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.

Value Measure

Academics

Citations per year in post (from the first professorial appointment, else the PhD year) · the 15 highest of 87 academics
1 Diptarka Hait Columbia Chemistry · Assistant · 1 years in post 341.0
2 Stacy Malaker Yale Chemistry · Associate · 5 years in post 295.0
3 Lilia Xie Princeton Chemistry · Assistant · 1 years in post 218.0
4 Phillip Milner Cornell Chemistry · Associate · 8 years in post 182.6
5 Jarad Mason Harvard Chemistry · Associate · 8 years in post 168.5
6 David R. Liu Harvard Chemistry · Distinguished · 27 years in post 154.0
7 Xiaowei Zhuang Harvard Chemistry · Distinguished · 25 years in post 148.5
8 Yusong Bai Brown Chemistry · Assistant · 4 years in post 123.5
9 Xiaoyang Zhu Columbia Chemistry · Distinguished · 33 years in post 105.5
10 Robert Knowles Princeton Chemistry · Distinguished · 15 years in post 104.1
11 Paul Robustelli Dartmouth Chemistry · Assistant · 6 years in post 100.7
12 Ou Chen Brown Chemistry · Full · 11 years in post 100.3
13 Emily Balskus Harvard Chemistry · Distinguished · 15 years in post 87.7
14 Joseph Subotnik Princeton Chemistry · Full · 16 years in post 87.0
15 Christina Woo Harvard Chemistry · Full · 10 years in post 82.0

44,665 citations over 1,183 of 1,624 works (73% carry a count). 65 of 87 academics have at least one counted work. A work with no citation count is unknown here, never a zero.

Publications by career year

Publications in each year of a career
Measure

Pooled over the 86 of 87 academics whose CV states a PhD year, across 8 departments; career year 0 is the PhD year. A career year fewer than 5 of them have reached is a gap. A total counts only the academics who had reached that career year, so the tail is low because fewer of them are in it; the mean and the median divide by exactly those academics.

What these filters select, over everything selected not what the graph divided by
Selected Academics CVScholarOpenAlex Works With a count
Everything selected 87 87/87 1.0% 46/87 0.5287356321839081% 87/87 1.0% 1624 1183/1624 0.728448275862069%
  • Over the academics these filters select, so a filtered page describes the academics it selected and not the whole department.
  • A row with nothing held draws no mark above: the graph is quiet there because the evidence is missing, not because the work is.
  • What there was to draw from, not what the graph divided by - the graph states its own denominator above.
  • A Scholar profile is attached when at least 3 of the CV titles are found on it, or 30% of them are; one at another institution or under a different name needs 10 titles, and a profile far larger than the CV that holds only a small part of it is refused. Its articles are then checked one by one: what is not a work, or not by this person, is not counted.

Citations by career year

Citations of the works published in each career year
Value Measure

Pooled over the 86 of 87 academics whose CV states a PhD year, across 8 departments; career year 0 is the PhD year. A career year fewer than 5 of them have reached is a gap. The citations of the works published in that career year, not the citations received in it. A total counts only the academics who had reached that career year, so the tail is low because fewer of them are in it; the mean and the median divide by exactly those academics.

What these filters select, over everything selected not what the graph divided by
Selected Academics CVScholarOpenAlex Works With a count
Everything selected 87 87/87 1.0% 46/87 0.5287356321839081% 87/87 1.0% 1624 1183/1624 0.728448275862069%
  • Over the academics these filters select, so a filtered page describes the academics it selected and not the whole department.
  • A row with nothing held draws no mark above: the graph is quiet there because the evidence is missing, not because the work is.
  • What there was to draw from, not what the graph divided by - the graph states its own denominator above.
  • A Scholar profile is attached when at least 3 of the CV titles are found on it, or 30% of them are; one at another institution or under a different name needs 10 titles, and a profile far larger than the CV that holds only a small part of it is refused. Its articles are then checked one by one: what is not a work, or not by this person, is not counted.

Publications per year

Works published in each year
Each point

One line over the 87 academics the filters select, by the year each work was published; an undated work is in no year. A work two colleagues share counts once for their department.

