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The focus here is on two sets of research collaboration variables, numbers of collaborators and the collaboration strategies employed.
The paper provides an overview of the disambiguation method, assesses its accuracy, and calculates network measures based on co-authorship and collaboration variables.
To address these methodological concerns, we constructed a longitudinal dataset of 65 biomedical scientists at a New Zealand university and coded collaboration variables by hand checking each of their publications in a period of 14 years.
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The collaboration process variables in subgroup 3 increased over time (see Table 4).
Details about the individual items of the collaboration process variables can be found in an additional publication [ 10].
ICPs in subgroup 1 made a substantial increase in the collaboration process variables over time (see Table 4).
None of the other baseline collaboration process variables had a predictive value for the perceived success.> -wrap-foot> ∆ change in collaboration process = (T1 score - T0 score)/T0 score.
In order to answer the first research question, Model 1 examined the baseline collaboration process variables that were associated with the perceived success of the partnership at T1.
In addition, correlations between the change in collaboration process variables ranged from moderate to strong and were all statistically significant (p < .01, rows 6 10).
No significant differences were found between subgroups in change in collaboration process variables with the multivariate test (F(10,72) = 1.57, ns).
Moderate to strong correlations were found between the collaboration process variables at T0 which were all statistically significant (p < .01) (Table 3, rows 1 5).
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