JDIS

Journal of Data and Information Science >

2018 , Vol. 3 >Issue 3: 43 - 80

DOI: https://doi.org/10.2478/jdis-2018-0014

Research Paper

Factors Influencing Cities’ Publishing Efficiency

  • Csomós György †
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  • Department of Civil Engineering, University of Debrecen, Hungary
† Corresponding author: Csomós György (E-mail: ).

Online published: 2018-12-06

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Open Access
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Abstract

Purpose: Recently, a vast number of scientific publications have been produced in cities in emerging countries. It has long been observed that the publication output of Beijing has exceeded that of any other city in the world, including such leading centres of science as Boston, New York, London, Paris, and Tokyo. Researchers have suggested that, instead of focusing on cities’ total publication output, the quality of the output in terms of the number of highly cited papers should be examined. However, in the period from 2014 to 2016, Beijing produced as many highly cited papers as Boston, London, or New York. In this paper, another method is proposed to measure cities’ publishing performance by focusing on cities’ publishing efficiency (i.e., the ratio of highly cited articles to all articles produced in that city).

Design/methodology/approach: First, 554 cities are ranked based on their publishing efficiency, then some general factors influencing cities’ publishing efficiency are revealed. The general factors examined in this paper are as follows: the linguistic environment of cities, cities’ economic development level, the location of excellent organisations, cities’ international collaboration patterns, and their scientific field profile. Furthermore, the paper examines the fundamental differences between the general factors influencing the publishing efficiency of the top 100 most efficient cities and the bottom 100 least efficient cities.

Findings: Based on the research results, the conclusion can be drawn that a city’s publishing efficiency will be high if meets the following general conditions: it is in a country in the Anglosphere-Core; it is in a high-income country; it is home to top-ranked universities and/or world-renowned research institutions; researchers affiliated with that city most intensely collaborate with researchers affiliated with cities in the United States, Germany, England, France, Canada, Australia, and Italy; and the most productive scientific disciplines of highly cited articles are published in high-impact multidisciplinary journals, disciplines in health sciences (especially general internal medicine and oncology), and disciplines in natural sciences (especially physics, astronomy, and astrophysics).

Research limitations: It is always problematic to demarcate the boundaries of cities (e.g., New York City vs. Greater New York), and regarding this issue there is no consensus among researchers. The Web of Science presents the name of cities in the addresses reported by the authors of publications. In this paper cities correspond to the spatial units between the country/state level and the institution level as indicated in the Web of Science. Furthermore, it is necessary to highlight that the Web of Science is biased towards English-language journals and journals published in the field of biomedicine. These facts may influence the outcome of the research.

Practical implications: Publishing efficiency, as an indicator, shows how successful a city is at the production of science. Naturally, cities have limited opportunities to compete for components of the science establishment (e.g., universities, hospitals). However, cities can compete to attract innovation-oriented companies, high tech firms, and R&D facilities of multinational companies by for example establishing science parks. The positive effect of this process on the city’s performance in science can be observed in the example of Beijing, which publishing efficiency has been increased rapidly.

Originality/value: Previous scientometric studies have examined cities’ publication output in terms of the number of papers, or the number of highly cited papers, which are largely size dependent indicators; however this paper attempts to present a more quality-based approach.

Key words： Cities; Publishing efficiency; Spatial scientometrics; Highly cited articles; Web of Science

Cite this article

Csomós György † . Factors Influencing Cities’ Publishing Efficiency[J]. Journal of Data and Information Science, 2018 , 3(3) : 43 -80 . DOI: 10.2478/jdis-2018-0014

1 Introduction

Both the total publication output of China (Andersson et al., 2014; Grossetti et al., 2014; Morrison, 2014; Zhou et al., 2009a) and its publication output in specific research areas (Kumar & Garg, 2005; Lu & Wolfram, 2010; Zou & Laubichler, 2017; Zhou et al., 2009b) have significantly increased in the past decades. The growth rate of China’s publication output is quite extreme; however, India (Gupta et al., 2011), Iran (Moin et al., 2005), Brazil (de Almeida & Guimarães, 2013; Leta et al., 2006), South Korea (Kim et al., 2012), and Taiwan (Miyairi & Chang, 2012) have also recently witnessed significant growth in their total publication output. At the same time, the global share of the publication output of the most developed countries (e.g., the United States, Canada, the Western European countries, Japan, and Australia) has been slowly decreasing. Naturally, the United States still has the highest publication output in the world (Leydesdorff & Wagner 2009; Nature Index, 2016), but it can easily be predicted that, due to China’s robust growth in the production of science, the global hegemony of the United States will soon cease.
Some cities in the world have long been considered as an outstanding locus of the production of science (Matthiessen & Schwarz, 1999; Van Noorden, 2010), and for some decades, an increasing number of cities have been involved in that process (Grossetti et al., 2014; Maisonobe et al., 2017). However, cities’ contribution to the global publication output has been changing over time. Before the rise of Chinese cities, most global output was primarily produced by Northern American cities (e.g., New York, Boston, and Los Angeles), Western European cities (e.g., London, Paris, and Rome), and Japanese cities (e.g., Tokyo, Kyoto, and Osaka). Currently, Beijing is producing the highest publication output in the world (Csomós, 2018; Van Noorden, 2010). Furthermore, some cities in emerging countries have been positioning themselves as major actors in the production of science. For example, the publication output of Seoul (South Korea), Tehran (Iran), and São Paulo (Brazil) has also increased significantly.
The question is whether the total publication output clearly represents the scientific performance of a city. Can we find another method to measure the scientific performance of a city, a method that is not based on total (or any kind of) output? Does the geographical pattern of the global production of science change if we focus on quality rather than quantity regarding cities’ publication output?
According to Van Noorden (2010), there are some alternatives to express the quality of a city’s scientific performance, for example, measuring the ‘average number of citations that a research paper from a city attracts’ or measuring the total number of Nature and Science articles published by researchers affiliated with that city. Recent studies recommend that, to measure the quality of cities’ publication output, the focus should be on the citation impact of the articles published in those cities. According to Bornmann and Leydesdorff (2011), Bornmann and Waltman (2011), Bornmann et al. (2011), Bornmann and Leydesdorff (2012), and Leydesdorff et al. (2014) as centres of excellence, cities can be assessed by counting the number of excellent papers (i.e., the top 1% most highly cited papers) produced in a city. These studies suggest that, based on the quality of the publication output, cities located in the most developed countries (i.e., the United States, Canada, the Western European countries, Japan, and Australia) are still in top positions.
It is, however, assumed that the higher a city’s total publication output is, the more likely it is that the output of highly cited papers will also be high (e.g., currently Beijing produces the greatest number of highly cited papers in the world). This context suggests that, instead of focusing on cities’ total publication output or the output of highly cited papers, we should focus on cities’ publishing efficiency (i.e., the ratio of highly cited papers to all papers).
Why is it important to measure a city’s publishing efficiency? It can be assumed that the higher the ratio of the number of highly cited papers to all articles produced in a city is, the more likely it is that researchers affiliated with that city conduct research resulting in new scientific breakthroughs (Van Noorden’s study also suggests this nexus). Thus, publishing efficiency shows how successful a city is at the production of science. In 2015, 2.28 percent of the world’s GDP was spent on research and development (R&D) but of course this value varied country to country. In some countries, a higher proportion of the GDP was spent on R&D (e.g., Israel, Japan, and Sweden spent more than three percent of their GDP on R&D), while most countries’ R&D expenditures remain under the world average (e.g., the United Kingdom spent less than two percent of their GDP on R&D). Publishing efficiency is a measure that informs governments on how effectively the R&D expenditures have been used (for example, the mean publishing efficiency of UK cities is almost twice as much as that of Japanese cities, while Japan has a much higher R&D expenditure). Furthermore, because publishing efficiency is measured on the city level, it allows governments to introduce more effective regional development policies.
There are many factors influencing cities’ publishing efficiency, some of which are city specific and some of which are general. Most of the city-specific factors are related to human factors (for example, how prolific a researcher or a team of researchers is), which, due to their nature, vary city to city. However, based on the general factors, typical geographical patterns can be revealed. In this paper, I aim to measure cities’ publishing efficiency worldwide and present the most significant general factors that might influence their publishing efficiency.
The structure of the paper is as follows. In Section 2, I present the data collection process and the methodology. Section 3 is divided into two subsections. In the first subsection, I rank cities based on their publishing efficiency, and in the second subsection, the most significant general factors are presented. Finally, in Section 4, I discuss the results and draw the conclusions.

2 Data and methodology

In the analysis, only cities that had at least 3,000 journal articles published in the period from 2014 to 2016 (i.e., at least 1,000 articles per year) are included. This criterion was met by 554 cities. Data of scientific publications were provided by the Clarivate Analytics’ Web of Science database. Two constraints were implemented to improve the objectivity of the study: 1) Only journal articles were selected for the analysis, and 2) journals should be included in the Science Citation Index Expanded (SCI-EXPANDED), the Social Sciences Citation Index (SSCI), and the Arts & Humanities Citation Index (A&HCI) databases.
The reason for the first constraint is that journal articles are generally considered the most prestigious of scientific publications since they are ‘the basic means of communicating new scientific knowledge’ (Braun et al., 1989). Therefore, I excluded all other types of publications indicated by the Web of Science (e.g., meeting abstracts, book reviews, editorial materials, reviews, proceedings paper, etc.). It should be noted, however, that certain scientific fields conference proceedings (e.g., in computer sciences) and books (e.g., in social sciences and humanities) are also important publishing channels for researchers. However, two-thirds of the documents indexed in the Web of Science are journal articles, therefore the results are only slightly biased towards health sciences and natural sciences.
The reason for the second constraint is that, in 2015, Clarivate Analytics launched a new database in the Web of Science, the Emerging Sources Citation Index (ESCI), which includes journals of regional importance from emerging scientific fields but that are not yet listed in the Journal Citation Report (i.e., they do not have an impact factor).
The publishing efficiency of a given city (x) in the period from 2014 to 2016 (y) is obtained by dividing the number of the highly cited articles1（1The definition of highly cited papers in the Web of Science is as follows: highly cited papers received enough citations as of July/August 2017 to place them in the top 1% of their academic fields based on a highly cited threshold for the field and publication year.）by the number of all articles produced by authors affiliated with that city (the value is multiplied by 100 to show a percentage). The formula is as follows:
where HCA is the number of highly cited articles2（2 In the analysis, I focused on the document type ‘articles’ only; therefore, I narrowed the category of highly cited ‘papers’ to the category of highly cited ‘articles’.） indicated by the Web of Science and A denotes all articles indexed by the Web of Science.

