JDIS

Journal of Data and Information Science >

2018 , Vol. 3 >Issue 3: 14 - 31

DOI: https://doi.org/10.2478/jdis-2018-0012

Research Paper

A Comparison of Citation Disciplinary Structure in Science between the G7 Countries and the BRICS Countries

  • Ting Yue 1, 2 ,
  • Liying Yang , 1† ,
  • Per Ahlgren 1, 3 ,
  • Jielan Ding 1, 2 ,
  • Shuangqing Shi 4 ,
  • Rainer Frietsch 5
Expand
  • 1National Science Library of the Chinese Academy of Sciences, Beijing, 100190, China
  • 2University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
  • 3KTH Library, KTH Royal Institute of Technology, Osquars backe 25, Stockholm, 10044, Sweden
  • 4Library of Peking University Health Science Center, Beijing, 100083, China
  • 5Fraunhofer Institute for Systems and Innovation Research ISI, Karlsruhe, Germany
† Corresponding author: Liying Yang (E-mail: ).

Online published: 2018-12-06

Copyright

Open Access
Fold

Abstract

Purpose: This study aims to compare the characteristics of citation disciplinary structure between the G7 countries and the BRICS countries.

Design/Methodology/Approach: In this contribution, which uses about 1 million Web of Science publications and two publications years (1993 and 2013), we compare the G7 countries and the BRICS countries with regard to this type of structure. For the publication year 2013, cosine similarity values regarding the citation disciplinary structures of these countries (and of nine other countries) were used as input to cluster analysis. We also obtained cosine similarity values for a given country and its citation disciplinary structures across the two publication years. Moreover, for the publication year 2013, the within-country Jeffreys-Matusita distance between publication and citation disciplinary structure was measured.

Research limitations: First, the citation disciplinary structures of countries depend on multiple and complex factors. It is therefore difficult to completely explain the formation and change of the citation disciplinary structure of a country. This study suggests some possible causes, whereas detailed explanations might be given by future research. Second, the length of the citation window used in this study is three years. However, scientific disciplines differ in their citation practices. Comparison between citations across disciplines using the same citation window length may affect the citation discipline structure results for some countries.^Practical limitations: First, the results of this study are based on the WoS database. However, in this database some fields are covered to a greater extent than others, which may affect the results for the citation discipline structure for some studied countries. In future research, we might repeat this study using another database (like Scopus) and, in that case, we would like to make comparisons between the two outcomes. Second, the use of a constant journal set yielded that a large share of the journals covered by WoS year 2013 is ignored in the study. Thus, disciplinary structure is studied based on a quite restricted set of publications. The three mentioned limitations should be kept in mind when the results of this study are interpreted.

Originality/value: Disciplinary structure on country level is a highlighted topic for the S&T policy makers, especially for those come from developing countries. This study observes the disciplinary structure in the view of academic impact, and the result will provide some evidence to make decision for the discipline strategy and funding allocation. Besides, Jeffreys-Matusita distance is introduced to measure the similarity of citation disciplinary structure and publication disciplinary structure. By applying this measure, some new observations were drawn, for example, “Based on the comparison of publication disciplinary structure and citation disciplinary structure, the paper finds most BRICS counties have less impact with more publications”.

Findings: The outcome of the cluster analysis indicates that the G7 countries and BRICS countries are quite heterogeneous regarding their citation disciplinary structure. For a majority of the G7 countries, the citation disciplinary structure tend to be more stable compared to BRICS countries with regard to the years 1993 and 2013. Most G7 countries, with United States as an exception, turned out to have lower values on the Jeffreys-Matusita distance than BRICS countries, indicating a higher degree of heterogeneity between the publication and the citation disciplinary structure for the latter countries. In other words, BRICS countries still receive much less citations in most disciplines than their publication output would suggest. G7 countries can still expect more citations than is to be expected based on their publication output, thereby generating relatively more impact than BRICS countries.

Key words： Citation disciplinary structure; Country; Bibliometrics

Cite this article

Ting Yue , Liying Yang , Per Ahlgren , Jielan Ding , Shuangqing Shi , Rainer Frietsch . A Comparison of Citation Disciplinary Structure in Science between the G7 Countries and the BRICS Countries[J]. Journal of Data and Information Science, 2018 , 3(3) : 14 -31 . DOI: 10.2478/jdis-2018-0012

1 Introduction

The concept of revealed comparative advantage (Balassa, 1965) suggests a specialization of countries in terms of their export portfolio. National economies tend to focus on what they do—relatively, not necessarily absolutely—best. Transferring this idea to science systems (Grupp, 1998; Schmoch et al., 2000; van Raan, 2003) profiles of relative advantage and disadvantage of the disciplinary profile of countries gets into focus. Independent of both disciplinary and country size effects, areas of intensive activity in the profile, one could also say in the disciplinary structure, of each country can be identified.
For individual countries, scientific disciplinary structure could reflect the distribution of scientific funds over disciplines and the characteristics of Science & Technology (S&T) strategy. S&T input, as well as output, could be used to analyze disciplinary structure of countries. The analysis of disciplinary structure based on S&T input might reveal how resources and funds are distributed over disciplines (Julio, 1998; Shibayama, 2011; Zhu, 2006; Wu, 2015). For S&T output, research publications are typically used to analyze disciplinary structure. In this study, we deal with two types of publication-based disciplinary structures: publication volume disciplinary structure and citation disciplinary structure. For a given country, publication volume disciplinary structure could mirror the features of S&T strategy policy for different disciplines, while citation disciplinary structure could reflect the general composition of the academic impact within individual disciplines.
Publication volume and citation disciplinary structure have been addressed in earlier research. Glänzel et al. (2006) and Yue (2008) described the local characteristics of disciplinary structure, whereas a number of studies have compared differences in disciplinary profiles between countries (e.g. Liang et al., 2006; Kozlowski et al., 1999; Yang et al., 2012; Schulz & Manganote, 2012), or treated the evolution of such profiles over time (e.g. Bongioanni et al., 2014; Yang et al., 2012; Li N., 2017). Ding et al. (2018) compared the publication volume disciplinary structure in three top journals (Nature, Science, Proceedings of the National Academy of Sciences of the United States of America) between countries. Aksnes et al. (2014) investigated how the increasing publication output of China influences Netherland’s disciplinary profile. A model to explain a country’s research profile and competitiveness in certain disciplines was developed by Harzing & Giroud (2014). Daraio et al. (2017) used the pseudo-likelihood approach (Aurell & Ekeberg, 2012), introduced in the physics of complex systems, to assess interdependencies between scientific disciplinary profiles at the country level. Other studies have focused on the citation impact of disciplines at the country level, or compared citation impact and publication volume regarding individual disciplines (e.g. Kozlowski et al., 1999; King, 2004; Yue, 2008; Radosevic & Yoruk, 2014).
Yang et al. (2012) investigated the disciplinary structures of the publication volume of the G7 countries (Canada, France, Germany, Italy, Japan, United Kingdom and United States) and the BRICS countries (Brazil, China, India, Russia and South Africa). The results showed that the two groups had different characteristics in terms of publication volume disciplinary structure. Our study, however, focuses on citation disciplinary structure, and compares the G7 countries and the BRICS countries with regard to this type of structure. Since the publication volume and the citation disciplinary structure for a given country might be more or less similar, we also analyze the relation between publication volume and citation disciplinary structure for the countries of the study. We thereby address the question how the publication output and the impact—reflected in citations—of science systems of different countries differ.
The remainder of this paper is structured as follows. In the next section, the data, indicators and methods of the study are described. The third section reports the results and discusses them, whereas the final section put forward conclusions.