What these filters select, per academic not what the graph divided by
Academic Academics CVScholarOpenAlex Works With a count
Andrew B. Bocarsly 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
David Spiegel 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Eric Jacobsen 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Erin E. Stache 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
F. Jon Kull 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Gregory Scholes 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Herschel Rabitz 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
John T. Groves 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Jose Roque 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Mark Johnson 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Martin F. Semmelhack 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Richard A. Friesner 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Robert J. Cava 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Seth Herzon 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Todd K. Hyster 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Tomislav Rovis 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Victor Batista 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Virgil Percec 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Virginia W. Cornish 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Nilay Hazari 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 2 0/2 0.0%
Nozomi Ando 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 1 0/1 0.0%
Richard Stratt 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 1 0/1 0.0%
Abraham Nitzan 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 26 26/26 1.0%
Adam Cohen 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 29 28/29 0.9655172413793104%
Amit Basu 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 3 3/3 1.0%
Barbara A. Baird 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 8/8 1.0%
Benjamin McDonald 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 7 6/7 0.8571428571428571%
Brenda Rubenstein 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 59 37/59 0.6271186440677966%
Christina Woo 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 38 34/38 0.8947368421052632%
Christoph Rose-Petruck 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 3/5 0.6%
David R. Liu 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 91 8/91 0.08791208791208792%
Diptarka Hait 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 18 17/18 0.9444444444444444%
Dirk Trauner 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 59 57/59 0.9661016949152542%
E. Chui-Ying Yan 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 9 9/9 1.0%
Emily Balskus 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 31 31/31 1.0%
Emily Sprague-Klein 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 24 17/24 0.7083333333333334%
Eunsuk Kim 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 6/6 1.0%
Ivan Aprahamian 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 29 27/29 0.9310344827586207%
Ivan J. Dmochowski 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 25 22/25 0.88%
James G. Anderson 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 9 4/9 0.4444444444444444%
Jarad Mason 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 33 29/33 0.8787878787878788%
Jeffrey D. Winkler 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 4/8 0.5%
Jeffrey R. Long 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 49 3/49 0.061224489795918366%
Jerome Robinson 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 9 9/9 1.0%
Jimmy Wu 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 4 4/4 1.0%
Jon Ellman 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 32 31/32 0.96875%
Jonathan S. Owen 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 12 1/12 0.08333333333333333%
Joseph Subotnik 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 87 76/87 0.8735632183908046%
Kang-Kuen Ni 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 38 20/38 0.5263157894736842%
Kyle M. Lancaster 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 24 24/24 1.0%
Lai-Sheng Wang 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 15 2/15 0.13333333333333333%
Lilia Xie 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 8/8 1.0%
Makeda Tekle-Smith 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 4/4 1.0%
Marissa Weichman 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 14 14/14 1.0%
Matthew Zimmt 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 1 1/1 1.0%
Megan Kizer 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 3/3 1.0%
Megan L. Matthews 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 19 16/19 0.8421052631578947%
Michael Hecht 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 6/6 1.0%
Monica E. McCallum 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 3/4 0.75%
Ou Chen 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 44 38/44 0.8636363636363636%
Patrick Holland 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 39 36/39 0.9230769230769231%
Patrick J. Walsh 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 26 26/26 1.0%
Paul Chirik 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 22 22/22 1.0%
Paul Robustelli 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 13 13/13 1.0%
Paul Williard 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 3/3 1.0%
Peter Weber 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 5 4/5 0.8%
Phillip Milner 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 66 59/66 0.8939393939393939%
Ralph Kleiner 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 16 16/16 1.0%
Robert A. DiStasio Jr. 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 24 12/24 0.5%
Robert Knowles 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 36 32/36 0.8888888888888888%
Salvatore Torquato 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 48 44/48 0.9166666666666666%
Sarah Delaney 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 11 11/11 1.0%
Sharon Hammes-Schiffer 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 111 28/111 0.25225225225225223%
Shouheng Sun 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 6/8 0.75%
Stacy Malaker 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 39 36/39 0.9230769230769231%
Theodore Betley 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 17 2/17 0.11764705882352941%
Tianquan “Tim” Lian 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 50 49/50 0.98%
Timothy Newhouse 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 24 21/24 0.875%
Tom Muir 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 9 7/9 0.7777777777777778%
Tristan Lambert 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 37 25/37 0.6756756756756757%
Wenlin Zhang 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 27 15/27 0.5555555555555556%
William Jorgensen 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 2 2/2 1.0%
William M. Jacobs 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 21 21/21 1.0%
Xiaowei Zhuang 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 18 17/18 0.9444444444444444%
Xiaoyang Zhu 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 44 44/44 1.0%
Yusong Bai 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 14 10/14 0.7142857142857143%
Zahra Fakhraai 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 46 27/46 0.5869565217391305%
  • Over the academics these filters select, so a filtered page describes the academics it selected and not the whole department.
  • A row with nothing held draws no mark above: the graph is quiet there because the evidence is missing, not because the work is.
  • What there was to draw from, not what the graph divided by - the graph states its own denominator above.
  • A Scholar profile is attached when at least 3 of the CV titles are found on it, or 30% of them are; one at another institution or under a different name needs 10 titles, and a profile far larger than the CV that holds only a small part of it is refused. Its articles are then checked one by one: what is not a work, or not by this person, is not counted.