3 Results

3.1 Relationship between cities’ publication output and publishing efficiency

In the past decades, the publication output of China has radically increased, and the growth rate has exceeded that of any other countries in the world. Furthermore, the publication outputs of some emerging countries, such as South Korea, Taiwan, India, and Iran, have also significantly increased (Csomós, 2018; Grossetti et al., 2014; Maisonobe et al., 2017). Naturally, the annual outputs of the United States, Canada, the Western European countries, and Japan have also increased, but they have witnessed a much smaller growth rate than the emerging countries. Therefore, the share of the most developed countries in the production of science has been decreasing for decades (Leydesdorff & Wagner, 2009).
Some cities in the world have long been considered as an outstanding locus of the production of science (Matthiessen & Schwarz, 1999; Van Noorden, 2010), and the growth rate of these cities’ publication output is much higher than that of the countries in which they are located. By the beginning of the 2010s, the annual publication output of Beijing surpassed that of any other city in the world (Table 1). In the period from 2014 to 2016, it produced a greater number of scientific publications than Japan. Furthermore, Seoul, Tehran, and São Paulo have also experienced a significant increase in their publication output, and due to this development, their position in the ranking has approached that of Tokyo, Paris, New York, and Boston.
Table 1 Top 50 cities producing the greatest number of articles between 2014 and 2016.
Rank Country City Total number of articles (2014-2016)
1 China Beijing 201260
2 China Shanghai 98227
3 England London 92453
4 South Korea Seoul 86447
5 Japan Tokyo 77440
6 France Paris 75033
7 China Nanjing 70320
8 USA New York, NY 68577
9 USA Boston, MA 63789
10 China Guangzhou 51922
11 China Wuhan 50343
12 Russia Moscow 47871
13 Spain Madrid 47061
14 Iran Tehran 46173
15 China Xi’an 44052
16 Spain Barcelona 40393
17 Brazil São Paulo 39916
18 USA Cambridge, MA 39121
19 China Hong Kong 39032
20 China Hangzhou 39029
21 USA Los Angeles, CA 38740
22 Canada Toronto, ON 38497
23 Australia Sydney, NSW 37676
24 USA Chicago, IL 37560
25 Singapore Singapore 37523
26 USA Baltimore, MD 36528
27 Germany Berlin 36509
28 USA Philadelphia, PA 36117
29 China Chengdu 36032
30 USA Houston, TX 33869
31 USA Atlanta, GA 32564
32 Canada Montreal, PQ 31820
33 China Tianjin 31764
34 England Oxford 31605
35 Germany Munich 30886
36 USA Seattle, WA 30779
37 Netherlands Amsterdam 30498
38 USA Washington, DC 29986
39 Switzerland Zürich 29242
40 Australia Melbourne, VIC 29198
41 Sweden Stockholm 28599
42 England Cambridge 27907
43 China Changsha 27442
44 USA Ann Arbor, MI 27322
45 Japan Osaka 26594
46 China Jinan 26557
47 China Harbin 26386
48 Denmark Copenhagen 25538
49 Italy Rome 25378
50 China Hefei 24911
However, many researchers wonder about the quality of publications produced in Brazilian, Chinese, Indian, Iranian, and even South Korean cities, which is also reflected in their low citation impact (Andersson et al., 2014; Maisonobe et al., 2017; Xie et al., 2014; Van Noorden, 2010; Zhou et al., 2009a). In the period from 2014 to 2016, the greatest number of highly cited articles were produced in Beijing (see Table 2), which is not surprising, if we consider the extremely high total publication output of Beijing in terms of the number of articles. Table 2 shows that the difference between the output of Beijing and Boston in terms of the number of highly cited articles is very small, while the total output of Beijing is three times greater than that of Boston (see Table 1). Comparing the rankings in Tables 1 and 2, the positions of some top-ranked cities in terms of total output (e.g., Tokyo and Seoul) have dropped in the ranking of cities producing the greatest number of highly cited articles. In addition, such emerging cities, such as Tehran and São Paulo, which both produced a substantial number of articles between 2014 and 2016, have disappeared from the ranking of the top 50 cities with the greatest number of highly cited articles.
Table 2 Top 50 cities producing the greatest number of highly cities articles between 2014 and 2016.
Rank Country City Number of highly cited articles (2014-2016)
1 China Beijing 2650
2 USA Boston, MA 2387
3 England London 2337
4 USA New York, NY 2237
5 USA Cambridge, MA 1827
6 France Paris 1601
7 China Shanghai 1208
8 USA Seattle, WA 1191
9 USA Los Angeles, CA 1142
10 England Oxford 1083
11 USA Stanford, CA 1058
12 USA Philadelphia, PA 1050
13 USA Baltimore, MD 1047
14 Canada Toronto, ON 1024
15 USA Chicago, IL 991
16 USA Atlanta, GA 971
17 USA Houston, TX 964
18 Spain Barcelona 920
19 USA Berkeley, CA 911
20 USA San Francisco, CA 910
21 England Cambridge 885
22 Singapore Singapore 871
23 China Nanjing 866
24 USA Bethesda, MD 825
25 Netherlands Amsterdam 806
26 Spain Madrid 772
27 USA Ann Arbor, MI 765
28 Germany Munich 764
29 Japan Tokyo 734
30 Switzerland Zürich 730
31 Denmark Copenhagen 729
32 Australia Sydney, NSW 723
33 China Hong Kong 720
34 Sweden Stockholm 708
35 South Korea Seoul 698
36 Germany Berlin 678
37 USA Washington, DC 674
38 USA Durham, NC 660
39 USA New Haven, CT 659
40 Canada Montreal, PQ 656
41 Australia Melbourne, VIC 643
42 China Wuhan 637
43 Germany Heidelberg 627
44 Canada Vancouver, BC 615
45 USA Pittsburgh, PA 588
46 USA Princeton, NJ 587
47 China Guangzhou 552
48 Switzerland Geneva 534
49 Saudi Arabia Jeddah 532
50 USA St. Louis, MO 511
A different geographical pattern will emerge if we focus on measuring cities’ publishing efficiency (Figure 1). The mean publishing efficiency of the 554 cities included in the analysis is 1.818, which means that an average of 1.818% of all articles published in these cities in the period from 2014 to 2016 received enough citations to belong to the top 1% of highly cited articles. However, there are significant geographic differences behind the mean value. Figure 1 shows that the publishing efficiency of most Chinese, Japanese, and South Korean cities (many of which have high publication output in terms of the number of articles) is quite low, while the publishing efficiency of most Northern American and Western European cities is considerably higher. This information is not novel since, directly or indirectly, it has also been described by Van Noorden (2010), Bornmann and Waltman (2011), and Leydesdorff et al. (2014).
However, a more fundamental question is whether there are factors influencing cities’ publishing efficiency. Are there any general factors producing high publishing efficiency? Can we find general factors characterising cities having low publishing efficiency? Why is the publishing efficiency of Villejuif (France), Menlo Park, California (United States), or Jeddah (Saudi Arabia) high, and what are the reasons behind the low publishing efficiency of Tehran (Iran), Shenyang (China), and Niigata (Japan)? To answer these questions, we should explore and compare the general factors characterising the most efficient and least efficient cities.
The mean publishing efficiency of the top 100 most efficient cities is 3.179, while that of the bottom 100 least efficient cities is 0.621. In the top 100 cities, in the period from 2014 to 2016, a mean of 13,830 articles per city was produced, of which a mean of 444.71 articles per city received enough citations to belong to the top 1% of highly cited articles. In the same period, in the bottom 100 cities, a mean of 8,885 articles per city was produced, of which only a mean of 60.44 articles per city received enough citations to belong to the top 1% of highly cited articles. That is, the total output in terms of the number of articles of the top 100 most efficient cities is only 1.5 times greater than that of the bottom 100 least efficient cities. In contrast to the results above, there is a difference of more than 7.4 times between the number of highly cited articles produced in the top 100 cities and those produced in the bottom 100 cities. When exploring the general factors influencing cities’ publishing efficiency, I will focus on presenting the differences between the top 100 most efficient cities and the bottom 100 least efficient cities.
Figure 1. Geographic visualisation of cities’ publishing efficiency.
The general factors examined in this paper are as follows: the linguistic environment of cities, cities’ economic development level (both derived from country-level data), the location of excellent organisations, cities’ international collaboration patterns, and their scientific field profile. The full list of the top 100 most efficient cities is available in Appendix 1, and the list of the bottom 100 least efficient cities is in Appendix 2.
Table 1 1. Appendix 1. The top 100 most efficient cities.
Rank Country City Efficiency
1 France Villejuif 6.174
2 USA Menlo Park, CA 5.676
3 USA Princeton, NJ 4.978
4 USA Cambridge, MA 4.670
5 USA Stanford, CA 4.658
6 Saudi Arabia Jeddah 4.541
7 USA Santa Cruz, CA 4.430
8 USA Pasadena, CA 4.400
9 USA San Francisco, CA 3.993
10 USA Berkeley, CA 3.932
11 USA Upton, NY 3.920
12 USA Bethesda, MD 3.912
13 USA Seattle, WA 3.870
14 USA Rochester, MN 3.830
15 USA Santa Barbara, CA 3.778
16 USA Boston, MA 3.742
17 USA Greenbelt, MD 3.679
18 USA Rockville, MD 3.667
19 USA Richland, WA 3.618
20 Switzerland Geneva 3.566
21 USA New Haven, CT 3.565
22 UK Oxford 3.427
23 USA Durham, NC 3.400
24 USA Evanston, IL 3.388
25 UK Didcot 3.366
26 USA Boulder, CO 3.288
27 USA Dallas, TX 3.272
28 USA New York, NY 3.262
29 Italy Perugia 3.219
30 USA Riverside, CA 3.201
31 Germany Heidelberg 3.177
32 UK Cambridge 3.171
33 UK Brighton 3.162
34 USA Nashville, TN 3.122
35 France Créteil 3.111
36 Israel Rehovot 3.096
37 USA Portland, OR 3.091
38 USA Palo Alto, CA 3.080
39 Switzerland Basel 3.050
40 Italy Trieste 3.036
41 USA St. Louis, MO 3.029
42 Netherlands Rotterdam 3.028
43 Canada Vancouver, BC 3.024
44 UK Norwich 3.006
45 USA Aurora, CO 3.004
46 USA Atlanta, GA 2.982
47 UK Lancaster 2.976
48 Netherlands Nijmegen 2.963
49 USA San Antonio, TX 2.961
50 France Gif-sur-Yvette 2.955
51 USA Los Angeles, CA 2.948
52 USA Chapel Hill, NC 2.937
53 Canada Victoria, BC 2.924
54 UK Dundee 2.916
55 USA Philadelphia, PA 2.907
56 UK Leicester 2.906
57 UK Edinburgh 2.898
58 USA Research Triangle Park, NC 2.897
59 South Africa Cape Town 2.896
60 Netherlands Wageningen 2.886
61 Germany Garching bei München 2.877
62 USA Baltimore, MD 2.866
63 Switzerland Lausanne 2.866
64 Denmark Copenhagen 2.855
65 USA Rochester, NY 2.852
66 USA Houston, TX 2.846
67 Estonia Tartu 2.842
68 USA Providence, RI 2.840
69 USA Denver, CO 2.837
70 USA Birmingham, AL 2.826
71 South Africa Durban 2.818
72 France Clermont-Ferrand 2.802
73 USA Ann Arbor, MI 2.800
74 Italy Ferrara 2.797
75 USA Cleveland, OH 2.788
76 Canada Hamilton, ON 2.768
77 UK Southampton 2.758
78 UK Cardiff 2.738
79 UK Exeter 2.738
80 USA San Diego, CA 2.734
81 USA Hanover, NH 2.715
82 Germany Mainz 2.714
83 USA Gaithersburg, MD 2.691
84 USA Worcester, MA 2.687
85 Switzerland Villigen 2.686
86 UK Birmingham 2.685
87 Denmark Lyngby 2.684
88 Germany Bonn 2.678
89 Canada Toronto, ON 2.660
90 UK Newcastle 2.658
91 Switzerland Bern 2.657
92 USA Amherst, MA 2.652
93 USA Eugene, OR 2.650
94 Netherlands Amsterdam 2.643
95 USA Chicago, IL 2.638
96 Germany Essen 2.627
97 Belgium Brussels 2.614
98 Italy Pavia 2.611
99 USA Winston-Salem, NC 2.594
100 USA Tallahassee, FL 2.591
Table 2 2. Appendix 2. The bottom 100 least efficient cities.
Rank Country City Efficiency
1 Tunisia Sfax 0.132
2 Russia Yekaterinburg 0.161
3 South Korea Cheonan 0.260
4 Iran Shiraz 0.268
5 Romania Iași 0.273
6 India Kharagpur 0.283
7 China Mianyang 0.325
8 Poland Lublin 0.333
9 Brazil São Carlos 0.333
10 China Wenzhou 0.348
11 India Varanasi 0.399
12 China Shijiazhuang 0.416
13 South Korea Cheongju 0.424
14 Japan Gifu 0.436
15 Iran Tabriz 0.444
16 Chile Concepción 0.452
17 Brazil Curitiba 0.454
18 Japan Kumamoto 0.456
19 Malaysia Serdang 0.462
20 Tunisia Tunis 0.484
21 Egypt Giza 0.487
22 China Nantong 0.494
23 Israel Beer-Sheva 0.501
24 Japan Ibaraki 0.503
25 India Kanpur 0.513
26 China Baoding 0.516
27 Turkey Konya 0.535
28 South Korea Busan 0.537
29 Iran Tehran 0.550
30 China Shenyang 0.551
31 Egypt Alexandria 0.552
32 Japan Niigata 0.556
33 France Villeneuve-d’Ascq 0.562
34 Spain Alicante 0.563
35 South Korea Gwangju 0.563
36 South Korea Jeonju 0.566
37 Brazil Fortaleza 0.567
38 Poland Poznań 0.568
39 Brazil Viçosa 0.576
40 Turkey Izmir 0.587
41 India Lucknow 0.587
42 Portugal Aveiro 0.587
43 China Zhengzhou 0.588
44 China Guilin 0.594
45 China Yantai 0.595
46 South Korea Daejeon 0.596
47 Brazil Belo Horizonte 0.601
48 India Kolkata 0.606
49 China Ürümqi 0.613
50 India Chennai 0.617
51 Japan Shizuoka 0.626
52 China Nanning 0.632
53 India Hyderabad 0.632
54 Japan Saitama 0.634
55 Japan Kawasaki 0.649
56 Brazil Recife 0.654
57 Italy Messina 0.661
58 Egypt Cairo 0.670
59 Turkey Istanbul 0.673
60 China Changzhou 0.682
61 South Korea Yongin 0.682
62 China Kunming 0.689
63 Pakistan Lahore 0.690
64 Japan Sapporo 0.695
65 Argentina Córdoba 0.720
66 Japan Kanazawa 0.732
67 Poland Gdańsk 0.736
68 Poland Wrocław 0.736
69 China Qingdao 0.737
70 Ukraine Kiev 0.749
71 China Jinan 0.749
72 China Xinxiang 0.754
73 India New Delhi 0.755
74 Poland Łódź 0.756
75 China Ningbo 0.758
76 India Bangalore 0.783
77 South Korea Jinju 0.783
78 Turkey Ankara 0.791
79 Japan Chiba 0.796
80 Japan Sagamihara 0.798
81 South Africa Pretoria 0.801
82 Russia Novosibirsk 0.804
83 South Korea Goyang 0.804
84 South Korea Daegu 0.806
85 South Korea Seoul 0.807
86 China Nanchang 0.809
87 China Taiyuan 0.810
88 China Guiyang 0.813
89 India Roorkee 0.829
90 Russia Moscow 0.836
91 China Wuxi 0.840
92 Brazil Porto Alegre 0.844
93 Brazil Florianópolis 0.855
94 Russia Saint Petersburg 0.856
95 India Mumbai 0.865
96 Japan Sendai 0.865
97 UK Loughborough 0.868
98 China Xuzhou 0.869
99 Brazil Campinas 0.884
100 Poland Kraków 0.891

