1. Introduction

A quality of a scientific publication of any country can be predicted, partly at least, from the GDP per capita but also from the percentage of money that was spent on R&D by this country (Allik 2013a, King 2004, Vinkler 2018). Hence, only very rich nations spending a considerable amount of the produced wealth on R&D afford to produce high-quality scientific papers, which have an impact on science. It was also noticed that open countries whose scientists collaborate with their foreign colleagues are likely to produce scientific output of higher quality (European Commission 2015, Moed 2005, Wagner and Jonkers 2017). Although wealth and money are important factors, countries differ considerably in terms of the efficiency of turning financial input into bibliometrically measurable output (King 2004, Leydesdorff and Wagner 2009, Vinkler 2008). This indicates that not all R&D money is necessarily turned into the high quality scientific output; some of it has been lost in translation. It was observed that countries differ in their ability to transform scientific research into immediate economic return (Vinkler, 2008). Besides money, achieving scientific excellence also requires reasonable science policies, research ethos, and even a culture that supports discovery of new ideas (Jurajda Kozubek, Munich, and Skoda 2017, Moed 2005, Ntuli, Inglesi-Lotz, Chang, and Pouris 2015, van Leeuwen and Moed 2012, van Leeuwen, Visser, Moed, Nederhof, and van Raan 2003).

In the study of factors that could determine scientific excellence, the progress of science in the three Baltic states--Estonia, Latvia, and Lithuania--may be particularly informative (Allik 2003, Kristapsons, Martinson, and Dagyte 2003). By a coincidence, all three countries published only approximately 300 papers each year in journals covered by the Web of Science (WoS; or its predecessor, Clarivate Analytics) around the moment when the Soviet Union collapsed in 1991 (Allik 2003). Only fifteen or so years later, Lithuania's scientists published about 1,300 papers in the peer-reviewed journals against only about 400 papers that were authored by Latvian researchers in 2007 (Allik 2008; Figure 1). Although the three Baltic countries are often confused, the progress in their science output, both in quantity and quality, has diverged remarkably during the years after regaining independence in 1991. In spite of very similar historical, political, and economic experiences, the progress of science measured on the basis of their bibliometric indicators have been dramatically different (Allik 2011, 2015). To a certain extent, it looks like a natural experiment where three different subjects experienced different treatments with a purpose to observe how it could affect their scientific progress.

In this paper we intend to provide an overview of the Estonian science, using Latvia and Lithuania as a benchmark, based on the latest release (March 15, 2018) of the Essential Science Indicators (ESI; Clarivate Analytics) covering 11 years long period from 2007 until 2017. As we hope to demonstrate, the progress of Estonian science, especially during the last decade, has been spectacularly fast. This progress of turning financial input into bibliometrically measurable output can be even called miraculous, because according to the Statistics Estonia, investments to R&D have diminished in the past three last years, despite the embarrassing fact that it is only 0.8% of Estonia's GDP (https://www.stat.ee/news-release-2017-128). We are not expecting to solve this puzzle--turning diminishing financial input into increasing bibliometric output--completely. Instead we hope to provide some additional knowledge how to avoid mistakes in nurturing such a delicate process as scientific excellence.

2. Method

Data were collected from the latest ESI release (updated on March 15, 2018) covering 11 years long period from January 1, 2007 until December 31, 2017. All journals, except universal such as Nature, Science and the Proceedings of the National Academy of Sciences (PANAS), are divided into 21 scientific areas in addition to Multidisciplinary containing papers, which are difficult to assign to any of these areas. When ESI was designed, it was decided to exclude humanities from the list of scientific areas. Thus, ESI data cannot tell anything specific about the state in the humanities for any country or institution.

ESI followed more than 12 million articles in more than 12,000 journals that were published during 11-year observation period and indexed in the WoS. Inclusion in ESI is dependent upon meeting certain citation thresholds. Only the most highly cited individuals, institutions, journals, countries and papers are included in ESI. Researchers, institutions, and highly cited papers must exceed 1% top-citation threshold to be included in ESI. For instance, to be included as a highly cited researcher in any of 22 areas, the total number of citations to a person's output must be in the top 1% when compared to all other researchers in that particular area, who have published papers in this area during the last 11 years. Thresholds for areas are remarkably different. For example, a computer scientist enters ESI collecting at least 322 citations to papers published during the last 11 years while the threshold for a physicist is as high as 7,999 citations. Understandably, countries/territories and journals need to be among the top 50% in order to enter ESI.

Because ESI includes countries/territories producing perhaps only a small number of papers during the 11-year observation period, we excluded from the further analysis all countries/territories publishing fewer than 4,000 papers. For example, over 3,000 papers were published by Senegal, Panama, Malawi, Uzbekistan, Zimbabwe, Macedonia, Sudan, and Burkina-Faso. It could be also mentioned that Bermuda, Seychelles, and Vatican published each fewer than 300 papers included in ESI over 11 years.

