25 research outputs found
Quantifying the long-term influence of scientific publications
We consider the long-term indirect influence of publications on subsequent publications. In particular, we are here interested in long-term scientific influence at the level of disciplines. We present a novel method to quantify the long-term scientific influence of publications, considering both direct and indirect, or higher-order citations. We apply this method to Web of Science data at the level of disciplines. Preliminary results for a specific operationalization of the notion of long-term scientific influence suggest that long-term influence is dominated by a few disciplines: Astronomy and Astrophysics, Basic Life Sciences, and Physics and Material Science.</p
Quantifying the long-term influence of scientific publications
We consider the long-term indirect influence of publications on subsequent publications. In particular, we are here interested in long-term scientific influence at the level of disciplines. We present a novel method to quantify the long-term scientific influence of publications, considering both direct and indirect, or higher-order citations. We apply this method to Web of Science data at the level of disciplines. Preliminary results for a specific operationalization of the notion of long-term scientific influence suggest that long-term influence is dominated by a few disciplines: Astronomy and Astrophysics, Basic Life Sciences, and Physics and Material Science.</p
From the margin to the centre?:A relational analysis of discursive contention in the minority integration debate in the Low Countries
Old questions, new techniques: a research note on the computational identification of political elites
Merit, Expertise and Measuremen
Investigating scientific mobility in co-authorship networks using multilayer temporal motifs
Algorithms and the Foundations of Software technolog
Italian sociologists: a community of disconnected groups
Examining coauthorship networks is key to study scientific collaboration patterns and structural characteristics of scientific communities. Here, we studied coauthorship networks of sociologists in Italy, using temporal and multi-level quantitative analysis. By looking at publications indexed in Scopus, we detected research communities among Italian sociologists. We found that Italian sociologists are fractured in many disconnected groups. The giant connected component could be split into five main groups with a mix of three main disciplinary topics: sociology of culture and communication (present in two groups), economic sociology (present in three groups) and general sociology (present in three groups). By applying an exponential random graph model, we found that collaboration ties are mainly driven by theresearch interestsof these groups. Other factors, such aspreferential attachment,genderandaffiliation homophilyare also important, but the effect of gender fades away once other factors are controlled for. Our research shows the advantages of multi-level and temporal network analysis in revealing the complexity of scientific collaboration patterns.Merit, Expertise and Measuremen
Exploring the mobility of mobile phone users
Mobile phone datasets allow for the analysis of human behavior on an
unprecedented scale. The social network, temporal dynamics and mobile behavior
of mobile phone users have often been analyzed independently from each other
using mobile phone datasets. In this article, we explore the connections
between various features of human behavior extracted from a large mobile phone
dataset. Our observations are based on the analysis of communication data of
100000 anonymized and randomly chosen individuals in a dataset of
communications in Portugal. We show that clustering and principal component
analysis allow for a significant dimension reduction with limited loss of
information. The most important features are related to geographical location.
In particular, we observe that most people spend most of their time at only a
few locations. With the help of clustering methods, we then robustly identify
home and office locations and compare the results with official census data.
Finally, we analyze the geographic spread of users' frequent locations and show
that commuting distances can be reasonably well explained by a gravity model.Comment: 16 pages, 12 figure
Impact factors: Is the Nature Index at odds with DORA? (Correspondence)
Merit, Expertise and Measuremen