3.2 Exploring factors influencing cities’ publishing efficiency

Before exploring and evaluating the general factors influencing cities’ publishing efficiency, it is necessary to present the geographical location of cities included in the analysis. The geographical location of a given city does not directly influence its publishing efficiency but allows us to draw indirect conclusions.
Most cities producing high publication output in terms of the number of articles (i.e., at least 3,000 articles in the period from 2014 to 2016) are in three geographical regions in the world: Europe, Asia, and Northern America (Table 3). The aggregate proportion of cities from other regions i.e., Africa, Latin America, and Australia/New Zealand) does not reach 9%. Not just the output but also the mean publishing efficiency of cities differs from each other depending on where they are located. Northern American cities produce the highest publishing efficiency, which is almost one-third greater than that of the European cities ranked second. However, if we divide Europe, the most complex region (there are 29 European countries in the analysis), into sub-regions, we obtain a more realistic picture. The mean publishing efficiencies of the Northern European 3（3Northern Europe includes the countries of the United Kingdom as defined by the United Nations Statistics Division in its geoscheme.）and the Western European cities are much higher than that of the Southern European and Eastern European cities, and while the publishing efficiencies of the former groups approach the efficiencies of the Northern American cities, those of the Eastern European cities are rather close to the efficiencies of the Latin American cities. In Asia, significant differences emerge as well. The mean publishing efficiencies of cities in Southern Asia and Eastern Asia are under the mean efficiencies of Western Asian cities. Furthermore, cities located in the former two Asian sub-regions produce the lowest mean publishing efficiencies in the world.
Table 3 Number of cities and their mean publishing efficiencies by region and sub-region*.
Regions/Sub-regions Number of cities Percentage
in the dataset		 Cities’ mean publishing efficiency
Africa 11 1.99 1.306
Asia 131 23.65 1.009
Eastern Asia 88 15.88 0.950
Southern Asia 24 4.33 0.876
Western Asia 14 2.53 1.521
Europe 230 41.52 1.948
Eastern Europe 25 4.51 0.989
Northern Europe 60 10.83 2.260
Southern Europe 54 9.75 1.673
Western Europe 91 16.43 2.168
Latin America 21 3.79 0.952
Northern America 145 26.17 2.497
Canada 18 3.25 1.970
USA 127 22.92 2.572
Australia/New Zealand 16 2.89 1.918
World 554 1.818

*Regions and sub-regions are defined by the United Nations Statistics Division in its geoscheme.

