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

Bayesian Analysis

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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’ median241,065.0 · 100%
B · citations per work1,026.6 · 99%
N · works20 · 2%
P · academics1 · 0%
Formula result1.381

From this institution

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

Works19
Citations11,293 over 11 of 19 works
Citations per counted work1,026.6
Weighted citations—
Academics1
Departments1
First seen2006

Works placed per year

19 of these 19 works carry a year; an undated work is in no year. Works placed, under these filters.

Departments publishing there 1

DepartmentWorks
Columbia Political Science19

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

Works placed there 19

YearTitleAcademicsCitations
2006Prior distributions for hierarchical variance parametersAndrew Gelman6,489
2021Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMCAndrew Gelman3,065
2018Using stacking to average Bayesian predictive distributions (with discussion and rejoinder)Andrew Gelman896
2008Objections to Bayesian statistics (with discussion and rejoinder)Andrew Gelman387
2015Bayesian nonparametric weighted sampling inferenceAndrew Gelman116
2025Simulation-based calibration checking for Bayesian computation: The choice of test quantities shapes sensitivityAndrew Gelman107
2022Bayesian hierarchical stacking: Some models are (somewhere) usefulAndrew Gelman95
2021Improving multilevel regression and poststratification with structured priorsAndrew Gelman80
2024Nested R-hat: Assessing the convergence of Markov chain Monte Carlo when running many short chainsAndrew Gelman54
2023Fast methods for posterior inference of two-group normal-normal modelsAndrew Gelman3
2010Comment on Article by RobertAndrew Gelman1
2013Nonparametric models can be checked. Discussion of “Bayesian nonparametric inference — why and how,” by Peter Muller and Riten MitraAndrew Gelman—
2006Comment on article by van Dyk et alAndrew Gelman—
2013Bayesian Nonparametric Inference Why and How CommentAndrew Gelman—
2006Deconvolution in High-Energy Astrophysics: Science, Instrumentation, and Methods CommentAndrew Gelman—
2010The Search for Certainty: A critical assessment CommentAndrew Gelman—
2023Gaussian Variational Approximations for High-dimensional State Space Models......................................... M. Quiroz, DJ Nott, and R. Kohn 989 Post-Processed …Andrew Gelman—
2018Bayesian AnalysisAndrew Gelman—
2015Bayesian AnalysisAndrew Gelman—

Citations as counted, before any journal weight: 11 of these 19 works carry a count, and a work with no count is unknown, never a zero.