2 Data and methods

2.1 Data

The data source of the study is Bibmet (2017 Quarter 1 version), the bibliometric version of the Web of Science (WoS) at KTH Royal Institute of Technology (Sweden). Bibmet is a relational database, based on WoS raw data purchased from Clarivate Analytics. Bibmet, which is updated quarterly, contains about 51 million WoS publications from the publication year 1980 onwards. The database also includes citation relations between WoS source publications.
To compare the disciplinary structure over time, two publication years were used: 1993 and 2013. The year 2013 was selected in order to have a fairly recent end year, and at the same time ensuring that all (or almost all) citations from WoS publications to publications published in the end year were present in the Bibmet version used in the study. Further, we first used 2003 as start year, but since very small differences in disciplinary structure, compared to year 2013, were observed, we decided to use 1993 as start year instead. For citations, we used 3-year windows. For example, for the publication year 1993, the citation window is 1993-1995. The data were collected in the following way:
1. We used a list of journals obtained from the 2013 Journal Citation Reports Science Edition. This list was named JCR_list. JCR_list contains 8,534 journals, together with their ISSNs.
2. Each journal in JCR_list has been mapped to exactly one Essential Science Indicators (ESI) class. These classes—except “Economics & Business”, “Multidisciplinary” and “Social Sciences, General”—were used in the study. However, classes related to biology and medicine were manually merged by the authors, which resulted in 10 broad science fields. Thus, each journal in JCR_list is mapped to exactly one of these 10 fields. In Table 1, the mapping from ESI classes to fields is displayed.
3. Standardization of the ISSNs in the JCR_list was performed. For instance, long hyphens were replaced by short hyphens.
4. For the two considered publication years, 1993 and 2013, we retrieved from Bibmet all publications published in one of these two years. However, we only used publications of the WoS document types “Article” and “Review” and appearing in the journal indexes of WoS. The ISSNs for (the journals of) the publications were standardized (cf. step 3 above). Publications such that their journals lacked an ISSN were excluded from the study.
5. For both publication years, we extracted the unique ISSNs from the two corresponding publication sets of step 4. This gave rise to two lists of ISSNs, one per year.
6. In order to have a constant journal set—with respect to the two involved publication years—JCR_list was reduced to those ISSNs that are present in both ISSN lists of step 5. Our final list includes 3,221 journals. If a constant journal set would not be used, the interpretation of the results of the study would be difficult. For instance, an increase in relative publication volume for a given country in a given science field between 1993 and 2013 might be an effect of an increase in the number of journals (in the field) in WoS for the country from the former to the latter year.
7. For each of the two publication years and for each ISSN in our final list of journals, the publications published in the year, satisfying the conditions of step 4, and appearing in the journal corresponding to the ISSN, were extracted and assigned to the science field of the journal. This gave rise to 1,085,849 publications in total. However, 14,415 publications turned out to lack addresses and were therefore excluded. Thus, the number of publications included in the study is equal to 1,071,434.
8. For the 1,071,434 publications, address data was extracted in order to obtain the contribution of countries to a publication.
9. Finally, citation counts were gathered from Bibmet.
With reference to point 8 above, a publication might involve more than one country because of co-authorship. Due to this, we applied fractional counting of publications and citations. Since there are no associations in WoS between author names and addresses before the publication year 2008, address fractionalization was used. For instance, if a publication has two China addresses and three United States addresses, and if it has received 10 citations, China is assigned 2/5 and United States 3/5 of the publication, while China is assigned 2/5 x 10 = 4 and United States 3/5 x 10 = 6 citations.
Table 1 The mapping from ESI classes to science fields.
Field(full) Field(abb.) ESI class
Agricultural Sciences AGR Agricultural Sciences
Environment/Ecology ENV Environment/Ecology
Geosciences GEO Geosciences
Engineering ENG Computer Science
Engineering
Materials Science
Biology BIO Biology & Biochemistry
Microbiology
Molecular Biology & Genetics
Plant & Animal Science
Medicine MED Clinical Medicine
Immunology
Neuroscience & Behavior
Pharmacology & Toxicology
Psychiatry/Psychology
Mathematics MAT Mathematics
Chemistry CHE Chemistry
Physics PHY Physics
Space Science SPA Space Science
In Table 2, journal and publication volume statistics are reported.
Table 2 Journal and publication volume by publication year.
Publication year # journals # publications
1993 3,221 436,440
2013 3,221 634,994
Total 3,221 1,071,434