Figure 2 shows the geographical location of the top 100 most efficient cities. Most of the top 100 cities are in two major regions: Northern America (primarily in the United States) and Europe (primarily in Northern Europe and Western Europe). In this group, only three cities are outside the above regions: two of them are in Southern Africa (more precisely in South Africa), and two of them can be found in Western Asia (Saudi Arabia and Israel).
Figure 2. Geographical location of the top 100 most efficient cities.
The bottom 100 least efficient cities are primarily in three major regions in the world: Asia (primarily in Eastern Asia and Southern Asia), Europe (primarily in Eastern Europe), and Latin America. There is no Northern American city among the least efficient cities, and only two cities from Northern Europe and Western Europe belong to this group. Compared to the number of cities from other regions in the world, the number of African cities (6 out of 100 cities) is insignificant in this group; however, 55% of all African cities in the dataset of 554 cities belong to the bottom 100 least efficient cities.
The geographical location of the top 100 most efficient cities and the bottom 100 least efficient cities is indicative information but allows us to deduce some of the general factors influencing cities’ publishing efficiency. One of the most crucial factors is related to linguistic features, more precisely to the dominance of the English language.
3.2.1 The linguistic environment of cities as a factor influencing cities’ publishing efficiency
It is a generally accepted fact that English has acquired an almost exclusive status as the international language of scientific communication (i.e., the neutral “lingua franca”), leaving little space for other languages in science (Björkman, 2011; López-Navarro et al., 2015; Tardy, 2004; van Weijen, 2012). Although the most important indexing and abstracting databases (i.e., the Web of Science and Scopus) have been including an increasing number of non-English language journals, English language journals are still significantly overrepresented (Li et al., 2014; Mongeon & Paul-Hus, 2016). According to Paasi (2005), ‘Anglo-American journals dominate the publishing space in science’, and the international journal publication space is ‘particularly limited to the English-speaking countries’. Furthermore, as Braun and Dióspatonyi (2005), Braun et al. (2007), and Leydesdorff and Wagner (2009) asserted in terms of gatekeepers like editors-in-chief and editorial board member positions, the dominance of the United States is still unchallenged. Considering the above facts, the English language is assumed to be one of the main factors that influence cities’ publishing efficiency.
In this paper, I classified cities according to the Anglosphere system introduced by Bennet (2007). In this system, the United States, the United Kingdom, Canada, Australia, New Zealand, and Ireland belong to the Anglosphere-Core. Countries in the Anglosphere-Middle sphere (e.g., Nigeria and South Africa) have several official languages, including English (which is the principal language of administration and commerce), but ‘where the primary connections to the outside world are in English’. The Anglosphere-Outer sphere consists of English-using states of other civilisations, including India, Pakistan, Bangladesh, the Arab states formerly under British control (primarily in the Middle East), and the Islamic former colonies of Britain (e.g., Malaysia and African states).
A total of 230 cities (out of 554 cities included in the analysis) are in countries in the Anglosphere, from which 195 cities are in countries in the Anglosphere-Core. Countries outside the Anglosphere are home to 324 cities. The mean publishing efficiency of cities in countries in the Anglosphere is 2.271, while that of the rest of the cities is 1.497. That is, the mean publishing efficiency of cities in countries in the Anglosphere is greater than that of the rest of the cities by 50%. If we focus on the mean publishing efficiency of cities located in countries in the Anglosphere-Core, it increases to 2.439.
As for the top 100 most efficient cities, 73% of them are in countries in the Anglosphere, and 70% of them can be found in countries in the Anglosphere-Core. The mean publishing efficiency of cities belonging to the latter group is 3.235. In contrast, 85% of the bottom 100 least efficient cities are in countries outside the Anglosphere, and 99% of them are in countries outside the Anglosphere-Core. Loughborough (England), having a publishing efficiency of 0.868, is the only city in the group of the bottom 100 cities that can be found in the Anglosphere-Core.
In conclusion, the publishing efficiency of cities located in countries in the Anglosphere (especially in the Anglosphere-Core) is much higher than that of any other cities located in countries outside the Anglosphere. That is, English is not only the international language of scientific communication but also the most fundamental factor influencing cities’ publishing efficiency.
Figure 3. Geographical location of the bottom 100 least efficient cities.
3.2.2 Economic development level of cities as a factor influencing publishing efficiency
Some researchers have observed linear or exponential correlation between scientometric indicators (e.g., the number of publications) and economic development indicators (e.g., GDP per capita or income per capita) (de Solla Price, 1978; Kealey, 1996; King, 2004), while others assert that the correlation between these different sets of indicators is far from clear (Lee at al., 2011; Meo et al., 2013; Vinkler, 2008; Vinkler, 2010). It is, however, more commonly accepted that the higher the GDP per capita or the income level of a country is, the more likely it is that a greater number of publications will be produced in that country. The question is whether there is a relationship between cities’ publishing efficiency (as a scientometric indicator) and cities’ per capita income level (derived from country-level data).
The classification of countries (and cities) by income level is based upon data obtained from the World Bank Country and Lending Groups database4（4 World Bank Country and Lending Groups https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups）. In this database, countries are classified into four income-level groups: low-income countries (GNI per capita of $1,005 or less in 2016), lower middle-income countries (GNI per capita between $1,006 and $3,955), upper middle-income countries (GNI per capita between $3,956 and $12,235), and high-income countries (GNI per capita of $12,236 or more).
Results show that 434 out of 554 cities included in the analysis are in high-income countries, 93 of them are in upper middle-income countries, and only 27 cities can be found in lower middle-income countries. None of the cities are in low-income countries. That is, most cities producing high publication output in terms of the number of articles (i.e., at least 3,000 articles in the period from 2014 to 2016) are in high-income countries. The mean publishing efficiency of cities from high-income countries is 2.057, that of cities located in upper middle-income countries is 0.997, and the mean publishing efficiency of cities from lower middle-income countries is only 0.881. There is a difference of more than double between the mean publishing efficiency of cities located in high-income countries and that of cities located in upper middle-income countries. The difference between the mean publishing efficiency of cities in upper middle-income countries and that of cities in lower middle-income countries seems to be insignificant.
As for the top 100 most efficient cities, 98% of them are in high-income countries, and only 2% of them can be found in upper middle-income countries. As compared to the quasi-homogeneous group of the top 100 cities, the bottom 100 least efficient cities show a more complex picture; 18% of them are in lower middle-income countries, 46% of them are in upper middle-income countries, but 36% of the bottom 100 least efficient cities are in high-income countries. Based on former studies available in the literature, this latter result might not have been expected; therefore, it requires more explanation.
As was mentioned above, most of the top 100 cities were in Northern America (primarily in the United States) and Europe (primarily in Northern European and Western European countries). Almost all countries in these regions are high-income countries. Contrary to the most efficient cities, none of the least efficient cities are in Northern America. Furthermore, only 17% of the bottom 100 cities are in European countries; except for five cities, all of them are in Eastern European countries (including Russia). Results show that 11 out of the 17 least efficient European cities are in high-income countries, and six of them are in Poland. Figure 4 illustrates that many cities producing low publishing efficiency are in Eastern Asian high-income countries. Half of these cities are in South Korea (11 cities), and another half are in Japan (12 cities); i.e., in countries that belong to the most developed countries in the world in terms of income level. One might suggest that if South Korea and Japan are high-income countries, cities located in South Korea and Japan should produce high publishing efficiency. One reason for this discrepancy may be that both Korean and Japanese languages are considered language isolates (Campbell, 2010), and it is well studied how problematic it is for Japanese people to acquire sufficient communicative skills in English (even if they are researchers) (Butler & Iino, 2005; Iwai, 2008). In contrast, for example, one survey shows that the Dutch have the world’s best non-native English skills (see, EF EPI English Proficiency Index, https://www.ef.co.hu/epi/). Therefore, it is not surprising that the collaboration intensity of both Japan and South Korea with the United States is lower (the proportion of co-authored papers indexed in the WoS during 2014-2016 was 10.6 and 13.8 percent for these countries, respectively) than that of European high-income countries, such as Germany and the Netherlands (the proportion of co-authored papers indexed in the WoS during 2014-2016 was 15.9 and 18.0 percent for these countries, respectively). It has however been determined that the higher the intensity of the collaboration between a country and the United States is, the more likely it is that co-authored papers will receive a higher number of citations (Pan et al., 2012; Sud & Thelwall, 2016), and have a greater chance to become highly cited papers.
Figure 4. Geographical location of the bottom 100 least efficient cities in terms of countries’ income levels.
Loughborough (England) is the only city in the bottom 100 least efficient cities that is in a high-income country belonging to the Anglosphere-Core. Beer-Sheva (Israel), a city in the group of the bottom 100 least efficient cities, is also in a high-income country, but is in the Anglosphere-Outer sphere. In fact, many of the bottom 100 cities are in countries in the Anglosphere-Outer sphere, but all of them are in lower middle-income countries, primarily in Southeast Asia (11 cities are in India, and one is in Pakistan).
East Asia is home to 46% of the bottom 100 least efficient cities. Beside Japan and South Korea, most of these cities are in China. While none of the East Asian countries included in the analysis belong to the group of the low-income or lower middle-income countries (Japan and South Korea are high-income countries, and China is an upper middle-income country), the publishing efficiency of the East Asian cities is rather low. Kawaguchi (Japan), the city producing the highest publishing efficiency in the region, is ranked only 138th. The facts above suggest that the economic development level of the cities is a key factor influencing publishing efficiency, which is reinforced by the fact that almost all cities in the group of the top 100 cities are in high-income countries, but it is not the most important factor.
The examination of factors like the dominance of the English language and cities’ economic development level will bring us closer to understanding why cities’ publishing efficiency differs from each other; however, we need deeper insight to obtain a precise picture of publishing efficiency. For example, country-level data allows us to understand why the publishing efficiency of Canadian and Chinese cities significantly differ from each other but does not help us to understand why the publishing efficiency of Kawaguchi is higher or why that of Niigata is lower than the mean publishing efficiency of Japanese cities. To examine cities’ publishing efficiency in a more precise way, we need to focus on some general as well as more city-specific factors, like the location of excellent organisations, cities’ international collaboration patterns, and the productivity of specific research areas.
For example, in Kawaguchi, most publications were produced by the Japan Science and Technology Agency, one of Japan’s excellent scientific organisations; therefore, the publishing efficiency of Kawaguchi is considerably higher than that of other Japanese cities. That is, which cities in the world are home to excellent organisations (e.g., universities and governmental and international research institutions) should be examined. The question is whether these organisations are exclusively located in cities producing high publishing efficiency or whether some of them might be found in cities with low publishing efficiency.
3.2.3 Location of excellent organisations as a factor influencing cities’ publishing efficiency
In the paper by Van Noorden (2010) an important question arose: What is the reason Boston ranks top in several analyses of scientific quality? A brief answer was given by José Lobo, a statistician and economist who was affiliated with Arizona State University at Tempe: ‘Take three or four of the best universities in the world, put them in a city with a seaport, and voilà!’ Naturally, the question requires a more complex answer (as was later also explained by Van Noorden), but it calls attention to a key factor: the scientific performance of cities significantly depends on whether they are home to top-ranked universities.
Although many research institutions, hospitals, governmental organisations (e.g., ministries and departments), NGOs, and companies have a significant publication output (Archambault & Larivière, 2011; Csomós & Tóth, 2016; Hicks, 1995), scientific publications are primarily produced by universities all over the world. In recent years, university rankings have gained in popularity. The main goal of ranking and comparing universities in terms of scientific output (of which the publication output is a vital component) is to make the most prestigious universities visible worldwide. There are several different world university rankings available (e.g., CWTS Leiden Ranking, The Times Higher Education World University Rankings, QS World University Rankings, and Academic Ranking of World Universities - ARWU), which are all based upon different input data. However, each ranking attributes more or less significance to bibliometric indicators, such as the number of publications produced in a given university, the quality (citation impact) of scientific publications, or the number of articles published in top journals (Docampo et al., 2015; Frenken et al., 2017; Piro & Sivertsen, 2016; Shehatta & Mahmood, 2016). Naturally, the methodologies of how university rankings are produced differ from each other; thus, university rankings are different in terms of top university rankings (Abramo & D’Angelo, 2015; Lin et al., 2013).
From the point of view of this analysis, university rankings contain indicative information only. I chose to use the Academic Ranking of World Universities (ARWU) published annually by the Shanghai Ranking Consultancy because the importance of the Shanghai ranking has become recognized by both governments and universities; further, according to Docampo and Cram (2014), the ‘ranking has become a major resource for exploring the characteristics and quality of academic institutions and university systems worldwide.’ I examined whether there is a relationship between the location of top-ranked universities and cities’ publishing efficiency. Top-ranked universities correspond to universities having been ranked among the top 100 universities on one of the ARWU lists of 2014, 2015, and 2016.
In the period from 2014 to 2016, the top 100 universities were in 95 cities, some of which were home to more than one top-ranked university (e.g., New York, London, Boston, Pittsburgh, Munich, Stockholm, and Zurich). The publishing efficiency of cities that were home to the top 100 universities averages 2.641, while that of the rest of the cities averages 1.648. That is, the mean publishing efficiency of cities that are home to the top 100 universities is higher than that of the rest of the cities by 60%. These results suggest that the location of top-ranked universities significantly influences cities’ publishing efficiency. In other words, it seems to be a logical assumption that top-ranked universities are primarily located in the most efficient cities. Thus, we should examine which of the top 100 most efficient cities are home to top-ranked universities.
Figure 5 shows that it is not an exclusive privilege of the most efficient cities to be home to top-ranked universities. Only 43% of the top 100 universities are in the top 100 most efficient cities. Furthermore, there are many cities worldwide (including Chinese and Japanese cities), that do not belong to the top 100 most efficient cities; yet, they are home to top-ranked universities. In the group of the bottom 100 cities, Moscow (Russia) is the only city that is home to a top-ranked university.
Figure 5. Geographical location of cities that are home to the top 100 universities as ranked by ARWU.
The location of top-ranked universities is considered an important but not decisive factor influencing cities’ publishing efficiency. Examining the ranking of the most efficient cities, there are two cities (Villejuif, France and Menlo Park, California, USA) topping the ranking that are not home to top-ranked universities as ranked by the ARWU.5（5 It should be noted that ARWU is just one of the alternatives to rank universities. Naturally, other organisations produce different rankings with different universities in top positions. For example, out of the top 10 universities, only Harvard University and Stanford University appear in both the CWTS Leiden Ranking of 2017 and the ARWU list of 2017. Contrary to this example, the groups of the top 10 universities in the QS World University Rankings of 2017 and the ARWU list differ from each other by only three universities. In addition, there are many top-ranked universities that are not included in the group of top 100 universities on the ARWU list but are in cities with high publishing efficiency. For example, Rotterdam, the forty-second most efficient city in the world, is home to the Erasmus University Rotterdam, which ranked 101-150 (i.e., outside but close to the top 100 universities).）
The question arises as to what kind of organisations (but not universities) are in cities like Villejuif, Menlo Park, California, Upton, New York (United States), Greenbelt, Maryland (United States), Didcot (England), etc., which produce very high publishing efficiency. The explanations are as follows.
Villejuif, the city with the highest publishing efficiency in the world, is home to the ‘Institut Gustave Roussy’, one of the world’s leading cancer-research institutions and the premier oncology centre and teaching hospital in Europe. Although Villejuif is a city (commune) having 50 thousand inhabitants, it is a suburb of Paris, about seven kilometres from its centre.
Menlo Park is home to the SLAC National Accelerator Laboratory, a linear accelerator that is owned by the US Department of Energy and operated by the Stanford University. Currently, SLAC is the world’s largest linear accelerator and is one of top research centres for accelerator physics. The city of Menlo Park, with a population of 32 thousand, is in the San Francisco Bay Area between San Francisco and San Jose (i.e., in one of the fastest growing regions in the world that is home to many innovative companies and top-ranked universities). Additionally, Didcot has 25 thousand inhabitants and is 16 km south of Oxford. Didcot is home to the Rutherford Appleton Laboratory, a world-renowned research centre for particle physics and space science.
Cities such as Villejuif, Menlo Park, and Didcot can be characterised the same way; they are smaller cities, towns, or villages located in metropolitan areas and are home to quasi-independent research institutions (e.g., national laboratories) generally operating under the umbrella of prestigious universities. Naturally, top research institutions are in large cities as well, but being surrounded by universities, their visibility in terms of publication output is much lower, even if they produce very high publishing efficiency. For example, the total publication output of Geneva (Switzerland) is produced by many organisations, including the European Organisation for Nuclear Research (CERN), the World Health Organization (WHO), and the University of Geneva. In the period from 2014 to 2016, almost 60% of Geneva’s total publication output came from the University of Geneva, which has been ranked among the top 100 universities on the ARWU list, and which publishing efficiency is as high as 3.33. However, if we compare the publishing efficiency of the University of Geneva to that of the CERN (5.37) and the WHO (6.86), it seems rather low. The same pattern appears in large cities like New York, London, Paris, Los Angeles, and Tokyo.
In conclusion, a positive relationship can be detected between the location of top-ranked universities and cities’ high publishing efficiency. However, it should be noted that publications, primarily in large cities, come from different types of organisations, many of which have lower publishing efficiency than universities. Thus, some cities that are home to top-ranked universities have not been included in the top 100 most efficient cities. Furthermore, there are several top-ranked cities that are not home to top-ranked universities (or any universities); yet, they produce a very high publishing efficiency.
3.2.4 International collaboration pattern as a factor influencing cities’ publishing efficiency
In recent years, the number of publications produced by single authors has been decreasing, while the number of co-authored publications and number of co-authors in publications have been increasing rapidly (Abramo et al., 2017; Castelvecchi, 2015; Uddin et al., 2012). Therefore, cities’ international collaboration patterns
have become more complex (i.e., authors affiliated with a given city have been collaborating with a growing number of co-authors affiliated with other cities in other countries). Naturally, cities’ international collaboration patterns are influenced by many factors, including differences between the productivity of scientific disciplines (Larivière et al., 2006; Paul-Hus et al., 2017; Zhou et al., 2009b), the size of the national research system (Van Raan 1998), and linguistic features (Csomós, 2018; Maisonobe et al., 2016). These facts might suggest that international collaboration patterns vary city to city worldwide, making it impossible to predict cities’ publishing efficiency. However, this question remains to be answered.
In this section, I aim to examine whether cities with high publishing efficiency and cities with low publishing efficiency are characterised by specific international collaboration patterns. Data obtained from the Web of Science database allows us to reveal countries with which the co-authors are affiliated. For example, in the period from 2014 to 2016, 27,322 articles were produced in Ann Arbor, Michigan (United States), from which 765 received enough citations to belong to the top 1% highly cited articles. If we focus on the international collaboration pattern of all articles produced in Ann Arbor between 2014 and 2016, 8.76% of the articles were written with co-authors affiliated with China, 7.23% had co-authors affiliated with Canada, 7.13% had co-authors affiliated with England, 6.78% had co-authors affiliated with Germany, 5.16% had co-authors affiliated with France, and so on. That is, in the case of all articles, the top collaborator with Ann Arbor is China, and the second ranked collaborator is Canada, and so on.
However, if we focus on the international collaboration pattern of the highly cited articles, a different pattern will emerge. Most highly cited articles were written with co-authors affiliated with England (27.32%), with 25.49% from Canada, 23.53% from Germany, 20.26% from France, 17.39% from Italy, and so on. That is, in the case of highly cited articles, the top collaborator of Ann Arbor is England (replacing China as the top collaborator in all articles), and the second ranked collaborator is Canada, and so on.
I examine which countries are the top collaborators (i.e., collaborators ranked 1-5) in the case of all articles and in the case of highly cited articles produced in a given city in the period from 2014 to 2016. Furthermore, I compare the typical international collaboration patterns of the top 100 most efficient cities to that of the bottom 100 least efficient cities. My aim is to reveal whether there is a relationship between cities’ international collaboration patterns and cities’ publishing efficiencies and whether there is a difference between the typical collaboration patterns of the top 100 cities and the bottom 100 cities. When examining cities’ international collaboration patterns, I implemented a geographical constraint. The group of the top 100 cities was divided into two sub-groups (i.e., the most efficient non-US cities and the most efficient US cities), and they were examined separately.
Table 4 shows the countries occupying the top 1-5 positions as collaborators in all articles and their frequency of occurrence in those positions. The top collaborator of the most efficient non-US cities (48 out of the top 100 cities) is the United States, whose frequency of occurrence in the top 1-5 positions is 100% (in the top position in 81.25% of the cases). This means that the United States has a very intense collaboration with every single city belonging to the group of the most efficient non-US cities. Germany is ranked second by collaborating with 87.50% of the most efficient non-US cities in one of the top 1-5 positions. As compared to that of the United States, the frequency of occurrence of Germany in the top position is only 8.33%. In the case of all articles, the top 1-5 collaborators of the most efficient non-US cities are the United States, Germany, England, France, and Italy. As top collaborators, other countries (like the Netherlands, Australia, Spain, etc.) are rather marginal, primarily appearing in the top 4-5 positions.
In the case of all articles produced in the most efficient US cities (52 out of the top 100 cities), the most frequently occurring countries as collaborators in the top 1-5 positions are Germany, England, China, Canada, and France (Table 4). China, the top collaborator of the most efficient US cities, has surpassed England by almost 2%. The United States has had a traditionally close scientific relationship with Western European countries (especially the United Kingdom) and Canada (Adams, 2013), but on the city level, China has recently been occupying a more significant position (Csomós, 2018; Tian, 2016). Naturally, the top international collaborator of most Chinese cities has been the United States for a long time (He, 2009; Wang et al., 2013; Zhang & Guo, 1997). If we merge the groups of the most efficient non-US cities and the most efficient US cities into a single group, it turns out that all the co-authors are affiliated with 21 countries occupying one of the top 1-5 positions.
Table 4 Top collaborators* in the case of all articles.
Top collaborators
of the most efficient non-US cities occurring in the 1-5 positions		 Frequency
of occurrence
in the 1-5 positions in percentage		 Top collaborators
of the most efficient US cities occurring in the 1-5 positions		 Frequency
of occurrence
in the 1-5 position in percentage		 Top collaborators
of the least efficient cities occurring in the 1-5 positions		 Frequency
of occurrence
in the 1-5 positions in percentage
1 USA 100.00 Germany 98.11 USA 98.00
2 Germany 87.50 England 98.11 Germany 78.00
3 England 75.00 China 94.34 England 69.00
4 France 75.00 Canada 84.91 France 39.00
5 Italy 52.08 France 66.04 China 31.00
6 Netherlands 27.08 Australia 15.09 Australia 30.00
7 Australia 18.75 Italy 15.09 Japan 25.00
8 Spain 18.75 Japan 5.66 Canada 23.00
9 China 16.67 Netherlands 5.66 Italy 23.00
10 Scotland 8.33 South Korea 5.66 South Korea 18.00

