An evaluation of impacts in “Nanoscience & nanotechnology”: steps towards standards for citation analysis
 2.9k Downloads
 11 Citations
Abstract
One is inclined to conceptualize impact in terms of citations per publication, and thus as an average. However, citation distributions are skewed, and the average has the disadvantage that the number of publications is used in the denominator. Using hundred percentiles, one can integrate the normalized citation curve and develop an indicator that can be compared across document sets because percentile ranks are defined at the article level. I apply this indicator to the set of 58 journals in the WoS Subject Category of “Nanoscience & nanotechnology,” and rank journals, countries, cities, and institutes using nonparametric statistics. The significance levels of results can thus be indicated. The results are first compared with the ISIimpact factors, but this Integrated Impact Indicator (I3) can be used with any set downloaded from the (Social) Science Citation Index. The software is made publicly available at the Internet. Visualization techniques are also specified for evaluation by positioning institutes on Google Map overlays.
Keywords
Citation Impact Evaluation Nanotechnology Statistics StandardsIntroduction
In recent decades, scientometric indicators have increasingly been used for evaluative purposes both in publicpolicy domains and, for example, in hiring and promoting faculty. Policy makers and research managers need to rely on the quality of these indicators. Recent years, however, have witnessed fierce debate about the use of statistics and standards in evaluation research (Gingras and Larivière 2011). A transition from parametric statistics (using averages) towards nonparametric statistics (using percentiles) provides advantages, but implies a different conceptualization of “impact.”
Nonparametric statistics enable us to honour both productivity and quality, whereas the impact may be lower in the case of averaging for the sole reason of a higher productivity. These statistics share this appreciation of both productivity and citation rates with the hindex (Hirsch 2005), but they differ from the hindex in that a range of tests for the significance of the impact (above or below expectation) becomes available. Lesscited papers can thus be appreciated proportionally, while the hindex uses the hvalue as a threshold for the cutoff of the tails of the distributions (cf. Glänzel 2007; Vinkler 2010, 2011).
Scientometric indicators first require normalization because of differences in publication and citation practices among fields of science. For example, impact factors (IFs) in psychology are on average larger than in sociology by an order of magnitude (Leydesdorff 2008). Mathematicians provide short reference lists (on the order of five references), while lifescientists often provide more than 40 references. The chances of being highlycited and positively evaluated would thus vary among the sciences for statistical reasons (Garfield 1979a).
Two conventions have been devised in the past for normalization: (1) normalization in terms of fields of science, and (2) comparison of the citation rate with the world average. For the delineation of fields of science, scientometricians often turn to journal classification schemes provided by the database producer—such as the WoS Subject Categories of the (Social) Science Citation Index (SCI and SSCI).^{1} Journals, however, can be assigned to more than a single category, and this categorization is not “literary warranted”; that is, it is not updated systematically to follow the development of the scientific literature (Bensman and Leydesdorff 2009; Chan 1999).
The sources of error in these field delineations are difficult to control using journals as units of analysis because it is questionable whether journals themselves are monodisciplinary units in terms of their content (Boyack and Klavans 2011; Pudovkin and Garfield 2002; Rafols and Leydesdorff 2009). Furthermore, one can expect the institutional units under study—such as universities—to be organized disciplinarily and interdisciplinarily, but from a different perspective. Therefore, refinement of the journal categories cannot solve the problem of field delineation (Leydesdorff 2006; Rafols et al. in press). Perhaps, paperbased classifications such as the Medical Subject Headings (MeSH) of the Medline database would be more appropriate, but the complexity of the handling of citations in relation to more than a single database has hitherto been technically difficult (Bornmann et al. 2008, 2011).
The second problem—that is, the comparison to a world average—was addressed early in the scientometric enterprise by Schubert and Braun (1986), who proposed comparing the mean observed citation rate (MOCR) with the corresponding mean expected citation rate (MECR) as the average of papers of the same datatype and publication year within a reference set (representing, for example, a field of science). The Relative Citation Rate (RCR = MOCR/MECR) is thus normalized with unity as the world average.
Similarly, the current evaluation standard of the ECOOM center in Louvain (Belgium) uses the Normalized Mean Citation Rate (NMCR; Glänzel et al. 2009) based on the same principles, but sometimes using their own classification system of journals for the field delineation (Glänzel and Schubert 2003; cf. Rafols and Leydesdorff 2009).
In reaction to this critique by Opthof and Leydesdorff (2010; cf. Lundberg 2007), the Centre for Science and Technology Studies (CWTS) in Leiden has changed the crown indicator (Waltman et al. 2011a), but not all other centers have followed suit. CWTS called this “new crown indicator” the Mean Normalized Citation Score or MNCS. One advantage of this indicator is that the mean is a statistics with a standard deviation, and consequently a standard error for the measurement can be defined. Waltman et al. (2011b) have shown that this new indicator is mathematically consistent (cf. Egghe 2012; Vinkler 2012).
In the sideline of these debates about citation indicators, two further steps were taken. First, Leydesdorff and Opthof (2010) proposed abandoning the idea of journal classification, instead using the citing articles as the reference set of the cited articles, and then normalizing by counting each citation in proportion to the length of the reference list in the citing paper (1/NRef; the field NRef is available in the Science Citation Index). Differences in citation behaviour among fields of science can thus be normalized at the source. This fractional counting of citations was previously used by Small and Sweeney (1985) for the mapping of cocitations; and “sourcenormalization” has been proposed by Zitt and Small (2008) and Moed (2010), albeit in other contexts.
Leydesdorff and Bornmann (2011a) could show that the differences in IFs among fields were no longer statistically significant when using such fractional counting instead of counting each citation as a full point. Another advantage of this methodology is the possibility to evaluate interdisciplinary units fractionally across different disciplines. Zhou and Leydesdorff (2011), for example, have shown that the department of Chinese Language and Literature of the Tsinghua University in Beijing was upgraded in the evaluation from 19th to 2nd position by using fractional counting (given the scarcity of journal citations in the humanities; Leydesdorff et al. 2011a; Nederhof 2006). In an evaluation of interdisciplinary programs in innovation studies, Rafols et al. (in press) found fractional counting of the citations to improve the measurement greatly, particularly if the set was limited to documents with more than ten references (cf. Leydesdorff 2012; Leydesdorff et al. in preparation).
In a second contribution to this controversy, Bornmann and Mutz (2011) proposed using percentile ranks instead of averages (Seglen 1992, 1997), in accordance with the standard practice of the Science & Engineering Indicators of the US National Science Board (NSB, 2010). Six percentile ranks are distinguished: the top1 %, top5 %, top10 %, top25 %, top50 %, and bottom50 %. If a one is assigned to a paper in the bottom category, and a six to a paper in the top1 %, it can be shown that the random expectation is a score of 1.91.
