A sensitivity analysis of factors influential to the popularity of shared data in data repositories

With their rapid development, data repositories usually provide abundant metadata—including data types, keywords, downloads, stars, forks, and citations—along with the data content. These rich metadata can be used as valuable resources to study the factors that facilitate data sharing. However, few previous studies have attempted to study which metadata are correlated with the popularity of data. This study overcomes these issues by extracting the major factors for each dataset from a well-known data repository, the UCI Machine Learning Repository, and a popular open-source software repository, GitHub. We trained a neural network model and measured the influence of these features on quantified popularity metrics using the weight product of connecting neurons. We grouped the UCI factors into two categories (intrinsic and extrinsic) and the GitHub factors into three categories (intrinsic, extrinsic, and web-related) to analyze their influence on popularity at each level. The quantified influence was used to predict the popularity of the data or software. We conducted a statistical analysis to explore the relationship between these factors and popularity with five different domains (life sciences, physical sciences, computer science/engineering, social sciences, and others) for the UCI repository. This study’s findings contribute to understanding the factors that affect the popularity of open datasets or software for providing guidance on data sharing, reuse, and organization.     

How to consider fractional counting and field normalization in the statistical modeling of bibliometric data: A multilevel Poisson regression approach

The numerical-algorithmic procedures of fractional counting and field normalization are often mentioned as indispensable requirements for bibliometric analyses. Against the background of the increasing importance of statistics in bibliometrics, a multilevel Poisson regression model (level 1: publication, level 2: author) shows possible ways to consider fractional counting and field normalization in a statistical model (fractional counting I). However, due to the assumption of duplicate publications in the data set, the approach is not quite optimal. Therefore, a more advanced approach, a multilevel multiple membership model, is proposed that no longer provides for duplicates (fractional counting II). It is assumed that the citation impact can essentially be attributed to time-stable dispositions of researchers as authors who contribute with different fractions to the success of a publication’s citation. The two approaches are applied to bibliometric data for 254 scientists working in social science methodology. A major advantage of fractional counting II is that the results no longer depend on the type of fractional counting (e.g., equal weighting). Differences between authors in rankings are reproduced more clearly than on the basis of percentiles. In addition, the strong importance of field normalization is demonstrated; 60% of the citation variance is explained by field normalization.