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

Statistica Sinica

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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’ median241,065.0 · —
B · citations per work924.2 · —
N · works7 · —
P · academics1 · —
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.

Works7
Citations3,697 over 4 of 7 works
Citations per counted work924.2
Weighted citations—
Academics1
Departments1
First seen1996

Works placed per year

7 of these 7 works carry a year; an undated work is in no year. Works placed, under these filters.

Departments publishing there 1

DepartmentWorks
Columbia Political Science7

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

Works placed there 7

YearTitleAcademicsCitations
1996Posterior predictive assessment of model fitness via realized discrepancies (with discussion and rejoinder)Andrew Gelman3,527
2003A Bayesian approach to the selection and testing of latent class modelsAndrew Gelman100
1996Bayesian model-building by pure thought: Some principles and examplesAndrew Gelman64
2007Bayes: radical, liberal, or conservative?Andrew Gelman6
1996Rejoinder: On posterior predictive p-valuesAndrew Gelman—
1996On posterior predictive p-values-ReplyAndrew Gelman—
2010S-1 Correlated normal target distributionAndrew Gelman—

Citations as counted, before any journal weight: 4 of these 7 works carry a count, and a work with no count is unknown, never a zero.