2.2 Indicators and methods

As we use fractionalization, when we refer to the number of publications and the number of citations for countries, we actually mean magnitude of publication fractions and magnitude of fractions of citations counts, respectively.
For country i, we define Pijy as the number of publications of i in field j in publication year y and Cijy as the number of citations to i in j during the three-year window starting with y. Let Pjy (Cjy) be the total number of publications (citations) in j in y (during the three-year window staring with y). We then define RPijy and
RCijy as
Thus, RPijy gives the percentage of the publications of country i in field j in publication year y relative to the total number of publications in j in y, whereas RCijy gives the percentage of the citations to country i in field j during the three-year window starting with y, relative to the total number of citations in field j during the three-year window starting with y.
The disciplinary structure of the publication volume for a country i in publication year y can now be operationalized as the vector VPiy, whereas the disciplinary structure of the citations for country i for the three-year window starting with year y can be operationalized as the vector VCiy:
where n = 10.
The cosine measure (Salton & McGill, 1983) was applied to measure the similarity between the citation disciplinary structures for one country in different publication years (1993, 2013), and between such structures for different countries in the same publication year (2013):
where i׳ and c׳ stand for a country and a year, respectively.
A cluster analysis was used to group country（region） based on cosine similarity values for pairs of countries. We used the average linkage, a well-known hierarchical method, for the clustering (Everitt et al., 2001). The Silhouette measure (Kaufman & Rousseeuw, 1990) was used in order to obtain the best number of clusters.
This measure contrasts coherence to separation by comparing within-cluster dissimilarity to between-cluster dissimilarity. Since the measure is defined in terms of dissimilarities, we converted the cosine similarity values to corresponding dissimilarity values by subtracting a given similarity value from 1. The cluster solution with the highest overall Silhouette value turned out to have two clusters for publication volume disciplinary structure and five clusters for citation disciplinary structure.
It might be the case that there are G7 and/or BRICS countries that are more similar, with respect to the citation disciplinary structure, to country（region） outside the G7-BRICS group than to countries within this group. In order to have a more comprehensive view of the citation disciplinary structure patterns, we extended the set of country（region） used in the cluster analysis with the nine countries (apart from the G7-BRICS countries) with the largest publication volumes in 2013. Thus, 21 country（region） were used in the cluster analysis.
With regard to the comparison of the disciplinary structure of the publication volume and of the citations within a given country i, we are interested in the distance between the publication and citation percentages RPijy and RCijy (j = 1, …, 10). More precisely, we are interested in the distance between the vectors VPiy and VCiy. The Jeffreys-Matusita distance (Bruzzone L.et al., 1995) was applied to measure the distance for a given country i in year 2013. This measure (with respect to publication year 2013 and for country i) is defined as:
Clearly, the Jeffreys-Matusita distance is similar to the well-known Euclidian distance. However, the latter is more dependent on the magnitudes of the components of the involved vectors. For instance, the Euclidian distance between two vectors with very small components, and thus with small norms, might underrate the distance between the vectors. In view of this, and in our context, we consider the Jeffreys-Matusita distance to be a better choice than Euclidian distance.
Moreover, in order to have a summary value for country i in publication year 2013 across all 10 fields, we calculated means of the elements of the vectorsVPi2103 and VCi2013:

3 Results

This section reports citation disciplinary structure results for countries in 2013 and within countries across 1993 and 2013. The section further reports the outcome of the comparison of publication volume and citation disciplinary structure within countries in 2013. Note that 21 countries, and not only the 12 G7/BRICS countries, are taken into account with regard to the first subsection (cf. the section “Data and methods”).

3.1 Citation disciplinary structure patterns for the G7 and BRICS countries in 2013

Figure 1 visualizes the outcome of the cluster analysis, which is based on the citation disciplinary structures of the G7 and BRICS country（region）, as well as on the corresponding structures of the nine country（region）, apart from the G7/BRICS countries, with the largest publication volumes in 2013. The clusters and country（region） are represented horizontally, whereas the 10 fields are represented vertically, with the field names located in the bottom of the figure (see Table 1 for the full names of the fields). The dendrogram for the analysis occurs to the right of the last column of the figure. Table 3 gives the RCij2013 values (Equation 2) across the 10 fields for the 21 considered country（region） for year 2013.
Figure 1. Cluster analysis outcome of citation disciplinary structure of 21countries (2013).
Table 3 RCij2013 values for 21 country（region） (2013).
Field CHE ENG MAT PHY AGR GEO ENV BIO MED SPA
Australia 1.7 2.6 1.5 1.5 2.9 4.1 5.2 2.9 3.3 2.4
Brazil 0.9 0.9 1.5 0.9 4.4 0.8 1.5 1.6 1.4 0.8
Canada 2.2 2.7 2.6 2.0 3.1 3.9 4.6 3.5 4.0 2.8
China 24.8 23.7 17.5 16.0 14.1 12.6 10.7 9.3 6.4 3.0
France 3.7 3.3 6.1 4.7 3.4 5.8 3.8 3.8 3.5 6.1
Germany 6.2 3.9 5.3 8.0 3.7 6.1 5.0 6.1 5.7 8.6
India 4.5 3.6 1.7 2.9 3.2 1.8 2.1 2.0 1.1 1.4
Iran 1.8 2.5 1.8 1.0 1.8 0.7 0.7 0.5 0.5 0.4
Italy 2.4 3.2 4.9 3.7 4.8 3.8 2.5 3.0 4.4 5.0
Japan 5.2 3.0 2.8 5.8 2.3 3.2 1.6 4.6 4.0 3.4
Republic of Korea 3.3 4.5 1.8 3.0 3.0 0.8 1.1 2.1 2.1 0.8
Netherlands 1.1 1.1 0.8 1.3 1.8 1.7 2.2 2.0 3.2 2.1
Poland 1.0 0.9 2.1 1.1 1.3 0.6 1.0 0.9 0.6 0.6
Russia 1.0 0.6 2.3 2.6 0.2 1.6 0.3 0.5 0.1 1.7
South Africa 0.3 0.2 0.5 0.2 0.6 0.4 0.8 0.4 0.3 0.6
Spain 3.1 2.9 3.1 2.4 7.2 2.4 3.8 2.6 2.3 3.0
Switzerland 1.3 1.1 1.2 1.8 0.8 2.0 2.2 1.5 1.5 1.4
Chinese Taiwan 1.3 2.8 1.0 1.3 1.4 0.7 0.7 0.8 1.0 0.4
Turkey 0.6 1.3 1.7 0.6 1.7 0.6 0.7 0.4 0.9 0.2
United Kingdom 3.7 4.2 4.0 4.4 3.0 6.9 5.8 6.2 7.3 8.2
United States 18.9 17.3 19.6 23.1 15.0 27.9 26.4 32.5 34.9 37.5

*For each country（region）, values in bold face are the highest two RCij2013 values among the 10 fields

