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The importance of a research article is routinely measured by counting how many times it has been cited. However, treating all citations with equal weight ignores the wide variety of functions that citations perform. We want to automatically identify the subset of references in a bibliography that have a central academic influence on the citing paper. For this purpose, we examine the effectiveness of a variety of features for determining the academic influence of a citation. By asking authors to identify the key references in their own work, we created a data set in which citations were labeled according to their academic influence. Using automatic feature selection with supervised machine learning, we found a model for predicting academic influence that achieves good performance on this data set using only four features. The best features, among those we evaluated, were those based on the number of times a reference is mentioned in the body of a citing paper. The performance of these features inspired us to design an influence-primed h-index (the hip-index). Unlike the conventional h-index, it weights citations by how many times a reference is mentioned. According to our experiments, the hip-index is a better indicator of researcher performance than the conventional h-index.

There are two principal data sources for collaborative filtering recommenders in scholarly digital libraries: usage data obtained from harvesting a large, distributed collection of Open URL web logs and citation data obtained from the journal articles. This study explores the characteristics of recommendations generated by implementations of these two methods: the 'bX' system by ExLibris and an experimental citation-based recommender, Sarkanto. Recommendations from each system were compared according to their semantic similarity to the seed article that was used to generate them. Since the full text of the articles was not available for all the recommendations in both systems, the semantic similarity between the seed article and the recommended articles was deemed to be the semantic distance between the journals in which the articles were published. The semantic distance between journals was computed from the "semantic vectors" distance between all the terms in the full-text of the available articles in that journal and this study shows that citation-based recommendations are more semantically diverse than usage-based ones. These recommenders are complementary since most of the time, when one recommender produces recommendations the other does not., Comment: 4 pages, 4 figures, ACM Recommender Systems Workshop 2012, Dublin Ireland

It is becoming common to archive research datasets that are not only large but also numerous. In addition, their corresponding metadata and the software required to analyse or display them need to be archived. Yet the manual curation of research data can be difficult and expensive, particularly in very large digital repositories, hence the importance of models and tools for automating digital curation tasks. The automation of these tasks faces three major challenges: (1) research data and data sources are highly heterogeneous, (2) future research needs are difficult to anticipate, (3) data is hard to index. To address these problems, we propose the Extract, Transform and Archive (ETA) model for managing and mechanizing the curation of research data. Specifically, we propose a scalable strategy for addressing the research-data problem, ranging from the extraction of legacy data to its long-term storage. We review some existing solutions and propose novel avenues of research., Comment: 8 pages, Fourth Workshop on Very Large Digital Libraries, 2011