29,346 research outputs found
Exploring scholarly data with Rexplore.
Despite the large number and variety of tools and services available today for exploring scholarly data, current support is still very limited in the context of sensemaking tasks, which go beyond standard search and ranking of authors and publications, and focus instead on i) understanding the dynamics of research areas, ii) relating authors ‘semantically’ (e.g., in terms of common interests or shared academic trajectories), or iii) performing fine-grained academic expert search along multiple dimensions. To address this gap we have developed a novel tool, Rexplore, which integrates statistical analysis, semantic technologies, and visual analytics to provide effective support for exploring and making sense of scholarly data. Here, we describe the main innovative elements of the tool and we present the results from a task-centric empirical evaluation, which shows that Rexplore is highly effective at providing support for the aforementioned sensemaking tasks. In addition, these results are robust both with respect to the background of the users (i.e., expert analysts vs. ‘ordinary’ users) and also with respect to whether the tasks are selected by the evaluators or proposed by the users themselves
The distorted mirror of Wikipedia: a quantitative analysis of Wikipedia coverage of academics
Activity of modern scholarship creates online footprints galore. Along with
traditional metrics of research quality, such as citation counts, online images
of researchers and institutions increasingly matter in evaluating academic
impact, decisions about grant allocation, and promotion. We examined 400
biographical Wikipedia articles on academics from four scientific fields to
test if being featured in the world's largest online encyclopedia is correlated
with higher academic notability (assessed through citation counts). We found no
statistically significant correlation between Wikipedia articles metrics
(length, number of edits, number of incoming links from other articles, etc.)
and academic notability of the mentioned researchers. We also did not find any
evidence that the scientists with better WP representation are necessarily more
prominent in their fields. In addition, we inspected the Wikipedia coverage of
notable scientists sampled from Thomson Reuters list of "highly cited
researchers". In each of the examined fields, Wikipedia failed in covering
notable scholars properly. Both findings imply that Wikipedia might be
producing an inaccurate image of academics on the front end of science. By
shedding light on how public perception of academic progress is formed, this
study alerts that a subjective element might have been introduced into the
hitherto structured system of academic evaluation.Comment: To appear in EPJ Data Science. To have the Additional Files and
Datasets e-mail the corresponding autho
Modeling and Analysis of Scholar Mobility on Scientific Landscape
Scientific literature till date can be thought of as a partially revealed
landscape, where scholars continue to unveil hidden knowledge by exploring
novel research topics. How do scholars explore the scientific landscape , i.e.,
choose research topics to work on? We propose an agent-based model of topic
mobility behavior where scholars migrate across research topics on the space of
science following different strategies, seeking different utilities. We use
this model to study whether strategies widely used in current scientific
community can provide a balance between individual scientific success and the
efficiency and diversity of the whole academic society. Through extensive
simulations, we provide insights into the roles of different strategies, such
as choosing topics according to research potential or the popularity. Our model
provides a conceptual framework and a computational approach to analyze
scholars' behavior and its impact on scientific production. We also discuss how
such an agent-based modeling approach can be integrated with big real-world
scholarly data.Comment: To appear in BigScholar, WWW 201
Inheritance patterns in citation networks reveal scientific memes
Memes are the cultural equivalent of genes that spread across human culture
by means of imitation. What makes a meme and what distinguishes it from other
forms of information, however, is still poorly understood. Our analysis of
memes in the scientific literature reveals that they are governed by a
surprisingly simple relationship between frequency of occurrence and the degree
to which they propagate along the citation graph. We propose a simple
formalization of this pattern and we validate it with data from close to 50
million publication records from the Web of Science, PubMed Central, and the
American Physical Society. Evaluations relying on human annotators, citation
network randomizations, and comparisons with several alternative approaches
confirm that our formula is accurate and effective, without a dependence on
linguistic or ontological knowledge and without the application of arbitrary
thresholds or filters.Comment: 8 two-column pages, 5 figures; accepted for publication in Physical
Review
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