For each country（region） i and field j represented in Figure 1, the darkness of the corresponding grid is related to the value of RCij2013: the darker the color is, the larger the value is. country（region） belonging to the same cluster tend to be represented by similar rows with respect to darkness levels across the fields. Thus, such country（region） tend to have similar citation disciplinary structures, given our operationalization of such structures.
For the 5-cluster solution, i.e. the solution with the highest overall Silhouette value, the G7 countries belong to the same cluster, together with Australia, Netherlands, South Africa and Switzerland, whereas the five BRICS countries are distributed over four groups. We observe that South Africa is the only BRICS country that belongs to the same cluster as the G7 countries. Thus, all the cluster neighbors of the other four BRICS countries are non-G7 country（region）. These results indicate that the G7 countries and BRICS countries are quite heterogeneous regarding citation disciplinary structure.
United States and United Kingdom are the two countries that are grouped first in the hierarchical clustering process (Figure 1), which indicates that they are similar in citation disciplinary structure. Because the country（region） are grouped based on cosine similarity values, and cosine-based measure is independent of size effects and only focuses on the disciplinary profiles of the citations. The data in Table 3 suggest, in more detail, why they are similar: Both country（region） have their highest RCij2013 values in Space Science and Medicine, and their smallest value in Agriculture. As is visible in Figure 1, Germany, France and Italy are also grouped early in the process. Space Science is the strongest field for those three countries in terms of citation impact. For example, for Germany, the RCij2013 value of Space Science is 8.6%, while the RCij2013 value of other fields are lower than 8% (Table 3). Apart from Space Science, Italy and France are comparatively strong in Mathematics from a citation point of view. Table 3 shows that for France and for a majority of the fields, the RCij2013 value is lower than 4%, while the RCij2013 value for Mathematics exceeds 6%. Like Germany, France and Italy, Canada Australia and Switzerland are grouped early. For these three countries, Life Sciences (Medicine, Biology) and Earth Sciences (Environment/Ecology, Geosciences) are prominent fields regarding citation impact.
For China and India, both grouped together early in the clustering process, the citation disciplinary structure is dominated by Chemistry and Engineering. For China, Chemistry and Engineering have RCij2013 values of about 25% and 24%, respectively, whereas the percentages in the other fields are less than 18%. Agriculture Sciences dominates for both Brazil and South Africa, even if the two countries do not belong to the same cluster until the next last step in the process has been executed, when the number of clusters is equal to 2 (Figure 2). For Brazil, the world share of Agriculture Science is 4.41%, compared to less than 2% in the other fields. Also South Africa has a better performance in Agriculture Sciences compared to the other fields.
Figure 2. RCij1993 and RCij2013 values across fields for Brazil and China.
Note that Russia is not grouped with another country until the last step of the clustering process has been executed, when the number of clusters is equal to 1. This indicates Russia has unique citation disciplinary structure characteristics, relative to the other considered country（region）. Russia’s academic achievements in Mathematics and Physics are globally acknowledged. This is reflected by the citation disciplinary structure of Russia, where the RCij2013 values of these two fields are much higher compared to the other fields.

3.2 Citation disciplinary structure within G7 and BRICS countries across 1993 and 2013

Table 4 gives similarity values, which are based on the citation disciplinary structures in 1993 and 2013, for the G7 countries and BRICS countries. A given value indicates the similarity between the 1993 and the 2013 citation disciplinary structures of the corresponding country. The closer the similarity value is to 1, the more similar are the citation disciplinary structures for a country with respect to the years 1993 and 2013.
Table 4 Similarity values based on citation disciplinary structures in 1993 and 2013.
Country Sim(VCi1993, VCi2013) Country Sim(VCi1993, VCi2013)
Brazil 0.739 Italy 0.942
Canada 0.971 Japan 0.950
China 0.907 Russia 0.951
France 0.978 South Africa 0.963
Germany 0.969 United Kingdom 0.968
India 0.974 United States 0.982
It is clear from Table 4 that all cosine values, except the one for Brazil, are greater than 0.9. Thus, in general the citation disciplinary structures within countries changed to a small extent across the two considered years (the similarity value is equal to or greater than 0.95 for nine of the 12 countries). We can see that the similarity values of most G7 countries are slightly higher compared to a majority of BRICS countries. For most G7 countries, then, the citation disciplinary structure tend to be more stable compared to BRICS countries with regard to the periods 1993 and 2013.
China has a lower similarity value than the other countries, apart from Brazil (0.907, Table 4). Compared to the other G7 and BRICS countries, the citation disciplinary structures of Brazil and China are more dissimilar regarding the two years. In view of this, we studied the citation disciplinary structures of China and Brazil in more detail. Figure 2 displays the RCij1993 and RCij2013 values across fields for Brazil and China.
For 1993 publications, Brazil’s citation impact in Mathematics, Space Science and Physics are relatively strong, whereas the performance in Biology and Medicine is relatively poor. For 2013 publications, however, Agricultural Sciences exhibits the (by far) strongest performance, with a large increase in world share compared to 1993. Besides, the corresponding shares for Biology, Environment/Ecology and Medicine have also increased a lot. For instance, the values for Environment/Ecology are 0.4% (1993) and 1.5% (2013).
Regarding China, several fields show quite remarkable increases in world shares, especially Chemistry and Engineering. The world shares of Chemistry and Engineering increased with more than 20 percentage units from 1993 to 2013, while the shares for Biology and Medicine only increased with less than 10 percentage points. The findings indicate that the enormous upsurge of publications in the
20 years of our observation period hardly had an impact on the citation structure of the countries under analysis. The upsurge resulted in an absolute upgrade of the numbers of citations, but the disciplinary profiles are rather stable, even in transition countries like Brazil or China. However, there are changes in the profiles as the example of China shows. While the basic research fields of mathematics and physics were the largest in terms of citations in the Chinese profile at the beginning of the 1990s, they shifted towards the more application oriented fields of engineering and also chemistry (including material sciences). These changes, however, have a measurable but limited impact on the overall citation profile. There is more stability than change in the system.

