Summary database

Unique authors

Collaboration patterns across domains/career stage


For each college/career stage, what is the proportion/count of collaborations with researchers who are either younger, same age, or older? In the the default setting, we can see that, for example, people from the physical sciences collaborate more extensively early on (during Phd; see last table for department to 'college' mapping), but engineering people catch up during their postDoc years. Humanists work more by themselves.
The data is unbalanced. Some college/career stage values might be still slightly misleading as there are only few individuals within each. This is the cost to pay to show proportion that are easy to compare.

Normalized: Comparing relative proportion of collaboration for two different facets showing different aspect of the data:

  • college facet: within college, how do collaboration with people of different author age strata changes over time? This facet focuses on comparing colleges for a given career stage. For instance, `computer scientists` collaborating extensively with younger folks.
  • career stage facet: within career stage, how do collaboration with people of different author age strata changes across colleges? This facet focuses on comparing career stage across colleges. For instance, `engineering` people seems to be collaborating with more senior researchers than other colleges.

    • Raw count: Comparing how many total collaborators with bar heights.

    The meaning for facets is the same than when y-axis is normalized.

    Raw table

    Professors' collaboration rate with younger academics (>7 years younger)

    Each tick is a faculty's rate of collaboration with collaborators seven years younger than them (cut point is the average time in our data to get from first publication to tenure-track position).

    Collaboration ''norms''

    We are interested in the relationship between group size, collaboration norms, and the emergence of computational works. More precisely, we want to know whether strong collaboration patterns could help humanities transitioning into the computational realm.

    At the moment, we simply calculate density of interactions among faculties' younger collaboraters as proxy for collaboration norms. The idea is that a group with strong collaboration norms is characterized by early career researchers working togeher early on.

    One issue with density is that it doesn't take into account the recurrent relationships, aka whether younger folks keep collaborating on each others' papers.