Trends in the Production of Scientific Knowledge

1. Trends in the Production of Scientific Knowledge Paula Stephan Georgia State University and NBER pstephan@gsu.edu Pecs July 2009

2. Overview • Focus will be on production of scientific research in the university sector • Draws from updated article “Economics of Science”

3. Role of Academic Sector • Academic sector plays important role in knowledge production. • In US, 74% of all articles (fractional counts) produced in academe; academe and PROs play similar role in Europe • Role of academe in patenting is increasing, but considerably smaller • 4.5%--grown from 1.5% in U.S. • Similar order of magnitude in Europe but more difficult to measure

4. Domain of Academe • Academe historically been more focused on basic research • Evidence that individuals who value independence in choosing research agendas are more likely to work in academe than they are in industry. • Individuals working in industry generally place higher value on monetary rewards.

5. Examples of Research • Bertozzi’s lab (Berkely )has 20 PhD students, 10 postdoctoral fellows & 10 undergrad students. Senior staff scientist & research associate also work in lab as do 3 administrative staff & a biosafety facility director. • High Performance Networking Group at Stanford, led by Nick Mckeown, includes 12 PhD students, two masters students, an administrative assistant, three visitors, three associates, and a research engineer. • Fluid physicist David Quéré, (on the faculty of the Ecole Superieure de Physique et Chimie Industrielles of France) & research director at CNRS leads a CNRS research group composed of a researcher, seven graduate students and one postdoc. • Research of hydrologist Elizabeth Screaton (University of Florida), which “investigates the interrelationship of fluid flow and deformation in subduction zones,” combines field work—on board drilling vessels—with lab work and numerical modeling.

6. Other examples • Caltech Observational Cosmology Group is composed of 17 individuals: One professor (Andrew Lange), an administrative assistant, an electronics engineer, 6 postdocs, 5 graduate students, 1 undergrad student and two visiting associates. Group’s focus is development of novel instruments to “study the birth and evolution of the universe.” It has designed instruments that collect data at South Pole Viper telescope as well as at other locations. • Susan Lindquist’s lab at MIT, which studies protein folding (and which we discussed in Chapter 3) has 37 members: 20 postdocs, 7 graduate students, 1 visiting scientist, 1 staff scientist, 3 technicians, 4 administrators and Lindquist. • Zhong Lin (ZL) Wang’s Nano Research Group in the College of Engineering at the Georgia Institute of Technology includes two postdocs, two visiting scientists, six research scientists and 14 graduate students.

7. Commonalities and Differences • All are doing science and engineering • All share certain common characteristics • But environments in which they work, the importance of equipment in the research that they do and way in which their work is structured and supported varies considerably.

8. Production Function Approach • No one model of production fits all of science and engineering. • Mathematicians, chemists, biologists, high energy physicists, engineers, and oceanographers share certain common characteristics in terms of production. • All require time and cognitive inputs. • In other dimensions there is considerable variability. • Way in which research is organized is case in point. • Mathematicians & theoretical physicists rarely work in labs (although they may identify with a group and work with coauthors) while most chemists, life scientists, engineers and many experimental physicists do. • Role of equipment provides another dimension. In some fields, equipment required to do research is fairly minimal, as in the case of certain areas of math, chemistry and fluid physics. In others, research is almost entirely organized and defined by equipment, as in the case of astronomy and high energy experimental physics. Materials also play a role. In vivo experiments require access to living organisms. For many biomedical researchers this means having—and taking care of—large numbers of mice, and, in recent years, zebra fish.

9. Production Functions • Long tradition in economics of studying production processes—or functions. When auto and steel plants were important components of economies there were studies of the productivity of the industry and production processes within the firms. • But when economists study science rarely think of how science is produced. • Instead—like sociologists-- economists focus on people as unit of observation. Not surprising. People are faces—and brains—behind science. • But important to think of science as having multiple inputs…not just inputs brought by people

10. How Science Is Produced • K=f(Cg, R, t, e) • K is knowledge being produced • Cg= cognitive resources • R=other resources, such as equipment, materials, lab assistants • t=time of researchers • e is some error term, encompassing among other things serendipity and uncertainty.

11. Scientist(s) • Effort • Science takes time; common observation is that scientists work exceptionally long hours (52.6 hours per week in U.S.) • Also requires motivation. “Informed observers have long described high-producing scientists as driving and indefatigable workers.” (Fox.)

