Current Status and Enhancement of Collaborative Research in the World: A Case Study of Osaka University

Osaka University (OU) has enacted a Medium-term Plan (Osaka University, 2018a) every six years, and the third Medium-term Plan (April 2016 – March 2022) is being enforced as of 2020. OU has set the following numerical targets in order to promote globalization of its research.

To run International Joint Research Promotion Program (Osaka University, 2018b), and to build approximately 80 International Joint Labs until March 2022. [ID: 6-2]

OU has already opened ASEAN (Association of Southeast Asian Nations) campuses in Brunei, Thailand, Vietnam, and Indonesia. In our previous research (Iwami et al., 2019), the analysis was performed to check the validity of the already-opened ASEAN campuses and other ASEAN campus candidates were found. The next step is developing the campuses globally, and this research will provide the clues of the global campuses.

The purpose of this research is to provide an overview of the current status of collaborative research between OU and other countries/regions and as a way to promote new collaborative research activities. In order to promote International Joint Research by the university's strategic headquarters, this work proposes a methodology of identifying: (1) strong academic collaborative countries/regions for building global campuses, and (2) researchers performing international joint research to request actions as a main player in university strategy. The merit to use co-authoring relations as an indicator is to perform matching on the scientific fields at the same time when researchers are found.

The bibliographic data of 108,948 academic papers were retrieved from the Web of Science; the data were provided by Clarivate Analytics (as of December 24, 2019). The data were retrieved with the query, OG=(“Osaka University”), from publication year 2001 to 2018.

Integer counting (Leydesdorff, 1989; Park, Yoon, & Leydesdorff, 2016) was used. However, academic papers with more than 1,000 authors were excluded. For example, in large-scale projects such as large-scale accelerators where many authors are usually involved. In light of the purpose of this research, these co-authorships of “kilo-paper” do not fit on the extraction of deep collaborations.

OU makes strategy every six years: 2004–2009, 2010–2015, and 2016–2021. Thus, co-authoring relations were counted every half of strategy period in addition to the previous three years (2001–2003).

The co-authoring relation is defined as the most important factor. Additionally, GDP (gross domestic product) growth (World Bank Group, 2019a) as capitals and population growth (World Bank Group, 2019b) as young receivers are added to select countries/regions. The refinement of countries/regions is performed in the order of co-authoring, GDP growth, and population growth.

To overview the research collaborations, Figure 1 illustrates contries/regions’ number of co-authoring papers with OU, and Table 4 in the Appendix tabulates the corresponding numbers. The co-authorships of “kilo-paper” are removed from the results.

Figure 1

In this research, the top countries are the USA (the United States of America), China, Germany, and England, that are also the top four countries in the total number of academic papers in 2018. The characteristic countries/regions are South Korea and Chinese Taiwan, which are geographically close to OU. Similar to a previous research (Iwami et al., 2019), Thailand, Singapore, and Vietnam are ranked within the top 25.

After the top 50 countries/regions were selected on the basis of the number of the co-authoring relations in 2016–2018 as shown in Table 4 in the Appendix, the top 15 countries/regions were selected on the basis of GDP growth or population growth. Capital, labor force and technological capability contribute to the economic growth of a country (Solow, 1956; Swan, 1956). This Solow-Swan model won the Novel Prize in economics in 1987 (Nobel Media AB, 2020). Considering that the number of co-authoring is an indicator of technological capability, GDP growth and population growth were carefully selected as indicators related to capital and labor force. Surely, taxation, infrastructure, education and the others could be considered as additional indicators, and the analysis could be even more extensive. However, since the existing ASEAN campuses were selected based on researchers’ connections, the indicators in addition to research collaboration must require the strong grounds that are related to the Nobel Prize in economics.

Table 1 shows the selected countries from Table 4 in the Appendix on the basis of 2018 GDP growth provided by the World Bank (World Bank Group, 2019a). Based only on Table 1, Ireland, Middle Europe around Hungary, Egypt, China, India-Bangladesh region, Philippines, Malaysia, and Chile are candidates for the future global campuses. Based only on Table 2, Middle East around Egypt, South Africa, Sweden, New Zealand, Australia, Philippines, Malaysia, and Chile are candidates. The bold countries (Egypt, Bangladesh, Indonesia, Philippines, Malaysia, and Chile) were also selected as common candidates in the Table 1 and Table 2.