* In this context, collaborators correspond to countries with which co-authors are affiliated.

Table 4 illustrates that the international collaboration patterns of the bottom 100 least efficient cities resemble a mixture of the international collaboration patterns of the most efficient non-US cities and US cities. The United States (in the top position in 85% of the cases), Germany, England, France, and China appear in the top 1-5 positions in most cases. However, two facts should be highlighted: 1) As for the international collaboration patterns of the bottom 100 cities, the frequency of occurrence of countries following the United States is much lower than in the case of the most efficient non-US cities. The mean frequency of occurrence of the top 1-5 collaborator countries in articles produced in the most efficient non-US cities is 77.92%. This value is 88.30% in articles produced in the most efficient US cities, but it reaches only 63% in the bottom 100 least efficient cities. 2) The least efficient cities collaborate with a greater number of countries (33) occupying one of the top 1-5 positions than the most efficient cities (21). Many of these countries (e.g., Saudi Arabia, Brazil, Iran, Russia, and South Korea) produce low publishing efficiency; thus, the collaboration has a negative effect on cities’ publishing efficiency (i.e., these collaborations result in a smaller number of articles that receive enough citations to belong to the top 1% highly cited articles).
It is, however, more important to know which countries (more precisely the co-authors affiliated with that country) are the top collaborators of cities (more precisely the authors affiliated with that city) in highly cited articles. According to my hypothesis, countries as the top 1-5 collaborators of cities in highly cited articles differ from those occupying top positions in the total number of articles. The publishing efficiency of cities is heavily influenced by where the top collaborators are in the case of highly cited articles.
Table 5 shows that the collaboration pattern of the most efficient non-US cities in highly cited articles is almost the same as the collaboration pattern that emerged in the total number of articles; however, the relative weight of Germany, France, and England has increased. In the total number of articles, the mean frequency of occurrence of the top 1-5 collaborators was 77.92, while in highly cited articles, this value has increased to 79.17. In highly cited articles produced in the most efficient US cities, the frequency of occurrence of England is 100%, which means that England occupies one of the top 1-5 positions of every single city (in the top position in 57.69% of the cases). Germany has the same frequency of occurrence in highly cited articles than in the total number of articles, but the frequency of occurrence of Canada and especially that of France has significantly increased. China, the third most frequently occurring country in the total number of articles, has vanished from the group of the top collaborators in highly cited articles. This means that, although the total number of articles in US cities shows intense collaboration with China, the collaboration results in only a small number of highly cited articles. In highly cited articles, the mean frequency of occurrence of the most efficient US cities with the top 1-5 collaborators is 81.92%, which is a bit less than in the total number of articles.
Table 5 Top collaborators* in the case of the highly cited articles.
Top collaborators of the most efficient non-US cities occurring in the 1-5 positions Frequency of occurrence in the 1-5 positions in percentage Top collaborators of the most efficient US cities occurring in the 1-5 positions Frequency of occurrence in the 1-5 position in percentage Top collaborators of the least efficient cities occurring in the 1-5 positions Frequency of occurrence in the 1-5 positions in percentage
1 USA 100.00 England 100.00 USA 98.00
2 Germany 89.58 Germany 98.08 Germany 72.00
3 France 79.17 France 88.46 England 70.00
4 England 77.08 Canada 86.54 France 43.00
5 Italy 50.00 Australia 36.54 Australia 34.00
6 Netherlands 22.92 Italy 34.62 China 29.00
7 Spain 18.75 China 21.15 Italy 29.00
8 Switzerland 16.67 Spain 9.62 Spain 26.00
9 Australia 14.58 Netherlands 9.62 Canada 25.00
10 Canada 12.50 Switzerland 7.69 Japan 13.00

* In this context, collaborators correspond to countries with which co-authors are affiliated.

Not surprisingly, in highly cited articles, the bottom 100 least efficient cities have a very intense collaboration with the United States. In 98 cities, the United States occupies one of the top 1-5 positions and is in the top position in 79% of the cases. The frequency of occurrence of countries following the United States is much lower than in the most efficient cities. The mean frequency of occurrence of the top 1-5 collaborators in highly cited papers produced in the least efficient cities is only 63.4%. In the top 1-5 positions, the bottom 100 least efficient cities collaborate with a total of 30 countries, while this value in the top 100 most efficient (non-US and US) cities is 16.
In the case of the highly cited articles, there are fundamental differences between the international collaboration patterns of the most efficient cities and the least efficient cities. Although both groups of cities have roughly the same top collaborators, the least efficient cities collaborate with a much greater number of countries than the most efficient cities. It seems that this difference significantly influences the publishing efficiency of cities.
In conclusion, if co-authors are primarily from countries of the United States, Germany, England, France, Canada, and Italy, which are leading countries in science, articles will have a greater chance to receive enough citations to belong to the top 1% highly cited articles.
3.2.5 The scientific field profiles of cities as a factor influencing publishing efficiency
Beside the factors detailed above, cities’ publishing efficiency is significantly influenced by the productivity of scientific disciplines. The most productive disciplines vary city to city, and the productivity of different disciplines in terms of highly cited articles differs as well (Bornmann et al., 2011). In each city, the most productive disciplines will be revealed both in the case of all articles and in the case of highly cited articles.
For example, in the period from 2014 to 2016, authors from Ann Arbor, Michigan produced articles in 151 disciplines: 8.16% of the 27,322 articles were published in the discipline of physics, 7.41% in engineering, 6.43% in ‘science, technology, and other topics’ (as it is indicated in the WoS), 4.99% in chemistry, 4.91% in psychology, and so on. The greatest number of highly cited articles was produced in quite different disciplines; 15.11% of the 765 highly cited articles were written in ‘science, technology, and other topics’, 11.27% in general internal medicine, 9.35% in physics, 9.22% in oncology, 5.89% in astronomy and astrophysics, and so on.
To obtain a better understanding of why the publishing efficiency of the most efficient cities and that of the least efficient cities differ significantly, we need to reveal the characteristics of the most productive discipline in those cities. Table 6 shows that, in the case of the top 100 cities, the most productive discipline occurring in the top 1-5 positions is ‘science, technology, and other topics’. In the Web of Science, articles published in multidisciplinary journals (e.g., Nature, Science, Proceedings of the National Academy of Sciences of the United States of America, and PlosONE) are classified into the discipline of ‘science, technology, and other topics’. It is well-known that articles published in high-impact multidisciplinary journals become highly cited at a very great proportion. For example, 45.67% of all articles published between 2014 and 2016 in Nature and 40.44% of all articles published in the same period in Science have received enough citations to belong to the top 1% highly cited articles.
In general, articles published in the top 100 most efficient cities can be classified into two major scientific fields: natural sciences (e.g., physics, chemistry, and engineering) and health sciences (e.g., neurosciences and neurology, oncology, and psychology). Contrary to the top 100 cities, most articles produced in the bottom 100 least efficient cities can be classified into disciplines that are natural sciences, while the field of health sciences is almost absent. In the case of the least efficient cities, oncology is the most frequently occurring health science discipline with a frequency of occurrence of only 12% (i.e., it occurs in the top 1-5 positions in only 12% of the least efficient cities). In contrast to health sciences, natural sciences (e.g., chemistry, engineering, physics, and material science) produce a very high frequency of occurrence (Table 6). Chemistry is in the top 1-5 positions in almost every bottom 100 city, and it occupies the top position in 54% of the cases. This means that, in more than half of the least efficient cities, chemistry is the most productive research area.
Table 6 Most productive scientific disciplines in all articles.
The most productive scientific disciplines occurring in the top 1-5 positions in the most efficient cities Frequency of occurrence in the 1-5 positions in percentage The most productive scientific disciplines occurring in the top 1-5 positions in the least efficient cities Frequency
of occurrence in the 1-5 positions in percentage
1 Science, Technology, and Other Topics 84.00 Chemistry 99.00
2 Physics 63.00 Engineering 85.00
3 Neurosciences and Neurology 47.00 Physics 84.00
4 Chemistry 45.00 Materials Science 80.00
5 Engineering 41.00 Science, Technology, and Other Topics 54.00
6 Astronomy and Astrophysics 38.00 Mathematics 15.00
7 Oncology 29.00 Environmental Sciences and Ecology 12.00
8 Environmental Sciences and Ecology 21.00 Oncology 12.00
9 Psychology 20.00 Pharmacology and Pharmacy 11.00
10 Materials Science 16.00 Agriculture 7.00
In the case of the highly cited articles published in the top 100 most efficient cities, the discipline of ‘science, technology, and other topics’ is even more dominant; it is in the top 1-5 positions in 91% of all cities but occurs in the top position in only 20% of the cases. In 35% of the top 100 cities, general internal medicine occupies the top position but ranked second based on the aggregate frequency of occurrence (Table 7). In highly cited articles produced in the most efficient cities, both the number and frequency of occurrence of health disciplines are greater than in all articles. When examining all articles produced in the most efficient cities, general internal medicine is in the top 1-5 positions in only 5% of cases, but in the highly cited articles, this value increases to 69%. Furthermore, the frequency of occurrence of oncology and the discipline of the cardiovascular system and cardiology increased by more than 50%.
In highly cited articles produced in the bottom 100 least efficient cities, most of the dominant disciplines are in natural sciences. In the least efficient cities, the discipline of ‘science, technology, and other topics’ occupies the top position, but its frequency of occurrence is less than in the most efficient cities.
Table 7 Most productive scientific disciplines in highly cited articles.
The most productive scientific disciplines occurring in the top 1-5 positions in the most efficient cities Frequency
of occurrence in the 1-5 positions in percentage		 The most productive scientific disciplines occurring in the top 1-5 positions in the least efficient cities Frequency
of occurrence in the 1-5 positions in percentage
1 Science, Technology, and Other Topics 91 Science, Technology, and Other Topics 74
2 Physics 69 Chemistry 66
3 General Internal Medicine 69 Physics 56
4 Astronomy and Astrophysics 54 Engineering 54
5 Oncology 42 General Internal Medicine 37
6 Chemistry 28 Materials Science 37
7 Cardiovascular System and Cardiology 21 Astronomy and Astrophysics 25
8 Biochemistry and Molecular Biology 18 Environmental Sciences and Ecology 20
9 Environmental Sciences and Ecology 15 Oncology 20
10 Neurosciences and Neurology 14 Mathematics 17
When we examined the international collaboration patterns in both the cases of all articles and in highly cited articles produced in the top 100 cities and produced in the bottom 100 cities, respectively, we found that they differ in the frequency of occurrence of the top collaborators. However, the countries with which they collaborate (i.e., the location of co-authors) were primarily the same. As for the scientific disciplines, there are significant differences between the top 100 cities and the bottom 100 cities in not only the frequency of occurrence of the most productive disciplines but also in the disciplines themselves. In the most efficient cities, highly cited articles are produced in disciplines that are in natural sciences and health sciences to almost the same degree, while, in the least efficient cities, health disciplines are rather marginal. Furthermore, the frequency of occurrence of the discipline of ‘science, technology, and other topics’ is much higher in articles produced in the most efficient cities than in articles produced in the least efficient cities. This fact suggests that, in the most efficient cities, a greater number of articles are published in high-impact multidisciplinary journals than in the least efficient cities.