3. Results

Table 1 presents a list of countries who entered ESI and published more than 4,000 documents in the period 2007-2017. The listed countries are ranked according to the mean citations per paper (the 5th column Cites/Paper). The 6th column (Top Paper %) show the percentage of papers which reached the top 1% rank in their citations. The next, the 7th column (HSDI Rank) demonstrates country ranking on the High Quality Science Index, which was proposed to combine average citation rate with the percentage of papers reaching the top 1% (Allik 2013a). To compute HQSI both indicators, the mean citation rate and the percentage of top papers, were transformed into normalized scores after which their mean value was found. The last column show changes in the ranking position compared to a similar ranking list for the period 1997-2007 (Allik 2008; Table 1). Several countries (Luxembourg, Nepal, Ecuador, Qatar, Macau, Bosnia and Herzegovina, and Iraq) were missing from the previous list and we cannot compute the change in ranking for them.

Small countries such as Iceland, Switzerland, and Scotland were able to produce science of the highest impact. Together with the Netherlands and Denmark they produced papers with the highest mean citation rate from which the highest percentage reached the top of citations. If we compare rankings, 1997-2007 (Allik 2008; Table 1) with the current one, then three countries, i.e. the Republic of Georgia, Singapore, and Saudi Arabia have improved their position most by increasing 50, 31, and 19 positions respectively. Three countries who dropped most in their ranking were Vietnam (-26), Poland (-18), and Russia (-18). Estonia improved 11 positions in the ranking while Latvia and Lithuania dropped 13 and 16 positions respectively in the ranking during the last 10 years.

There were worries that Americans produce higher quality science than the EU countries, with a gap between them widening (Albarran, Crespo, Ortuno, and Ruiz-Castillo 2010, European Commission 2015, Leydesdorff, Wagner, and Bornmann 2014). Inspecting the table above, there is no foundation for these fears. USA not only lost 5 rank positions compared with the previous ranking 10 years ago, but its HQSI rank (15) is 8 positions behind the overall ranking (7) based on the mean citations. The negative gap can be used as a Mediocrity Index pointing to countries, which produce unexpectedly small number of highly influential papers compared with the total number of papers indexed in ESI (Allik 2013a). As an example, experts noticed already several years ago that Scandinavian countries, including Sweden, may have fallen into the comfort zone trap producing an unexpectedly small number of highly cited papers (Karlsson and Persson 2012). If we compare rankings on the mean citation rate and the percentage of highly cited papers, we see that unlike other Scandinavian countries, Sweden and Finland are producing fewer highly cited papers than it could be expected on the basis of the impact of their papers in general. This may indicate that their researchers have become complaisant with regularly good papers and do not aim to produce scientific breakthroughs.

Based on the HQSI ranking, Estonia has currently the 12th position, which is even of a higher ranking that Sweden (14), USA (15), and Finland (19). Latvia occupies the 56th and Lithuania the 77th position. Russia has the 95th position, which is only three positions away from the very bottom.

Next, we demonstrate how the mean citation rate of papers authored by Estonian scientists has changed during the last eleven years. Figure 1 shows the percentage of the citing rate relative to the ESI average. In 2006, Estonian papers were cited approximately 20% less than papers in ESI on average. By the end of 2017, papers written by Estonian scholars were cited 30% more times than papers in ESI on average. The impact of Estonian papers increased approximately 8% faster than the impact of all ESI papers have increased on average during the last five years. If it had been a growth of economic indicators it would have been a sensation.

By the number of citations per paper, Estonia shares approximately the same position as France and Israel, which are much wealthier countries compared to Estonia. For comparison, France had in 2017 GDP per capita $38,578 and Israel 37,778. Estonia's GDP per capita in 2017 was about $17,853, which is approximately 50% of GDP in these two countries. Nevertheless, Estonian authors were able to publish papers, which were cited as frequently as papers that were written by the French and Israeli scientists. Please note that Estonia has never won a Nobel Prize compared to 68 Nobel Prize winners in France and 12 in Israel. Like Finland, Estonia has a relatively high national IQ (Pullmann, Allik, & Lynn, 2004; see also http://www.oecd.org/estonia/pisa-2015-estonia.htm), but one of the lowest number of Nobel prizes (Dutton, Nijenhuis, & Roivainen, 2014). It is also useful to remember that France and Israel spend respectively 2.3% and 4.3% of their GDP on R&D. It is even embarrassing to say that Estonia's R&D expenditures are falling the third year in a row, below 0.8% of the GDP (Estonian Research Council, 2017, p. 12; Figure 1.1).

