OBJECTIVES To develop a method for investigating co-authorship patterns and author team characteristics associated with the publications in high-impact journals through the integration of public MEDLINE data and institutional scientific profile data. METHODS For all current researchers at Columbia University Medical Center, we extracted their publications from MEDLINE authored between years 2...

OBJECTIVES To assess the prevalence of honorary and ghost authors in six leading general medical journals in 2008 and compare this with the prevalence reported by authors of articles published in 1996. DESIGN Cross sectional survey using a web based questionnaire. SETTING International survey of journal authors. PARTICIPANTS Sample of corresponding authors of 896 research articles, review...

PaperPuppy is a system designed to allow the exploration and analysis of a network of ISWC conference authors and publications. Using proceeding data from the first four ISWC conferences, we show co-authorship, citation, institutional affiliation, co-depiction in photographs, and other connections supported by the underlying ontologies. We demonstrate the rich browsing experience with the Paper...

We use a convolutional neural network to perform authorship identification on a very homogeneous dataset of scientific publications. In order to investigate the effect of domain biases, we obscure words below a certain frequency threshold, retaining only their POS-tags. This procedure improves test performance due to better generalization on unseen data. Using our method, we are able to predict...

Burrows’s Delta is the most established measure for stylometric difference in literary authorship attribution. Several improvements on the original Delta have been proposed. However, a recent empirical study showed that none of the proposed variants constitute a major improvement in terms of authorship attribution performance. With this paper, we try to improve our understanding of how and why ...

Authorship guidelines have established criteria to guide author selection based on significance of contribution and helped to define associated responsibilities and accountabilities for the published findings. However, low awareness, variable interpretation, and inconsistent application of these guidelines can lead to confusion and a lack of transparency when recognizing those who merit authors...

We present data analysis and modeling of two particular cases of study in the field of growing networks. We analyze World Wide Web data set and authorship collaboration networks in order to check the presence of correlation in the data. The results are reproduced with good agreement through a suitable modification of the standard Albert-Barabási model of network growth. In particular, intrinsic...

We examine two popular statistical models, the hidden Markov model and mixed membership stochastic blockmodel. Using the hierarchical Dirichlet process, we define nonparametric variants of these models. We develop a memoized online variational inference algorithm that uses a new objective function to properly penalize the addition of unneeded states in either model. Finally, we demonstrate that...