The Use of Usage

2. The Use of Usage Michael J. Kurtz Harvard-Smithsonian Center for Astrophysics

3. Collaborators • Johan Bollen • Edwin Henneken • Alberto Accomazzi • http://www.cfa.harvard.edu/~kurtz/PSP.pptx

4. Literatureall in arXiv • 2013 Kurtz & Henneken, “Finding and Recommending Scholarly Articles” in Bibliometrics and Beyond, MIT Press • 2010 Kurtz & Bollen, “Usage Bibliometrics” Ann Rev Information Science & Tech • 2005 Kurtz et al. “Bibliometric Properties of Article Readership Information” J. Am Soc Information Science & Tech

7. http://dx.doi.org/10.1787/603233448430.

8. Four Types of Readers • Researchers (=authors?) • Practitioners • Students • General Public

9. General Public (Google)

10. Students (Google Scholar)

11. Co-Usage • Recommendations • What are people like me reading • What are people who read this also reading • Queries • Sort by most downloaded • Sort by filtered downloads

14. Scholarly Articles Are Different Scholarly Articles are MUCH denser than films • Netflix ~ 25,000 films • ADS ~ 10,000,000 articles • 172,000 contain the word “cosmology” • >12,000 in 2012

15. Scholarly Articles Are Different Scholarly Articles are read by scholars • Typically they have subject matter doctorates • Their needs are often highly specific • There are not many of them

17. Usage vs Age – 90 weeks

18. Aggregation • Articles • Readers • Enhance Signal to Noise • Engineering Issues • What are you trying to achieve • Who are you doing this for • How fast must it be

24. What Can Be Measured • The Interconnectedness of Science • Fields of Study • Countries • Journals • Organizations • People • Instruments • Articles

25. Usage map • 200M usage events • 2006 usage only • JCR journals (+-7600) Red, orange= psych, cogn Green = phys, chem Olive = material science Blue = biology Purple = pharma Bollen et al, 2009a

26. Networks - ADS

27. Weak Lensing (84) AGN Spectra (101) Comparing Sub-Fields

28. Measuring Countries • Authors are from countries, reads as a function of author’s country has yet to be studied • Readers are from countries, their activities allow one to measure the Scientific Wealth of Nations

29. ADS use vs per capita GDP

30. Astronomy is representative ADS predicts average of Cites+articles better than cites predicts articles

31. Measuring Changes

32. Measuring Journals • Beyond the Impact Factor • Reads vs Cites differences will be important here • The New York Times would have a low Impact Factor

33. Standard Usage Measures • COUNTER • David Nicholas, Ian Rowland & collaborators • University College, London • CIBER-Research

34. Bollen et al, 2009b

35. Measuring Departments

36. Department Size Can Matter

37. Berkeley Princeton

38. Measuring Individuals • The number of times one’s articles are read is a valid measure of one’s scientific impact, similar to citation counts • Use has different properties than cites, together they form a two dimensional view of productivity

39. The Read-Cite Diagram

40. Professional Astronomers

41. 2 dimensional productivity model

42. Different productivities - histories

43. Productivity vs Age - Cites

44. Productivity vs Age - Reads

45. Reads and Cites History for M. Kurtz

46. Papers, Reads, and Cites for Hectospec

47. Papers, Read, Cites for NAG5-4445

48. Reads, Cites for 1998PASP..110..934K

49. Final Thoughts • Much remains to be done • Implement “best” methods • Develop standard data • Problems abound • Privacy • Open Access • Who exactly is reading this paper? • Why do I care? (not money)