12. Persistence • > 50% of physicists chose persistence from list of 25 adjectives of what it takes to be successful. No other quality came close. • Many examples • Judah Folkman • Lorenz

13. Dimensions of Cognitive Resources • Ability: studies document that as a group scientists have above average IQs. • Knowledge base: Important in choosing and solving problems. • Education; • Does scientist keep up? • Raises possibility of obsolescence and related vintage effects. • Public nature of knowledge intensifies races in discovery.

14. Embodied or Disembodied Knowledge? • Different types of research rely more heavily on one than the other. • Nuclear physicist Leo Szilard, who left physics to work in biology, told the biologist Sydney Brenner that he could never have a comfortable bath after he left physics. “When he was a physicist he could lie in the bath and think for hours, but in biology he was always having to get up to look up another fact.”

15. Too Much Knowledge? • One can be encumbered by “too” much knowledge • One reason young may have an edge

16. Importance of Tacit Knowledge • Difference between codified and tacit knowledge • Only way to acquire tacit knowledge is to work with someone with the knowledge • Lab rotations as a mechanism • Visiting other labs • Transgenic mice as an example—need to have “magic hands”

17. Collaboration • Research rarely done in isolation • Often done in labs—common for individuals to specialize • Staffing of labs varies across countries • U.S. model relies on “temporary workers”—postdocs & doctoral students; • European model: permanent staff—employees of CNRS, Max Planck, etc.

18. Responsibility for Funding • U.S. faculty has responsibility for funding graduate students & most postdocs. Also faculty member’s time. • Grad student: $28,000 stipend plus $25,000 tuition. • Postdoc: $38,000 • Europe: permanent staff generally employees of state or PRO. • Graduate students receive stipend from state

19. Biological and Medical Sciences Postdocs by Source of Support Source:http://www.nsf.gov/statistics/gradpostdoc/ 19

20. Full Time Biological and Medical Sciences Graduate Students in Doctorate Granting Departments by Mechanism of Support Source:http://www.nsf.gov/statistics/gradpostdoc/ 20

21. Labs “belong” to faculty in U.S. • Most have web pages • Lab is named for PI • Sometimes lab members are referred to using PI’s name as in “Sharpies” for Philip Sharp’s students at MIT

22. Lab Structure: Example • 415 labs affiliated with a nanotech center • Average lab has 12 technical staff, excluding PI • 50% are graduate students; 16% are postdocs and 10% are undergraduates; rest are staff scientists, etc.

23. Team Behind Science’s 2008 Breakthrough of the Year: University of Wisconsin James Thomson Lab Back View J. Yu—first author

24. Amon Lab: Whitehead Institute • Amon, HHMI investigator, works on cell division, focusing on how “cells make sure their chromosomes separate in the right way.”

25. Christine White and Group: U. of Illinois, Chemistry

26. Interface & Company ESCPI Quéré with group

27. Example from Science Three-Dimensional Super-Resolution Imaging by Stochastic Optical Reconstruction Microscopy Bo Huang,1,2 Wenqin Wang,3 Mark Bates,4 Xiaowei Zhuang1,2,3* Science, February 8, 2008 1 Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA.2 Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA.3 Department of Physics, Harvard University, Cambridge, MA 02138, USA.4 School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.

28. Members of team • Xiaowei Zhuang, Prof of Chemistry & Chemical Biology, Prof of Physics, HHMI, Harvard • Bo Huang, post-doctoral fellow in Zhuang lab. • Wenqin Wang, graduate student, Dept of Physics, Harvard; member Zhuang lab • Mark Bates, graduate student, Division of Engineering and Applied Sciences, Harvard; member of Zhuang lab

29. Birth origin of PI Matters in terms of lab staffing—at least in U.S. • Korean-directed labs have 29% more Koreans than labs directed by U.S.-born PIs • Chinese-directed labs have 38% more Chinese students than labs directed by U.S.-born PIs • Indian-directed labs have 27% more Indians than in labs directed by U.S.-born PIs • Turkish-directed labs have 36% more Turkish students than in labs directed by U.S.-born Pis.

30. Why? • Networks and role PI has in staffing lab • Efficient: language

31. Collaboration • Common and growing in science • Within labs and across labs • Several ways of seeing trends

32. Evidence Concerning Teams Source: Adams et al

33. Wuchty, Jones & Uzzi • Analysis of approximately 13 million published papers in S&E over the 45 year period 1955 to 2000 found team size to increase in virtually every one of the 172 subfields studied. • On average team size nearly doubled, going from 1.9 to 3.5 authors per paper (Wuchty, Jones & Uzzi, 2006). • Team size even increased in mathematics-- seen as the domain of individuals working alone and field least dependent on capital equipment:

34. Teams Increasingly Have Members from Another Institution • Jones, Wutchy and Uzzi • Study 662 U.S. institutions which have received NSF funding. • Find collaboration in S&E across these institutions, which was rare in 1975, grew in each and every year between 1975-2005, reaching approximately 40 percent by 2005. • My own work with Wolfgang Glänzel, Katholieke Universiteit Leuven, Steunpunt O&O finds similar results using Thomson Reuters ISI data for 1300 plus four year institutions in the U.S.

