In this study we design, develop, implement and test an analytical framework and measurement model to detect scientific discoveries with 'breakthrough' characteristics. To do so we have developed a series of computerized search algorithms that data mine large quantities of research publications. These algorithms facilitate early-stage detection of 'breakout' papers that emerge as highly cited and distinctive and are considered to be potential breakthroughs.Combining computer-aided data mining with decision heuristics, enabled us to assess structural changes within citation patterns with the international scientific literature. In our case studies we applied a citation impact time window of 24-36 months after publication of each research paper.In this paper, we report on our test results, in which five algorithms were applied to the entire Web of Science database. We analysed the citation impact patterns of all research articles from the period 1990-1994. We succeeded in detecting many papers with distinctive impact profiles (breakouts). A small subset of these breakouts is classified as 'breakthroughs': Nobel Prize research papers; papers occurring in Nature's Top-100 Most Cited Papers Ever; papers still (highly) cited by review papers or patents; or those frequently mentioned in today's social media. We also compare the outcomes of our algorithms with the results of a 'baseline' detection algorithm developed by Redner in 2005, which selects the world's most highly cited 'hot papers'.The detection rates of the algorithms vary, but overall, they present a powerful tool for tracing breakout papers in science. The wider applicability of these algorithms, across all science fields, has not yet been ascertained. Whether or not our early-stage breakout papers present a 'breakthrough' remains a matter of opinion, where input from subject experts is needed for verification and confirmation, but our detection approach certain helps to limit the search domain to trace and track important emerging topics in science.
We investigate publications in medical research that have gone unnoticed for a number of years after being published and then suddenly become cited to a significant degree. Such publications are called Sleeping Beauties (SBs). This study focuses on SBs that are cited in patents. We find that the increasing trend of the relative number of SBs comes to an end around 1998. However, still a constant fraction of publications becomes an SB. Many SBs become highly cited publications, they even belong to the top-10 to 20% most cited publications in their field. We measured the scaling of the number of SBs in relation to the sleeping period length, during-sleep citation-intensity, and with awakening citation-intensity. We determined the Grand Sleeping Beauty Equation for these medical SBs which shows that the probability of awakening after a period of deep sleep is becoming rapidly smaller for longer sleeping periods and that the probability for higher awakening intensities decreases extremely rapidly. The exponents of the scaling functions show a time-dependent behavior which suggests a decreasing occurrence of SBs with longer sleeping periods. We demonstrate that the fraction of SBs cited by patents before scientific awakening exponentially increases. This finding shows that the technological time lag is becoming shorter than the sleeping time. Inventor-author self-citations may result in shorter technological time lags, but this effect is small. Finally, we discuss characteristics of an SBs that became one of the highest cited medical papers ever.
In September 2015 Thomson Reuters published its Ranking of Innovative Universities (RIU). Covering 100 large research-intensive universities worldwide, Stanford University came in first, MIT was second and Harvard in third position. But how meaningful is this outcome? In this paper we will take a critical view from a methodological perspective. We focus our attention on the various types of metrics available, whether or not data redundancies are addressed, and if metrics should be assembled into a single composite overall score or not. We address these issues in some detail by emphasizing one metric in particular: university–industry co-authored publications (UICs). We compare the RIU with three variants of our own University–Industry R&D Linkage Index, which we derived from the bibliometric analysis of 750 research universities worldwide. Our findings highlight conceptual and methodological problems with UIC-based data, as well as computational weaknesses such university ranking systems. Avoiding choices between size-dependent or independent metrics, and between single-metrics and multi-metrics systems, we recommend an alternative ‘scoreboard’ approach: (1) without weighing systems of metrics and composite scores; (2) computational procedures and information sources are made more transparent; (3) size-dependent metrics are kept separate from size-independent metrics; (4) UIC metrics are selected according to the type of proximity relationship between universities and industry.
Some say that world science has become more ‘applied’, or at least more ‘application-oriented’, in recent years. Replacing the ill-defined distinction between ‘basic research’ and ‘applied research’, we introduce ‘research application orientation’ domains as an alternative conceptual and analytical framework for examining research output growth patterns. To distinguish possible developmental trajectories we define three institutional domains: ‘university’, ‘industry’, ‘hospitals’. Our macro-level bibliometric analysis takes a closer look at general trends within and across some 750 of the world’s largest research-intensive universities. To correct for database changes, our time-series analysis was applied to both a fixed journal set (same research journals and conference proceedings over time) and a dynamic journal set (changing set of publication outlets). We find that output growth in the ‘hospital research orientation’ has significantly outpaced the other two application domains, especially since 2006/2007. This happened mainly because of the introduction of new publication outlets in the WoS, but also partially because some universities—especially in China—seem to have become more visible in this domain. Our analytical approach needs further broadening and deepening to provide a more definitive answer whether hospitals and the medical sector are becoming increasingly dominant as a domain of scientific knowledge production and an environment for research applications.
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