From Sole Investigator to Team Scientist

Leahey于2017年在Annual Review of Sociology上发表了题为 From Sole Investigator to Team Scientist的论文。1 这是一篇综述类的论文,介绍了关于合作研究(research collaboration)的现状和趋势,其核心当然是团队科学(team science)

This article reviews trends in the practice and study of research collaboration, focusing on journal publications in academic science. I briefly describe the different styles and types of collaboration and then focus on the drivers of the trend toward increased collaboration and on its consequences for both individual researchers and science more generally. Scholarship on collaboration seems partial to delineating its benefits; this review highlights the increasing body of research that focuses instead on the possible costs of collaboration. The synthesis reveals several topics that are ripe for investigation, including the impact of collaboration on the contributing authors and their work, the use of multiple methods and measures, and research integrity. I applaud a few recent efforts to overcome the perennial file-drawer problem by gaining access to collaborations that do not result in publication and thus are typically removed from public review and the research analyst’s eye.

Introduction

科研领域存在两个两个发现(模式):

  • The move away from research produced by individual scientists toward team-based production (West et al. 20132, Wuchty et al. 2007 3)
  • Team size is also growing. 3

The aim of this review is to identify gaps in this literature and to suggest fruitful avenues for future research. I highlight the contributions that sociologists, in particular, are well suited to make.

What Drives Collaboration?

  • cost of scientific facilities and instruments promotes collaborative and often interdisciplinary research efforts.
  • problems are increasingly ill defined, technically complex, and interdisciplinary, requiring expertise in diverse topics.
  • Institutions, such as universities and research centers have actively promoted collaboration among faculty.
  • Policy pushes at the federal level have also been supportive of this trend.
  • competition may be partly responsible, too, as scientists aim to work with (rather than against) scientists interested in similar topics.
  • specialization. Science is increasingly complex and specialized, and it is becoming harder for any one person, subfield, or field to comprehensively address a scientific question
    • First, specialty areas are large and highly productive.
    • Second, science that spans boundaries (e.g., subfields, fields) is highly regarded (Natl. Acad. Sci. et al. 2005) and valued by researchers for its novelty and usefulness (Leahey et al. 2015).
  • resources: the availability of—and competition for—financial resources drives collaboration
  • instrument/technology requirements
  • electronic communication makes collaborations easier.

there are few empirical assessments of these posited drivers. no comprehensive theory of scientific collaboration exists.

BENEFITS OF COLLABORATION

Collaboration ensures funding flows and career advancement (Abramo et al. 2014, Jeong & Choi 2015), collaboration is beneficial for individual scientists (Presser 1980) and perhaps for scientific progress more generally (Hara et al. 2003).

  • collaboration increases productivity.
    • collaborating increases grant activity
    • collaborating has a positive effect on a “normal count” of publications
  • Collaborative teams produce more high-impact articles and garner more citations
  • collaboration brings legitimacy to an idea.

COSTS OF COLLABORATION

  • communication and coordination costs
    • communication is difficult
    • processes and routines need to be developed so that work can be distributed and synchronized and progress can be monitored effectively
  • role strain
    • balance an independent research program with collaborative work, as well as in multiple collaborations.
    • while enhancing creativity, also make skepticism “more likely to occur and more challenging to manage
    • Initial collaborative success prompts growth in the size and diversity of the team, but these changes endanger the conditions that prompted success in the first place, producing yet more tensions in collaborative work.
  • free riding: Some coauthors do not do much (or any) of the work but are still included as coauthors.
  • potential exploitation
  • inequality

TYPES OF COLLABORATION

  • Collaborative research that reinforces and consolidates known areas of expertise is, arguably, an extension of sole-authored research.
  • domain-spanning collaborationsthat aims to complement and extend.
  • A mentoring style of collaboration
  • International collaboration, Collaboration across scientific domains (interdisciplinary collaborations), Cross-university collaboration

Put simply, integrative work can be more or less innovative, depending on the relationship between the integrated entities (Carnabuci & Bruggerman 2009, p. 608).

Research in this vein finds that benefits are most pronounced when collaboration occurs across subfields (Leahey & Moody 2014), departments (Bikard et al. 2015, p. 23), and disciplines (Leahey et al. 2015), especially those that are more cognitively distant (Larivie` re et al. 2015a, Uzzi et al. 2013)

The costs of collaboration may also depend on the type of collaboration

CONCLUSION

Alternative Directions

Perhaps because the data requirements are so taxing, a few scholars (largely in information science) have turned to simulation and agent-based modeling to better understand what drives collaboration.

Newman (2001) identifies the existence of small worlds (clusters) of scientists in three fields.4

In widely celebrated work, de Solla Price (1922–1983) identified the basic mechanism driving citation counts, in which current visibility and lucky events drive a positive feedback loop that amplifies future visibility.5 6 7 这正是后来为人所熟知的Price Model的起源,也就是今天所说的BA Model所描绘的Preferential Attachment。

Guimera (2005) empirically and theoretically identify micro-level team assembly mechanisms that determine the macro-level collaborative structure of science as well as the success of certain teams. 8

  • number of members
  • probability of selecting members who already belong to the research network
  • and propensity of these members to select past (repeat) collaborators

Milojevic (2014) constructs simulated team size distributions and validates them with data from 150,000 publications in the field of astronomy to explain how the discipline has changed in the last 50 years. She identifies two modes of knowledge production 9:

  • core teams, formed by a Poisson process, and
  • extended teams, formed by a power-law process and thus leading to the existence of some extremely large teams.

Uzzi B, Mukherjee S, Stringer M, Jones B. 2013. Atypical combinations and scientific impact. Science 342:468–72

Lee Y N, Walsh J P, Wang J. Creativity in scientific teams: Unpacking novelty and impact. Research Policy, 2015, 44(3):684-697.

novelty

To read:

  • Milojevic S. 2014. Principles of scientific research team formation and evolution. PNAS 111:3984–89
  • Guimerà R, Uzzi B, Spiro J, et al. Team Assembly Mechanisms Determine Collaboration Network Structure and Team Performance. Science, 2005, 308(5722):697-702.

参考文献

  1. Leahey, E. (2016). From solo investigator to team scientist: trends in the practice and study of research collaboration. Sociology, 42(1) 

  2. West JD, Jacquet J, King MM, Correll SJ, Bergstrom CT. 2013. The role of gender in scholarly authorship. PLOS ONE 8:e66212 

  3. Wuchty S, Jones BF, Uzzi B. 2007. The increasing dominance of teams in production of knowledge. Science 316:1036–39  2

  4. Newman MEJ. 2001. The structure of scientific collaboration networks. PNAS 98:404–09 

  5. de Solla Price DJ. 1963. Little Science, Big Science. New York: Columbia Univ. Press 

  6. de Solla Price DJ, 1965. Networks of scientific papers. Science 149, 510–515. 

  7. Price, Derek De Solla. “A general theory of bibliometric and other cumulative advantage processes.” Journal of the Association for Information Science and Technology 27.5(1976):292–306. 

  8. Guimera R, Uzzi B, Spiro J, Amaral LAN. 2005. Team assembly mechanisms determine collaboration network structure and team performance. Science 308:697–702 

  9. Milojevic S. 2014. Principles of scientific research team formation and evolution. PNAS 111:3984–89 

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