It is unlikely that small countries have equal strengths in all areas into which science is in ESI divided. Table 2 provides ESI bibliometric statistics in each of ESI research areas for Estonia, Latvia, and Lithuania. Estonia passed 50% citation threshold in all 22, Lithuania in 21 areas, and Latvia in 17 research fields. Another success story, the Republic of Georgia passed the ESI thresholds only in 11 research areas. The strength of a country can be measured by the impact of papers measured relative to the ESI world average in this field. For example, in 10 research fields papers authored by Estonian scientists have a higher impact than papers on average in this field (these fields are marked with red). Latvian scientists publish papers with above average impact in two fields: Clinical Medicine and Molecular Biology & Genetics. Lithuania performed above ESI average in three fields: Immunology, Molecular Biology & Genetics, and Plant & Animal Science.

The observations we can make are very similar to those about science in the three Baltic States after the first decade of independence (Allik 2003). Lithuania published the largest number of papers (22,435) exceeding Estonia (16,818) and particularly Latvia (6,478) by more than three times. However, in terms of the paper's quality, which can be measured by the number of times they have been cited, Latvia lags more than 20% behind of the ESI world average. It seems that Lithuania failed to improve the quality of its scientific publications because their citation rate is 36% below ESI world's average citation rate. Thus, out of the three Baltic countries only Estonia was able to increase not only the volume of its publications but also their mean impact (Allik 2013a).

The mean citation rate--cites per paper--tells only a part of the story about a country's science. There were many proposals how to supplement the mean citation rate with additional indicators, which could improve the quality of bibliometric indicators. For example, researchers were concerned how much self-citation could distort the mean citation rate (Aksnes 2003, Jaffe 2011, Thijs and Glanzel 2006). In addition to individual self-citation, there may also be a country-level self-citation bias: the degree to which authors from one country cite works carried out by the researchers of their own country relative to the work that was performed outside of that country (Allik 2013b, Jaffe 2011). In addition to the percentage of highly cited papers, the other end of citation frequencies--the percentage of not cited papers--is a sensitive indicator of the scientific quality (Leydesdorff and Wagner 2009, Okubo 1997). Of course, the number of researchers per each country who have reached the top 1% cites could be an additional indicator of the quality of research in any country. Unfortunately, the ESI's search engine does not allow sorting researchers according to their affiliations. We tested potential Estonian researchers one by one and were able to identify 66 researchers with Estonian affiliation (see Appendix 1).

Luckily, Clarivate Analytics composes another, even shorter list of about 3,500 highly cited researchers who have reached the top of about 160 most cited researchers in each out of 21 research fields (Clarivate Analytics, 2017; https://clarivate.com/hcr/researchers-list/archived-lists/). In 2017-year's list, Estonia was represented by seven highly cited researchers: Martin Zobel (Environment/Ecology), Tonu Esko (Molecular Biology & Genetics), Andres Metspalu (Ibid.), Markus Perola (Ibid.), Urmas Koljalg (Plant & Animal Science), Ulo Niinemets (Ibid.), and Leho Tedersoo (Ibid.). For a reference, nobody from Latvian or Lithuanian researches were included into the list of highly cited researchers, which is perhaps not very surprising considering other bibliometric indicators. Although Russia outperforms Estonia approximately 20 times in the number of published papers, only three Russian researchers have reached the list of highly cited researchers.

4. Discussion

Even after 25 years that have passed from the collapse of the Soviet Union, most post-communist countries are still lagging behind their EU counterparts in the quality of science they produce (Jurajda et al. 2017, Kozak, Bornmann, and Leydesdorff 2015, Must 2006, Pajic 2015, Vinkler 2008). If there is one post-communist country that has managed to escape the curse of the past, it is Estonia occupying the highest position in rankings among all post-communist countries (Allik 2003, 2008, 2011, 2015, 2017). Although the Republic of Georgia is only two positions behind, this was achieved by supporting science only in few limited areas having practically no publications in others. The former flagship of the post-communist science Hungary is on the 37th position falling 5 compared with the situation ten years ago. Some observers were able to foresee this decline (Izsvak, Ivics, and Mates 2006).

Usually, the lack of money is blamed for the lagging behind of the rest of Europe. In transitional economies, however, it is very difficult to convince policymakers to allocate more money for science because there is no convincing evidence that investment into R&D will have immediate return in the form of economic growth (Hatemi-J, Ajmi, El Montasser, Inglesi-Lotz, and Gupta 2016, Solarin and Yen 2016, Yasgul and Guris 2016). Some countries show a causal relationship from the output of research to real GDP, but some other countries do not (Hatemi-J et al., 2016). Although economic and scientific wealth, as we said above, are related in general (King, 2004), there are many factors that could intervene to alter straightforward relationship. A good example is Estonia together with the Republic of Georgia who are two exceptions violating a relatively uncomplicated relationship between economic wealth and the impact of scientific papers written by researchers in a given country. Luxembourg is a good example of the opposite deviation because $105,914 of the GDP per capita of Luxembourg in 2017 expects higher position than the 36th in Table 1 (King 2004; Table 1 and Figure 2).

