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Bottom Up Repositories: Why UCT-CS has a Repository

Bottom Up Repositories: Why UCT-CS has a Repository. Hussein Suleman hussein@cs.uct.ac.za University of Cape Town Department of Computer Science Advanced Information Management Laboratory High Performance Computing Laboratory July 2007. EPrints Software Author self-submission

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Bottom Up Repositories: Why UCT-CS has a Repository

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  1. Bottom Up Repositories:Why UCT-CS has a Repository Hussein Suleman hussein@cs.uct.ac.za University of Cape Town Department of Computer Science Advanced Information Management Laboratory High Performance Computing Laboratory July 2007

  2. EPrints Software Author self-submission Checking of submissions Archive-everything! UCT-CS-specific metadata and classification systems Hierarchical browsing Simple and fielded searching OAI-PMH compliance The UCT-CS Repository: Revision

  3. Why we started a repository project • It was faster than simply waiting! • CS departments internationally archive technical reports (NCSTRL). • Research websites don’t last long (enough). • UCT doesn’t have an ETD project yet. • We need to improve ACCESS to our work. • We need to preserve our research output. • Bureaucracy (UCT, NRF, DoE, etc.) requires tracking publications. • We (think we) know what we are doing.

  4. Submissions to Archive over Time

  5. Analysis of Access • 187 items • 478520 log file entries • 798 unique user agent types • 65% from crawlers and bots • 34 from metadata harvesters • Data extraction from log file: • Unique IP addresses (visitors) • Source of access • Which resources are accessed • Access patterns of popular/old resources • Effect of submission sequence • Distribution of accesses • Analysis of partial responses

  6. Disclaimer • Web log analysis is at best an approximation! • Assumptions: • download = access • if useragent != bot, then real user • no caching • every user has a single IP address • …

  7. Unique Local IP Addresses

  8. Unique Non-local IP Addresses

  9. Unique Crawler IP Addresses

  10. Source of Access

  11. Which Resources are Accessed

  12. Access Patterns – Most Popular Resources

  13. Access Patterns – Oldest Resources

  14. Effect of Submission Sequence

  15. Distribution of Accesses

  16. Probability of Success on First Download

  17. Percentage of Partial Responses

  18. Summary of Results • The archive is regularly and aggressively indexed by search services. • The vast majority of access to resources comes from outside UCT. • Most users find their way to resources through Google and other search engines. • Most resources are used in most months – by both crawlers/harvesters and end users. • Access appears to be dictated by content, and not age of resources. • While many downloads are successful immediately, larger files result in more partial downloads.

  19. Conclusions • The results support our initial reasons for the repository! • A document repository is a simple and effective way to increase visibility to the research of a department or institution. • But it is imperative that documents are archived as soon as possible, to maximise visibility!

  20. Links • Open Archives Initiative • http://www.openarchives.org/ • Budapest Open Access Initiative • http://www.soros.org/openaccess/ • BioMed Central • http://www.biomedcentral.com/ • UCT CS Research Archive • http://pubs.cs.uct.ac.za • EPrints • http://www.eprints.org/ • DSpace • http://www.dspace.org/

  21. That’s all Folks! direct all questions and comments to: hussein@cs.uct.ac.za

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