Trackademia

Welcome to Trackademia

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.
Trackademia
Equation Rank the departments by it

The equation is over the departments these filters select, on citations per academic. Change the filters or the value in the Departments tab and the same equation is applied to what you chose there.

total the department's total. A work two of its academics wrote counts once.
mean the mean of its academics' own figures. A work two of them wrote counts for each.
median the median of the same. Unmoved by one very cited academic.
people how many academics it has. After the rank filter, so a filtered list is measured on what it kept.
functions log, ln, log10, sqrt, exp, abs, min, max, round, with ^ for a power.

Ready-made

What each one says about size
mean * log(1 + people) Size counts, but not in proportion The default. A department twice the size is worth about a fifth more, not twice as much. mean Size does not count The mean academic, whatever the headcount. The same as the per-academic reading of the leaderboard. total Size counts in full The department total. The same as the total reading of the leaderboard. median * sqrt(people) The typical academic, times the square root of size The median ignores a department carried by one outlier; the square root credits size more gently than a total and more firmly than a logarithm. mean * 0.7 + median * 0.3 Mostly the mean, steadied by the median A compromise for a department whose mean is pulled up by one very cited person.

What it does here

Citations per academic
Series: value.Harvard GovernmentHarvard Government 52,550.852,550.8Columbia Political ScienceColumbia Political Science 46,986.946,986.9Penn Political SciencePenn Political Science 26,179.126,179.1Princeton PoliticsPrinceton Politics 22,134.422,134.4Yale Political ScienceYale Political Science 18,006.118,006.1Dartmouth GovernmentDartmouth Government 16,876.916,876.9Brown Political ScienceBrown Political Science 10,255.510,255.5Cornell GovernmentCornell Government 9,290.39,290.30.010,000.020,000.030,000.040,000.050,000.0DepartmentScore
Department TotalMeanMedianAcademics Score
1 Harvard Government Harvard 459,74714,059.83,991.541 52,550.8
2 Columbia Political Science Columbia 451,32412,652.82,738.540 46,986.9
3 Penn Political Science Penn 239,3727,196.82,439.537 26,179.1
4 Princeton Politics Princeton 266,7115,474.72,677.056 22,134.4
5 Yale Political Science Yale 178,6424,787.31,599.042 18,006.1
6 Dartmouth Government Dartmouth 128,7585,012.01,290.028 16,876.9
7 Brown Political Science Brown 83,4982,986.51,816.530 10,255.5
8 Cornell Government Cornell 81,8372,592.51,138.035 9,290.3

Each row carries what went into its score, because an equation can only be judged against the numbers it was given. A department with no measured academics has no score and is not ranked : a formula that cannot be worked out is unknown, never a zero.