Because the gap between Estonia's economic and scientific performances was so obvious, we proposed that there must be a considerable amount of 'hidden money' (Allik 2003, 2008). Indeed, the unrealistically low cost of scientific articles suggests that a considerable amount of 'hidden money' must be involved, not reflected in the official expenditures. One possibility is collaboration with partners from more affluent countries. Typically, these collaborative projects are chiefly financed by wealthy Western partners and domestic contribution is primarily a qualified but still cheap labor (Allik 2003, 2008). However, it was clear that the 'hidden money', if there was any, was not enough to fill the gap between recorded expenses and disproportionately high scientific output.

The next obvious candidate to explain differences in the counties' economic and scientific performance was the efficiency of the R&D system to transform financial input into bibliometrically-measured output. For instance, differences between Estonia, Latvia, and Lithuania in their scientific productivity and quality, which were virtually absent in the early 1990s, can be explained with different approaches and practices of their R&D systems (Kristapsons et al. 2003, Martinson 2015). There are several plausible reasons that alone or in combination with others could explain stagnation in Latvian and inflation in Lithuanian science. For example, one obvious mistake in Latvian science was the elimination of permanent science financing replacing it with a temporary grant system only (Allik 2003). Lithuania, on the other hand, created its own cottage industry of scientific journals instead of competing with the rest of the word for publishing in the leading international journals. Although damaging, one of the main mistakes that Latvia and Lithuania made was not building an impartial R&D system, with the only goal of promoting scientific excellence.

As it was already mentioned, among factors that are behind the recent success of Estonian science is a relatively strong competition for limited funds (Allik 2015). Ever since Estonia regained its independence in 1991, most research funding applications had to be written in English, which allowed using foreign experts as impartial judges. An inevitable consequence of the project-based funding is to make the fairness of the decision-making process almost compulsory. In addition, writing all applications in English was an invaluable practice for writing scientifically sound articles, to say nothing about internationally competitive and successful grant applications themselves. For the transparency of the decision process, all scientific assessment and decision-making in Estonia was given to panels consisting of top-level researchers who were mandated to make sovereign decisions that have been rarely reversed by non-scientific authorities. Panels consisting of the best active scientists decided what question was important to study and proposals were selected based on their scientific merits, not what science bureaucrats typically think about the importance for particular institutions and Estonian economy and society in general. It is not surprising that bureaucrats, who are responsible for science, became worried about too much autonomy and self-governance that scientists had in Estonia. Consequently, the amount of competitive and project-based funding was decreased in favor of more stable funding schemes where decisions can be made by the administrators of universities and other research institutions (Allik 2015).

Estonian politicians became very excited if foreign observers claimed, for example, that Estonia had become the digital leader of Europe (Gaskell, 2017) (1). Nevertheless, Estonia became the only country whose expenditures on the R&D have decreased in the third year in running. Local politicians even invented a story why the digital tiger did not need to invest more money into research. It was said that public did not understand the need for science and this is why it was not wise to discuss this question in the context of the forthcoming general elections. Officials declared that if Estonian scientists wanted more money for their research they needed to provide evidences that their research helped to increase productivity of Estonian economy. Only after Kristjan Vassil, the Vice Rector for Research, University of Tartu, published a paper in the largest newspaper, the tone of politicians became slightly more apologetic (Vassil 2018).

Summarizing, the economic and scientific wealth of nations are intimately related to each other (Allik 2013a, King 2004). Only very few rich countries can afford mediocre science because they have faith in their neighbors. However, as Estonia and its two neighbors, Latvia and Lithuania, demonstrate a successful science is inevitable because of the economic growth and prosperity. Many factors could intervene in the process of converting economic wealth into bibliometrically measurable scientific output. The mission of small countries is to be a trial case from which we can learn recipes for the growth of scientific wealth and, more important, how to avoid mistakes.

Kalmer Lauk (1) and Juri Allik (1,2)

(1) University of Tartu and (2) Estonian Academy of Sciences

Addresses:

Kalmer Lauk

University of Tartu Grant Office Ulikooli 18 50090 Tartu, Estonia

E-mail: [email protected]

Juri Allik

Institute of Psychology University of Tartu Naituse 2 50409 Tartu, Estonia

E-mail: [email protected]

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APPENDIX 1

The list of Estonian researchers who reached 1% top citation rate in
one or several research fields

    Researcher       Institution  Papers  Cites   Top Papers

 1  Abarenkov, K     UT            35      2,667  11

 2  Alavere, H       UT             8      2,873   4
 3  Allik, J         UT           104      2,286   4
 4  Bahram, M        UT           108      3,067  11

