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
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Journal

Statistics and Public Policy

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Citation weight

Tierunranked
Weight in force1×
Weightingoff

Weight inputs

Each figure with its percentile among the ranked journals. A is the median, over the academics with a work here, of that academic’s citations to the works this site counts for them - never a Google Scholar profile total. Over every academic of this field, all years, whatever the filters select.

A · academics’ median59,831.0 · —
B · citations per work34.3 · —
N · works8 · —
P · academics3 · —
Formula result—
Below the thresholdfewer than 20 works: unranked, and weighs 1

From this institution

Under these filters. A work with no citation count is unknown here, never a zero.

Works8
Citations240 over 7 of 8 works
Citations per counted work34.3
Weighted citations—
Academics3
Departments2
First seen2014

Works placed per year

8 of these 8 works carry a year; an undated work is in no year. Works placed, under these filters.

Departments publishing there 2

DepartmentWorks
Columbia Political Science6
Harvard Government2

A work held by two departments counts once in each, so these add up to more than the works above.

Works placed there 8

YearTitleAcademicsCitations
2017ADGN: An Algorithm for Record Linkage Using Address, Date of Birth, Gender, and NameStephen Daniel Ansolabehere58
2020The Essential Role of Empirical Validation in Legislative Redistricting SimulationKosuke Imai55
201719 things we learned from the 2016 election (with discussion and rejoinder)Andrew Gelman50
2021Failure and success in political polling and election forecastingAndrew Gelman37
2014The twentieth-century reversal: How did the Republican states switch to the Democrats and vice versa?Andrew Gelman26
2017The 2008 election: A preregistered replication analysisAndrew Gelman9
2022Reconciling evaluations of the Millennium Villages ProjectAndrew Gelman5
2017Rejoinder: How Special was 2016?Andrew Gelman—

Citations as counted, before any journal weight: 7 of these 8 works carry a count, and a work with no count is unknown, never a zero.