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

Every graph on this site

Publications

  • Ranking Ranked table
    • Value: Citations, Weighted citations, Publications
    • Measure: Total, Mean per work, Median per work
  • Works published per year Lines over years
    • Measure: Total, Mean per academic, Median per academic
    • Calendar year or years since PhD
    • In each year or cumulative
  • Citations received per year Lines over years
    • Value: Citations, Weighted citations
    • Measure: Total, Mean per work, Median per work, Mean per academic, Median per academic
    • In each year or cumulative
  • Citation distribution Bars
    • Value: Citations, Weighted citations
  • Citations by age of work Lines over years
    • Value: Citations, Weighted citations
    • Measure: Total, Mean per work, Median per work
  • Publication type mix Ranked bars
    • Value: Citations, Weighted citations, Publications
  • Top venues Ranked bars
    • Value: Citations, Weighted citations, Publications

Academicshere

Departments

  • Ranking Ranked table
    • Value: Citations, Weighted citations, Publications
    • Measure: Total, Mean per work, Median per work, Mean per academic, Median per academic, Formula
  • Publications over time Lines over years
    • Measure: Total, Mean per academic, Median per academic, Formula
    • Calendar year or years since PhD
    • In each year or cumulative
  • Citations over time Lines over years
    • Value: Citations, Weighted citations
    • Measure: Total, Mean per academic, Median per academic, Formula
    • In each year or cumulative
  • Makeup by tenure track Stacked bars
  • Output by rank Grouped bars
    • Value: Citations, Weighted citations, Publications
    • Measure: Total, Mean per work, Median per work, Mean per academic, Median per academic, Formula
  • Academics ranked against academics Grouped bars
    • Value: Citations, Weighted citations, Publications
    • How many ranks (opens on 5)
  • Years to promotion Grouped bars
  • Journals used Stacked bars
    • Value: Citations, Weighted citations, Publications

Journals

Fields

  • Ranking Ranked table
    • Value: Citations, Weighted citations, Publications
    • Measure: Total, Mean per work, Median per work, Mean per academic, Median per academic
  • Departments by field Bars
    • Measure: Total, Mean per work, Median per work, Mean per academic, Median per academic
  • University rollup Ranked bars
  • Coverage by field Ranked bars

Trackademia

Search the whole site

Search a person, a department, a journal or the title of a work. A person’s name brings up their profile, their publications, their department and the journals they publish in.
Trackademia
111 academics under these filters. Open a row to see where each number came from.
Name Department Rank Publications Citations h-index
Roberto Car Princeton Chemistry Unknown 98 119,575 124
David MacMillan Princeton Chemistry Unknown 100 77,173 142
Joshua Rabinowitz Princeton Chemistry Unknown 83 62,004 145
Hailiang Wang Yale Chemistry Unknown 74 58,065 95
Hongkun Park Harvard Chemistry Unknown 92 55,076 104
Christopher J. Chang Princeton Chemistry Unknown 100 52,470 133
Andrew M. Rappe Penn Chemistry Unknown 98 36,793 105
Suyang Xu Harvard Chemistry Unknown 76 33,870 82
Colin P. Nuckolls Columbia Chemistry Unknown 100 32,800 106
Gary Brudvig Yale Chemistry Unknown 98 29,500 110
Eric Heller Harvard Chemistry Unknown 97 26,079 82
Eugene I. Shakhnovich Harvard Chemistry Unknown 100 25,094 94
David W. Christianson Penn Chemistry Unknown 99 24,312 100
James Mayer Yale Chemistry Unknown 97 21,508 94
Timothy C Berkelbach Columbia Chemistry Unknown 97 20,713 46
Luis M. Campos Columbia Chemistry Unknown 95 16,617 58
Ming Xian Brown Chemistry Unknown 100 16,472 72
Frank C. Schroeder Cornell Chemistry Unknown 96 15,729 81
Bruce Ganem Cornell Chemistry Unknown 99 15,369 75
Tobias Baumgart Penn Chemistry Unknown 96 12,757 52
Jeremy M. Baskin Cornell Chemistry Unknown 84 12,737 39
Ann E. McDermott Columbia Chemistry Unknown 98 11,918 65
Joonho Lee Harvard Chemistry Unknown 92 11,888 46
Xavier Roy Columbia Chemistry Unknown 97 10,593 54
Leslie Schoop Princeton Chemistry Unknown 91 10,409 50
Katherine A. Mirica Dartmouth Chemistry Unknown 87 9,909 51
Squire J. Booker Penn Chemistry Unknown 97 9,715 62
Eric J. Schelter Penn Chemistry Unknown 99 9,096 58
Peng Chen Cornell Chemistry Unknown 89 8,995 47
Pamela Chang Cornell Chemistry Unknown 30 8,630 19
Miguel I. Gonzalez Dartmouth Chemistry Unknown 67 8,083 42
Yao Yang Cornell Chemistry Unknown 80 7,615 47
Elizabeth Rhoades Penn Chemistry Unknown 92 7,525 50
Dale F. Mierke Dartmouth Chemistry Unknown 97 6,944 52
Jason M. Crawford Yale Chemistry Unknown 90 6,924 45
Marsha I. Lester Penn Chemistry Unknown 99 6,407 52
E. James Petersson Penn Chemistry Unknown 97 6,269 46
Kai Chen Penn Chemistry Unknown 39 6,128 33
Laura J. Kaufman Columbia Chemistry Unknown 93 5,902 37
Angelo Cacciuto Columbia Chemistry Unknown 83 5,886 35
David S. Glueck Dartmouth Chemistry Unknown 94 5,487 43
Sarah Slavoff Yale Chemistry Unknown 36 5,210 29
Ruben L Gonzalez Jr Columbia Chemistry Unknown 70 5,186 37
Dean E. Wilcox Dartmouth Chemistry Unknown 115 4,423 41
David M. Chenoweth Penn Chemistry Unknown 90 4,387 39
Gregory Sion Ezra Cornell Chemistry Unknown 92 4,237 39
Jane E. G. Lipson Dartmouth Chemistry Unknown 97 3,780 37
Neel H. Shah Columbia Chemistry Unknown 45 3,737 30
John A. Marohn Cornell Chemistry Unknown 76 3,688 30
Brian Liau Harvard Chemistry Unknown 52 3,652 23
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