Using time series of US patents per million inhabitants, knowledge-generating cycles can be distinguished. These cycles partly coincide with Kondratieff long waves. The changes in the slopes between them indicate discontinuities in the knowledge-gene
rating paradigms. The knowledge-generating paradigms can be modeled in terms of interacting dimensions (for example, in university-industry-government relations) that set limits to the maximal efficiency of innovation systems. The maximum values of the parameters in the model are of the same order as the regression coefficients of the empirical waves. The mechanism of the increase in the dimensionality is specified as self-organization which leads to the breaking of existing relations into the more diversified structure of a fractal-like network. This breaking can be modeled in analogy to 2D and 3D (Koch) snowflakes. The boost of knowledge generation leads to newly emerging technologies that can be expected to be more diversified and show shorter life cycles than before. Time spans of the knowledge-generating cycles can also be analyzed in terms of Fibonacci numbers. This perspective allows for forecasting expected dates of future possible paradigm changes. In terms of policy implications, this suggests a shift in focus from the manufacturing technologies to developing new organizational technologies and formats of human interactions
Percentiles have been established in bibliometrics as an important alternative to mean-based indicators for obtaining a normalized citation impact of publications. Percentiles have a number of advantages over standard bibliometric indicators used fre
quently: for example, their calculation is not based on the arithmetic mean which should not be used for skewed bibliometric data. This study describes the opportunities and limits and the advantages and disadvantages of using percentiles in bibliometrics. We also address problems in the calculation of percentiles and percentile rank classes for which there is not (yet) a satisfactory solution. It will be hard to compare the results of different percentile-based studies with each other unless it is clear that the studies were done with the same choices for percentile calculation and rank assignment.
