Graph drawing by force-directed placement
Software—Practice & Experience
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Domain visualization using VxInsight for science and technology management
Journal of the American Society for Information Science and Technology
A clickstream-based collaborative filtering personalization model: towards a better performance
Proceedings of the 6th annual ACM international workshop on Web information and data management
The bibliometric properties of article readership information: Research Articles
Journal of the American Society for Information Science and Technology
Worldwide use and impact of the NASA Astrophysics Data System digital library: Research Articles
Journal of the American Society for Information Science and Technology
Toward alternative metrics of journal impact: a comparison of download and citation data
Information Processing and Management: an International Journal - Special issue: Infometrics
An architecture for the aggregation and analysis of scholarly usage data
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
A practical ontology for the large-scale modeling of scholarly artifacts and their usage
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Usage impact factor: The effects of sample characteristics on usage-based impact metrics
Journal of the American Society for Information Science and Technology
Learning to assess the quality of scientific conferences: a case study in computer science
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Grammar-based geodesics in semantic networks
Knowledge-Based Systems
Annual Review of Information Science and Technology
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Scholarly usage data holds the potential to be used as a tool to study the dynamics of scholarship in real time, and to form the basis for the definition of novel metrics of scholarly impact. However, the formal groundwork to reliably and validly exploit usage data is lacking, and the exact nature, meaning and applicability of usage-based metrics is poorly understood. The MESUR project funded by the Andrew W. Mellon Foundation constitutes a systematic effort to define, validate and cross-validate a range of usage-based metrics of scholarly impact. MESUR has collected nearly 1 billion usage events as well as all associated bibliographic and citation data from significant publishers, aggregators and institutional consortia to construct a large-scale usage data reference set. This paper describes some major challenges related to aggregating and processing usage data, and discusses preliminary results obtained from analyzing the MESUR reference data set. The results confirm the intrinsic value of scholarly usage data, and support the feasibility of reliable and valid usage-based metrics of scholarly impact.