Citations per year

Citations received in each year
Each point

One line over the 87 academics the filters select. Citations received in each year, from the academics’ verified Scholar profiles, which count every citation to everything they wrote: no publication filter reaches this line, and no journal weight either, which is why it offers no Value.

What these filters select, per academic not what the graph divided by
Academic Academics CVScholarOpenAlex Works With a count
Andrew B. Bocarsly 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
David Spiegel 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Eric Jacobsen 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Erin E. Stache 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
F. Jon Kull 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Gregory Scholes 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Herschel Rabitz 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
John T. Groves 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Jose Roque 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Mark Johnson 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Martin F. Semmelhack 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Richard A. Friesner 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Robert J. Cava 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Seth Herzon 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Todd K. Hyster 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Tomislav Rovis 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Victor Batista 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Virgil Percec 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Virginia W. Cornish 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 0
Nilay Hazari 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 2 0/2 0.0%
Nozomi Ando 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 1 0/1 0.0%
Richard Stratt 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 1 0/1 0.0%
Abraham Nitzan 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 26 26/26 1.0%
Adam Cohen 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 29 28/29 0.9655172413793104%
Amit Basu 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 3 3/3 1.0%
Barbara A. Baird 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 8/8 1.0%
Benjamin McDonald 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 7 6/7 0.8571428571428571%
Brenda Rubenstein 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 59 37/59 0.6271186440677966%
Christina Woo 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 38 34/38 0.8947368421052632%
Christoph Rose-Petruck 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 5 3/5 0.6%
David R. Liu 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 91 8/91 0.08791208791208792%
Diptarka Hait 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 18 17/18 0.9444444444444444%
Dirk Trauner 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 59 57/59 0.9661016949152542%
E. Chui-Ying Yan 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 9 9/9 1.0%
Emily Balskus 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 31 31/31 1.0%
Emily Sprague-Klein 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 24 17/24 0.7083333333333334%
Eunsuk Kim 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 6/6 1.0%
Ivan Aprahamian 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 29 27/29 0.9310344827586207%
Ivan J. Dmochowski 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 25 22/25 0.88%
James G. Anderson 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 9 4/9 0.4444444444444444%
Jarad Mason 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 33 29/33 0.8787878787878788%
Jeffrey D. Winkler 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 4/8 0.5%
Jeffrey R. Long 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 49 3/49 0.061224489795918366%
Jerome Robinson 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 9 9/9 1.0%
Jimmy Wu 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 4 4/4 1.0%
Jon Ellman 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 32 31/32 0.96875%
Jonathan S. Owen 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 12 1/12 0.08333333333333333%
Joseph Subotnik 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 87 76/87 0.8735632183908046%
Kang-Kuen Ni 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 38 20/38 0.5263157894736842%
Kyle M. Lancaster 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 24 24/24 1.0%
Lai-Sheng Wang 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 15 2/15 0.13333333333333333%
Lilia Xie 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 8/8 1.0%
Makeda Tekle-Smith 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 4/4 1.0%
Marissa Weichman 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 14 14/14 1.0%
Matthew Zimmt 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 1 1/1 1.0%