 5  Blinova, I       NICPB         22      1,293   4
 6  Brosche, M       UT            50      1,788   6
 7  Choubey, V       UT            20      3,391   2
 8  Davison, J       UT           149      4,098   8
 9  Dubourguier, HC  EULS          15      2,060   4

10  Dumas, M         UT            98      1,066   1
11  Esko, T          UT           193     17,554  42
12  Giammanco, A     NICPB        716     21,746  48
13  Heinlaan, M      NICPB         15      1,261   4
14  Helm, A          UT            49      1,475   2
15  Ivask, A         NICPB         45      2,592   7

16  Junninen, H      UT           103      4,912  17
17  Kaasik, A        UT            57      3,547   2
18  Kadastik, M      NICPB        671     22,007  48
19  Kahru, A         NICPB         69      3,877   9

20  Kasemets, K      NICPB         32      1,867   6

21  Kivisild, T      UT            85      3,834   3
22  Kohout, P        UT            41      1,207   3
23  Koljalg, U       UT            52      5,196  13

24  Kollist, H       UT            33      1,460   6
25  Kutser, T        UT            39      1,314   2
26  Koressaar, T     UT             7      2,477   2
27  Laan, Maris      UT           121      4,110   6
28  Langel, U        UT           147      3,647   2
29  Leinsalu, M      NIHD          52      6,245  12
30  Leito, I         UT           125      2,205   0
31  Liira, J         UT            76      2,495   3
32  Magi, R          UT           116     10,904  26
33  Mander, U        UT            89      2,066   4
34  Merits, A        UT            83      1,369   0
35  Metspalu, A      UT           282     19,436  46
36  Metspalu, M      UT            53      3,137   4
37  Mihailov, E      UT            73      5,755  18
38  Milani, L        UT           161      7,026  14
39  Moora, M         UT            64      3,126  11

40  Morris, A P      UT           223     23,268  29
41  Muntel, M        NICPB        315     15,631  18
42  Naatanen, R      UT            71      3,250   3
43  Niinemets, U     EULS         181      7,564  22
44  Opik, M          UT            51      1,788   6
45  Org, E           UT            30      3,912   9
46  Parmasto, E      EULS           5        980   1
47  Partel, M        UT            78      2,887   8
48  Parts, L         UT            36      7,376   5
49  Perola, M        UT           218     19,496  39

50  Punab, M         UT           122      3,597   5
51  Poldmaa, K       UT            22      1,102   3
52  Raidal, M        NICPB/UT     731     23,728  54
53  Realo, Anu       UT            73      1,986   5
54  Rebane, L        NICPB        432     18,928  31
55  Remm, M          UT            54      3,424   3
56  Snieder, H       UT           202      9,028  19
57  Tammesoo, M-L    UT            14      3,313   8
58  Tammeveski, K    UT            99      2,631   5
59  Tedersoo, L      UT            86      5,264  18

60  Tiko, A          NICPB        572     16,960  39
61  Varnik, A        ESMHSI/       55      1,308   4
                     TLU
62  Veelken, C       NICPB        657     20,280  44
63  Viigimaa, M      NEMCF/        53     11,024  12
                     TUT
64  Villems, R       UT            73      3,972   3
65  Vilo, J          UT            49      1,857   6
66  Zobel, M         UT           119      5,694  16


    Fields           HCR2017

 1  Env/Ecol,
    Pla&AniSci
 2  MolBio&Gen
 3  Psy
 4  Env/Ecol,
    Pla&AniSci
 5  Env/Ecol
 6  Pla&AniSci
 7  MolBio&Gen
 8  Pla&AniSci
 9  Env/Ecol,
    Pharm&Tox
10  Compu
11  MolBio&Gen       Yes
12  Phys
13  Env/Ecol
14  Env/Ecol
15  Env/Ecol,
    Pharm&Tox
16  Geo
17  MolBio&Gen
18  Phys
19  Env/Ecol,
    Pharm&Tox
20  Env/Ecol,
    Pharm&Tox
21  MolBio&Gen
22  Env/Ecol
23  Pla&AniSci,      Yes
    Env/Ecol
24  Pla&AniSci
25  Env/Ecol
26  Biol&Biochem
27  MolBio&Gen
28  Pharm&Tox, Biol
29  ClinMed, SocSci
30  Chem
31  Env/Ecol
32  MolBio&Gen
33  Env/Ecol
34  Microb
35  MolBio&Gen       Yes
36  MolBio&Gen       Yes
37  MolBio&Gen
38  MolBio&Gen
39  Env/Ecol,
    Pla&AniSci
40  MolBio&Gen
41  Phys
42  Neurosci&Behav
43  Pla&AniSci       Yes
44  Pla&AniSci
45  MolBio&Gen
46  Pla&AniSci
47  Env/Ecol
48  MolBio&Gen
49  MolBio&Gen,      Yes
    ClinMed
50  ClinMed
51  Env/Ecol
52  Phys
53  Psy
54  Phys
55  Biol&Biochem
56  MolBio&Gen
57  MolBio&Gen
58  Chem
59  Pla&AniSci,      Yes
    Env/Ecol
60  Phys
61  SocSci