3.3 Comparison of publication volume and citation disciplinary structure within G7 and BRICS country（region） in 2013

Jeffreys-Matusita distances between publication and citation disciplinary structure within G7 and BRICS countries and in year 2013 are reported in Table 5. Also reported are the means of the elements of corresponding vectors, which are the world shares of publication volume and citations.
Table 5 Jeffreys-Matusita distances between publication and citation disciplinary structure and vector means within G7 and BRICS countries.
country Dist(VPi2013, VCi2013) Mean(VPi2013) Mean(VCi2013)
United States 2.1 19.2 25.3
Russia 1.9 2.6 1.1
Brazil 1.3 2.5 1.5
India 1.2 3.6 2.4
China 1.0 14.8 13.8
United Kingdom 0.8 4.3 5.4
Japan 0.8 4.6 3.6
Germany 0.7 5.0 5.9
France 0.5 3.9 4.4
Italy 0.4 3.5 3.8
South Africa 0.4 0.6 0.4
Canada 0.3 2.9 3.2
Most G7 countries, with United States as an exception, have lower values on the Jeffreys-Matusita distance than BRICS countries, indicating a higher degree of heterogeneity between the publication and the citation disciplinary structure for the latter countries. The distance for United States is much higher compared to the other G7 countries, which indicates a substantial difference between the two types of disciplinary structures.
For the G7 countries except Japan, Mean(VCi2013) is larger than Mean(VPi2013), which indicates that on average across the fields, the citation impact performance is stronger than publication volume performance. This result is particularly striking for USA, with Mean(VCi2013) equal to 25.3, much higher than Mean(VPi2013) (19.2), and with the largest Jeffreys-Matusita distance of 2.1, indicating that USA gets much more citation impact with fewer publications. For the BRICS countries and Japan, Mean(VCi2013) is less than Mean(VPi2013): The citation impact of these countries lags behind publication volume. This is the case especially for Russia. Mean(VCi2013) (1.09) is considerably smaller than Mean(VPi2013)(2.58), which indicates that Russia has less impact with more publications.

4 Discussion and conclusions

In this study, which uses about 1 million publications and two publications years (1993 and 2013), we have dealt with citation disciplinary structure at the country level. We focused on the G7 and the BRICS countries. For the publication year 2013, cosine similarity values regarding the citation disciplinary structures of these countries (and of nine other countries) were used as input to cluster analysis. For the same publication year, the within-country Jeffreys-Matusita distance between publication and citation disciplinary structure was measured. We also obtained cosine similarity values for a given country and its citation disciplinary structures across the two publication years.
The citation disciplinary structure patterns for the G7 and BRICS countries, regarding year 2013, are quite different, which is indicated by the outcome of the cluster analysis. For most G7 countries, the academic impact of Space Sciences exceeds the impact of the other fields. Space Sciences has the highest citation world share for these countries, with Canada and Japan as exceptions. Among the BRICS countries, the citation disciplinary structure patterns are heterogeneous. China and India perform well in Chemistry and Engineering compared to the other fields, Brazil and South Africa in Agriculture Sciences, whereas Russia performs comparatively well in Mathematics and Physics.
For a given country, the pattern of the publication disciplinary structure can be assumed to be related to the S&T level of the country (Kozlowski et al., 1999; King, 2004; Kostoff et al., 2007). Based on the results of this study, citation disciplinary structure is also relevant to the S&T level of a country. USA and United Kingdom represent high S&T level countries, and they have higher world share citation impact in Space science and Medicine. Some countries with lower S&T levels, like China and India, perform better in Chemistry, Physics and Engineering, while the citation impact in Medicine and Biology lags behind. Chemistry, Physics and Engineering can be seen as the material-related fields, focusing on the studies of non-living systems, whereas Medicine and Biology can be regarded as life-related fields, which are helpful in improving the quality of life. Countries with relatively low S&T level usually give priority to the development of material-related fields, and high S&T level countries often shift their development priorities to the life-related fields.
S&T policy or strategy might also make contributions to the evolution of disciplinary structure. About one decade ago, Brazil created a new economic model named “Natural Knowledge Economy” (DEMOS, 2008).The field changes for Brazil in world share citation impact from 1993 to 2013 (from best relative performance in Mathematics, Space Science and Physics in 1993 to best relative performance in Agricultural Sciences in 2013) might partly be explained by the establishment and maintenance of this economic model.
The citation disciplinary structure is related to a country’s national conditions (apart from S&T level and policy), such as history and natural resources. Both Mathematics and Space Science harvested more citations compared to other fields for France and Italy. In these two countries, research in astronomy and mathematics started many centuries ago, leading to the high impact of contemporary research in these fields. For Canada and South Africa, countries rich in natural resources, the citation impact in Environment/Ecology is relatively high.
Based on the analysis of the citation disciplinary structure within countries across publication years, we can see, comparing 1993 to 2013, that for most G7 and BRICS countries, their citation disciplinary structures did not change to any greater extent, which reveals that the evolution of the citation disciplinary structure can be seen as a drawn-out process. This is related to the fact that it might take quite a long time to raise the S&T level of a country. The drawn-out process is also related to the circumstance that the unique national conditions of a country typically do not change rapidly.
For the comparison of publication volume and citation disciplinary structure in the year 2013, the G7 countries get more academic impact with less publications, whereas the academic impact lags behind publication volume for BRICS countries. If we regard publication volume as input, and citation impact of the publications as output, then a comparison of input and output could tell us something about scientific output efficiency or at least productivity. In this study, the G7 countries tend to have higher output efficiency than the BRICS countries. The scientific output efficiency of a country is affected by its S&T level. On the other hand, the improvement of scientific output efficiency could promote the enhancement of the S&T level. Low S&T level countries (such as the BRICS countries) usually have relatively low output efficiency. To focus on publishing publications with high scientific quality, rather than on publication volume, might be a fruitful strategy for such countries regarding S&T level enhancement.
The citation disciplinary structures of countries depend on multiple and complex factors. It is therefore difficult to completely explain the formation and change of the citation disciplinary structure of a country. This study suggests some possible causes, whereas detailed explanations might be given by future research.
Finally, we have to mention three limitations. First, the results of this study are based on the WoS database. However, in this database some fields are covered to a greater extent than others, which may affect the results for the citation discipline structure for some studied countries. In future research, we might repeat this study using another database (like Scopus) and, in that case, we would like to make comparisons between the two outcomes. Second, the length of the citation window used in this study is three years. However, scientific disciplines differ in their citation practices. Comparison between citations across disciplines using the same citation window length may affect the citation discipline structure results for some countries. Third, the use of a constant journal set yielded that a large share of the journals covered by WoS year 2013 is ignored in the study. Thus, disciplinary structure is studied based on a quite restricted set of publications. The three mentioned limitations should be kept in mind when the results of this study are interpreted.