Megan Kizer 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 3/3 1.0%
Megan L. Matthews 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 19 16/19 0.8421052631578947%
Michael Hecht 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 6 6/6 1.0%
Monica E. McCallum 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 4 3/4 0.75%
Ou Chen 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 44 38/44 0.8636363636363636%
Patrick Holland 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 39 36/39 0.9230769230769231%
Patrick J. Walsh 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 26 26/26 1.0%
Paul Chirik 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 22 22/22 1.0%
Paul Robustelli 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 13 13/13 1.0%
Paul Williard 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 3 3/3 1.0%
Peter Weber 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 5 4/5 0.8%
Phillip Milner 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 66 59/66 0.8939393939393939%
Ralph Kleiner 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 16 16/16 1.0%
Robert A. DiStasio Jr. 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 24 12/24 0.5%
Robert Knowles 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 36 32/36 0.8888888888888888%
Salvatore Torquato 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 48 44/48 0.9166666666666666%
Sarah Delaney 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 11 11/11 1.0%
Sharon Hammes-Schiffer 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 111 28/111 0.25225225225225223%
Shouheng Sun 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 8 6/8 0.75%
Stacy Malaker 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 39 36/39 0.9230769230769231%
Theodore Betley 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 17 2/17 0.11764705882352941%
Tianquan “Tim” Lian 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 50 49/50 0.98%
Timothy Newhouse 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 24 21/24 0.875%
Tom Muir 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 9 7/9 0.7777777777777778%
Tristan Lambert 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 37 25/37 0.6756756756756757%
Wenlin Zhang 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 27 15/27 0.5555555555555556%
William Jorgensen 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 2 2/2 1.0%
William M. Jacobs 1 1/1 1.0% 0/1 0.0% 1/1 1.0% 21 21/21 1.0%
Xiaowei Zhuang 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 18 17/18 0.9444444444444444%
Xiaoyang Zhu 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 44 44/44 1.0%
Yusong Bai 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 14 10/14 0.7142857142857143%
Zahra Fakhraai 1 1/1 1.0% 1/1 1.0% 1/1 1.0% 46 27/46 0.5869565217391305%
  • Over the academics these filters select, so a filtered page describes the academics it selected and not the whole department.
  • A row with nothing held draws no mark above: the graph is quiet there because the evidence is missing, not because the work is.
  • What there was to draw from, not what the graph divided by - the graph states its own denominator above.
  • A Scholar profile is attached when at least 3 of the CV titles are found on it, or 30% of them are; one at another institution or under a different name needs 10 titles, and a profile far larger than the CV that holds only a small part of it is refused. Its articles are then checked one by one: what is not a work, or not by this person, is not counted.

h-index

Academics at each h-index
Series: Academics.0102030401–4 Academics 115–9 Academics 4410–19 Academics 8820–39 Academics 232340+ Academics 38381–45–910–1920–3940+Academicsh-index

The verified Scholar profile’s h-index where there is one, else computed from the OpenAlex citations of the academic’s CV works (definitions section 6): Scholar for 46 academics, OpenAlex citations of CV works for 28, 13 with no h-index. Never OpenAlex’s author-level figure, and no filter here moves it.

What these filters select, over everything selected not what the graph divided by
Selected Academics CVScholarOpenAlex Works With a count
Everything selected 87 87/87 1.0% 46/87 0.5287356321839081% 87/87 1.0% 1624 1183/1624 0.728448275862069%
  • Over the academics these filters select, so a filtered page describes the academics it selected and not the whole department.
  • A row with nothing held draws no mark above: the graph is quiet there because the evidence is missing, not because the work is.
  • What there was to draw from, not what the graph divided by - the graph states its own denominator above.
  • A Scholar profile is attached when at least 3 of the CV titles are found on it, or 30% of them are; one at another institution or under a different name needs 10 titles, and a profile far larger than the CV that holds only a small part of it is refused. Its articles are then checked one by one: what is not a work, or not by this person, is not counted.

