A Quantitative Study of History in the English Short-Title Catalogue (ESTC), 1470-1800

This article analyses publication trends in the field of history in early modern Britain and North America in 1470–1800, based on English Short- Title Catalogue (ESTC) data. Its major contribution is to demonstrate the potential of digitized library catalogues as an essential scholastic tool and part of reproducible research. We also introduce a novel way of quantitatively analysing a particular trend in book production, namely the publishing of works in the field of history. The study is also our first experimental analysis of paper consumption in early modern book production, and dem- onstrates in practice the importance of open-science principles for library and information science. Three main research questions are addressed: 1) who wrote history; 2) where history was published; and 3) how publishing changed over time in early modern Britain and North America. In terms of our main findings we demonstrate that the average book size of history publications decreased over time, and that the octavo-sized book was the rising star in the eighteenth century, which is a true indication of expand- ing audiences. The article also compares different aspects of the most popu- lar writers on history, such as Edmund Burke and David Hume. Although focusing on history, these findings may reflect more widespread publishing trends in the early modern era. We show how some of the key questions in this field can be addressed through the quantitative analysis of large-scale bibliographic data collections.Peer reviewe

The Anatomy of Eighteenth Century Collections Online (ECCO)

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Ebola epidemic model with dynamic population and memory

The recent outbreaks of Ebola encourage researchers to develop mathematical models for simulating the dynamics of Ebola transmission. We continue the study of the models focusing on those with a variable population. Hence, this paper presents a compartmental model consisting of 8-dimensional nonlinear dif- ferential equations with a dynamic population and investigates its basic reproduction number. Moreover, a dimensionless model is introduced for numerical analysis, thus proving the disease-free equilibrium is locally asymptotically stable whenever the threshold condition, known as a basic reproduction number, is less than one. Finally, we use a fractional differential form of the model to sufficiently fit long time-series data of Guinea, Liberia, and Sierra Leone retrieved from the World Health Organization, and the numerical results demonstrate the performance of the model.publishe

Dependency detection with similarity constraints

Unsupervised two-view learning, or detection of dependencies between two paired data sets, is typically done by some variant of canonical correlation analysis (CCA). CCA searches for a linear projection for each view, such that the correlations between the projections are maximized. The solution is invariant to any linear transformation of either or both of the views; for tasks with small sample size such flexibility implies overfitting, which is even worse for more flexible nonparametric or kernel-based dependency discovery methods. We develop variants which reduce the degrees of freedom by assuming constraints on similarity of the projections in the two views. A particular example is provided by a cancer gene discovery application where chromosomal distance affects the dependencies between gene copy number and activity levels. Similarity constraints are shown to improve detection performance of known cancer genes.Comment: 9 pages, 3 figures. Appeared in proceedings of the 2009 IEEE International Workshop on Machine Learning for Signal Processing XIX (MLSP'09). Implementation of the method available at http://bioconductor.org/packages/devel/bioc/html/pint.htm

Scaling up bibliographic data science

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Digital Humanities and study of history

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