Author Contributions

Liying Yang (yangly@mail.las.ac.cn, corresponding author) proposed the research idea, designed the research framework, and revised the manuscript. Ting Yue (yuet@mail.las.ac.cn) did the data analysis, drafted and revised the manuscript. Per Ahlgren (perahl@kth.se) collected the data, did the data statistics, drafted and revised the manuscript. Jielan Ding (dingjielan@mail.las.ac.cn) searched the literature and revised the manuscript. Shuangqing Shi (shisq@mail.las.ac.cn) processed the data, made the tables and drew the figures. Rainer Frietsch (rainer.frietsch@isi.fraunhofer.de) performed the research and revised the manuscript.

The authors have declared that no competing interests exist.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
Aksnes D. W., Leeuwen T. N. V., & Sivertsen G. (2014). The effect of booming countries on changes in the relative specialization index (RSI) on country level. Scientometrics, 101(2), 1391-1401.The Relative Specialization Index (RSI) is an indicator that measures the research profile of a country by comparing the share of a given field in the publications of a given country with the share of the same field in the world total of publications. If measured over time, this indicator may be influenced in the world total by the increased representation of certain other countries with different research profiles. As a case, we study the effect on the RSI for The Netherlands of the increased representation of China in the ISI Web of Science. Although the booming of China is visible in the RSI for The Netherlands, especially in the last decade and in fields where the countries have opposite specializations, the basic research profile as measured by the RSI remains the same. We conclude that the indicator is robust with regard to booming countries, and that it may suffice to observe the general changes in the research profile of the database if the RSI for a country is studied over time.

DOI

[2]
Aurell E., &Ekeberg ,M. (2012). Inverse Ising inference using all the data. Physical Review Letters, 108(9), 090201.DOI:

DOI PMID

[3]
Bongioanni I., Daraio C., & Ruocco G. (2014). A quantitative measure to compare the disciplinary profiles of research systems and their evolution over time. Journal of Informetrics, 8(3), 710-727.By modeling research systems as complex systems we generalize similarity measures used in the literature during the last two decades. We propose to use the mathematical tools developed within the spin-glasses literature to evaluate similarity within systems and between systems in a unified manner. Our measure is based on the verlap of disciplinary profiles of a set of research systems and can readily be integrated in the framework of traditional bibliometric profile analysis. The investigation of the distribution of the overlaps provides useful insights on the dynamics of the general system, that is whether it converges toward a unique disciplinary structure or to a differentiated pattern. We illustrate the usefulness of the approach by investigating the dynamics of disciplinary profiles of European countries from 1996 to 2011. We analyze several bibliometric indicators (including publications and citations) of European countries in the 27 Scopus subject categories. We compare the disciplinary profiles of European countries (i) among them; (ii) with respect to the European standard; and (iii) to the World reference. We find that there is a convergence toward a unique European disciplinary profile of the scientific production even if large differences in the scientific profiles still remain. The investigation of the dynamics by year shows that developing countries are converging toward the European model while some developed countries are departing from it.

DOI

[4]
Balassa B. (2010). Trade liberalisation and “revealed” comparative advantage, Manchester School, 33(2), 99-123.

[5]
Bruzzone L., Roli F., & Serpico S. B. (1995). An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection. IEEE Transations on Geoscience and Remote Sensing, 33(6), 1318-1321.The problem of extending the Jeffreys-Matusita distance to multiclass cases for feature-selection purposes is addressed and a solution equivalent to the Bhattacharyya bound is presented. This extension is compared with the widely used weighted average Jeffreys-Matusita distance both by examining the respective formulae and by experimenting on an optical remote-sensing data set.

DOI

[6]
Bound K. (2008). Brazil: The natural knowledge-economy. DEMOS.

[7]
Daraio C.,Fabbri F.,Gavazzi G.,Izzo M. G., Leuzzi L., Quaglia G., & Ruocco G. (2017). Assessing the interdependencies between scientific disciplinary profiles at the country level: A pseudo-likelihood approach. 2017 ISSI Conference Proceedings. China: Wuhan University, 2017, 1448-1459.

[8]
Ding J., Ahlgren P., Yang L., & Yue T. (2018). Disciplinary structures in Nature, Science, and PNAS: journal and country levels. Scientometrics(3), 1-36.This paper analyzes, using Web of Science publications and two time periods (2004–2006 and 2014–2016), the disciplinary structures in the three prestigious journals Nature, Science and PNAS, compared...

DOI

[9]
Everitt B., Landau ,S. &Leese , M. (2001). Cluster Analysis (Forth Edition.). London: Arnold.

[10]
Glänzel W., Debackere K., & Meyer M. (2008). “Triad” or “tetrad”? On global changes in a dynamic world. Scientometrics, 74(1), 71-88.The US-EU race for world leadership in science and technology has become the favourite subject of recent studies. Studies issued by the European Commission reported the increase of the European share in the world’s scientific production and announced world leadership of the EU in scientific output at the end of the last century. In order to be able to monitor those types of global changes, the present study is based on the 15-year period 1991–2005. A set of bibliometric and technometric indicators is used to analyse activity and impact patterns in science and technology output. This set comprises publication output indicators such as (1) the share in the world total, (2) subject-based publication profiles, (3) citation-based indicators like journal-and subject-normalised mean citation rates, (4) international co-publications and their impact as well as (5) patent indicators and publication-patent citation links (both directions). The evolution of national bibliometric profiles, ‘scientific weight’ and science-technology linkage patterns are discussed as well. The authors show, using the mirror of science and technology indicators, that the triad model does no longer hold in the 21 st century. China is challenging the leading sciento-economic powers and the time is approaching when this country will represent the world’s second largest potential in science and technology...

DOI

[11]
Glänzel W., Leta J., & Thijs B. (2006). Science in Brazil. Part 1: A macro-level comparative study, Scientometrics, 67(1), 67-86.In the present paper, the evolution of publication activity and citation impact in Brazil is studied for the period 1991-2003. Besides the analysis of trends in publication and citation patterns and of national publication profiles, an attempt is made to find statistical evidences of the relation between international co-authorship and both research profile and citation impact in the Latin American region. Despite similarities and strong co-publication links with the other countries in the region, Brazil has nonetheless a specific research profile, and forms the largest potential in the region.

DOI

[12]
Glänzel W., &Schlemmer B. (2007). National research profiles in a changing Europe (1983-2003): An exploratory study of sectoral characteristics in the Triple Helix. Scientometrics, 70(2), 267-275.Eight Eastern European countries joined the European Union in 2004. In this paper, bibliometric methods are used to analyse if the integration of these countries into the EU was accompanied by corresponding changes in their sectoral research profiles. In addition, the authors discuss changes in the national profiles of three accession countries and three EU15 member states during the last two decades. The results confirm that a process of European homogenisation and convergence is taking place, but also show that this process is slow and that member countries have maintained their individual peculiarities and preferences during this evolution.