4 Discussion and conclusion

In this paper, I examined whether there were general factors influencing cities’ publishing efficiency (i.e., the ratio of highly cited articles to all articles produced in that city). I have found the following five fundamental factors: the dominance of the English language, cities’ economic development level, the location of excellent organisations, cities’ international collaboration patterns, and the productivity of scientific disciplines.
The dominance of the English language seems to be one of the most (if not the most) significant factors influencing cities’ publishing efficiency. About three-quarters of the most efficient cities are in countries in the Anglosphere-Core, and the rest of are in Northern and Western European countries. Contrary to the most efficient cites, 99% of the least efficient cities are in countries outside the Anglosphere-Core.
The economic development level of cities (derived from country-level data) as a factor influencing the publishing efficiency seems less significant than the linguistic environment. Results show that 98% of the most efficient cities are in high-income countries. It might suggest that there is a relationship between cities’ high-income level and cities’ high publishing efficiency, but it turned out that one-third of the least efficient cities were also located in high-income countries. The reason for this is that countries that are home to cities with low efficiency but high-income level do not belong to the Anglosphere, reinforcing the fact that the dominance of the English language (i.e., the linguistic environment of cities) as a factor has a greater significance in influencing cities’ publishing efficiency than the cities’ economic development level has.
It is well-known fact that scientific publications are primarily produced by universities. We can assume that the most efficient cities should be home to the most prestigious universities in the world, while top-ranked universities are not expected to be in the least efficient cities. Results show that this hypothesis is basically correct, at least when we focus on the location of top-ranked universities in the least efficient cities. However, the picture is more complex in the case of the most efficient cities, because half of those cities are not home to top-ranked universities. Moreover, many top-ranked universities are in cities that are not the most efficient cities. The reason for this is that there are many towns and small or mid-sized cities that are home to world-renowned national or international research institutions producing even higher publishing efficiency than top-ranked universities. These settlements are all characterised by the fact that they are within metropolitan areas, while the research institutions they host operate under the umbrella of prestigious research universities.
In the case of the highly cited articles, an overlap can be detected between the international collaboration patterns of the most efficient cities and the least efficient cities. In both cases, the top collaborators are the United States (primarily in the top position), Germany, England, France, Canada, and Australia/Italy. If we merely focus on who the top collaborators of cities are, we cannot predict whether its publishing efficiency will be high. However, the magnitude of the collaboration intensity between cities (more precisely the authors affiliated with those cities) and the leading countries in science (more precisely the co-authors located in those countries) even more significantly influences cities’ publishing efficiency. The higher the collaboration intensity is, the more likely it is that cities will produce high publishing efficiency.
In the most efficient cities, highly cited articles are produced in disciplines of natural sciences and health sciences to the same degree. In the least efficient cities, almost all highly cited articles are produced in the field of natural sciences (primarily in chemistry), while hardly any articles are published in health sciences. In the case of both groups of cities, ‘science, technology, and other topics’ is the most frequently occurring discipline in highly cited articles; however, its frequency of occurrence in articles produced in the most efficient cities is much higher than in the least efficient cities.
Based on the above research results, we can draw the conclusion that a city’s publishing efficiency will be high if meets the following conditions:
1) It is in a country in the Anglosphere-Core;
2) It is in a high-income country;
3) It is home to top-ranked universities and/or world-renowned research institutions;
4) Researchers affiliated with that city most intensely collaborate with researchers affiliated with cities in the United States, Germany, England, France, Canada, and Australia/Italy; and 5) The most productive scientific disciplines of highly cited articles are ‘science, technology, and other topics’ (i.e., most articles are published in high-impact multidisciplinary journals), disciplines in health sciences (especially general internal medicine and oncology), and disciplines in natural sciences (especially physics, astronomy, and astrophysics).
Approximately 60% of the top 100 most efficient cities meet the above criteria, but if we expand the geographical dimension beyond the Anglosphere, 86% of the top 100 cities will meet the criteria.
Most of the bottom 100 least efficient cities are in countries outside the Anglosphere. If we do not consider the determinant significance of the linguistic factor, the patterns of the Japanese, South Korean, and European cities resemble the patterns of the most efficient cities. All of them are in high-income countries and have more or less similar international collaboration patterns as that of the most efficient cities. Moreover, most of the highly cited articles are produced in similar disciplines (although disciplines in natural sciences are overrepresented). Naturally, there are several top-ranked and prestigious universities and research institutions in Japanese and South Korean cities (especially in Tokyo, Kyoto, Nagoya, Osaka, and Seoul); yet, they produce low publishing efficiency.
The question is: What can the city administration do to increase the city’s performance in science (e.g., to increase the city’s publishing efficiency)? Naturally, cities have limited opportunities to compete for components of the science establishment. Universities, hospitals and most governmental research institutions are generally tied to their original loci. However, cities can compete to attract innovation-oriented companies, high tech firms, and R&D facilities of multinational companies by for example establishing science parks. The positive effect of this process on the city’s performance in science can be observed in the example of Beijing (Andersson et al., 2014; Liefner et al., 2006; Zhou, 2005). Furthermore, cities can compete to acquire cutting-edge international research facilities. For example, in 2009, founding member states of the European Spallation Source (ESS) (the most powerful linear proton accelerator in the world) decided to support for placing ESS in Lund, selecting it from the competition of three European cities. The ESS will attract thousands of researchers from all over the world to Lund.
Some of the further research directions based upon the results of the study are as follows: What kind of local factors influence cities’ publishing efficiency? If publishing efficiency is an indicator of cities’ performance in science, what can city administrations do to improve it? If cities have very different sizes and populations (even publication output) worldwide, what kind of territorial demarcation can be introduced to balance these differences?

The authors have declared that no competing interests exist.

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Publishing order | Descend order by publishing year | Descend order by cited within
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Kumar S., &Garg K.C. (2005). Scientometrics of computer science research in India and China. Scientometrics, 64(2), 121-132.An analysis of 2058 papers published by Chinese authors and 2678 papers published by Indian authors in the field of computer science during 1971-2000 indicates that India's output is significantly higher than the Chinese output. However, China is catching up fast. Chinese researchers prefer to publish their research results in domestic journals, while Indian researchers prefer to publish their research results in journals published in the advanced countries of the West. Also the share of papers in journals covered by SCI for India was higher than from China. However, no significant difference has been observed in the impact of the research output of the two countries as seen by different impact indicators. Team research is more common in India as compared to China.

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[35]
Larivière V., Gingras Y., & Archambault É. (2006). Canadian collaboration networks: A comparative analysis of the natural sciences, social sciences and the humanities. Scientometrics, 68(3), 519-533.A basic dichotomy is generally made between publication practices in the natural sciences and engineering (NSE) on the one hand and social sciences and humanities (SSH) on the other. However, while researchers in the NSE share some common practices with researchers in SSH, the spectrum of practices is broader in the latter. Drawing on data from the CD-ROM versions of the Science Citation Index , Social Sciences Citation Index and the Arts & Humanities Citation Index from 1980 to 2002, this paper compares collaboration patterns in the SSH to those in the NSE. We show that, contrary to a widely held belief, researchers in the social sciences and the humanities do not form a homogeneous category. In fact, collaborative activities of researchers in the social sciences are more comparable to those of researchers in the NSE than in the humanities. Also, we see that language and geographical proximity influences the choice of collaborators in the SSH, but also in the NSE. This empirical analysis, which sheds a new light on the collaborative activities of researchers in the NSE compared to those in the SSH, may have policy implications as granting councils in these fields have a tendency to imitate programs developed for the NSE, without always taking into account the specificity of the humanities.

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[36]
Lee L. C., Lin P. H., Chuang Y. W., & Lee Y. Y. (2011). Research output and economic productivity: A Granger causality test. Scientometrics, 89(2), 465-478.The correlation between GDP and research publications is an important issue in scientometrics. This article provides further empirical evidence connecting revealed comparative advantage in national research with effects on economic productivity. Using quantitative time series analysis, this study attempts to determine the nature of causal relationships between research output and economic productivity. One empirical result is that there is mutual causality between research and economic growth in Asia, whereas in Western countries the causality is much less clear. The results may be of use to underdeveloped nations deciding how to direct their academic investment and industry policy.

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[37]
Leta J., Glänzel W., & Thijs B. (2006). Science in Brazil. Part 2: Sectoral and institutional research profiles. Scientometrics, 67(1), 87-105.In the present study a bibliometric meso-level analysis of Brazilian scientific research is conducted. Both sectoral and publication profile of Brazilian universities and research institutions are studied. Publication dynamics and changing profiles allow to the conclusion that powerful growth of science in Brazil goes with striking structural changes. By contrast, citation-based indicators reflect less spectacular developments.

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[38]
Leydesdorff L., & Wagner, C. (2009). Is the United States losing ground in science? A global perspective on the world science system. Scientometrics, 78(1), 23-36.<a name="Abs1"></a>Based on the Science Citation Index-Expanded web-version, the USA is still by far the strongest nation in terms of scientific performance. Its relative decline in percentage share of publications is largely due to the emergence of China and other Asian nations. In 2006, China has become the second largest nation in terms of the number of publications within this database. In terms of citations, the competitive advantage of the American &#8220;domestic market&#8221; is diminished, while the European Union (EU) is profiting more from the enlargement of the database over time than the USA. However, the USA is still outperforming all other countries in terms of highly cited papers and citation/publication ratios, and it is more successful than the EU in coordinating its research efforts in strategic priority areas like nanotechnology. In this field, the People&#8217;s Republic of China (PRC) has become second largest nation in both numbers of papers published and citations behind the USA.