In the previous research (Iwami et al., 2019) about the relations with ASEAN, Thailand, Singapore and Vietnam had the largest share for OU in the latest period 2016–2018, and Malaysia was at the third rank in the previous period 2013–2015. Considering the growth rate of co-authoring relations, Indonesia will be an important partner with OU. Before this analysis, OU had decided ASEAN campuses in Thailand, Vietnam, Indonesia, and Brunei (Osaka University, 2017), and the former three countries’ selection matches these results.

Table 2 shows the selected countries from Table 4 in the Appendix on the basis of 2018 population growth provided by the World Bank (World Bank Group, 2019b). Considering common countries both in Table 1 and Table 2, Egypt, Bangladesh, Philippines, Malaysia, and Chile are candidates for the future global campuses. The effectiveness of the OU's past strategy may have led to this result that ASEAN countries increase the number of their co-authoring relations with OU. However, Malaysia, Philippines, and Indonesia don’t have ASEAN campuses yet, so it is considered that these three ASEAN countries are strong candidates.

Figure 2 illustrates organizations’ number of co-authoring papers with OU, and contries/regions are displayed with alpha-3 code of ISO 3166-1. Chinese Academy of Sciences and Seoul National University have decreased the number of co-authoring with OU, while other contries/regions have increased it. Meanwhile, the world's top universities such as Harvard University and Oxford University have increased it. However, the United States has kept stagnant about it. There is a trend that European universities such as Paris-Saclay University, University of York, and University Oslo have been developed as new co-authoring since 2010. In Figure 1, Germany is ranked at the third, but German organizations are not included within the top 25 in Figure 2. The reasons are that a large number of German organizations (500 German organizations / 5318 in 2016–2018) have a small number of co-authoring with OU, and MAX PLANCK was registered separately by departments.

Figure 2

After deciding the countries/regions, researchers having strong relations are identified. Here, assuming that a global campus will be opened in Philippines, Figure 3 shows a part of the breakdowns of world co-authoring countries/regions with the top researchers in OU. The reason is that the two authors are related to Philippines, and there is no problem to reveal the names for scientific productivity. The thickness of the ring becomes wider as the number of papers in that period increases. The same color is used for one organization in one researcher lane beyond the periods. The labels are represented using the initial letters of the university, and the labels and shares are rendered if the share is 10% or more. The number in square brackets written on the left of researcher's name in Figure 3 is the number of the author's papers. The number in square brackets written on the left of an organization is the number of the co-authors. The specialized field, written on the right of researcher's name in Figure 3, is extracted automatically based on the maximum field in the latest period 2016–2018.

Figure 3

The 1^st researcher has too many co-authoring organizations, so Philippines is not especially important for him. According to Figure 3, the 2^nd researcher is a suitable person to ask for opening a global campus in Philippines. The University of Philippines has several campuses, so “Univ Philippines *” belong to the same university system. The 6^th researcher belongs to the same lab with 2^nd researcher, so he will also join the 2^nd researcher's action.

To find strong relations, the OU researchers are ordered by the maximum number of co-authoring with each organization. As a result, researchers of astronomy are ranked up due to papers having a large number of co-author. It does not match the purpose, because such co-authoring means weak relations. Thus, the number of co-authoring internationally was adopted.

Table 3 shows the dependence rate for Philippines in 2001–2018, and the high dependence rate will indicate the high potential of the collaboration about the strategy towards the world. The dependence rate of each author is simply defined as below: Dependence rate =[number of co−authoring papers with target countries][number of all papers]Dependence\;rate\; = {{\left[ {number\;of\;co - authoring\;papers\;with\;target\;countries} \right]} \over {\left[ {number\;of\;all\;papers} \right]}}

How to select data is always an issue. Since available data amount of analysis depends on computer resources, bigger data is not always good. However, in this research, as a result of narrowing the initial data set to the scope of OU, it became impossible to trace if the researchers belonging to OU have a relationship with world organizations under the name other than OU.

There are issues that cannot be solved even if data cleaning was performed – for examples, variations of person's name or organization name, people with the same name, indefinite pairs between affiliation and a person. The analysis result should be interpreted as a trend, but it is necessary to pay attention that bias will occur if the amount of data is small.