The teams of Bornmann & Mutz and Leydesdorff & Opthof thereafter joined forces and evaluated the seven principal investigator (PIs) of the set that originally triggered this debate (Opthof and Leydesdorff 2010), but using percentile ranks, and then compared the results with those obtained by using the full range of hundred percentiles. In this study, they proposed one further step: one should distinguish evaluation of the seven document sets (of publications) as independent samples—which is the current practice—from their evaluation as subsamples of a reference set.
Among the seven PIs, for example, the one ranked highest had 23 papers of which three were in the top1 %. Each of these three papers contributes (1/23) * 6 = 0.261 points to the total score of this PI (PI1). However, PI6 had 65 papers; a single paper in the top1 % would in this case add (1/65) * 6 = 0.092 points to his/her total score. In other words, one should no longer consider these samples as independent, but instead use the grand sum of the total set (N = 248) so that each PI is credited equally with (1/248) * 6 = 0.024 points for each paper in the top1 % category. By using this measure, PI6 was upgraded to first position, and PI1 downgraded to fifth in the ranking (Leydesdorff et al. 2011b, Table 1, at p. 1375).
The reference set thus provides a standard, and each subset is evaluated as a part of this superset. This reference set can be any encompassing superset, including, for example, a definition of the field in terms of relevant journals or specific keywords (e.g., Bonaccorsi and Vargas 2010). The crucial point is that each paper adds to the impact score proportionally, that is, according to its percentile rank. Impact is thus no longer dependent on dividing by the number of papers in a specific subset: if two researchers have an equal number of papers in the highest category, adding more papers to one of the sets will add to its percentage impact even if the added papers are in a lower category. In the case of averaging (or using the median) these additional papers would decrease the impact, and thus “punish” productivity.
Leydesdorff and Bornmann (2011b) have elaborated this approach into an indicator: the Integrated Impact Indicator (I3). In the meantime, a website is available at http://www.leydesdorff.net/software/i3 which provides the relevant routines. The indicator can formally be written as follows: I3 = ∑_{ i } x _{ i } * n(x _{ i }), in which x _{ i } denotes the percentile (rank) value i, and n the number of papers with this value. I3 leaves the parametric domain of averages behind and moves to nonparametric statistics using percentiles. The percentiles can be computed as a continuous random variablze. Aggregation of the values in the six percentile rank classes distinguished above, leads to an indicator (PR6) that weights highlycited papers more than lowercited ones. I shall use both these indicators throughout this study.
Using I3 or PR6, one can test whether a contribution is statistically significant above or below expectation using the ztest, and one can also compare citation distributions in terms of significant differences using Dunn’s test. These are two distinct questions which should not be confused. For example, the citation distribution of the Proceedings of the National Academy of Science of the USA and the Proceedings of the Estonian Academy of Science are not significantly different, but the impact of the former is significantly above the expected impact using I3, while this is not the case for the latter (Leydesdorff and Bornmann 2011a).
The concept of impact has thus been redefined. Using averages (as in the case of IFs), the N in the denominator has an adverse effect on productivity. When a researcher coauthors with his PhD students, the average citation rate of the group is likely to go down, but the total impact of this research project increases. As Bensman (2007) noted, Garfield (1972, 1979b) chose to normalize the ISIimpact factor by dividing by N in order to prevent the larger journals from overshadowing the smaller. Bensman then proposed using “Total Citations” instead as an indicator of reputation, because the latter correlated more clearly with the subjective appreciation of faculty than the IFs. Our percentilerank approach, however, appreciates that highlycited papers should be weighted more than less frequently cited ones.
The division by N was just too coarse: using an average assumes a normal distribution, whereas the skewness in different citation distributions can be made comparable when normalized on the 1–100 scale of percentiles. How the percentiles are aggregated, for example, into the six percentile ranks of the NSF used by Bornmann and Mutz (2011), or in terms of quartiles, is a normative decision which can be made independently from the technical analysis in terms of hundred percentiles. In what follows, I use both the scheme of six percentile ranks and the baseline of 100 percentiles to evaluate the case of journals subsumed under the WoS Subject Category of “Nanoscience & nanotechnology.” In order to make my results comparable with the IFs, I use whole counts and 2 years of cited data (2007 and 2008) and compare with the IF2009 (the most recent one available at the time of this research; Spring 2011). As noted, I3 is defined more abstractly; it can be used with any reference set and also with fractional counts.
The “nanoset” in the Science Citation Index
The WoS Subject Category “Nanoscience & nanotechnology” (NS) entered the SCI in 2005 with 27 journals and more than doubled to include 59 journals by 2009. One of these journals (ACM Applied Materials & Interfaces) was added to the database only in 2009, and consequently no records for the two previous years were included (as of the time of this study). I limit the set to the 31,644 citable items in these 58 journals during the years 2007 and 2008 (that is: 25,271 articles, 5,488 proceedings papers, 709 reviews, and 176 letters). The percentile of each paper is determined with reference to the set with the same publication year and document type, respectively.
A simple counting rule for percentiles is the number of papers with lower citation rates divided by the total number of similar records in the set. The resulting value can be rounded into percentiles or used as a continuous random variable (socalled “quantiles”). The advantage of this counting rule over other possible ones is that tied ranks are accorded their highest possible values. Other rules are also possible: Pudovkin and Garfield (2009), for example, first averaged tied ranks.
Figure 2 shows that Nano Letters outperforms Nature Nanotechnology in all six classes. However, if one divides the total scores by the number of publications in each category, Nano Letters becomes the less important journal (in all categories). Thus one should not divide, but integrate: impacts adds up. The impact of two collisions is the (vector) sum of the two momenta. The use of the word “impact factor” to denote an average value has confused the semantics for decades (Sher and Garfield 1965; Garfield 1972).
The integrals in this stepwise function are equal to ∑_{ i } x _{ i } * f(x _{ i }) in which x represents the percentile rank and f(x) the frequency of that rank. For example, i = 6 in the case above, or i = 100 when using all percentiles as classes. The hundred percentiles can be considered as a continuous random variable, and these “quantiles” form the baseline. Note that the percentile is thereafter a characteristic of each individual paper. Thus, different aggregations of papers are possible, for example in terms of journals, nations, institutes, cities, or authors. The function integrates both the number of papers (the “mass”) and their respective quality in terms of beingcited, but after normalization represents them as percentiles with reference to a set.
I3 correlates with both citations (r = 0.935, p ≤ 0.01) and publications (r = 0.963, p ≤ 0.01) more than these two values correlate mutually: this then takes both productivity and quality into account. PR6 is slightly more sensitive to high citation rates than I3 (based on hundred percentiles), but the difference between the two indicators is not significant.
Methods and materials
The 58 journals in the ISI Subject Category “Nanoscience & nanotechnology” conveniently contain a large sample: 31,644 citable items, with 65,173 addresses, and 146,432 authors. All records were downloaded on February 25, 2011. Dedicated software was developed and brought online at http://www.leydesdorff.net/software/i3. The routines add the values for the two relevant indicators (I3 and PR6) at the article level to a set downloaded from the WebofScience (WoS) and organized into relational databases. The percentiles can be summed into categories using the various possible schemes for an evaluation. As noted, I use the six ranks of the NSF as an example in addition to the quantiles (between 0 and 100) as a continuous random variable.