Makeup by rank

Academics at each current rank
Series: value.DistinguishedDistinguished 3636FullFull 2727AssociateAssociate 66AssistantAssistant 18180102030RankAcademics

The current rank of every academic the filters keep (definitions section 7). A rank is current as of the CV, so no year window or publication type moves it.

What these filters select, over everything selected not what the graph divided by
Selected Academics CVScholarOpenAlex Works With a count
Everything selected 87 87/87 1.0% 46/87 0.5287356321839081% 87/87 1.0% 1624 1183/1624 0.728448275862069%
  • Over the academics these filters select, so a filtered page describes the academics it selected and not the whole department.
  • A row with nothing held draws no mark above: the graph is quiet there because the evidence is missing, not because the work is.
  • What there was to draw from, not what the graph divided by - the graph states its own denominator above.
  • A Scholar profile is attached when at least 3 of the CV titles are found on it, or 30% of them are; one at another institution or under a different name needs 10 titles, and a profile far larger than the CV that holds only a small part of it is refused. Its articles are then checked one by one: what is not a work, or not by this person, is not counted.

Top collaborators

Works two of the selected academics both hold
Series: value.Abraham Nitzan · Joseph SubotnikAbraham Nitzan · Joseph Subotnik 99E. Chui-Ying Yan · Sharon Hammes-SchifferE. Chui-Ying Yan · Sharon Hammes-Schiffer 77Joseph Subotnik · Sharon Hammes-SchifferJoseph Subotnik · Sharon Hammes-Schiffer 33Phillip Milner · Tristan LambertPhillip Milner · Tristan Lambert 33Xiaoyang Zhu · Yusong BaiXiaoyang Zhu · Yusong Bai 33Jeffrey R. Long · Phillip MilnerJeffrey R. Long · Phillip Milner 22Kyle M. Lancaster · Patrick HollandKyle M. Lancaster · Patrick Holland 22Ou Chen · Yusong BaiOu Chen · Yusong Bai 22Patrick J. Walsh · Zahra FakhraaiPatrick J. Walsh · Zahra Fakhraai 22Sharon Hammes-Schiffer · Tianquan “Tim” LianSharon Hammes-Schiffer · Tianquan “Tim” Lian 22Abraham Nitzan · Sharon Hammes-SchifferAbraham Nitzan · Sharon Hammes-Schiffer 11Benjamin McDonald · Brenda RubensteinBenjamin McDonald · Brenda Rubenstein 11Benjamin McDonald · Emily Sprague-KleinBenjamin McDonald · Emily Sprague-Klein 11Brenda Rubenstein · Eunsuk KimBrenda Rubenstein · Eunsuk Kim 11Jarad Mason · Jeffrey R. LongJarad Mason · Jeffrey R. Long 1102468PairShared works

The pairs of the 87 selected academics who share the most of these works. 45 of the 1,624 selected works are held by two of them, making 22 pairs - a work three of them share is three pairs, so the two figures are different quantities and neither is a sum of the other. Only collaborations inside this selection are here: a work written with somebody outside it is counted everywhere else on this page and cannot be paired.

What these filters select, over everything selected not what the graph divided by
Selected Academics CVScholarOpenAlex Works With a count
Everything selected 87 87/87 1.0% 46/87 0.5287356321839081% 87/87 1.0% 1624 1183/1624 0.728448275862069%
  • Over the academics these filters select, so a filtered page describes the academics it selected and not the whole department.
  • A row with nothing held draws no mark above: the graph is quiet there because the evidence is missing, not because the work is.
  • What there was to draw from, not what the graph divided by - the graph states its own denominator above.
  • A Scholar profile is attached when at least 3 of the CV titles are found on it, or 30% of them are; one at another institution or under a different name needs 10 titles, and a profile far larger than the CV that holds only a small part of it is refused. Its articles are then checked one by one: what is not a work, or not by this person, is not counted.
First Second

Pick two academics to see how many of the selected works they share.