The occurrence of 'Sleeping Beauty' publications in medical research: Their scientific impact and technological relevance.

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.

Capturing 'R&D excellence': indicators, international statistics, and innovative universities.

Excellent research may contribute to successful science-based technological innovation. We define 'R&D excellence' in terms of scientific research that has contributed to the development of influential technologies, where 'excellence' refers to the top segment of a statistical distribution based on internationally comparative performance scores. Our measurements are derived from frequency counts of literature references ('citations') from patents to research publications during the last 15 years. The 'D' part in R&D is represented by the top 10% most highly cited 'excellent' patents worldwide. The 'R' part is captured by research articles in international scholarly journals that are cited by these patented technologies. After analyzing millions of citing patents and cited research publications, we find very large differences between countries worldwide in terms of the volume of domestic science contributing to those patented technologies. Where the USA produces the largest numbers of cited research publications (partly because of database biases), Switzerland and Israel outperform the US after correcting for the size of their national science systems. To tease out possible explanatory factors, which may significantly affect or determine these performance differentials, we first studied high-income nations and advanced economies. Here we find that the size of R&D expenditure correlates with the sheer size of cited publications, as does the degree of university research cooperation with domestic firms. When broadening our comparative framework to 70 countries (including many medium-income nations) while correcting for size of national science systems, the important explanatory factors become the availability of human resources and quality of science systems. Focusing on the latter factor, our in-depth analysis of 716 research-intensive universities worldwide reveals several universities with very high scores on our two R&D excellence indicators. Confirming the above macro-level findings, an in-depth study of 27 leading US universities identifies research expenditure size as a prime determinant. Our analytical model and quantitative indicators provides a supplementary perspective to input-oriented statistics based on R&D expenditures. The country-level findings are indicative of significant disparities between national R&D systems. Comparing the performance of individual universities, we observe large differences within national science systems. The top ranking 'innovative' research universities contribute significantly to the development of advanced science-based technologies.

Do younger Sleeping Beauties prefer a technological prince?

In this paper we investigate recent Sleeping Beauties cited in patents (SB-SNPRs). We find that the increasing trend of the relative number of SBs stopped around 1998. Moreover, we find that the time lag between the publication year of the SB-SNPRs and their first citation in a patent is becoming shorter in recent years. Our observations also suggest that, on average, in the more recent years SBs are awakened increasingly earlier by a 'technological prince' rather than by a 'scientific prince'. These observations suggest that SBs with technological importance are 'discovered' earlier in an application-oriented context. Then, because of this earlier recognized technological relevance, papers may be cited also earlier in a scientific context. Thus early recognized technological relevance may 'prevent' papers to become an SB. The scientific impact of Sleeping Beauties is generally not necessarily related to the technological importance of the SBs, as far as measured with number and impact of the citing patents. The analysis of the occurrence of inventor-author relations as well as the citation years of inventor-author patents suggest that the scientific awakening of Sleeping Beauties only rarely occurs by inventor-author self-citation.

University-industry R&D linkage metrics: validity and applicability in world university rankings.

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.