The simple counting rule for quantiles—specified above—may generate problems when a reference set is smaller than 100. For example, if a journal includes among its publications only 10 reviews each year, the highest possible percentile would be the 90 %—nine out of ten—whereas this could be the 99th percentile. Rüdiger Mutz (personal communication, February 18, 2011) suggested adding 0.9 to each percentile value, which solved part of the problem (Leydesdorff and Bornmann 2011b). Rousseau (2012) proposed adopting as a counting rule not “less than” (<), but “less than or equal to” (≤) the item under study. All papers would then have a chance to be in the top rank of the 100th percentile, but the resulting values are higher. I did not apply this solution in the current study (Leydesdorff and Bornmann in press). Most recently, Schreiber (in press) proposed another solution to this problem.
The file of 31,644 citable items each with a value for “times cited” (“TC” provided by the database), I3 (for the hundred percentiles), and PR6 (for the six classes of the NSF) can be imported into SPSS (v.18); and then the routine “Compare Means,” using the journals—countries, cities, etc., respectively—in each set as the independent (grouping) variable, can be used for determining all relevant statistics. (This routine also allows for bootstrapping and the determination of confidence levels.) The I3values and PR6values are based on summation for the units of analysis under evaluation. The standard error of measurement (SEM) values are based on averaging and will not be used below, but are also available from this routine.
The I3 and PR6 values can be recomposed by aggregating for each subset—for example, each journal or a subset with a specific institutional address—and expressed as a percentage of the total I3 (or PR6) value for the set. The routine isi2i3.exe also provides tables at the level of authors, institutes,^{4} countries, and journals, with aggregates of these variables. (Alternatively, one can use the procedure “Aggregate cases” in SPSS or generate pivot tables in Excel.) The absolute numbers can be expressed as percentages of the set which can then be compared and used for ranking. Examples will be provided in the results section below.
Both I3 and PR6 are sizedependent because a unit with 1,000 publications has 10 times more chance to have one in the top1 % range than a unit with only 100 publications. These indicator values can be tested against the publication rates: ex ante, all publications have an equal chance to be cited. One can use the ztest for two independent proportions to test observed versus expected rates for their statistical significance (Sheskin 2011, p. 656).^{5} Note that the impact I3 is not independent of the number of publications. I shall return to this issue below.
Citation curves (such as in Fig. 1) can be tested against each other using “Multiple Comparisons” with Bonferroni correction in SPSS. When the confidence levels do not intersect, the distributions are flagged as significantly different. In the case of nonparametric statistics, Dunn’s test can be simulated by using least significant differences (LSD) with familywise correction for TypeI error probability (Levine 1991, at pp. 68 ff.).^{6} The routine in SPSS is limited to a maximum of 50 comparisons. Alternatively, one can use the Mann–Whitney U test with the same Bonferroni correction between each individual pair.
Journals (etc.) with significantly different or similar citation patterns, can be visualized as a graph in which homogenous sets are connected while significantly different nodes are not. I shall use the algorithm of Kamada and Kawai (1989) in Pajek for this visualization. The kcore set which are most homogenous in terms of citation distributions can thus be visualized. Analogously, other variables which are attributed at the article level, such as the percentile values I3 and PR6, can be analyzed. Differences and similarities in citation distributions can be expected to change after normalization in terms of percentile values. The latter exhibit differences in impact (I3 and/or PR6).
In summary, one can assess each percentage impact (with reference to a set) in terms of whether the contribution (expressed as a percentage impact of the set) is significantly above or below the expectation. In accordance with the convention in SPSS but using plus and minus signs, I use double ^{++} for above and ^{− −} for below expectation at the 1 % level of significance testing, and single ^{+} and ^{−} at the 5 % level. The significance of the contribution is a different question from whether its ranking is based on significant differences in the underlying distributions of the citations or I3values. The latter analysis enables us to indicate groups which may differ in size but otherwise be homogenous.
Results
Journal evaluation
Rankings between 20 journals of “Nanoscience & nanotechnology” with highest values on I3 (expressed as percentages of the sum) compared with IFs, total citations, and % I3 (PR6)
Journal  No. of papers (a)  % I3 (b)  IF 2009 (c)  Total citations (d)  % I3 (PR6) (e)  

1  J Phys Chem C  5,585 [1]  20.71 [1]^{++}  4.224 [9]  62,072  18.79 [1]^{++} 
2  Mater Sci Eng AStruct Mater  3,222 [2]  8.82 [2]^{− −}  1.901 [12]  15,920  8.64 [2]^{− −} 
3  Nano Lett  1,507 [6]  7.99 [3]^{++}  9.991 [2]  42,159  8.38 [3]^{++} 
4  Nanotechnol  2,656 [3]  7.96 [4]^{− −}  3.137 [10]  18,748  7.22 [5]^{− −} 
5  Advan Mater  1,512 [5]  7.61 [5]^{++}  8.379 [3]  34,581  7.59 [4]^{++} 
6  Adv Funct Mater  883  4.24 [6]^{++}  6.990 [5]  16,629  4.04 [7]^{++} 
7  J Nanosci Nanotechnol  1,699 [4]  3.74 [7]^{− −}  1.435 [15]  5,946  4.07 [6]^{− −} 
8  Biosens Bioelectron  821  3.57 [8]^{++}  5.429 [8]  11,583  3.34 [8]^{++} 
9  Scripta Mater  1,153  3.43 [9]^{− −}  2.949 [11]  8,656  3.13 [11]^{− −} 
10  Microporous Mesoporous Mat  1,041  3.40 [10]^{++}  2.652 [12]  7,140  3.14 [10] 
11  Microelectron Eng  1,064  3.14 [11]^{− −}  1.488 [13]  4,090  3.17 [9]^{−} 
12  Small  595  2.58 [12]^{++}  6.171 [7]  9,811  2.40 [14]^{++} 
13  Lab Chip  520  2.34 [13]^{++}  6.342 [6]  8,930  2.21 [15]^{++} 
14  J Vac Sci Technol B  962  2.29 [14]^{− −}  1.460 [14]  3,471  2.43 [13]^{− −} 
15  Physica E  1,004  2.26 [15]^{− −}  1.177 [16]  3,126  2.64 [12]^{− −} 
16  J Micromechanic Microengineer  778  1.86 [16]^{− −}  0.541 [19]  186  1.86 [16]^{− −} 
17  Acs Nano  349  1.73 [17]^{++}  7.493 [4]  6,845  1.72 [17]^{++} 
18  Microelectron Rel  627  1.20 [18]^{− −}  1.117 [17]  1,767  1.44 [19]^{− −} 
19  Nat Nanotechnol  199  1.20 [19]^{++}  26.309 [1]  13,224  1.47 [18]^{++} 
20  Microsyst Technol  475  1.00 [20]^{− −}  1.025 [18]  1,265  1.17 [20]^{− −} 
Note that Nano Letters ranks only sixth in terms of the number of publications (column a), but third in terms of the impact I3 and PR6 (columns b and e, respectively). Its impact is thus (significantly!) above expectation. Mater Sci Eng AStruct Mater,^{7} in second position, is larger in terms of numbers of publications, but relatively low in terms of citations. Therefore, it scores significantly below the expected citation rates in columns (b) and (e). Analogously, the c/p ratio for Nature Nanotechnology (13,244/199 =) 66.45 corresponds to its high IF, but, as noted, this is entirely due to the low number in the denominator (N = 199). The I3 and PR6 values are determined by both productivity and citation rates. In this case, values for I3 and PR6 were virtually identical (r = 0.998; ρ = 0.966; p ≤ 0.01; N = 58).
