Acknowledgments in research publications, like citations, indicate influential contributions to scientific work. However, acknowledgments are different from citations; whereas citations are formal expressions of debt, acknowledgments are arguably more personal, singular, or private expressions of appreciation and contribution. Furthermore, many sources of research funding expect researchers to acknowledge any support that contributed to the published work. Just as citation indexing proved to be an important tool for evaluating research contributions, we argue that acknowledgments can be considered as a metric parallel to citations in the academic audit process. We have developed automated methods for acknowledgment extraction and analysis and show that combining acknowledgment analysis with citation indexing yields a measurable impact of the efficacy of various individuals as well as government, corporate, and university sponsors of scientific work. acknowledgment analysis ͉ information extraction ͉ machine learning
This paper examines the difference and similarities between the two on-line computer science citation databases DBLP and CiteSeer. The database entries in DBLP are inserted manually while the CiteSeer entries are obtained autonomously via a crawl of the Web and automatic processing of user submissions. CiteSeer's autonomous citation database can be considered a form of self-selected on-line survey. It is important to understand the limitations of such databases, particularly when citation information is used to assess the performance of authors, institutions and funding bodies. We show that the CiteSeer database contains considerably fewer single author papers. This bias can be modeled by an exponential process with intuitive explanation. The model permits us to predict that the DBLP database covers approximately 24% of the entire literature of Computer Science. CiteSeer is also biased against low-cited papers. Despite their difference, both databases exhibit similar and significantly different citation distributions compared with previous analysis of the Physics community. In both databases, we also observe that the number of authors per paper has been increasing over time.4 By public, we mean that access to the database is free of charge. Commercial databases are also available, the most well-known being the science-citation index [6] arXiv:cs/0703043v1 [cs.DL]
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.