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NISO Patron Privacy VM#3-Richard Entlich: user-based information offered by publishers


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May 22, 2015
NISO Patron Privacy in Digital Library and Information Systems

Published in: Education
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NISO Patron Privacy VM#3-Richard Entlich: user-based information offered by publishers

  1. 1. Patron data collected and offered by publishers NISO Patron Privacy Virtual Forum #3 May 22, 2015 Richard Entlich Collection Analyst Librarian Cornell University Library
  2. 2. The Landscape  Publishers collect a variety of information about user interaction with their systems ◦ Some is specifically at the request and for the benefit of libraries, such as COUNTER reports ◦ Some consists of proprietary reports of various kinds (outside of COUNTER) are provided by some publishers. Others will provide such reports upon request. ◦ Some is to meet publisher objectives, such as protecting intellectual property (e.g., to detect “excessive downloads”), or for marketing
  3. 3. Understanding publisher data collection activity  Libraries don’t know the scope of data that publishers are collecting about their patrons’ use of licensed e-resources  Publisher web site terms & conditions and privacy policy statements provide some information but don’t offer a complete picture  Looking at the data publishers are already providing to libraries can provide some insight
  4. 4. Examples of publisher provided data about users  Licensed e-journals  Licensed e-books  Web scale discovery systems
  5. 5. Licensed e-journals  Full-text article downloads by month by IP address, platform level ◦ Provided routinely by many publishers  American Chemical Society  Association for Computing Machinery  IEEE  Nature Publishing Group  Royal Society of Chemistry  … and many others ◦ Most publishers can provide such reports upon request, even if they don’t offer them on their administrative portals
  6. 6. Downloads by month by IP address, platform level ◦ Very similar in design to the COUNTER JR1 report, except by IP instead of journal title Full-text downloads for [Whatever] University (by month by IP address) IP Address Jan-2014 Feb-2014 Mar-2014 YTD total PDF total HTML total 3 6 8 17 17 0 6 15 17 38 38 0 9 20 22 51 51 0 12 25 27 64 40 24 15 8 10 33 33 0 9 16 14 39 39 0 9 15 29 53 53 0 Total 63 105 127 295 271 24
  7. 7. Downloads by IP, article level  Combines highly granular demographic and bibliographic data  At least one third party analytics provider, MPS, makes such a report an option for publishers using its MPSInsight product  Some publishers make the report available to libraries
  8. 8. Full-text article downloads by IP  Data returned (for a one month period) ◦ Journal [journal title] ◦ DOI [Digital object identifier] ◦ Title [article title] ◦ Volume ◦ Issue ◦ IP Address [full IPv4 in dot-decimal notation] ◦ Total Successful Full-Text Article Requests
  9. 9. Licensed e-books  Full-text page, section, or chapter downloads by IP address, platform level ◦ Provided routinely by some publishers ◦ Very similar in appearance to the comparable e-journal report ◦ Most publishers can provide such reports upon request, even if they don’t offer them on their administrative portals
  10. 10. Usage statistics with authentication details  Data returned (for a full calendar year) Customer Number # of Hits Collection Number of Pages Viewed MiL EAN/ISBN Number of Pages Downloaded Title [e-book title] Number of Pages Printed Publisher Checkouts Pub e-EAN/ISBN License Hardcover EAN/ISBN Authentication / Login type Paper EAN/ISBN Login Date LC Subject Heading IP Address [full IPv4 in dot-decimal notation] LC Class Session ID
  11. 11. Web scale discovery systems: query details
  12. 12. IP addresses and usage data  An IP address does not identify a person, but comes uncomfortably close  What level of bibliographic data is acceptable to combine with IP addresses?  Should publisher systems be retaining such data or sharing it with libraries?  Do libraries have the right to ask publishers not to collect it? Retain it? Share it? Sell it?
  13. 13. Library use for IP address data ◦ Platform level download counts by IP address can be very useful ◦ IP addresses can be converted into demographic categories and then removed, allowing for demographic analysis of licensed e-resource use (e.g. at the college or department level) ◦ Under the widely used IP-authentication model, publisher systems are the best source of such data
  14. 14. Recommended reading  Some publishers provided “too much information”  Some publishers declined to share IP-based data with the library, citing privacy concerns  The University of Virginia library significantly altered its funding model for licensed e- resources based on usage patterns that the IP data revealed eston