This paper addresses the problem of scientific research analysis across multiple research literature collections. We use topic modeling in three novel comparative tasks: (1) unsupervised discovery and comparison of scientific topics across multiple disciplines; (2) comparison of topics within the same discipline; (3) analysis of topic evolution over time within and across disciplines and trend ...

The birth and decline of disciplines are critical to science and society. How do scientific disciplines emerge? No quantitative model to date allows us to validate competing theories on the different roles of endogenous processes, such as social collaborations, and exogenous events, such as scientific discoveries. Here we propose an agent-based model in which the evolution of disciplines is gui...

The extraordinary scientific-technical, economic, and social transformations related to the widespread use of computers and to the whole information and communication technologies have not been accompanied by the development of a scientific “informational” perspective helping make a coherent sense of the spectacular changes occurring. Like in other industrial revolutions of the past, technical ...

The structure and interrelationships of formal knowledge produced in the scientific disciplines have long been of interest to academics. One increasingly important domain of Information Systems (IS) research is the study of the creation and evolution of knowledge. Recent discourse about the intellectual structure of IS has revealed Latent Semantic Analysis (LSA) as an analytic technique that re...

As climate change became an increasingly prominent topic in both scientific and public discourse over the past two decades, technical terms like "stationarity" and "nonstationarity" also became more conspicuous. Historically, such terms arose in the mathematics literature, and have been commonly used in engineering and related disciplines. Indeed, stationarity and nonstationarity have tradition...

Computational intelligence generally comprises a rather large set of – in a wider sense – adaptive and human-like data analysis and modelling methods. Due to some superior features – such as generalisation, trainability, coping with incomplete and inconsistent data, etc. – computational intelligence has found its way into numerous applications in almost all scientific disciplines. A very promin...

A concise sketch of the self-model theory of subjectivity (SMT; Metzinger, 2003a), aimed at empirical researchers. Discussion of some candidate mechanisms by which self-awareness could appear in a physically realized information-processing system like the brain, using empirical examples from various scientific disciplines. The paper introduces two core-concepts, the "phenomenal self-model" (PSM...

Almost any scientific information contains mathematical formulae. For may disciplines the mathematics that is needed is rather limited, but even areas like law need to be able to speak of lengths, areas, weights and volumes. Hence they need to be able to express information involving mathematical concepts. Sometimes these concepts have to be translated into equivalent other concepts, say like U...