DOI

[13]
Grupp H. (1998). Foundations of the Economics of Innovation: Theory, measurement, and practice. Books, 30(4), 379-379.Measurement of technical change have focused on the characteristics of price and quantity whilst useful theories and reliable indicators of the quality of innovation in new products habe been sorely lacking. The author examines the theoretical foundations of the measurement of technical change and extends the analysis to consider the econometric and empirical perspective in the process of innovation. He outlines the key contributions to innovation research reviewing both the English-language literature and providing a very useful guide to the most important contributions in other languages. In the measurement of the quality of innovation, the techniques used in the author's contribution to new 'technometrics' are presented and explained in detail and be applied to the most important topical problems in innovation management.

DOI

[14]
Harzing A.W., &Giroud A. (2014). The competitive advantage of nations: An application to academia. Journal of Informetrics, 8(1), 29-42.Within the field of bibliometrics, there is sustained interest in how nations ompete in terms of academic disciplines, and what determinants explain why countries may have a specific advantage in one discipline over another. However, this literature has not, to date, presented a comprehensive structured model that could be used in the interpretation of a country's research profile and academic output. In this paper, we use frameworks from international business and economics to present such a model. Our study makes four major contributions. First, we include a very wide range of countries and disciplines, explicitly including the Social Sciences, which unfortunately are excluded in most bibliometrics studies. Second, we apply theories of revealed comparative advantage and the competitive advantage of nations to academic disciplines. Third, we cluster our 34 countries into five different groups that have distinct combinations of revealed comparative advantage in five major disciplines. Finally, based on our empirical work and prior literature, we present an academic diamond that details factors likely to explain a country's research profile and competitiveness in certain disciplines.

DOI

[15]
Julio C. R. P., Maria M. L. E., & Dirce, M. T. Z.(1998). Brazilian sciences and government funding at the state of Sao Paulo. Scientometrics, 43(2), 177-188.The State of S茫o Paulo plays an important role in national research activities. The Foundation for the Support of Research of S茫o Paulo State (FAPESP) is commissioned to manage funds for these activities. The profile of Brazilian sciences is investigated and contrasted to FAPESP's investments. ISI date relative to years 1981 to 1995 are analysed through multivariate methods (Factor and Cluster Analysis) as to provide weighted ranks of research fields, relationships among them as geometric distances, and group classification. This information is compared to public reports on FAPESP's expenditures. Brazilian scientific production grows at 244 papers/year in the period, and its global share is almost doubled. Impact shows no trend. Best performers are from exact and biological sciences. Though impact varies greatly among research fields, their major distinctive feature is magnitude of production. The six top research fields identified (Physics, Biology & Biochemistry, Clinical Medicine, Engineering, Chemistry, Plant & Animal Sciences) were found to equally be the prime beneficiaries FAPESP's investments. The Brazilian scenario is one of increasing production under an international pattern. This trend is not accompanied by changes in impact. As a corollary, volume of publications rises as an important issue. Public investments from S茫o Paulo State adequately conform this scenario giving reassurance that resouces are not being squandered.

DOI

[16]
Kaufman ,L. &Rousseeuw, P.J. (1990). Finding groups in data: An introduction to cluster analysis. DBLP, New York:Wiley.ABSTRACT This is a book. We are not allowed to upload or share it, sorry.

DOI

[17]
King D.A. (2004). The scientific impact of nations. Nature, 430(6997), 311-6.To measure the quantity and quality od science in different nations, the author has analysed the numbers of research papers and reviews, and their citations. All data are were provided by Thomson ISI, previously known as the Institute for Scientific Information.

DOI PMID

[18]
Kozlowski J., Radosevic S., & Ircha D. (1999). History matters: the inherited disciplinary structure of the post-communist science in countries of central and Eastern Europe and its restructuring. Scientometrics, 45(1), 137-166.The inherited disciplinary structure of the science of post-communist countries of CEE carries a strong common features of its past. The communist heritage is present in: a) a relatively homogeneous research profile among post-communist countries; b) the similar structure of disciplinary comparative advantages of post-communist countries; c) the unbalanced and concentrated disciplinary structure of comparative advantages. The analysis is based on ISI databases and uses statistics on papers and citations for the 1992-1997 period for all central and eastern European countries as well as for other world regions. In the conclusions we discuss the relevance of the results for the restructuring of science in the countries of Central and Eastern Europe.

DOI

[19]
Li N.(2017). Evolutionary patterns of national disciplinary profiles in research:1996-2015 .Scientometrics, 111(1), 1-28.Abstract This paper presents an empirical study of the evolutionary patterns of national disciplinary profiles in research using a dataset extracted from Scopus covering publications from 45 nations for the period 1996–2015. Measures of disciplinary specializations and statistical models are employed to examine the distribution of disciplinary specializations across nations, the patterns of structural changes in the world’s disciplinary profiles, and the evolutionary patterns of research profiles in individual nations. It is found that, while there has been a continuous process of convergence in national research profiles, nations differ greatly in their evolutionary patterns. Changes in national disciplinary profiles are decomposed into the regression effect and the mobility effect and both effects are analyzed for individual nations. The G7 and the BRICs countries are used as cases for the in-depth scrutiny. Policy implications based on the findings and directions for future research are discussed.

DOI

[20]
Liang L. M., Havemann F., Heinz M., & Wagner-Dobler R. (2006). Structural similarities between science growth dynamics in China and in western countries. Scientometrics, 66(2), 311-325.To compare science growth of different countries is both, of theoretical and of pragmatic interest. Using methods for the analysis of complex growth processes introduced by H. E. Stanley and others, we exhibit quantitative features of Chinese science growth from 1986 to 1999 and compare them with corresponding features of western countries. Patterns of growth dynamics of Chinese universities publication output do not differ significantly from those found in the case of western countries. The same is valid for Chinese journals when compared to international journals. In nearly all cases the size distribution of output over universities or journals is near to a lognormal one, the growth rate distribution is Laplace-like, and the standard deviations of the corresponding conditional distributions with regard to size decay according to a power law. This means that regarding some structural-dynamical properties China's recent science system cannot be distinguished from a western one - despite different prehistory and different political and economic environment.

