three factors could enhance managerial utilization: capability to exploit huge volumes of available information, ways to do so very quickly, and informative representations that help manage emerging technologies. This paper reports on efforts to address these three factors via partially automated processes to generate helpful knowledge from text quickly and graphically. We first illustrate a process to generate a family of technology maps that help convey emphases, players, and patterns in the development of a target technology. Second, we exemplify the generation of particular “innovation indicators” that measure particular facets of R&D activity to relate these to technological maturation, contextual influences, and market potential. Both technology mapping and innovation indicators rely upon searches in huge, easily accessible, abstract databases and text mining software. We augment these through “macros” (programming scripts) that automatically sequence the necessary steps to generate particular desired information products. These analytical findings can be tailored to the needs of particular technology managers.

On the Future of Technological Forecasting

Technological forecasting is now poised to respond to the emerging needs of private and public sector organizations in the highly competitive global environment. The history of the subject and its variant forms, including impact assessment, national foresight studies, roadmapping, and competitive technological intelligence, shows how it has responded to changing institutional motivations. Renewed focus on innovation, attention to science-based opportunities, and broad social and political factors will bring renewed attention to technological forecasting in industry, government, and academia. Promising new tools are anticipated, borrowing variously from fields such as political science, computer science, scientometrics, innovation management, and complexity science.

Translation of Innovative Designs Into Phase I Trials

PURPOSE Phase I clinical trials of new anticancer therapies determine suitable doses for further testing. Optimization of their design is vital in that they enroll cancer patients whose well-being is distinctly at risk. This study examines the effectiveness of knowledge transfer about more effective statistical designs to clinical practice. METHODS We examined abstract records of cancer phase I trials from the Science Citation Index database between 1991 and 2006 and classified them into clinical (dose-finding trials) and statistical trials (methodologic studies of dose-escalation designs). We then mapped these two sets by tracking which trials adopted new statistical designs. RESULTS One thousand two hundred thirty-five clinical and 90 statistical studies were identified. Only 1.6% of the phase I cancer trials (20 of 1,235 trials) followed a design proposed in one of the statistical studies. These 20 clinical studies showed extensive lags between publication of the statistical paper and its translation into a clinical paper. These 20 clinical trials followed Bayesian adaptive designs. The remainder used variations of the standard up-and-down method. CONCLUSION A consequence of using less effective designs is that more patients are treated with doses outside the therapeutic window. Simulation studies have shown that up-and-down designs treated only 35% of patients at optimal dose levels versus 55% for Bayesian adaptive designs. This implies needless loss of treatment efficacy and, possibly, lives. We suggest that regulatory agencies (eg, US Food and Drug Administration) should proactively encourage the adoption of statistical designs that would allow more patients to be treated at near-optimal doses while controlling for excessive toxicity.

AN INDICATOR OF CROSS-DISCIPLINARY RESEARCH

Study of interdisciplinary research processes and performance is hampered by a lack of data. This project investigated possible indicators based in the open scientific literature to measure such processes. Focusing on theJournal Citation Reports as a suitable data base, alternative indicators were validated on a sample of 383 articles drawn from 19 journals. The results support the use ofCitations Outside Category as an indicator of cross-disciplinary research activity. An estimated version of this indicator is used to examine three research categories — Demography, Operations Research/Management Science, and Toxicology — as to the extent of cross-disciplinary citation occurring by the journals in these categories and to them. Results suggest thatCitations Outside Category can be a quite informative bibliometric measure. A key substantive finding is that citation across broad field categories (engineering, life sciences, physical sciences, and social sciences) is extremely infrequent.

How interdisciplinary is nanotechnology

Facilitating cross-disciplinary research has attracted much attention in recent years, with special concerns in nanoscience and nanotechnology. Although policy discourse has emphasized that nanotechnology is substantively integrative, some analysts have countered that it is really a loose amalgam of relatively traditional pockets of physics, chemistry, and other disciplines that interrelate only weakly. We are developing empirical measures to gauge and visualize the extent and nature of interdisciplinary interchange. Such results speak to research organization, funding, and mechanisms to bolster knowledge transfer. In this study, we address the nature of cross-disciplinary linkages using “science overlay maps” of articles, and their references, that have been categorized into subject categories. We find signs that the rate of increase in nano research is slowing, and that its composition is changing (for one, increasing chemistry-related activity). Our results suggest that nanotechnology research encompasses multiple disciplines that draw knowledge from disciplinarily diverse knowledge sources. Nano research is highly, and increasingly, integrative—but so is much of science these days. Tabulating and mapping nano research activity show a dominant core in materials sciences, broadly defined. Additional analyses and maps show that nano research draws extensively upon knowledge presented in other areas; it is not constricted within narrow silos.

Interdisciplinary research: meaning, metrics and nurture

Recognizing prior research and reflection, we offer a definition of interdisciplinary research (IDR) that focuses on integration of concepts, techniques and/or data. We note that this need not entail teaming. Building upon this definition, we discuss its implications for accurate measurement. We then synthesize contextual and process factors expected to foster knowledge integration. These suggest a rich set of research questions concerning the implications for successful IDR of actions by universities, funding organizations, professional associations, and the science media, including journal editors. We seek to engage social scientists who study research practices, organizations, and policy in consideration of interdisciplinary research processes and their evaluation. Copyright , Beech Tree Publishing.

Research profiling: Improving the literature review

We propose enhancing the traditional literature review through “research profiling”. This broad scan of contextual literature can extend the span of science by better linking efforts across research domains. Topical relationships, research trends, and complementary capabilities can be discovered, thereby facilitating research projects. Modern search engine and text mining tools enable research profiling by exploiting the wealth of accessible information in electronic abstract databases such as MEDLINE and Science Citation Index. We illustrate the potential by showing sixteen ways that “research profiling” can augment a traditional literature review on the topic of data mining.