If we repeat this analysis with the distributions of I3—instead of citation distributions across these fifty journals (not shown), all journals are connected in a single graph (24 in a k = 17 core with 10 more journals connected to this component at the level of k = 11). However, the I3distribution of Nature Nanotechnology is statistically similar only to the one of Nano Today, while this latter journal is connected to five other, among them Nano Letters. Not surprisingly, the impact pattern of Nano Letters is not otherwise different from ACS Nano, another journal of the American Chemical Society. ACS Nano was ranked 17th in Table 1, and is another highly specialized journal with a relatively high IF (7.493), but a lower value of I3.
In summary, I3 and the IF measure very different aspects of impact. Whereas I3 can be considered as a measure of impact comparable to the hindex, but sophisticated in its statistics and elaboration, the IF is based on a division by the number of citable items in the denominator. Bensman (2007) noted that Garfield thus wished to distinguish this indicator from “total cites” in order to control for the size of journals. However, the choice of the mean as a measure for impact has unfortunately led to the mistaken assumption that impact can be measured by averaging.
Evaluation of nations
Because I3 is not an average over a distribution, but a summation of values of times cited normalized at the level of individual papers, aggregations other than those in terms of journals are equally possible. Since Narin (1976) and Small and Garfield (1985) one has been inclined to consider the two major aggregations in terms of journals and nations as the orthogonal axes of a matrix for citation analysis; but sets can also be aggregated using criteria such as keywords or author groups across journals and/or institutional addresses. Each paper has a percentile rank with reference to the set which represents the relevant field. Each subset can be tested on the significance of the contribution, that is, above or below statistical expectation.
Rankings among top20 countries in terms of I3 (expressed as percentages of the sum) compared with number of publications, total citations, and % I3 (PR6) for the 58 journals in the ISI Subject Category “Nanoscience & nanotechnology”
Publications  Citations  Cit/Pub  % I3  % PR6  

1  USA  16,039  234,370  14.61  27.73^{++}  27.93^{++} 
2  Peoples R China  8,653  83,079  9.60  12.88^{−}  12.74^{−} 
3  Japan  4,798  39,469  8.23  7.08^{−}  7.00^{−} 
4  Germany  3,802  43,231  11.37  6.54^{++}  6.46^{++} 
5  South Korea  4,250  32,772  7.71  6.10^{−}  6.14^{−} 
6  France  2,803  21,099  7.53  4.04^{−}  3.92^{−} 
7  England  1,963  24,875  12.67  3.24^{++}  3.24^{++} 
8  Taiwan  2,529  19,068  7.54  3.24^{−}  3.30^{−} 
9  Italy  2,021  15,738  7.79  3.00^{−}  2.92^{−} 
10  India  2,215  13,593  6.14  2.67^{−}  2.72^{–} 
11  Spain  1,527  14,692  9.62  2.45^{++}  2.33^{−} 
12  Canada  1,360  13,418  9.87  2.09^{++}  2.04^{++} 
13  Singapore  1,219  13,785  11.31  1.94^{++}  1.91^{++} 
14  Netherlands  872  10,951  12.56  1.53^{++}  1.51^{++} 
15  Australia  919  9,560  10.40  1.51^{++}  1.45^{++} 
16  Switzerland  682  9,565  14.02  1.29^{++}  1.27^{++} 
17  Belgium  668  5,683  8.51  1.05^{++}  1.04^{++} 
18  Sweden  566  6,661  11.77  0.96^{++}  0.96^{++} 
19  Israel  525  5,662  10.78  0.86^{++}  0.78^{++} 
20  Russia  1,023  3,410  3.33  0.81^{− −}  1.11^{−} 
Rankings among the 20 topinstitutes in terms of I3 (expressed as percentages of the sum) compared with number of publications and citations, and % I3 (PR6) for the 58 journals in the ISI Subject Category “Nanoscience & nanotechnology”
Publications  Citations  c/p  % I3  % PR6  

1  Chinese Acad Sci, Beijing, Peoples R China  824  11,258  13.66  1.43^{++}  1.42^{++} 
2  MIT, Cambridge, USA  420  8,372  19.93  0.89^{++}  0.90^{++} 
3  Univ Calif Berkeley, Berkeley, USA  425  8,639  20.33  0.88^{++}  0.92^{++} 
4  Natl Univ Singapore, Singapore, Singapore  470  6,483  13.79  0.83^{++}  0.82^{++} 
5  Northwestern Univ, Evanston, USA  304  7,594  24.98  0.63^{++}  0.68^{++} 
6  Univ Michigan, Ann Arbor, USA  301  5,633  18.71  0.63^{++}  0.63^{++} 
7  Univ Illinois, Urbana, USA  307  6,905  22.49  0.61^{++}  0.62^{++} 
8  Georgia Inst Technol, Atlanta, USA  307  5,658  18.43  0.61^{++}  0.62^{++} 
9  Nanyang Technol Univ, Singapore, Singapore  383  4,110  10.73  0.60^{++}  0.59^{++} 
10  Penn State Univ, University Park, USA  293  4,519  15.42  0.52^{++}  0.53^{++} 
11  Univ Cambridge, Cambridge, UK  276  4,677  16.95  0.52^{++}  0.52^{++} 
12  Seoul Natl Univ, Seoul, South Korea  314  4,135  13.17  0.51^{++}  0.51^{++} 
13  Peking Univ, Beijing, Peoples R China  285  3,698  12.98  0.50^{++}  0.50^{++} 
14  Rice Univ, Houston, USA  239  6,170  25.82  0.49^{++}  0.55^{++} 
15  Purdue Univ, Lafayette, USA  282  3,931  13.94  0.48^{++}  0.48^{++} 
16  Tohoku Univ, Miyagi, Japan  349  2,274  6.52  0.47^{− −}  0.47^{− −} 
17  Univ Washington, Seattle, USA  210  5,126  24.41  0.47^{++}  0.50^{++} 
18  Natl Inst Mat Sci, Ibaraki, Japan  271  2,831  10.45  0.46^{++}  0.44^{++} 
19  Natl Cheng Kung Univ, Tainan, Taiwan  340  2,256  6.64  0.43^{− −}  0.43^{− −} 
20  Stanford Univ, Stanford, USA  200  5,894  29.47  0.42^{++}  0.47^{++} 
More specific delineation of the nanoset
Because of its interdisciplinarity, the WoS Subject Category “Nanoscience & nanotechnology” can be considered as a mixed bag (Leydesdorff and Rafols 2012; Rafols et al. 2010). Factor analysis of the citation matrix of these 58 journals in terms of the cited patterns provided us with a first factor showing robustly the same 15 journals with factor loadings >0.4 using both four or five factors for the extraction. This factor 1 can be considered as the representation of a more homogenous set of journals which publish about nanoscience and nanotechnology from the perspective of condensed matter physics and chemistrybased nanotechnology. Other factors are focused on micro and nanofluidics (factor 2), nanomedicine (factor 3), and nanoelectronics (factor 4; Ismael Rafols, personal communication, 20 March 2011). Factor 1 explains approx. 36 % of the variance in the matrix of aggregated citation relations among the 58 journals under study.
