The idea behind Trackademia is to build on existing academic databases like Google
Scholar and OpenAlex so that you can trust the data and use it for whatever you want. That comes down
to two things.
More data, and more ways to use it. We aggregate publication data from every
major source into a single dataset and add information pulled from faculty CVs. Then we let you slice
it however you want: choose which sources to build from, write your own formulas, weight journals,
adjust for department size.
More accurate, and honest about where it isn’t. We focus on a smaller set of
schools so we can verify everything carefully. Faculty lists come from department websites rather than
being inferred from publication records, so we know who should be in the data before we go looking.
When we can’t find data on someone, we record the gap instead of dropping them. Every number comes with
a coverage rate you can check against ground truth.
Everything in Trackademia is built from seven units. AI merges duplicate records across
sources and tags each publication so these categories hold up:
- Publication — citation counts, authors, journal, peer review status, and more
- Publication group — versions of the same work combined into one entity, such as a working
paper and the article it became
- Academic
- Department
- University
- Field
- Journal
There are two modes. In Database mode you browse the underlying records and filter by any of
the seven units. In Analytics mode you get graphs and comparisons, and you switch views to
compare two departments, compare one department against the average of the others, or see them all at
once. Tabs let you focus on a particular level of the data.
Brown Chemistry
Dartmouth Chemistry
Columbia Chemistry
Cornell Chemistry
Harvard Chemistry
Princeton Chemistry
Yale Chemistry