35. Percent of papers with U.S. author at another U.S. institution by tier Work is joint with Wolfgang Glänzel, Katholieke Universiteit Leuven, Steunpunt O&O.

36. Percent of U.S. papers with an international author Work is joint with Wolfgang Glänzel, Katholieke Universiteit Leuven, Steunpunt O&O.

37. Why Increase? • Importance of interdisciplinary research • Systems biology is case in point • Researchers are arguably acquiring narrower expertise and thus have more to benefit through collaboration • Vast amount of data that has become available—Human Genome project; PubChem • Increased complexity of equipment—accelerators and telescopes are a case in point. CERN’s four colliders have combined team size of just under 6,000. • Rapid spread of connectivity decreases cost of collaboration

38. Spread of Connectivity: Examples from U.S. • Twenty five years ago, only way to work with someone at another institution was to talk with them by phone, visit in person, or fax them material • Phone calls & travel were expensive. Cheapest trip to Europe cost around 1800 in today’s dollars. • Internet, as we know it, did not exist; e-mail not a possibility. • This changed with inauguration of BITNET.

39. BITNET • Conceptualized by the Vice Chancellor of University Systems at the City University of New York (CUNY) • BITNET’s first adopters were CUNY and Yale in May 1981 (Bitnet history). • At its peak in 1991-1992, BITNET connected about 1,400 organizations (almost 700 academic institutions) in 49 countries (CREN). • By the mid-1990s BITNET was eclipsed by Internet as we know it today and began to fade away. • We have collected information on date of adoption of BITNET for 1300 four-year institutions in U.S.

40. Adoption of BITNET by Tier

41. BITNET replaced by Internet as we know it today • Key requirement for efficient communication on internet was development of domain name system—such as gsu.edu. • We have collected information on date that almost every 4-year institution in U.S. took a domain name.

42. Adoption of DNS by Tier

43. Concurrently, increased incentives to publish encourages collaboration • Occurs at both the system level and at the individual level • Budgets of universities and departments in certain countries depend heavily on publication and citation counts. • Funding for research of individual scientists depends increasingly on publication track record. • Bonus payments based on publications

44. Examples • UK—ranking of departments and allocation of funds based in part on publications and citations. (Research Assessment Exercise). • Australia—funding of departments based in part on publications/citations. • Flemish Science Foundation makes research awards based in part on reputation of faculty as established through publication. • NIH in U.S. (with $29 billion budget) places considerable emphasis on publication record of grant applicants. • Chinese researchers who place in top half of colleagues in terms of bibliometric measures can earn three to four times salaries of co-workers. Some institutes pay cash bonus for publishing in Science, Nature or Cell.

45. Increased emphasis on networking encourages collaboration • Government agencies have bought heavily into the importance of networks • “Networks of excellence” funding in EU • Network funding at NIH through “glue” grants and P01s.

46. Which grows faster: Lab size or collaboration across labs? • Number of names on an article has increased by 50% • Number of addresses has increased by 37%. • Suggests lab size growing slightly faster than institutional collaboration

47. Equipment • Science heavily influenced by availability of technology • Exceptions exist but • Increasingly science requires access to complex equipment • In genetics: DNA gene sequencer and synthesizer, protein synthesizer & sequencer comprise the technological foundation for contemporary molecular biology. Super Computers • tunneling microscopy—key in nanotechnology • Accelerators • Cell lines • Mice—90% of all mammals used in research are mice—13,000 published

48. Equipment changes output of lab • 1990 best-equipped lab could sequence 1000 base pairs a day • January 2000 the 20 labs mapping human genome were collectively sequencing 1000 base pairs a second, 24/7 • Measured in base pairs sequenced per person per day, for researchers operating multiple machines, productivity increased more than 20,000 fold from early 1990s to 2007, doubling approximately every 12 months. • Costs per finished base pair fell from $10.00 in 1990 to roughly $.01 in 2007

50. Just beginning… • New technology for sequencing emerged recently • Does work of 100 earlier sequencing machines • Ads • “A billion a day, soon a billion an hour. “ (A billion an hour is what it would take to do the human genome for $1000). • “More applications lead to more publications” • “length really matters”