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

Journal of the Royal Statistical Society Series A (Statistics in Society)

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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’ median29,203.5 · 97%
B · citations per work202.2 · 79%
N · works30 · 33%
P · academics10 · 16%
Formula result1.258

From this institution

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

Works23
Citations4,964 over 20 of 23 works
Citations per counted work248.2
Weighted citations—
Academics9
Departments4
First seen1977

Works placed per year

23 of these 23 works carry a year; an undated work is in no year. Works placed, under these filters.

Departments publishing there 4

DepartmentWorks
Harvard Government11
Columbia Political Science8
Princeton Politics4
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 23

YearTitleAcademicsCitations
2019Visualization in Bayesian workflow (with discussion and rejoinder)Andrew Gelman1,519
2008Misunderstandings among Experimentalists and Observationalists: Balance Test Fallacies in Causal InferenceGary King · Kosuke Imai1,400
2013Experimental Designs for Identifying Causal MechanismsDustin Tingley · Kosuke Imai688
2013Reply to Discussions of “Experimental Designs for Identifying Causal Mechanisms”Kosuke Imai416
2017Beyond subjective and objective in statistics (with discussion and rejoinder)Andrew Gelman354
2015Enhancing a Geographic Regression Discontinuity Design Through Matching to Estimate the Effect of Ballot Initiatives on Voter TurnoutRocío Titiunik188
2001Models, assumptions, and model checking in ecological regressionsAndrew Gelman · Stephen Daniel Ansolabehere135
2023Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Risk Assessment InstrumentKosuke Imai102
2015Hierarchical models for estimating state and demographic trends in U.S. death penalty public opinionAndrew Gelman71
2018Legislative Behavior Absent Reelection Incentives: Findings from a Natural Experiment in the Arkansas SenateRocío Titiunik22
2020A Placebo Design to Detect Spillovers from an Education-Entertainment Experiment in UgandaDonald P. Green21
2025Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam WarKosuke Imai14
2025A calibrated BISG for inferring race from surname and geolocationAndrew Gelman12
2020Identification and Sensitivity Analysis of Contagion Effects with Randomized Placebo-Controlled TrialsKosuke Imai11
2025Hierarchical Bayesian models to mitigate systematic disparities in prediction with proxy outcomesAndrew Gelman9
2004Finding New Information for Ecological Inference Models: A Comment on Jon Wakefield, ‘Ecological Inference in 2 × 2 Tables’Gary King1
2025Randomization Inference for Before-and-After Studies with Multiple Units: An Application to a Criminal Procedure Reform in UruguayRocío Titiunik1
2023Authors’ Reply to the Discussion of ‘Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment’Kosuke Imai0
2015Response to “Statistical modelling of citation exchange between statistics journals” by Varin, C, Cattelan, M and Firth, D.Arthur Spirling0
2026Donald P. Green’s and Jonas M. Mikhaeil’s contribution to the Discussion of ’Balanced and Robust Randomized Treatment Assignments’ by Chattopadhyay et al.’Donald P. Green0
2004Discussion on the paper by WakefieldGary King—
2013Reply to Discussions of ’Experimental Designs for Identifying Causal Mechanisms”’Dustin Tingley—
1977Credibility—Theory and ApplicationsJulia Gray—

Citations as counted, before any journal weight: 20 of these 23 works carry a count, and a work with no count is unknown, never a zero.