Integrated impact (% I3) of journals and rankings of (15 top) countries, cities, and institutes in the domain of the 15 journals (in column a) more specifically selected as condensedmatter and chemistrybased nanotechnology
Journals (N = 15) (a)  Countries (N = 95) (b)  Cities (N = 1,694) (c)  Institutes (N = 5,726) (d)  

1  J Phys Chem C ^{− −}  USA^{++}  Beijing, Peoples R China^{++}  Chinese Acad Sci, Beijing, Peoples R China^{++} 
2  Nano Lett ^{++}  Peoples R China^{− −}  Singapore, Singapore  Univ Calif Berkeley, Berkeley, USA^{++} 
3  Advan Mater ^{++}  Germany^{++}  Seoul, South Korea^{− −}  MIT, Cambridge, USA^{++} 
4  Nanotechnology ^{− −}  Japan^{− −}  Cambridge, USA^{++}  Northwestern Univ, Evanston, USA^{++} 
5  Adv Funct Mater ^{++}  South Korea  Berkeley, USA^{++}  Natl Univ Singapore, Singapore, Singapore^{++} 
6  Physica E ^{− −}  UK  Shanghai, Peoples R China^{− −}  Univ Michigan, Ann Arbor, USA^{++} 
7  Small ^{++}  France^{− −}  Ibaraki, Japan  Georgia Inst Technol, Atlanta, USA^{++} 
8  ACS Nano ^{++}  Italy^{− −}  Tokyo, Japan^{− −}  Univ Illinois, Urbana, USA^{++} 
9  Nat Nanotechnol ^{++}  Taiwan^{–}  Evanston, USA^{++}  Rice Univ, Houston, USA^{++} 
10  Nano ^{− −}  Spain^{− −}  Atlanta, USA^{++}  Peking Univ, Beijing, Peoples R China^{++} 
11  Nano Res ^{−}  Canada^{− −}  Houston, USA^{++}  Univ Cambridge, Cambridge, UK^{++} 
12  Int J Nanotechnol ^{− −}  Singapore  Changchun, Peoples R China^{++}  Univ Washington, Seattle, USA^{++} 
13  Nano Today ^{++}  India^{− −}  Taipei, Taiwan  Penn State Univ, University, USA^{++} 
14  J Nanomater ^{− −}  Australia^{−}  Hong Kong, Peoples R China  Nanyang Technol Univ, Singapore, Singapore^{−} 
15  Plasmonics ^{− −}  Netherlands^{++}  Ann Arbor, USA^{++}  Univ Calif Los Angeles, Los Angeles, USA^{++} 
The table confirms the impression obtained above: Asian units are more concentrated in large metropoles; at the higher level of aggregation of countries the USA is still dominant; and China performs below expectation because of lower citation rates. Except for the UK (University of Cambridge), European institutes and cities do not appear to play a leading role. Japanese cities and centers tend to score below expectation, but several Chinese centers are leading along with American ones.
One can use Dunn’s test (as above in Fig. 4) for the evaluation, for example, of the extent to which the impactprofiles of the 15 institutions are significantly different and/or homogenous. The I3distributions of ten American universities and the University of Cambridge in the UK are statistically homogenous. Three of these universities (Rice University, Penn State, the University of Cambridge, UK) and the National University of Singapore have stronger roles at the interface with the otherwise differently profiled Chinese universities. In other words, the rankordering among the AmericanBritish universities in this core set is also an effect of size, as it is among the Asian ones. If we use citations instead of I3 values for Dunn’s test, the results are not different except that the University of California in Los Angeles (number 15 in rank) is no longer included among the core set of American/UK universities. Thus, the analysis in terms of I3 refines our ability to compare citation distributions involving multiple comparisons.
Figure 6 shows that many cities other than Cambridge, UK, perform significantly above expectation, in Germany, for example. These, however, are often smaller towns with smaller universities, but at the aggregated level of countries Germany is ranked third and significantly above expectation, whereas the UK is ranked sixth and not different from expectation. The larger map informs us that papers from France and Mediterranean countries on average have less impact in this field than expected.
Specification of expectation
Because I3—and mutatis mutandis PR6—is based on a summation, the values for units of analysis such as countries or journals can be large. For example, the I3value of Nano Letters in the set of 58 journals was 117,450, representing only 8 % of the total I3 of the set, namely 1,469,253. For the expected value of I3, this latter value was normalized using the share of publications in the set (n _{ i }/∑_{ i } n _{ i } = 1,507/31,644). Therefore, in this case: exp(I3) = 1,469,253 * (1,507/31,644) = 69,971. The differences between observed and expected values can thus be large; this so easily generates significance using the ztest that the test perhaps loses its discriminating value. The measure I3 is very sensitive to differences in performance.^{10}
An obvious alternative would be to test the percentage I3 for each unit of analysis against the percentage of publications.^{11} However, I3 is dependent on the N of publications, and percentages (or permillages) may be small in the case of cities or institutes (<0.1 %). Using relative publication rates as expected values, for example, only Nano Letters and Advances in Materials were flagged as impacting significantly above expectation in Table 1 (p ≤ 0.01), but using this alternative option none of the smaller journals remain significantly above or below expectations. In Table 4, all significance indications for countries, cities, and institutes would disappear, and the Google Map accordingly would become uniformative. This alternative is therefore unattractive.
A third option could be to compare the observed impact per paper with the expected one per paper. This specification prevents the accumulation of I3 values and thus differences from expected values. The expected value of I3/paper on the basis of the set could then also be considered as a “world average.” Instead of dividing the observed average by the expected one [as in the case of MOCR/MECR (Budapest), NMCR (Louvain), or the “old Leiden crown indicator”], the expected value could be used as a yardstick for the testing. However, one would expect the results to suffer from the same problems as the previous use of CPPs, namely, that the N of papers is used in the denominator so that larger units of analysis are relatively disadvantaged and smaller units foregrounded.