DOI

[21]
Radosevic S., &Yoruk, E. (2014). Are there global shifts in the world science base? Analysing the catching up and falling behind of world regions. Scientometrics, 101(3), 1897-1924.This paper explores the changing role of world regions (North America, EU15, South EU, Central and Eastern Europe (CEE), Former-USSR, Latin America, Asia Pacific and the Middle East) in science from 1981 to 2011. We use bibliometric data extracted from Thomson Reuter’s National Science Indicators (2011) for 21 broad disciplines, and aggregated the data into the four major science areas: life, fundamental, applied and social sciences. Comparing three sub-periods (1981–1989, 1990–2000 and 2001–2011), we investigate (i) over time changes in descriptive indicators such as publications, citations, and relative impact; (ii) static specialization measured by revealed comparative advantage (RCA) in citations and papers; and (iii) dynamic specialization measured by absolute growth in papers. Descriptive results show a global shift in science largely in quantity (papers) and much less in impact (citations). We argue this should be interpreted as a shift in science’s absorptive capacity but not necessarily a shift of knowledge generation at the world science frontier, which reflects the nature of science systems operating with high inertia and path dependency in areas of their historically inherited advantages and disadvantages. In view of their common historical legacy in science we are particularly interested in the process of convergence/divergence of the catching-up/transition regions with the world frontier regions. We implement an interpretative framework to compare regions in terms of their static and dynamic specialization from 1981–1989 to 2001–2011. Again, our analysis shows that while science systems are mostly characterised by strong inertia and historically inherited (dis)advantages, Asia Pacific, Latin America and CEE show strong catching-up characteristics but largely in the absorptive capacity of science.

DOI PMID

[22]
Schulz P. A,& Manganote E. J. T. (2012). Revisiting country research profiles: learning about the scientific cultures. Scientometrics, 93(2), 517-531.AbstractIn the present work we analyze the Country Profiles, open access data from ISI Thomson Reuter Science Watch. The country profiles are rankings of the output (indexed in Web of Science) in different knowledge fields during a determined time span for a given country. The analysis of these data permits defining a Country Profile Index, a tool for diagnosing the activity of the scientific community of a country and their possible strengths and weakness. Furthermore, such analysis also enables the search for identities among research patterns of different countries, time evolution of such patterns and the importance of the adherence to the database journals portfolio in evaluating the productivity in a given knowledge field.

DOI

[23]
Salton G., &McGill ,M.J. (1983). Introduction to modern information retrieval. New York: McGraw-Hill.CiteSeerX - Scientific documents that cite the following paper: Introduction to Modern Information Retrieval

DOI

[24]
Schmoch U., Licht G., & Reinhard M. (eds.) (2000). Wissens-und technologietransfer in

Deutschland. Stuttgart: Fraunhofer IRB Verlag.

[25]
Shibayama S. (2011). Distribution of academic research funds: a case of Japanese national research grant. Scientometrics, 88(1), 43-60.AbstractDrawing on a database of the competitive research funds in the Japanese academia, this study examines the distribution of research grants at the university and individual levels. The data indicates high inequality at the university level and slightly lower inequality at the individual level. Over the last three decades, the total grant budget has greatly increased and an increasing number of researchers have received the funds. Simultaneously, large-size grants have become more common and multiple awarding (i.e., one researcher receives more than one grant simultaneously) has become more frequent. These changes taken together, the level of inequality has not been changed substantially. The extent of inequality largely differs between scientific fields; especially high in basic natural sciences and relatively low in social sciences. A close examination of inequality over researchers career indicates different patterns of transition between fields and cohorts. Finally, both at the university and individual levels, the funding distribution is found more unequal than the distribution of publications as an output indicator.

DOI

[26]
Raan A. F. J.V. (2003). The use of bibliometric analysis in research performance assessment and monitoring of interdisciplinary scientific developments. Technikfolgenabschautzung - Theorie und Praxis(1), 20-29.

[27]
Wu J.(2015). Distributions of scientific funding across universities and research disciplines.Journal of Informetrics, 9(1), 183-196.Distributing scientific funding to the suitable universities and research fields is very important to the innovation acceleration in science and technology. Using a longitudinal panel dataset of the National Natural Science Foundation of China (NSFC), the total 224,087 sponsored projects is utilized to investigate the distributions of scientific funding across universities and research disciplines. The inequality of funding distribution is studied through the investigation of Gini coefficient, and its fundamental rules are discovered through the technique of distribution fitting. It is found that the inequality of distributions of NSFC funding across 1971 universities is decreasing, and the distribution of funding and supported universities of 971 research fields follow Generalized Pareto distribution and Geometric distribution function, respectively. This study is dedicated to give an entire landscape to help make policy of distributing scientific funding.

DOI

[28]
Yang L. Y., Yue T., Ding J. L., & Han T. (2012). A comparison of disciplinary structure in science between the G7 and the BRIC countries by bibliometric methods. Scientometrics, 93(2), 497-516.AbstractUsing a collection of papers gathered from the Web of Science, and defining disciplines by the JCR classification, this paper compares the disciplinary structure of the G7 countries (representing high S&T level countries) and the BRIC countries (representing fast breaking countries in S&T) by using bibliometric methods. It discusses the similarity and the balance of their disciplinary structure. We found that: (1) High S&T level countries have a similar national disciplinary structure; (2) In recent years the disciplinary structure of the BRIC countries has become more and more similar to that of the G7 countries; (3) The disciplinary structure of the G7 countries is more balanced than that of the BRIC countries (4) In the G7 countries more emphasis goes to the life sciences, while BRIC countries focus on physics, chemistry, mathematics and engineering.

DOI

[29]
Yue H.J. (2008). Prediction and relative dynamic performance on field of research of China.Studies in Science of Science, 26(3), 530-538.With the ESI database,this paper makes dynamic analysis on 20 Fields of research performance of China from growth in pa- pers and citations ,revealed comparative advantage ,assimilated coefficient,grey prediction.Some quantitative characteristics of the science structure of the science system are revealed.

[30]
Zhu W.T. (2006). A study on NSFC’s interdisciplinary research funding system. Bulletin of National Natural Science Foundation of China. 20(3), 184-189.Key Words】：

DOI

Options
Outlines

/

〈 〉

京ICP备05002861号-43

Copyright © 2023 All rights reserved Journal of Data and Information Science

E-mail: jdis@mail.las.ac.cn　Add:No.33, Beisihuan Xilu, Haidian District, Beijing 100190, China

Support by Beijing Magtech Co.ltd　E-mail: support@magtech.com.cn