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[39]
Leydesdorff L., Wagner C. S., & Bornmann L. (2014). The European Union, China, and the United States in the top-1% and top-10% layers of most-frequently cited publications: Competition and collaborations. Journal of Informetrics, 8(3), 606-617.The percentages of shares of world publications of the European Union and its member states, China, and the United States have been represented differently as a result of using different databases. An analytical variant of the Web-of-Science (of Thomson Reuters) enables us to study the dynamics in the world publication system in terms of the field-normalized top-1% and top-10% most-frequently cited publications. Comparing the EU28, USA, and China at the global level shows a top-level dynamic that is different from the analysis in terms of shares of publications: the United States remains far more productive in the top-1% of all papers; China drops out of the competition for elite status; and the EU28 increased its share among the top-cited papers from 2000 to 2010. Some of the EU28 member states overtook the United States during this decade; but a clear divide remains between EU15 (Western Europe) and the Accession Countries. Network analysis shows that China was embedded in this top-layer of internationally co-authored publications. These publications often involve more than a single European nation. (C) 2014 Elsevier Ltd. All rights reserved.

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[40]
Li J., Qiao L., Li W., & Jin Y. (2014). Chinese-language articles are not biased in citations: Evidences from Chinese-English bilingual journals in Scopus and Web of Science. Journal of Informetrics, 8(4), 912-916.This paper examined the citation impact of Chinese- and English-language articles in Chinese-English bilingual journals indexed by Scopus and Web of Science (WoS). Two findings were obtained from comparative analysis: (1) Chinese-language articles were not biased in citations compared with English-language articles, since they received a large number of citations from Chinese scientists; (2) a Chinese-language community was found in Scopus, in which Chinese-language articles mainly received citations from Chinese-language articles, but it was not found in WoS whose coverage of Chinese-language articles is only one-tenth of Scopus. The findings suggest some implications for academic evaluation of journals including Chinese-language articles in Scopus and WoS.

DOI

[41]
Liefner I., Hennemann S., & Lu X. (2006). Cooperation in the innovation process in developing countries: Empirical evidence from Zhongguancun, Beijing. Environment and Planning A, 38(1), 111-130.

[42]
Lin C. S., Huang M. H., & Chen D. Z. (2013). The influences of counting methods on university rankings based on paper count and citation count. Journal of Informetrics, 7(3), 611-621.In an age of intensifying scientific collaboration, the counting of papers by multiple authors has become an important methodological issue in scientometric based research evaluation. Especially, how counting methods influence institutional level research evaluation has not been studied in existing literatures. In this study, we selected the top 300 universities in physics in the 2011 HEEACT Ranking as our study subjects. We compared the university rankings generated from four different counting methods (i.e. whole counting, straight counting using first author, straight counting using corresponding author, and fractional counting) to show how paper counts and citation counts and the subsequent university ranks were affected by counting method selection. The counting was based on the 1988鈥2008 physics papers records indexed in ISI WoS. We also observed how paper and citation counts were inflated by whole counting. The results show that counting methods affected the universities in the middle range more than those in the upper or lower ranges. Citation counts were also more affected than paper counts. The correlation between the rankings generated from whole counting and those from the other methods were low or negative in the middle ranges. Based on the findings, this study concluded that straight counting and fractional counting were better choices for paper count and citation count in the institutional level research evaluation.

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[43]
López-Navarro I., Moreno A. I., Quintanilla M. Á., & Rey-Rocha J. (2015). Why do I publish research articles in English instead of my own language? Differences in Spanish researchers’ motivations across scientific domains. Scientometrics, 103(3), 939-976.

[44]
Lu K., & Wolfram, D. (2010). Geographic characteristics of the growth of informetrics literature 1987-2008. Journal of Informetrics, 4(4), 591-601.<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">Recent studies have concluded that American contributions to science literature have been in relative decline, whereas contributions from other parts of the world such as the European Union and Asia have increased. Is the same true for the areas of bibliometrics, informetrics and scientometrics? This study investigates the growth and geographic distribution of metrics research for the period 1987&ndash;2008. Similar to studies of other disciplines or science in general, the findings reveal that the United States continues to dominate, but there has been a recent relative decline in North American contributions overall. European and Asian contributions have grown substantially. National and institutional collaborations that contribute to this growth do not necessarily follow close geographic proximity, although European nations have been more active with international collaborations overall, both within Europe and elsewhere.</p>

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[45]
Maisonobe M., Eckert D., Grossetti M., Jégou L., & Milard B. (2016). The world network of scientific collaborations between cities: domestic or international dynamics? Journal of Informetrics, 10(4), 1025-1036.An earlier publication (Grossetti et al., 2014) has established that we are attending a decreasing concentration of scientific activities within “world-cities”. Given that more and more cities and countries are contributing to the world production of knowledge, this article analyzes the evolution of the world collaboration network both at the domestic and international levels during the 2000s. Using data from theScience Citation Index Expanded, scientific authors’ addresses are geo-localized and grouped by urban areas. Our data suggests that interurban collaborations within countries increased together with international linkages. In most countries, domestic collaborations increased faster than international collaborations. Even among the top collaborating cities, sometimes referred to as “world cities”, the share of domestic collaborations has gained momentum. Our results suggest that, contrary to common beliefs about the globalization process, national systems of research have been strengthening during the 2000s.

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[46]
Maisonobe M., Grossetti M., Milard B., Jégou L., & Eckert D. (2017). The global geography of scientific visibility: a deconcentration process (1999-2011). Scientometrics, 113(1), 479-493.Abstract This article aims to ascertain whether the territorial redistribution observed in the geography of scientific production between 1999 and 2008 translated into a redistribution of the geography of citations, and therefore of scientific visibility. Are publications from formerly marginal locations able to influence researchers based in “central locations”, or is their impact mostly “provincial”? Because the distribution of citations is extremely asymmetrical, it could very well be that the geographic de-concentration of production activities did not lead to the geographic de-concentration of citations, but instead contributed to creating increasingly asymmetrical flows of information for the benefit of “central” cities and countries. This article aims to verify whether this is the case by analysing the geographic distribution of citations received, using a method for localising the publications indexed in the Web of Science by urban areas. Results show a growing convergence between the geography of scientific production and that of scientific citations. The number of citations received by the world’s 30 top publishing countries and cities tended to edge closer to the global average. While Singapore, China, India and Iran suffered from a deficit of visibility in 2000, their level considerably improved by 2007. Moreover, a decrease in the discrepancy between cities’ scientific visibility is observed in almost all countries of the world, except for three: Sweden, Egypt and Denmark. To finish, our results show that the gap between the share of citations and the share of publications has decreased across all disciplines. A significant asymmetry in favour of English-speaking countries has remained in the distribution of citations in humanities and social sciences (but it is diminishing).

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[47]
Matthiessen C.W., &Schwarz , A.W. (1999). Scientific centres in Europe: An analysis of research strength and patterns of specialisation based on bibliometric indicators. Urban Studies, 36(3), 453-477.This paper presents the first analysis of scientific strength by output (papers in the Science Citation Index 1994-96) produced by authors from the 'greater' urban regions of Europe. Top lists of European centres are indicated. Four agglomerations constitute the European super-league of science: London, Paris, Moscow and the Dutch urban agglomeration of Amsterdam, the Hague, Rotterdam and Utrecht. The next layer could be named the primary league and comprises 19 large research centres. A third group of 16 cities forms a secondary league of 16 smaller research centres. These upper-level research strongholds are categorised in the paper where patterns of specialisations by absolute and relative distribution of research disciplines for each city are identified and families of cities by research pattern are analysed and compared within the perspective of urban economic growth and change.

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[48]
Meo S. A., Al Masri A. A., Usmani A. M., Memon A. N., & Zaidi S. Z. (2013). Impact of GDP, Spending on R&D, Number of Universities and Scientific Journals on Research Publications among Asian Countries. PLoS ONE, 8(6), e66449

[49]
Miyairi N., & Chang, H. W. (2012). Bibliometric characteristics of highly cited papers from Taiwan, 2000-2009. Scientometrics, 92(1), 197-205.AbstractThe present study analyzes bibliometric characteristics of Taiwan’s highly cited papers published from 2000 to 2009. During this period, Taiwan ranked within the top 30 countries by number of highly cited papers, defined in Thomson Reuters’ Essential Science Indicators (ESI) as those that rank in the top 102% by citations for their category and year of publication. Taiwan made notable progress in world-class research in the two consecutive 5-year periods 2000–2004 and 2005–2009. For the group of highly cited papers from Taiwan, USA, China, Germany, and Japan were the top collaborating countries over the decade. In recent years, Taiwan has increasingly collaborated with European countries whose output of highly cited papers is relatively high and increasing, rather than with its neighboring countries in Asia. Overall, Taiwan produced highly cited papers in all the 22 ESI subject categories during the 10-year period. Taiwan’s output of highly cited papers was greatest in the categories of Engineering, Clinical Medicine, and Physics, while those in Agricultural Sciences and Mathematics exceeded the expected output level in relative terms. More detailed analyses would be useful for a holistic understanding of Taiwan’s research landscape and their progress in world-class research, combining both bibliometric and non-bibliometric data, such as researcher mobility, research grants, and output from internationally-collaborated research programs.

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[50]
Moin M., Mahmoudi M., & Rezaei N. (2005). Scientific output of Iran at the threshold of the 21st century. Scientometrics, 62(2), 239-248.<a name="Abs17"></a>Using the method of bibliometrics, a 1999-2002 biochemistry and molecular biology database was constructed for China from the <emphasis type=""></emphasis><img src="/content/J3W865Q255VGR260/xxlarge8221.gif" alt="rdquo" align="MIDDLE" border="0">Italic<img src="/content/J3W865Q255VGR260/xxlarge8221.gif" alt="rdquo" align="MIDDLE" border="0">>Science Citation Index Expanded (SCI-Expanded). Based on this database, the author quantitatively analyzed the current research activity in biochemistry and molecular biology in China. Results show that almost half the publications were published in Chinese journals. The percentage of articles published by Chinese authors in the total articles from the world is increasing. The number of articles published in high influence journals is continuously increasing. The research outputs are mainly located in Beijing, Shanghai and Hong Kong. The sites of the China Science Academy and National Universities are the important locations for these studies. The collaboration rate of Chinese output is low as compared to results from other countries. USA and Japan are the main international collaborating countries.

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[51]
Mongeon P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics, 106(1), 213-228.Abstract Bibliometric methods are used in multiple fields for a variety of purposes, namely for research evaluation. Most bibliometric analyses have in common their data sources: Thomson Reuters’ Web of Science (WoS) and Elsevier’s Scopus. The objective of this research is to describe the journal coverage of those two databases and to assess whether some field, publishing country and language are over or underrepresented. To do this we compared the coverage of active scholarly journals in WoS (13,605 journals) and Scopus (20,346 journals) with Ulrich’s extensive periodical directory (63,013 journals). Results indicate that the use of either WoS or Scopus for research evaluation may introduce biases that favor Natural Sciences and Engineering as well as Biomedical Research to the detriment of Social Sciences and Arts and Humanities. Similarly, English-language journals are overrepresented to the detriment of other languages. While both databases share these biases, their coverage differs substantially. As a consequence, the results of bibliometric analyses may vary depending on the database used. These results imply that in the context of comparative research evaluation, WoS and Scopus should be used with caution, especially when comparing different fields, institutions, countries or languages. The bibliometric community should continue its efforts to develop methods and indicators that include scientific output that are not covered in WoS or Scopus, such as field-specific and national citation indexes.

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[52]
Morrison J.(2014). China becomes world’s third-largest producer of research articles. Nature News, 06/02/2014. doi:10.1038/nature.2014.14684But quantity is being favoured over quality, experts say.

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[53]
Nature Index (2016). US tops global research performance. Nature Index, 20/04/2016.

[54]
Paasi A. (2005). Globalisation, academic capitalism, and the uneven geographies of international journal publishing spaces. Environment and Planning A, 37(5), 769-789.