One can draw the same map as Fig. 6, but using this indicator (not shown here, but available at http://www.leydesdorff.net/nano2011/nano2011b.htm). Indeed, Cambridge UK (which is sized as before; N = 228) is no longer flagged as impacting significantly above expectation. Smaller centers such as Ludwigshaven in Germany (N = 9) and Maastricht in the Netherlands (N = 8) are now flagged as performing significantly above expectation. (A minimum of N = 5 was used for the test for reasons of reliability.)
Nevertheless, this map provides another view to the same data, and I decided to include it in the routine at the Internet. In addition to ztest.txt, a file ri3r.txt is generated by i3cit2.exe (and i3inst2.exe, respectively) which enables the user to draw this map. The zscore is also saved in ucities.dbf with the fieldname “zaverage.”^{12} Unlike previous indicators (derived from RCR = MOCR/MECR; Schubert and Braun 1986), this indicator is not based on a division of means, but tests citations in terms of the percentile scores of the cited publications against a “world average” as the expectation. Let me call this indicator the Relative I3Rate (RI3R) in honour of the RCR which has served as an indicator for 25 years.
In summary, one can distinguish between testing integrated (I3) and average (I3/n) citation impact. Integrated impact is more easily significantly different from expectation than average impact. Furthermore, the latter is also determined by the n in the denominator.
Let me finally note that one could further inform the expectation depending on the research question (Cassidy Sugimoto, personal communication, August 16, 2011). For example, one could assume that the chances of being cited are not equal for established authors and newcomers, and thus weigh publications according to the specification of this expectation. The statistical tests remain the same, but the values of the expectations are then different.
Conclusions and discussion
The integrated impact indicator I3 provides us with a versatile instrument which (i) can be applied to institutional units of analysis and journals (or other document sets); (ii) takes both publication and citation rates into account like the hindex; and (iii) enables us to use nonparametric statistics on skewed distributions as against using averages. The values of I3 in terms of percentiles can (iv) be summed in evaluative schemes such as the six ranks used by the NSF (and otherwise; e.g. quartiles).
The first requirement is a careful decision about the reference set. This set can be based on a sophisticated search string in the Web of Science or on a delineated journal set. One can vary the citation windows and use fractional instead of integer counting. The measure is formalized much more abstractly, namely by reducing any set to one hundred percentiles and thus making unequal distributions comparable without giving up the notion of quality either by abandoning the tales of the distribution (as in the case of the hindex) or by using “total cites” instead of more sophisticated statistics.
Let me emphasize that the new measure does not prevent one from taking an average citation over publication rate as another measure. However, some of the issues in the debate over impact indicators that has raged for the past 2 years can be solved by using nonparametric statistics on which this new measure is based. I would particularly recommend its use combined with fractional citation counting as proposed by Leydesdorff and Opthof (2010) and hitherto applied in a limited number of studies (Leydesdorff and Bornmann 2011a; Leydesdorff and Shin 2011; Prathap 2011; Zhou and Leydesdorff 2011; Rafols et al. in press; cf. Radicchi and Castellano 2012). Fractional counting would correct for disciplinary structures (and biases) in the potentially interdisciplinary reference sets. In the case of nanoscience and nanotechnology, I have shown how one can identify 15 journals (out of 58) that are specific for condensedmatter and chemistrybased nanotechnology.
These analytical advantages can be combined with practicalities such as the straightforward option to show the results of an evaluation at the city or institute level by using Google Maps. As noted, fractional counting (in terms of authors and addresses) can be expected to further refine these maps. The software is available at http://www.leydesdorff.net/software/i3. This software routinely provides the I3 based on 100 percentiles as well as the six percentile classes distinguished above.
Normative implications
The use of citation and publication analysis for evaluative purposes is a normative choice. Many good reasons can be given why one should not rely too much on statistics when taking decisions, particularly when the set is small, as in the case of individual hiring or promotion decisions. Peer review, however, also has constraints (e.g., Bornmann et al. 2010), and in the case of large sets the reading of actual papers may be too timeconsuming. Bibliometric indicators can then serve as proxies provided that error can be specified and the illusion of clarity conveyed by quantification can be avoided.

Citationbased indicators should accommodate various normative schemes such as the six categories of the NSF;

Citationbased indicators should be applicable to different sets of reference such as fields of science, nations, institutions, etc.;

The indicator should allow productivity to be taken into account. One should, for example, be able to compare two papers in the 39th percentile with a single one in the 78th percentile (perhaps after weighing in an evaluation scheme);

The indicator should result in a rather straightforward measure such as a percentage of maximum performance;

Error estimation and statistics should enable the analyst to specify the significance of individual contributions and differences in rankings.
By developing the apparatus of I3 and showing its comparability with the NSF scheme of six ranks, its decomposability in terms of percentages of contributions, and its relative straightforwardness in being based on percentiles in a distribution that allows for the use of nonparametric statistics, Leydesdorff and Bornmann (2011b) have shown how one can meet these criteria. This study has elaborated the technique for a highly policyrelevant field of science, notably, “Nanoscience & nanotechnology,” and integrated I3 with the geographic evaluation of excellence using Google Maps (Bornmann and Leydesdorff 2011). The instrument is ready to be used.
Footnotes
 1.
Scopus of Elsevier provides similar classification schemes.
 2.
 3.
Second in rank in 2009 was the journal Nano Today, with IF = 13.237.
 4.
The aggregation at the city level presumes further processing the address field using i3cit1.exe, available at http://www.leydesdorff.net/software/i3.
 5.
Leydesdorff and Bornmann (2011b) mention this option, but use the standardized residuals which can also be expected to be Chisquare distributed. Unlike the ztest, however, this assumption is not yet mathematically proven in the literature.
 6.
This error probability increases with the number of pairwise comparisons c: c = n * (n − 1)/2. For 50 cases (e.g., journals), the number of pairwise comparisons is 50 * 49/2 = 1,225, and one should therefore test at a significance level of 0.05/1225 = 0.000041 instead of p ≤ 0.05.
 7.
The full title of this journal is: Materials Science and Engineering AStructural Materials Properties Microstructure and Processing.
 8.
Alternatively, “fractional counting” attributes addresses proportionally to the number of addresses/paper.
 9.
The number of addresses is 32,134, and the number of authors: 73,342.
 10.
The ztest is casebased. BonferroniHolm correction (Holm 1979) can then be used for correcting familywise accumulation of the probability of TypeI errors in multiple comparisons among cases by adjusting the αlevel (Rüdiger Mutz, personal communication, August 25, 2011). However, the accumulation in this case is not a consequence of multiple comparisons but of summation of I3values within each case (e.g., city, journal).
 11.
Leydesdorff and Bornmann (2011b) used this expectation, but these authors used the standardized residuals of the Chisquare for the test. The latter test is less conservative and less reliable than the ztest.
 12.
Similarly, the file zperc.txt contains the overlay map when % I3 is tested against % of publications. The score is stored in the field “zperc” within ucities.dbf.
Notes
Acknowledgments
I thank Lutz Bornmann for continuous discussion of these issues. I also wish to thank Cassidy Sugimoto, Ismael Rafols, and an anonymous referee for comments on a previous version of this paper. I am grateful to ThomsonReuters for access to the data.
