***** To join INSNA, visit http://www.insna.org *****
The Integrated Impact Indicator (I3)
<at http://www.leydesdorff.net/software/i3/index.htm >
One is inclined to conceptualize impact in terms of citations per
publication, and thus as an average. The so-called Impact Factor of
journals, for example, is also an average. However, citation distributions
are skewed and the average has the disadvantage that the number of
publications is used in the denominator. Thus, a principal investigator has
a higher average citation rate than s/he and her junior team together.
However, the impact of the group is larger than that of the individual. In
other words, size matters for impact.
Leydesdorff & Bornmann (2011) therefore replaced averaging with integration
of the citation curve, but not after qualifying the underlying publications
in terms of their respective percentiles: a top-1% publication obtains 100
percent points whereas an average publication gets only 50 points. This
rescaling from zero to hundred makes it possible to compare different sets
and different citation distributions in terms of their impact. The results
of the measurement can be used as input to non-parametric statistics which
are, for example, available in SPSS.
This website provides routines to compute I3 for a set of papers downloaded
from the Web-of-Science (v5). First, this set can be organized in a
relational database using ISI.exe. ISI.exe uses as input the download in the
tagged format of the WoS which is available in the same folder and named
“data.txt”. The output is a set of databases (.dbf) which can be read using
Excel or SPSS. For example, authors are organized into au.dbf and email
addresses into em.dbf. (The various files are related in terms of the field
“nr;” MSAccess can be used for relational database management.)
The resulting files can be used by isi2i3.exe as input. This program will
transform core.dbf into i3core.dbf, au.dbf into i3au.dbf, and cs.dbf into
i3cs.dbf. The program may take a while; in the case of large files, one can
perhaps leave it over night. When one uses the c-prompt, the routine either
finishes (after a while) or provides an informative error message.
The resulting files (e.g., i3core.dbf) are only different from the input
files in a number of additional fields: the field i3f provides the value of
i3 normalized as percentiles in relation to the set under study (“the
field”), and i3j is normalized at the level of each journal. Analogously,
r6f and r6j provide these values for the six percentile ranks used by the
NSF: top-1%, top-5%, top-10%, top-25%, top-50%, and bottom-50%.
Isi2i3.exe furthermore generates a number of summary tables that one can
use: i3so.dbf summarizes the data after aggregation at the journal level
(“so” for source); i3cntry.dbf for aggregation at the country level; i3city
and i3inst at the city and institutional levels, respectively; i3au at the
level of authors. These aggregations can also be made by using pivot tables
in Excel or “Aggregate cases” in SPSS. Note that the results for authors and
addresses are “integer counted”: each record is counted as one, whereas
fractional counting would imply attributing credit proportionally in the
case of multi-authored papers.
I3cs.dbf can be used as input for the generation of overlays to Google Maps
strictly analagous to the procedures used by Leydesdorff & Persson (2010)
<at http://www.leydesdorff.net/maps> and Bornmann & Leydesdorff (2011) <at
http://www.leydesdorff.net/topcity>. Instead of cities1.exe and cities2.exe,
one uses i3cit1.exe and i3cit2.exe. Instead of inst1.exe and inst2.exe, one
uses i3inst1.exe and i3inst2.exe. I3cit2.exe and i3inst2.exe directly
produce the various output files among which ztest.txt. A third step is not
needed; between the first and second step cities.txt or inst.txt has to be
geocoded. An example is provided at
http://www.leydesdorff.net/nano2011/nano2011.htm which shows a Google Map
with the performance of cities worldwide in the field of 15 core journals of
nanotechnology (Leydesdorff, in preparation).
The following paper explains the concept of integrated impact indicators:
• Loet Leydesdorff & Lutz Bornmann, Integrated Impact Indicators (I3)
compared with Impact Factors (IFs): An alternative design with policy
implications. Journal of the American Society for Information Science and
Technology (in press).
For the use of Google maps:
• Lutz Bornmann and Loet Leydesdorff, Which cities produce worldwide
excellent papers more than expected? A new mapping approach—using Google
Maps—based on statistical significance testing. Journal of the American
Society for Information Science and Technology (in press); [software &
manual at http://www.leydesdorff.net/topcity];
• Loet Leydesdorff & Olle Persson, Mapping the Geography of Science:
Distribution Patterns and Networks of Relations among Cities and Institutes,
Journal of the American Society for Information Science & Technology 61(8)
(2010) 1622-1634; <pdf-version> <software and manual at
A study entitled “An Evaluation of Impacts in ‘Nanoscience and
Nanotechnology:’ Steps towards standards and statistics for citation
analysis” is in preparation for the Atlanta Conference on Science,
Technology, and Innovation Policy, September 15-17, 2011.
** apologies for cross-postings
Professor, University of Amsterdam
Amsterdam School of Communications Research (ASCoR)
Kloveniersburgwal 48, 1012 CX Amsterdam.
Tel. +31-20-525 6598; fax: +31-842239111
[log in to unmask] ; http://www.leydesdorff.net/
Visiting Professor, ISTIC, Beijing; Honorary Fellow, SPRU, University of
SOCNET is a service of INSNA, the professional association for social
network researchers (http://www.insna.org). To unsubscribe, send
an email message to [log in to unmask] containing the line
UNSUBSCRIBE SOCNET in the body of the message.