Data Quality for the Information Age
Data Quality for the Information Age
Two supervised learning approaches for name disambiguation in author citations
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Name disambiguation in author citations using a K-way spectral clustering method
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Comparative study of name disambiguation problem using a scalable blocking-based framework
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Detecting Patch Submission and Acceptance in OSS Projects
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
Proceedings of the 2008 international working conference on Mining software repositories
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
Effective self-training author name disambiguation in scholarly digital libraries
Proceedings of the 10th annual joint conference on Digital libraries
User-contributed descriptive metadata for libraries and cultural institutions
ECDL'10 Proceedings of the 14th European conference on Research and advanced technology for digital libraries
An analysis of the evolving coverage of computer science sub-fields in the DBLP digital library
ECDL'10 Proceedings of the 14th European conference on Research and advanced technology for digital libraries
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Adding user-editing to a catalogue of cartoon drawings
ECDL'06 Proceedings of the 10th European conference on Research and Advanced Technology for Digital Libraries
A study of citations in users' online personal collections
ECDL'07 Proceedings of the 11th European conference on Research and Advanced Technology for Digital Libraries
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Defective metadata is a significant problem of digital libraries. So far, automatic error detectors have been in the focus of research interest. However, recent public projects have shown that patrons are willing to invest time to report errors if they are called to contribute. In this case-study, we analyze the community contribution to error detection for DBLP, a public bibliographic collection. Our study is based on e-mails sent to the project between January 2007 and November 2010. We manually and automatically identify error reports and analyze their contribution to corrections of the DBLP collection. We show that users frequently report certain types of defects while others are ignored. The detection of homonym-name inconsistencies in particular strongly depends on user input. We also discuss who sends the reports and which communities are particularly active in this matter.