Open Access
This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
References
 Bensman, S. J. (2007). Garfield and the impact factor. Annual Review of Information Science and Technology, 41(1), 93–155.CrossRefGoogle Scholar
 Bensman, S. J., & Leydesdorff, L. (2009). Definition and identification of journals as bibliographic and subject entities: librarianship vs. ISI Journal Citation Reports (JCR) methods and their effect on citation measures. Journal of the American Society for Information Science and Technology, 60(6), 1097–1117.CrossRefGoogle Scholar
 Bonaccorsi, A., & Vargas, J. (2010). Proliferation dynamics in new sciences. Research Policy, 39(8), 1034–1050.CrossRefGoogle Scholar
 Bornmann, L., & Leydesdorff, L. (2011). Which cities produce excellent papers worldwide more than can be 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). http://arxiv.org/abs/1103.3216.
 Bornmann, L., Leydesdorff, L., & Van den Besselaar, P. (2010). A metaevaluation of scientific research proposals: Different ways of comparing rejected to awarded applications. Journal of Informetrics, 4(3), 211–220.CrossRefGoogle Scholar
 Bornmann, L., & Mutz, R. (2011). Further steps towards an ideal method of measuring citation performance: The avoidance of citation (ratio) averages in fieldnormalization. Journal of Informetrics, 5(1), 228–230.CrossRefGoogle Scholar
 Bornmann, L., Mutz, R., Neuhaus, C., & Daniel, H. D. (2008). Citation counts for research evaluation: standards of good practice for analyzing bibliometric data and presenting and interpreting results. Ethics in Science and Environmental Politics (ESEP), 8(1), 93–102.CrossRefGoogle Scholar
 Bornmann, L., Schier, H., Marx, W., & Daniel, H. D. (2011). Is interactive open access publishing able to identify high impact submissions? A study on the predictive validity of Atmospheric Chemistry and Physics by using percentile rank classes. Journal of the American Society for Information Science and Technology, 52(1), 61–71.CrossRefGoogle Scholar
 Boyack, K. W., & Klavans, R. (2011). Multiple dimensions of journal specificity: Why journals can’t be assigned to disciplines. In E. Noyons, P. Ngulube, & J. Leta (Eds.), The 13th conference of the international society for scientometrics and informetrics (Vol. I, pp. 123–133). Durban, South Africa: ISSI, Leiden University and the University of Zululand.Google Scholar
 Chan, L. M. (1999). A guide to the library of congress classification (5th ed.). Englewood, CO: Libraries Unlimited.Google Scholar
 Egghe, L. (2012). Averages of ratios compared to ratios of averages: Mathematical results. Journal of Informetrics, 6(2), 307–317.CrossRefGoogle Scholar
 Garfield, E. (1972). Citation analysis as a tool in journal evaluation. Science, 178(4060), 471–479.CrossRefGoogle Scholar
 Garfield, E. (1979a). Is citation analysis a legitimate evaluation tool? Scientometrics, 1(4), 359–375.CrossRefGoogle Scholar
 Garfield, E. (1979b). Citation indexing: Its theory and application in science, technology, and humanities. New York: Wiley.Google Scholar
 Gingras, Y., & Larivière, V. (2011). There are neither “king” nor “crown” in scientometrics: Comments on a supposed “alternative” method of normalization. Journal of Informetrics, 5(1), 226–227.CrossRefGoogle Scholar
 Glänzel, W. (2007). Characteristic scores and scales: A bibliometric analysis of subject characteristics based on longterm citation observation. Journal of Informetrics, 1(1), 92–102.CrossRefGoogle Scholar
 Glänzel, W., & Schubert, A. (2003). A new classification scheme of science fields and subfields designed for scientometric evaluation purposes. Scientometrics, 56(3), 357–367.CrossRefGoogle Scholar
 Glänzel, W., Thijs, B., Schubert, A., & Debackere, K. (2009). Subfieldspecific normalized relative indicators and a new generation of relational charts: Methodological foundations illustrated on the assessment of institutional research performance. Scientometrics, 78(1), 165–188.CrossRefGoogle Scholar
 Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the USA, 102(46), 16569–16572.CrossRefGoogle Scholar
 Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70.Google Scholar
 Huang, C., Notten, A., & Rasters, N. (2011). Nanoscience and technology publications and patents: a review of social science studies and search strategies. The Journal of Technology Transfer, 36(2), 145–172.CrossRefGoogle Scholar
 Kamada, T., & Kawai, S. (1989). An algorithm for drawing general undirected graphs. Information Processing Letters, 31(1), 7–15.MathSciNetzbMATHCrossRefGoogle Scholar
 Levine, G. (1991). A guide to SPSS for analysis of variance. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
 Leydesdorff, L. (2006). Can scientific journals be classified in terms of aggregated journaljournal citation relations using the journal citation reports? Journal of the American Society for Information Science and Technology, 57(5), 601–613.CrossRefGoogle Scholar
 Leydesdorff, L. (2008). Caveats for the use of citation indicators in research and journal evaluation. Journal of the American Society for Information Science and Technology, 59(2), 278–287.CrossRefGoogle Scholar
 Leydesdorff, L. (2012). Alternatives to the journal impact factor: I3 and the top10% (or top25%?) of the mosthighly cited papers. Scientometrics. doi: 10.1007/s11192110121066011196.
 Leydesdorff, L., & Bornmann, L. (2011a). How fractional counting affects the impact factor: Normalization in terms of differences in citation potentials among fields of science. Journal of the American Society for Information Science and Technology, 62(2), 217–229.CrossRefGoogle Scholar
 Leydesdorff, L., & Bornmann, L. (2011b). Percentile ranks and the integrated impact indicator (I3). Journal of the American Society for Information Science and Technology, 62(11), 2133–2146.CrossRefGoogle Scholar
 Leydesdorff, L., & Bornmann, L. (in press). Percentile ranks and the integrated impact indicator (I3). Journal of the American Society for Information Science and Technology. http://arxiv.org/abs/1112.6281.
