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Diversity is a central concept in many fields. Despite its importance, there is no unified methodological framework to measure diversity and its three components of variety, balance and disparity. Current approaches take into account…
Scientific research trends and interests evolve over time. The ability to identify and forecast these trends is vital for educational institutions, practitioners, investors, and funding organizations. In this study, we predict future trends…
Feature tracking in time-varying scalar fields is a fundamental task in scientific computing. Topological descriptors, which summarize important features of data, have proved to be viable tools to facilitate this task. The merge tree is a…
Novelty evaluation is vital for the promotion and management of innovation. With the advancement of information techniques and the open data movement, some progress has been made in novelty measurements. Tracking and reviewing novelty…
Scientific behavior is often characterized by a tension between building upon established knowledge and introducing novel ideas. Here, we investigate whether this tension is reflected in the relationship between the similarity of a…
This study presents, for the first time, a conceptual and formal model of postsingular science (PSS), which analyses and interprets changes in scientific knowledge driven by accelerating technological progress, singularity, and the…
In many areas of science multiple sets of data are collected pertaining to the same system. Examples are food products which are characterized by different sets of variables, bio-processes which are on-line sampled with different…
Scientific document embeddings contain a variety of rich features which can be harnessed for downstream tasks such as recommendation, ranking, and clustering. We explore which tangible insights can be drawn from scientific document…
Understanding the relationship between the composition of a research team and the potential impact of their research papers is crucial as it can steer the development of new science policies for improving the research enterprise. Numerous…
Several studies exist which use scientific literature for comparing scientific activities (e.g., productivity, and collaboration). In this study, using co-authorship data over the last 40 years, we present the evolutionary dynamics of multi…
Motivated by the results of recent laboratory experiments (Yoshida et al. Nature, 424, 303-306 (2003)) as well as many earlier field observations that evolutionary changes can take place in ecosystems over relatively short ecological time…
The academia and industry are characterized by a reciprocal shaping and dynamic feedback mechanism. Despite distinct institutional logics, they have adapted closely in collaborative publishing and talent mobility, demonstrating tension…
Bibliometric methods for the analysis of highly specialized subjects are increasingly investigated and debated. Information and assessments well-focused at the specialty level can help make important decisions in research and innovation…
Recent artificial intelligence has developed rapidly with significant interdisciplinary expansion, yet existing studies often treat it as a whole, lacking systematic long-term subfield comparisons and structural analyses, thereby limiting…
Bibliometric studies based on the Web of Science (WOS) database have become an increasingly popular method for analysing the structure of scientific research. So do network approaches, which, based on empirical data, make it possible to…
The emergence of intellectual property as an academic issue opens a big gate to a cross-disciplinary field. Different disciplines start a dialogue in the framework of the international multilateral treaties in the early 90's. As global…
In this paper we introduce and study three new measures for efficient discriminative comparison of phylogenetic trees. The NNI navigation dissimilarity $d_{nav}$ counts the steps along a "combing" of the Nearest Neighbor Interchange (NNI)…
Approaches form the foundation for conducting scientific research. Querying approaches from a vast body of scientific papers is extremely time-consuming, and without a well-organized management framework, researchers may face significant…
Over the past few years, we have seen fundamental breakthroughs in core problems in machine learning, largely driven by advances in deep neural networks. At the same time, the amount of data collected in a wide array of scientific domains…
Data science has emerged from the proliferation of digital data, coupled with advances in algorithms, software and hardware (e.g., GPU computing). Innovations in structural biology have been driven by similar factors, spurring us to ask:…