Networks of Neuroscientists: Professional Interactions within an Interdisciplinary Brain Research Institute.
Bibliographic record
Abstract
Abstract:This paper uses social network analysis to evaluate how the formation of an interdisciplinary brain research institute affected interaction and collaboration among neuroscientists at one Canadian university. The research institute, formed in 2004, has about 100 members representing ten different departments across the university campus. We conducted a whole network survey of the members in 2010, asking them to report on their professional interactions (advice seeking, co-supervising, co-teaching, co-authorship, holding grants, and organizing conferences together) with each of the other members during the five years before and the five years since the foundation of the Institute. Whole network measures examined include density, isolates, average degree and multiplexity. We compared these measures over time. Our findings indicate that professional interactions among the neuroscientists have increased since the founding of the Institute. The main networks of collaborators are now clustered around the three organizational themes of the Institute, which were formalized in 2010. We also examined how individual-level characteristics of the scientists affected professional interaction. We show that departmental co-membership, office co-location and Institute themes are all significant predictors of interaction among neuroscientists at this university since the foundation of the Institute. Social network analysis is a useful tool for evaluating the impact of the establishment of an interdisciplinary institute on scientists relationships.Keywords: interdisciplinary research; social network analysis; collaboration; neuroscienceIntroductionThis project aimed to assess how the formation of the Hotchkiss Brain Institute (HBI; www. ucalgary.ca/hbi) at the University of Calgary (UCalgary) in October 2004 affected professional interactions among neuroscience researchers. Prior to the formation of the HBI, there was no single administrative structure linking neuroscientists who work in different academic departments at UCalgary. Since the formation of the HBI, essentially all neuroscientists working at UCalgary (including new hires) are encouraged to become members of the HBI. Thus, the establishment of the HBI provides a case study through which to examine whether the formation of an explicitly interdisciplinary administrative unit affects professional interactions among scientists at one institution. We conducted a whole network survey of the members of the HBI in November, 2010. We asked all current HBI members (N = 95) to fill out an online survey reporting on their professional interactions with each of the other members since the foundation of the HBI (2005-2010). In addition, for those members who joined the HBI in 2005, we asked about their interactions with other members before the foundation of the Institute. Eighty-one scientists (a response rate of 85%) filled out the survey, indicating their working relationships with other HBI members.We analyzed the data using social network analytic techniques, described below, as well as descriptive statistics. We also examined whether individual-level characteristics of the scientists such as gender, rank, department, office location, research theme, and research pillar affected their relationships with other scientists. Research pillar is a term used in Canada to classify all health researchers into one of four categories - biomedical, clinical, health services or population health (Canadian Institutes of Health Research, 2009). Finally, for each of the professional interaction networks, we examined the positions of those who hold leadership roles in the HBI. We use our results to discuss the effect that the establishment of the HBI has had on professional relationships among neuroscientists at UCalgary. We conclude by reflecting on the usefulness of social network analysis as an evaluation method for interdisciplinary research institutes.BackgroundThe need for collaboration in science is well accepted (Adams, Black, Clemmons, and Stephan, 2005), and many academic fields now encourage interdisciplinary work (Hackett, 2005). …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".