 Leydesdorff, L., Bornmann, L., Mutz, R., & Opthof, T. (2011a). Turning the tables in citation analysis one more time: Principles for comparing sets of documents. Journal of the American Society for Information Science and Technology, 62(7), 1370–1381.CrossRefGoogle Scholar
 Leydesdorff, L., Hammarfelt, B., & Salah, A. A. A. (2011b). The structure of the arts & humanities citation index: A mapping on the basis of aggregated citations among 1,157 journals. Journal of the American Society for Information Science and Technology, 62(12), 2414–2426.Google Scholar
 Leydesdorff, L., & Opthof, T. (2010). Scopus’ source normalized impact per paper (SNIP) versus the journal impact factor based on fractional counting of citations. Journal of the American Society for Information Science and Technology, 61(11), 2365–2396.CrossRefGoogle Scholar
 Leydesdorff, L., & Persson, O. (2010). Mapping the geography of science: Distribution patterns and networks of relations among cities and institutes. Journal of the American Society of Information Science and Technology, 61(8), 1622–1634.Google Scholar
 Leydesdorff, L., & Rafols, I. (2012). Interactive overlays: A new method for generating global journal maps from webofscience data. Journal of Informetrics, 6(3), 318–332.CrossRefGoogle Scholar
 Leydesdorff, L., & Shin, J. C. (2011). How to evaluate universities in terms of their relative citation impacts: Fractional counting of citations and the normalization of differences among disciplines. Journal of the American Society for Information Science and Technology, 62(6), 1146–1155.CrossRefGoogle Scholar
 Leydesdorff, L., Zhou, P., & Bornmann, L. (in preparation). How can impact factors be normalized across fields of science? An assessment of science citation index 2010 in terms of percentile ranks and fractional counts.Google Scholar
 Lundberg, J. (2007). Lifting the crown—citation zscore. Journal of Informetrics, 1(2), 145–154.CrossRefGoogle Scholar
 Moed, H. F. (2010). Measuring contextual citation impact of scientific journals. Journal of Informetrics, 4(3), 265–277.CrossRefGoogle Scholar
 Moed, H. F., De Bruin, R. E., & Van Leeuwen, T. N. (1995). New bibliometric tools for the assessment of national research performance: Database description, overview of indicators and first applications. Scientometrics, 33(3), 381–422.CrossRefGoogle Scholar
 Narin, F. (1976). Evaluative bibliometrics: The use of publication and citation analysis in the evaluation of scientific activity. Washington, DC: National Science Foundation.Google Scholar
 National Science Board. (2010). Science and engineering indicators. Washington DC: National Science Foundation. http://www.nsf.gov/statistics/seind10/.
 Nederhof, A. J. (2006). Bibliometric monitoring of research performance in the social sciences and the humanities: A review. Scientometrics, 66(1), 81–100.MathSciNetCrossRefGoogle Scholar
 Opthof, T., & Leydesdorff, L. (2010). Caveats for the journal and field normalizations in the CWTS (“Leiden”) evaluations of research performance. Journal of Informetrics, 4(3), 423–430.CrossRefGoogle Scholar
 Prathap, G. (2011). Fractionalized exergy for evaluating research performance. Journal of the American Society for Information Science and Technology, 62(11), 2294–2295.Google Scholar
 Pudovkin, A. I., & Garfield, E. (2002). Algorithmic procedure for finding semantically related journals. Journal of the American Society for Information Science and Technology, 53(13), 1113–1119.CrossRefGoogle Scholar
 Pudovkin, A. I., & Garfield, E. (2009). Percentile rank and author superiority indexes for evaluating individual journal articles and the author’s overall citation performance. CollNet Journal of Scientometrics and Information Management, 3(2), 3–10.Google Scholar
 Radicchi, F., & Castellano, C. (2012). Testing the fairness of citation indicators for comparison across scientific domains: the case of fractional citation counts. Journal of Informetrics, 6(1), 121–130.CrossRefGoogle Scholar
 Rafols, I., & Leydesdorff, L. (2009). Contentbased and algorithmic classifications of journals: Perspectives on the dynamics of scientific communication and indexer effects. Journal of the American Society for Information Science and Technology, 60(9), 1823–1835.CrossRefGoogle Scholar
 Rafols, I., Leydesdorff, L., O’Hare, A., Nightingale, P., & Stirling, A. (in press). How journal rankings can suppress interdisciplinary research: A comparison between innovation studies and business & management. Research Policy.Google Scholar
 Rafols, I., Porter, A., & Leydesdorff, L. (2010). Science overlay maps: A new tool for research policy and library management. Journal of the American Society for Information Science and Technology, 61(9), 1871–1887.CrossRefGoogle Scholar
 Rousseau, R. (2012). Basic properties of both percentile rank scores and the I3 indicator. Journal of the American Society for Information Science and Technology, 63(2), 416–420.CrossRefGoogle Scholar
 Schreiber, M. (in press). Inconsistencies of Recently Proposed Citation Impact Indicators and how to Avoid Them. Journal of the American Society for Information Science and Technology. Available at arXiv:1202.3861.Google Scholar
 Schubert, A., & Braun, T. (1986). Relative indicators and relational charts for comparative assessment of publication output and citation impact. Scientometrics, 9(5), 281–291.CrossRefGoogle Scholar
 Seglen, P. O. (1992). The skewness of science. Journal of the American Society for Information Science, 43(9), 628–638.CrossRefGoogle Scholar
 Seglen, P. O. (1997). Why the impact factor of journals should not be used for evaluating research. British Medical Journal, 314, 498–502.CrossRefGoogle Scholar
 Sher, I. H., & Garfield, E. (1965). New tools for improving and evaluating the effectiveness of research. Paper presented at the second conference on research program effectiveness, July 27–29, Washington, DC.Google Scholar
 Sheskin, D. J. (2011). Handbook of parametric and nonparametric statistical procedures (5th ed.). Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
 Small, H., & Garfield, E. (1985). The geography of science: Disciplinary and national mappings. Journal of information science, 11(4), 147–159.CrossRefGoogle Scholar
 Small, H., & Sweeney, E. (1985). Clustering the science citation index using cocitations I. A comparison of methods. Scientometrics, 7, 391–409.CrossRefGoogle Scholar
 Vinkler, P. (1986). Evaluation of some methods for the relative assessment of scientific publications. Scientometrics, 10(3), 157–177.CrossRefGoogle Scholar
 Vinkler, P. (2010). The π_{v}index: A new indicator to characterize the impact of journals. Scientometrics, 82(3), 461–475.CrossRefGoogle Scholar
 Vinkler, P. (2011). Application of the distribution of citations among publications in scientometric evaluations. Journal of the American Society for Information Science and Technology, 62(10), 1963–1978.CrossRefGoogle Scholar
 Vinkler, P. (2012). The case of scientometricians with the “absolute relative” impact indicator. Journal of Informetrics, 6(2), 254–264.CrossRefGoogle Scholar
 Waltman, L., van Eck, N. J., van Leeuwen, T. N., Visser, M. S., & van Raan, A. F. J. (2011a). Towards a new crown indicator: An empirical analysis. Scientometrics, 87, 467–481.CrossRefGoogle Scholar
 Waltman, L., Van Eck, N. J., Van Leeuwen, T. N., Visser, M. S., & Van Raan, A. F. J. (2011b). Towards a new crown indicator: Some theoretical considerations. Journal of Informetrics, 5(1), 37–47.CrossRefGoogle Scholar
 Zhou, P., & Leydesdorff, L. (2011). Fractional counting of citations in research evaluation: A cross and interdisciplinary assessment of the Tsinghua University in Beijing. Journal of Informetrics, 5(3), 360–368.CrossRefGoogle Scholar
 Zitt, M., & Small, H. (2008). Modifying the journal impact factor by fractional citation weighting: The audience factor. Journal of the American Society for Information Science and Technology, 59(11), 1856–1860.CrossRefGoogle Scholar