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 American Statistical Association

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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’ median12,052.5 · 84%
B · citations per work229.7 · 81%
N · works49 · 66%
P · academics8 · 11%
Formula result1.206

From this institution

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

Works45
Citations9,861 over 42 of 45 works
Citations per counted work234.8
Weighted citations—
Academics7
Departments4
First seen1990

Works placed per year

44 of these 45 works carry a year; an undated work is in no year. Works placed, under these filters.

Departments publishing there 4

DepartmentWorks
Harvard Government23
Columbia Political Science18
Princeton Politics7
Dartmouth Government1

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

Works placed there 45

YearTitleAcademicsCitations
2007An analysis of the NYPD’s stop-and-frisk policy in the context of claims of racial biasAndrew Gelman1,413
2011Multivariate Matching Methods That are Monotonic Imbalance BoundingGary King1,385
2004Causal Inference With General Treatment Regimes: Generalizing the Propensity ScoreKosuke Imai1,359
2015Optimal Data-Driven Regression Discontinuity PlotsRocío Titiunik708
1996Physiological pharmacokinetic analysis using population modeling and informative prior distributionsAndrew Gelman485
2011Multivariate Regression Analysis for the Item Count TechniqueKosuke Imai481
2004Parameterization and Bayesian modelingAndrew Gelman394
2006How many people do you know in prison?: Using overdispersion in count data to estimate social structure in networksAndrew Gelman304
2015Design and Analysis of the Randomized Response TechniqueKosuke Imai · Yang-Yang Zhou286
2007On the Estimation of Disability-Free Life Expectancy: Sullivan’s Method and Its ExtensionKosuke Imai258
2019Causal Interaction in Factorial Experiments: Application to Conjoint AnalysisKosuke Imai234
1999Not Asked and Not Answered: Multiple Imputation for Multiple SurveysAndrew Gelman · Gary King227
2006Randomization Inference with Natural Experiments: An Analysis of Ballot Effects in the 2003 California Recall ElectionKosuke Imai213
1990Estimating the electoral consequences of legislative redistrictingAndrew Gelman · Gary King171
2021Prediction Intervals for Synthetic Control MethodsRocío Titiunik165
1998Estimating the probability of events that have never occurred: When is your vote decisive?Andrew Gelman · Gary King164
2015Robust Estimation of Inverse Probability Weights for Marginal Structural ModelsKosuke Imai158
2018Disentangling bias and variance in election pollsAndrew Gelman146
2017Some natural solutions to the p-value communication problem—and why they won’t workAndrew Gelman126
2021What are the most important statistical ideas of the past 50 years?Andrew Gelman123
2024An Automated Approach to Causal Inference in Discrete Settings.Jonathan Mummolo116
2021Causal Inference with Interference and Noncompliance in the Two-Stage Randomized ExperimentsKosuke Imai105
2021Extrapolating Treatment Effects in Multi-cutoff Regression Discontinuity DesignsRocío Titiunik105
2010Identifying Intraparty Voting Blocs in the U.K. House of CommonsArthur Spirling99
2008Estimating Incumbency Advantage and its Variation,as an Example of a Before-After StudyAndrew Gelman87
2024Policy Learning with Counterfactual Asymmetric UtilitiesKosuke Imai75
2023Experimental Evaluation of Individualized Treatment RulesKosuke Imai73
2025Safe Policy Learning through Extrapolation: Application to Pre-trial Risk AssessmentKosuke Imai65
2008Estimating incumbency advantage and its variation, as an example of a before/after study (with discussion and rejoinder)Andrew Gelman54
2015A model-based approach to climate reconstruction using tree-ring dataAndrew Gelman53
2022Dynamic Stochastic Blockmodel Regression for Social Networks: Application to International ConflictsKosuke Imai42
2001Post-stratification without population level information on the post-stratifying variable, with application to political pollingAndrew Gelman37
2010Scaling the Critics: Uncovering the Latent Dimensions of Movie Criticism With an Item Response ApproachArthur Spirling36
2025Estimating Racial Disparities When Race is Not ObservedKosuke Imai33
2019Comment: The challenges of multiple causesKosuke Imai27
1997Discussion of “Analysis of non-randomly censored ordered categorical longitudinal data from analgesic trials,” by L. B. Sheiner, S. L. Beal, and A. DunneAndrew Gelman15
2019Comment: The Challenges of Multiple CausesKosuke Imai14
1999Comment on “Review of ‘A Solution to the Ecological Inference Problem’”Gary King9
1998A Solution to the Ecological Inference ProblemGary King9
2014Bootstrap averaging: Examples where it works and where it doesn’t workAndrew Gelman4
1994Discussion of “A probabilistic model for the spatial distribution of party support in multiparty elections,” by S. MerrillAndrew Gelman2
2006Applications and Case Studies-Randomization Inference With Natural Experiments: An Analysis of Ballot Effects in the 2003 California Recall ElectionKosuke Imai1
2022Dynamic stochastic blockmodel regression for network data: Application to international militarized conflictsKosuke Imai—
—Identifying Intra-Party Voting Blocs in the UK House of CommonsArthur Spirling—
1994Discussion of “Approximate Bayesian inference and the weighted likelihood bootstrap,” by M. A. Newton and A. E. RafteryAndrew Gelman—

Citations as counted, before any journal weight: 42 of these 45 works carry a count, and a work with no count is unknown, never a zero.