The H-index is among the most widely used metrics for
evaluating the quality and quantity of a scientist’s publications; but what
about their influence on society? Here I introduce the Influence Score that can
help an outside reviewer better understand a person’s impact not just among other
scientists but among the general public and those outside of academia. If the ultimate goal is to evaluate a
person’s true overall role as a scientist, I think we should be considering how
they communicate with all people not just other scientists (which is the
case with the H-index). The new index can be used to accompany the H-index, but
also incorporates it. All elements of the Influence Score can be looked up
through simple Google and Twitter/Facebook searches.
The H-index can be easily calculated
in Google Scholar, I
prefer it to the Web of Science or Scopus because Google Scholar counts books
and other
non-traditional peer-reviewed publications: and it is free! To
calculate the H-index you essentially count down the number of publications and
their citations until the numbers no longer overlap. A person with an H-index
of 5, has at least 5 papers with 5 citations. Read more HERE to learn how to calculate the H-index.) Because a scientist’s main
role is still to communicate their science among peers (via peer review), the Influence
Score multiples their H-index by 100, and down weights the other elements,
which are a little easier to accumulate (e.g., # of twitter followers). I chose the H-index over say, total number of
citations (which might be more similar to # of followers), because it is easier
to calculate for a given researcher, especially one without a Google Scholar
profile (Fig.1).
 |
| Fig.1. Google Scholar Profile Page Showing H-Index in Red Box. |
The other measures of the Influence
score includes a measure of their visibility with the press (i.e, the Press
Index or P-Index for short). Using Google News, one simple puts the person’s
name in the search box and counts the number of articles that are found, which
Google also does for you (Fig.2). Because
Google News is only searching through
a relatively recent window of time, few scientists will have much more than a
few articles about them. Sometimes it is worth googling the person with “science”
following their name, as I did for James Watson (e.g., “James Watson science”),
to distinguish him from other news articles about people with the same name. This
measure largely is to bump up those scientists truly making a social impact as
newsmakers. That is without bumping them up too much, I’m trying to avoid giving too much influence to “celebrity
scientists” that don’t do much science of their own. Therefore, I suggest that
you divide the total number of search results of the Press Index by 100 so that
this score is not completely overweighing the person’s academic accomplishments
represented by the (albeit crude) measure of the H-index. Folks like James
Cameron that are great promoters of science, but are best known for other
things, are intentionally excluded here. If someone could separate press about
science related activities from all others, they obviously could still be
included. This is also the most dynamic element of the score because it can change so rapidly. Jane Goodall can skyrocket to the top of the list with the publication of a new book.
 | |
| Fig 2. Red box shows Google News P-index. |
 |
| Fig.3. Twitter and Facebook Fan pages showing # of followers. |
The third part of
the Influence Score considers your sway in social media (i.e, the Social Media
Index or SM-index for short), specifically Twitter or Facebook. For someone on
Twitter you get 1pt for every follower. For someone not on Twitter but that has
either a Facebook “Fan” Page or Facebook “followers,” you get 1pt per fan or
follower. You don’t get points for regular old Facebook “friends” because that
isn’t necessarily measuring your scientific influence. If they have both a
Facebook Fan page and a Twitter handle you only get points for whichever is the
higher value. To learn more about the role of twitter for outreach read David
Shiffman’s @whysharksmatter excellent article HERE).
As with the Press-Index you divide the total number by 100; again this
is to allow the more academic H-index to still have some weight. The reason being
that someone with 40,000 Twitter followers and an H-index of 0 might not really
be more influential than a scientist with an H-index of 40 and only 4000
twitter followers.
The Influence Score is then the
total of your (H-Index X 100) + (Press index/100) + (Social Media-Index/100). I
would round this to the nearest integer. All three can be discovered relatively
easily through searches (e.g. GoogleScholar, GoogleNews, and a Facebook/Twitter
search). Below I’ve compiled a list of some of the most well known scientists
and have calculated their Influence Score. I’ve also added folks randomly that I
admire that might not be the most famous folks but that I hope will be one day,
I think adopting the Influence Score might help them get the recognition they
deserve for the impact they have in society. This metric is imperfect: but I hope it is a good start.
I would like to thank Paige Brown @FromTheLabBench and her class #manship4002 for helping me figure out a more user-friendly way to compile this Influence Score. Also would like to thank Joshua Drew (@Drew_Lab) and David Shiffman (@WhySharksMatter) for their comments and advice.
Scientist
|
H-Index x 100
|
Press Index /100 (total articles according to GoogleNews)
|
Social Media Index
/100
(Twitter/or Facebook followers)
|
Influence Score
|
Stephen Hawking
|
8400
|
781
|
9,591
|
18,772
|
Neil deGrasse Tyson
|
600
|
14
|
16,100
|
16,714
|
Richard Dawkins
|
4000
|
36
|
8,870
|
12,906
|
Bill
Nye
|
100
|
140
|
11,500
|
11,740
|
Jared Diamond
|
10800
|
104
|
208
|
11,112
|
J. Craig Venter
|
9600
|
20
|
186
|
9,806
|
E.O. Wilson
|
8800
|
64
|
12
|
8,876
|
Steven Chu
|
8200
|
17
|
212
|
8,429
|
Buzz Aldrin
|
0
|
10
|
8,150
|
8,160
|
Sean B. Carroll
|
7500
|
.01
|
0
|
7,500
|
Jane
Goodall
|
4500
|
21
|
1,650
|
6,171
|
Ed Yong
|
0
|
.12
|
4,660
|
4,660
|
Jane Lubchenco
|
4600
|
.71
|
0
|
4,601
|
Jack Horner
|
3600
|
.16
|
18
|
3,618
|
Carl Zimmer
|
1700
|
3.83
|
1700
|
3,404
|
Amanda Vincent
|
3200
|
0
|
3
|
3,203
|
Neil Shubin
|
3100
|
3.94
|
9
|
3,113
|
David Attenborough
|
1500
|
45
|
1,423
|
2,968
|
James Watson
|
2600
|
18
|
0
|
2,618
|
Hope Jahren
|
2200
|
0.01
|
24
|
2,224
|
Eugenie Clark
|
2200
|
0.01
|
0
|
2,200
|
Eugenie Scott
|
1800
|
0.06
|
71
|
1,871
|
David Shiffman
|
200
|
0.04
|
1,470
|
1,670
|
Sylvia Earle
|
1400
|
1
|
213
|
1,614
|
Note: As always I
would like feedback on this post and if people have suggestions for changes or
additions to the metric. Please e-mail me at prosanta@lsu.edu