62  Phys
63  ClinMed

64  MolBio&Gen
65  Biol&Biochem
66  Env/Ecol,        Yes
    Pla&AniSci

Notes: UT = University of Tartu; TUT TalTech?? = Tallinn University of
Technology; NICPB = National Institute of Chemical Physics and
Biophysics; EULS = Estonian University of Life Sciences; NIHD =
National Institute for the Health Development; NEMCF = North Estonia
Medical Centre Foundation; ESMHSI = Estonian-Swedish Mental Health and
Suicidology Institute; Env/Ecol = Environment/Ecology; Biol&Biochem =
Biology & Biochemistry; ClinMed = Clinical Medicine; Phys = Physics;
Chem = Chemistry; Psy = Psychiatry/Psychology; MolBio&Gen = Molecular
Biology & Genetics; PlaAniSci = Plant & Animal Science; Neurosci&Beha =
Neuroscience & Behavior; Microb = Microbiology; ParmTox = Pharmacology
& Toxicology; Compu = Computer Science; HRC2017 = Highly Cited
Researchers 2017. Krista Fischer from University of Tartu is likely to
be included but she cannot be separated from similar name variants.

(1) https://www.forbes.com/sites/adigaskell/2017/06/23/how-estonia-became-the-digital-leaders-of-europe/#50cd890256da

DOI: https://doi.org/10.3176/tr.2018.4.01

Table1. List of countries who published more than 4,000 ESI papers
during the last 11 years, in the period 2007-2017

                         Web of Science
Rank  Countries/Regions  Documents       Cites       Cites/Paper

 1    ICELAND                9,775          227,554  23.3
 2    SWITZERLAND          281,839        5,974,440  21.2
 3    SCOTLAND             149,732        3,050,642  20.4
 4    NETHERLANDS          382,711        7,734,062  20.2
 5    DENMARK              161,671        3,141,880  19.4
 6    ENGLAND              976,296       18,091,235  18.5
 7    USA                4,018,935       73,894,592  18.4
 8    BELGIUM              210,940        3,843,680  18.2
 9    WALES                 51,446          936,240  18.2
10    SWEDEN               255,231        4,578,903  17.9
11    SINGAPORE            117,749        2,080,794  17.7
12    IRELAND               78,858        1,381,373  17.5
13    GERMANY            1,063,985       18,088,194  17.0
14    CANADA               659,943       11,134,985  16.9
15    NORTHERN              25,197          424,684  16.9
      IRELAND
16    AUSTRIA              145,599        2,451,730  16.8
17    FINLAND              124,726        2,099,606  16.8
18    NORWAY               121,843        1,987,122  16.3
19    FRANCE               744,687       12,117,539  16.3
20    ESTONIA               16,818          273,488  16.3
21    ISRAEL               141,052        2,247,131  15.9
22    REPUBLIC OF            5,637           88,227  15.7
      GEORGIA
23    AUSTRALIA            540,607        8,417,798  15.6
24    PERU                   9,186          141,639  15.4
25    ITALY                642,089        9,864,393  15.4
26    HONG KONG            124,997        1,870,561  15.0
27    NEW ZEALAND           89,996        1,345,522  15.0
28    KENYA                 14,895          222,062  14.9
29    UGANDA                 8,565          125,887  14.7
30    SPAIN                554,312        7,991,814  14.4
31    COSTA RICA             5,333           76,815  14.4
32    GREECE               116,369        1,627,653  14.0
33    TANZANIA               7,983          110,706  13.9
34    PHILIPPINES           11,123          149,008  13.4
35    PORTUGAL             125,877        1,665,554  13.2
36    LUXEMBOURG             8,439          110,511  13.1
37    HUNGARY               69,002          893,493  13.0
38    URUGUAY                8,684          111,700  12.9
39    JAPAN                854,526       10,751,287  12.6
40    CYPRUS                 9,899          124,414  12.6
41    ARMENIA                7,391           90,356  12.2
42    SRI LANKA              6,652           78,509  11.8
43    CZECH REPUBLIC       115,152        1,316,297  11.4
44    SOUTH AFRICA         108,477        1,235,866  11.4
45    ARGENTINA             88,002          988,965  11.2
46    CHILE                 68,167          759,513  11.1
47    GHANA                  7,438           82,232  11.1
48    SLOVENIA              39,276          433,690  11.0
49    TAIWAN               277,054        2,949,603  10.7
50    THAILAND              68,382          724,041  10.6
51    SOUTH KOREA          519,213        5,419,516  10.4
52    LEBANON               11,052          115,357  10.4
53    INDONESIA             15,999          166,435  10.4
54    NEPAL                  5,066           51,812  10.2
55    BULGARIA              25,356          253,415  10.0
56    ECUADOR                6,486           63,910   9.9
57    CHINA MAINLAND     2,168,070       21,231,438   9.8
58    COLOMBIA              35,519          346,482   9.8
59    LATVIA                 6,478           62,508   9.7
60    SAUDI ARABIA          86,543          816,025   9.4
61    SLOVAKIA              34,783          327,297   9.4
62    VENEZUELA             11,948          112,289   9.4
63    CROATIA               36,711          342,701   9.3
64    MEXICO               125,334        1,166,844   9.3
65    BANGLADESH            14,928          138,281   9.3
66    QATAR                 10,797           97,882   9.1
67    CUBA                   8,791           79,208   9.0
68    CAMEROON               7,373           65,874   8.9
69    OMAN                   5,912           52,452   8.9
70    POLAND               249,900        2,204,107   8.8
71    UNITED ARAB           16,699          147,343   8.8
      EMIRATES
72    INDIA                554,273        4,839,616   8.7
73    ETHIOPIA               9,419           81,245   8.6
74    BELARUS               11,849          101,338   8.6
75    BRAZIL               407,396        3,420,751   8.4
76    VIETNAM               22,629          187,197   8.3
77    MOROCCO               17,460          141,446   8.1
78    AZERBAIJAN             4,955           39,663   8.0
79    MALAYSIA              87,529          697,892   8.0
80    LITHUANIA             22,435          178,357   8.0
81    KUWAIT                 7,749           60,473   7.8
82    EGYPT                 82,585          639,236   7.7
83    JORDAN                13,048          100,485   7.7
84    SERBIA                48,720          361,052   7.4
85    IRAN                 250,418        1,825,070   7.3
86    PAKISTAN              67,815          490,947   7.2
87    TURKEY               270,114        1,953,060   7.2
88    ROMANIA               76,027          539,922   7.1
89    TUNISIA               33,944          236,083   7.0
90    NIGERIA               23,821          163,055   6.9
91    UKRAINE               52,492          352,886   6.7
92    RUSSIA               332,508        2,150,853   6.5
93    ALGERIA               23,791          148,391   6.2
94    MACAU                  4,693           27,961   6.0
95    BOSNIA &               4,475           26,101   5.8
      HERZEGOVINA
96    IRAQ                   7,351           38,394   5.2
97    KAZAKHSTAN             5,718           27,394   4.8

