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

The American Statistician

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Citation weight

TierA
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’ median27,607.0 · 94%
B · citations per work231.8 · 82%
N · works27 · 23%
P · academics3 · 4%
Formula result1.265

From this institution

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

Works8
Citations1,726 over 8 of 8 works
Citations per counted work215.8
Weighted citations—
Academics2
Departments2
First seen2017

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 Science7
Penn Political Science1

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
2018R-squared for Bayesian regression modelsAndrew Gelman1,207
2017The Perils of Balance Testing in Experimental Design: Messy Analyses of Clean DataDiana C. Mutz256
2019Large scale replication projects in contemporary psychological researchAndrew Gelman149
2019Multiple perspectives on inference for two simple statistical scenariosAndrew Gelman50
2022A proposal for informative default priors scaled by the standard error of estimatesAndrew Gelman36
2024Causal quartets: Different ways to attain the same average treatment effectAndrew Gelman22
2023“Two truths and a lie” as a class-participation activityAndrew Gelman6
2026The ladder of abstraction in statistical graphicsAndrew Gelman0

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