[55]
Pan K. R., Kaski K., & Fortunato S. (2012). World citation and collaboration networks: uncovering the role of geography in science. Scientific Reports, 2, 902Modern information and communication technologies, especially the Internet, have diminished the role of spatial distances and territorial boundaries on the access and transmissibility of information. This has enabled scientists for closer collaboration and internationalization. Nevertheless, geography remains an important factor affecting the dynamics of science. Here we present a systematic analysis of citation and collaboration networks between cities and countries, by assigning papers to the geographic locations of their authors affiliations. The citation flows as well as the collaboration strengths between cities decrease with the distance between them and follow gravity laws. In addition, the total research impact of a country grows linearly with the amount of national funding for research & development. However, the average impact reveals a peculiar threshold effect: the scientific output of a country may reach an impact larger than the world average only if the country invests more than about 100,000 USD per researcher annually.

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[56]
Paul-Hus A., Mongeon P., Sainte-Marie M.,& Larivière V. (2017). The sum of it all: Revealing collaboration patterns by combining authorship and acknowledgements. Journal of Informetrics, 11(1), 80-87.Acknowledgments are one of many conventions by which researchers publicly bestow recognition towards individuals, organizations and institutions that contributed in some way to the work that led to publication. Combining data on both co-authors and acknowledged individuals, the present study analyses disciplinary differences in researchers credit attribution practices in collaborative context. Our results show that the important differences traditionally observed between disciplines in terms of team size are greatly reduced when acknowledgees are taken into account. Broadening the measurement of collaboration beyond co-authorship by including individuals credited in the acknowledgements allows for an assessment of collaboration practices and team work that might be closer to the reality of contemporary research, especially in the social sciences and humanities.

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[57]
Piro F.N., &Sivertsen G.(2016). How can differences in international university rankings be explained? Scientometrics, 109(3), 2263-2278.University rankings are typically presenting their results as league tables with more emphasis on final scores and positions, than on the clarification of why the universities are ranked as they are. Finding out the latter is often not possible, because final scores are based on weighted indicators where raw data and the processing of these are not publically available. In this study we use a sample of Scandinavian universities, explaining what is causing differences between them in the two most influential university rankings: Times Higher Education and the Shanghai-ranking. The results show that differences may be attributed to both small variations on what we believe are not important indicators, as well as substantial variations on what we believe are important indicators. The overall aim of this paper is to provide a methodology that can be used in understanding universities different ranks in global university rankings.

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[58]
Shehatta I., &Mahmood , K. (2016). Correlation among top 100 universities in the major six global rankings: policy implications. Scientometrics, 109(2), 1231-1254.Abstract The discrepancies among various global university rankings derive us to compare and correlate their results. Thus, the 2015 results of six major global rankings are collected, compared and analyzed qualitatively and quantitatively using both ranking orders and scores of the top 100 universities. The selected six global rankings include: Academic Ranking of World Universities (ARWU), Quacquarelli Symonds World University Ranking (QS), Times Higher Education World University Ranking (THE), US News & World Report Best Global University Rankings (USNWR), National Taiwan University Ranking (NTU), and University Ranking by Academic Performance (URAP). Two indexes are used for comparison namely, the number of overlapping universities and Pearson/Spearman correlation coefficients between each pair of the studied six global rankings. The study is extended to investigate the intra-correlation of ARWU results of the top 100 universities over a 5-year period (2011鈥2015) as well as investigation of the correlation of ARWU overall score with its single indicators. The ranking results limited to 49 universities appeared in the top 100 in all six rankings are compared and discussed. With a careful analysis of the key performance indicators of these 49 universities one can easily define the common features for a world-class university. The findings indicate that although each ranking system applies a different methodology, there are from a moderate to high correlations among the studied six rankings. To see how the correlation behaves at different levels, the correlations are also conducted for the top 50 and the top 200 universities. The comparison indicates that the degree of correlation and the overlapping universities increase with an increase in the list length. The results of URAP and NTU show the strongest correlation among the studied rankings. Shortly, careful understanding of various ranking methodologies are of utmost importance before analysis, interpretation and usage of ranking results. The findings of the present study could inform policy makers at various levels to develop policies aiming to improve performance and thereby enhance the ranking position.

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[59]
Sud P.,& Thelwall ,M. (2016). Not all international collaboration is beneficial: The Mendeley readership and citation impact of biochemical research collaboration. Journal of the Association for Information Science and Technology, 67(8), 1849-1857.Abstract Biochemistry is a highly funded research area that is typified by large research teams and is important for many areas of the life sciences. This article investigates the citation impact and Mendeley readership impact of biochemistry research from 2011 in the Web of Science according to the type of collaboration involved. Negative binomial regression models are used that incorporate, for the first time, the inclusion of specific countries within a team. The results show that, holding other factors constant, larger teams robustly associate with higher impact research, but including additional departments has no effect and adding extra institutions tends to reduce the impact of research. Although international collaboration is apparently not advantageous in general, collaboration with the United States, and perhaps also with some other countries, seems to increase impact. In contrast, collaborations with some other nations seems to decrease impact, although both findings could be due to factors such as differing national proportions of excellent researchers. As a methodological implication, simpler statistical models would find international collaboration to be generally beneficial and so it is important to take into account specific countries when examining collaboration.

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[60]
Tardy C.(2004). The role of English in scientific communication: Lingua franca or Tyrannosaurus rex? Journal of English for Academic Purposes, 3(3), 247-269.The use of English as an international language of science (EILS) is by now well documented; depending on one’s orientation, English may be seen as a neutral lingua franca or it may be seen more insidiously as a dominating and overpowering force. This paper explores these co-existing roles of EILS through various perspectives. It begins by outlining conversations regarding EILS found in the literature of applied linguistics and the scientific community. The paper then turns to the perspective of international graduate students studying at an American university through a small-scale questionnaire and focus group interview study that attempts to understand these students’ attitudes toward English and its role in scientific communication. Findings from the study are discussed in light of published conversations of EILS and implications for an EAP classroom that aims to recognize the dual roles of English in scientific communication.

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[61]
Tian P. (2016). China’s diaspora key to science collaborations. Nature Index, 23/06/2016.

[62]
Uddin S., Hossain L., Abbasi A., & Rasmussen K. (2012). Trend and efficiency analysis of co-authorship network. Scientometrics, 90(2), 687-699.AbstractAlthough co-authorship in scientific research has a long history the analysis of co-authorship network to explore scientific collaboration among authors is a relatively new research area. Studies of current literature about co-authorship networks mostly give emphasis to understand patterns of scientific collaborations, to capture collaborative statistics, and to propose valid and reliable measures for identifying prominent author(s). However, there is no such study in the literature which conducts a longitudinal analysis of co-authorship networks. Using a dataset that spans over 20years, this paper attempts to explore efficiency and trend of co-authorship networks. Two scientists are considered connected if they have co-authored a paper, and these types of connections between two scientists eventually constitute co-authorship networks. Co-authorship networks evolve among researchers over time in specific research domains as well as in interdisciplinary research areas. Scientists from diverse research areas and different geographical locations may participate in one specific co-authorship network whereas an individual scientist may belong to different co-authorship networks. In this paper, we study a longitudinal co-authorship network of a specific scientific research area. By applying approaches to analyze longitudinal network data, in addition to known methods and measures of current co-authorship literature, we explore a co-authorship network of a relatively young and emerging research discipline to understand its trend of evolution pattern and proximity of efficiency.

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[63]
Van Noorden, R. (2010). Cities: Building the best cities for science. Nature, 467(7318), 906-908.Nature - the world's best science and medicine on your desktop

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[64]
Van Raan, A. F. J. (1998). The influence of international collaboration on the impact of research results: Some simple mathematical considerations concerning the role of self-citations. Scientometrics, 42(3), 423-428.

[65]
Van Weijen,D. (2012). The Language of (Future) Scientific Communication. Research Trends, 31, 11/2012.

[66]
Vinkler P.(2008). Correlation between the structure of scientific research, scientometric indicators and GDP in EU and non-EU countries. Scientometrics, 74(2), 237-254.<a name="Abs1"></a>Significant discrepancies were found in the ratio and relative impact of the journal papers of several scientific fields of some Central and Eastern European (CEE) countries compared to the European Community member states, the US and Japan (EUJ countries). A new indicator, characterizing the Mean Structural Difference of scientific fields between countries has been introduced and calculated for CEE countries. For EUJ countries correlation between the GDP and number of publications of a given year proved to be non-significant. Longitudinal studies showed, however, significant correlations between the yearly values of GDP and number of papers published. Studying data referring to consecutive time periods revealed that there is no direct relationship between the GDP and information production of countries. It may be assumed that grants for R&amp;D do not actually depend on real needs, but the fact is that rich countries can afford to spend more whilst poor countries only less money on scientific research.

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[67]
Vinkler P. (2010). The Evaluation of Research by Scientometric Indicators.Oxford: Chandos Publishing.

[68]
Wang X., Xu S., Wang Z., Peng L., & Wang C. (2013). International scientific collaboration of China: Collaborating countries, institutions and individuals. Scientometrics, 95(3), 885-894.AbstractUsing bibliometric methods, we investigate China’s international scientific collaboration from three levels of collaborating countries, institutions and individuals. We design a database in SQL Server, and make analysis of Chinese SCI papers based on the corresponding author field. We find that China’s international scientific collaboration is focused on a handful of countries. Nearly 9502% international co-authored papers are collaborated with only 20 countries, among which the USA account for more than 4002% of all. Results also show that Chinese lineage in the international co-authorship is obvious, which means Chinese immigrant scientists are playing an important role in China’s international scientific collaboration, especially in English-speaking countries.

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[69]
Xie Y., Zhang C., & Lai Q. (2014). China’s rise as a major contributor to science and technology. Proceedings of the National Academy of Sciences of the United States of America, 111(26), 9437-9442.Abstract In the past three decades, China has become a major contributor to science and technology. China now employs an increasingly large labor force of scientists and engineers at relatively high earnings and produces more science and engineering degrees than the United States at all levels, particularly bachelor's. China's research and development expenditure has been rising. Research output in China has been sharply increasing since 2002, making China the second largest producer of scientific papers after the United States. The quality of research by Chinese scientists has also been improving steadily. However, China's rise in science also faces serious difficulties, partly attributable to its rigid, top-down administrative system, with allegations of scientific misconduct trending upward.

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[70]
Zhang H., &Guo ,H. (1997). Scientific research collaboration in China. Scientometrics, 38(2), 309-319.The purpose of this study is to analyze the characteristics of scientific research collaboration in China by bibliometric indicators, collaborative index, degree of collaboration and level of collaboration, based on the articles published in 1218 titles of Chinese scientific and technical periodicals in the year 1993. The results suggest that the current trend of collaboration among multiauthors and multiinstetutions for producing scientific articles may have reflected the multidimensional science of China.

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[71]
Zhou P., Thijs B., & Glänzel W. (2009a). Regional analysis on Chinese scientific output. Scientometrics, 81(3), 839-857.Based on data from the Science Citation Index Expanded (SCIE) and using scientometric methods, we conducted a systematic analysis of Chinese regional contributions and international collaboration in terms of scientific publications, publication activity, and citation impact. We found that regional contributions are highly skewed. The top positions measured by number of publications or citations, share of publications or citations are taken by almost the same set of regions. But this is not the case when indicators for relative citation impact are used. Comparison between regional scientific output and R&D expenditure shows that Spearman rank correlation coefficient between the two indicators is rather low among the leading publication regions.

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[72]
Zhou P., Thijs B., & Glänzel W. (2009b). Is China also becoming a giant in social sciences? Scientometrics, 79(3), 593-621.At present China is challenging the leading sciento-economic powers and evolving to one of the world’s largest potentials in science and technology. Jointly with other emerging economies, China has already changed the balance of power among the formerly leading nations as measured by scientific production. In the present paper, the evolution of China’s publication activity and citation impact in the social sciences is studied for the period 1997–2006. Besides the comparative analysis of trends in publication and citation patterns and of national publication profiles, an attempt is made to interpret the results in both the regional and global context.

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[73]
Zhou Y.(2005). The making of an innovative region from a centrally planned economy: Institutional evolution in Zhongguancun Science Park in Beijing. Environment and Planning A, 37(6), 1113-1134.

[74]
Zou Y., &Laubichler , M.D.(2017). Measuring the contributions of Chinese scholars to the research field of systems biology from 2005 to 2013. Scientometrics, 110(3), 1615-1631.Systems biology is a new field of biology that has great implications for agriculture, medicine, and sustainability. In this article we explore the contributions of Chinese authors to systems biology

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