                                 Change in the Rank
Rank  Top Papers (%)  HQSI Rank  since 2007

 1    3.05             1           5
 2    2.70             2          -1
 3    2.61             3           2
 4    2.45             4           0
 5    2.49             5          -2
 6    2.16             9           2
 7    1.85            15          -5
 8    2.21            10           4
 9    2.22             8           9
10    2.00            14          -3
11    2.46             6          31
12    2.17            13           9
13    1.72            21           0
14    1.85            18          -3
15    1.78            20           7

16    2.05            16          -1
17    1.84            19          -8
18    1.99            17          -2
19    1.62            26          -2
20    2.41            12          11
21    1.65            27          -7
22    2.54            11          50

23    1.93            23          -4
24    2.80             7           5
25    1.49            32          -5
26    1.93            24         -16
27    1.75            28          -4
28    2.05            22           0
29    1.56            33          -5
30    1.45            35          -3
31    1.56            34          -5
32    1.47            38           8
33    1.48            39           3
34    2.16            25           1
35    1.32            41           2
36    1.87            30         n.a.
37    1.40            40          -5
38    1.21            44          -8
39    0.85            55         -14
40    1.96            31           8
41    1.79            36           6
42    2.09            29          18
43    1.19            48           1
44    1.48            43          -5
45    0.96            57          -7
46    1.17            49         -13
47    1.40            45           9
48    1.15            51           5
49    0.69            70           8
50    0.93            59           1
51    0.85            63           8
52    1.47            47          11
53    1.25            52         -10
54    1.30            50         n.a.
55    1.02            60           7
56    1.59            46         n.a.
57    1.05            61          15
58    1.33            54         -12
59    1.31            56         -13
60    2.25            37          19
61    0.87            73           0
62    0.87            72          -7
63    0.94            67           5
64    0.87            74         -15
65    1.20            58           1
66    1.95            42         n.a.
67    0.73            79           0
68    0.88            75         -14
69    1.17            62          15
70    0.83            78         -18
71    1.08            64          10

72    0.61            84          -1
73    1.06            66          -8
74    1.05            69          11
75    0.64            85         -17
76    1.11            68         -26
77    0.75            80           0
78    1.21            65          16
79    1.12            71          -3
80    1.00            77         -16
81    0.80            81          -6
82    0.64            87          -4
83    0.81            82           7
84    0.86            83         -15
85    0.68            88           2
86    1.14            76          -3
87    0.54            91          -7
88    0.81            86         -15
89    0.43            96          -1
90    0.73            89           1
91    0.59            93          -5
92    0.51            95         -18
93    0.69            92          -4
94    1.98            53         n.a.
95    0.87            90         n.a.

96    0.84            94         n.a
97    0.61            97          -5

Table 2. ESI bibliometric indicators characterizing Estonia, Latvia,
and Lithuania during the period 2007-2007

    Research Fields                            Estonia
                              Pap     Cites    C/P    C/P (%)  TopP


 1  AGRICULTURAL SCIENCES        379    3,143   8.29   -6.2      5
 2  BIOLOGY & BIOCHEMISTRY       757   15,463  20.43   18.6     13
 3  CHEMISTRY                  1,479   18,801  12.71  -13.6     10
 4  CLINICAL MEDICINE          1,581   42,973  27.18  107.2     83
 5  COMPUTER SCIENCE             211      890   4.22  -34.2      0
 6  ECONOMICS & BUSINESS         295    1,571   5.33  -36.5      1
 7  ENGINEERING                  732    4,328   5.91  -20.8      5
 8  ENVIRONMENT/ECOLOGY        1,321   26,948  20.40   54.3     42
 9  GEOSCIENCES                1,219   12,725  10.44  -16.9     11
10  IMMUNOLOGY                   271    4,808  17.74   -9.2      3
11  MATERIALS SCIENCE            707    6,458   9.13  -24.7      4
12  MATHEMATICS                  330    1,331   4.03   -9.2      1
13  MICROBIOLOGY                 257    4,442  17.28   10.5      5
14  MOLEC. BIOLOGY &             759   37,094  48.87   96.6     46
    GENETICS
15  MULTIDISCIPLINARY             52      686  13.19  -10.8      2
16  NEUROSCIENCE & BEHAVIOR      473    9,324  19.71    6.4      7
17  PHARMACOL. &                 280    4,800  17.14   30.8      8
    TOXICOLOGY
18  PHYSICS                    1,849   34,036  18.41   59.7     64
19  PLANT & ANIMAL SCIENCE     1,675   25,635  15.30   60.7     63
20  PSYCHIATRY/PSYCHOLOGY        470    6,398  13.61    7.7     11
21  SOCIAL SCIENCES, GENERAL   1,451    7,345   5.06  -27.2     17
22  SPACE SCIENCE                270    4,289  15.89  -13.2      4
 0  ALL FIELDS                16,818  273,488  16.26   30.8    405

                   Latvia                      Lithuania
    Pap    Cites   C/P    C/P    TopP  Pap     Cites    C/P    C/P
                          (%)                                  (%)

 1    177   1,378   7.79  -11.9   3       883    4,160   4.71  -46.7
 2    215   2,209  10.27  -40.4   2       759    8,664  11.42  -33.7
 3    872   5,757    6.6  -55.1   1     2,057   16,303   7.93  -46.1
 4    735  16,451  22.38   70.6  38     2,731   28,824  10.55  -19.6
 5     75     391   5.21  -18.7   0       583    2,575   4.42  -31.0
 6    124     612   4.94  -41.1   1     1,315    7,899   6.01  -28.4
 7    584   1,821   3.12  -58.2   1     3,487   13,294   3.81  -48.9
 8    246   3,190  12.97   -1.9   5       981    9,263   9.44  -28.6
 9                                        392    3,281   8.37  -33.4
10                                        194    5,648  29.11   49.0
11    671   4,273   6.37  -47.5   2     1,602    7,747   4.84  -60.1
12                                        930    2,362   2.54  -42.8
13     84   1,240  14.76   -5.6   1       251    3,666  14.61   -6.6
14    149   8,039  53.95  117.0   5       253    9,641  38.11   53.3

15
16     62     866  13.97  -24.6   0       259    2,972  11.47  -38.1
17    145   1,111   7.66  -41.5   1       239    2,051   8.58  -34.5

18  1,139   6,783   5.96  -48.3  12     2,946   36,069  12.24    6.2
19    440   3,300    7.5  -21.2   6     1,210    5,390   4.45  -53.3
20     44     459  10.43  -17.5   1       188    2,057  10.94  -13.4
21    262   1,472   5.62  -19.1   5       943    4,252   4.51  -35.1
22                                        227    2,159   9.51  -48.1
 0  6,478  62,508   9.65  -22.4  85    22,435  178,357   7.95  -36.0

Notes: Pap = WoS papers included in ESI; Cites = total number of
cites; C/P = Citations per paper; C/P (%) = Citations per paper
expressed as percentage relative to the ESI world average; TopP = the
number of papers reached the top 1% citation rate.