Scimago Lab Blog

While the current dynamics of Worldwide Science outputs are rising to the surface Research Institutions from developing countries with important results; the location of Institutions holding excellence outcomes, that is, those which represent a truly and significant science advancement does not seem to be changing.

Although a formal study would need a detailed analysis by scientific fields, an exploratory analysis of the group formed by the Research Institutions which annually publish 100 scientific papers or more (3,000 worldwide, as indexed in Scopus database) reveals that the resulting geographic distribution has a strong bias which is explained by research spending patterns in different regions of the world. There is a large concentration of institutions in North America, Europe, India, China and Japan, and to a lesser extent, in the southern hemisphere, in Chile, Argentina and more prominently in Brazil, South Africa and Australia.

Neighborhood influence

By depicting Research Institutions grouped into four levels of Normalized Impact (NI), a picture, as the one exposed, of the geographic distribution of Research Excellence around the world is obtained. Taking into account that, with odd exceptions, institutions reaching NI scores higher than the world average (values higher than 1) are concentrated in North America, Western Europe and Australia/New Zeeland, we must conclude that if the geographic bias in Scientific Outputs is high, it is even higher the bias affecting the Scientific Impact.

Impact can be considered to reflect the use researchers make of the scientific knowledge previously generated. With this in mind, the map suggests that those regions that produce the most (Western Europe and North America) firstly use the knowledge being generated in their area, justifying this way the concentration of large impacts in highly productive regions and implying that Research Institutions in the most productive regions worldwide accumulate a reputational capital, which is due to the geographical context where they are, and on the other hand, that is unattainable for institutions located in less productive regions. It is still to be seen what will happen with China, a newcomer to the elite of more productive scientific countries in the world. Put another way, the neighborhood of a Research Institution affects the scientific reputation it can achieve in global terms, unless it can go beyond its neighborhood through inter-regional alliances with reputed institutions from highly productive regions.

Scientific Dependence

To developing countries, an unintended consequence of the need to collaborate with researchers from highly productive regions is what it could be called “scientific dependence”. Even though it is difficult to measure the role played by researchers in scientific works by just studying the affiliation fields, what we really know is that certain Research Institutions show extremely high rates of International Collaboration (IC) which indeed can be associated to situations of scientific dependence. I.e. when an Institution’s IC reaches or exceeds 80% of its total output, in such a way that its exclusive production ends being marginal, it is clear that it operates in a “scientific dependence” situation. Furthermore, when an institution shows high collaboration rates jointly with an outstanding impact (NI) within its own country and/or region, such impact will be most likely due to the collaboration, and it may be concluded that there exists an external scientific dependence.

Some countries from Latin America constitute an example and an interesting case of scientific dependence in cases where a less scientific development, a highly international collaboration rate and a relatively high impact in a regional scale come together. See the high correlation between IC and NI values in the following table.

 

Country	Output	IC	NI
Brazil	135,259	25.35	0.77
Mexico	52,527	38.65	0.75
Argentina	34,927	42.93	0.90
Chile	20,534	52.64	0.95
Venezuela	8,489	44.40	0.65
Colombia	8,292	53.90	0.83
Cuba	6,915	39.38	0.46
Puerto Rico	4,476	57.95	1.00
Uruguay	2,711	64.66	0.95
Peru	2,654	75.77	1.13
Costa Rica	1,946	71.17	1.16

We are releasing here a small post with some interesting indicators that can help contribute to shed light on the performance of the Spanish Scientific System, these specifically are related to publicly-funded universities.

The table is available as a pdf and can be downloaded here: Science Indicators of Spanish Public Universities

The list below is a description of the indicators you will find on the table.

• Normalized Impact (NI): (ordering criteria) weighted citation average of universities. By normalizing citation counts for differences among fields this indicator represents, in an indirect way, the Average Scientific Quality of the universities. Analysis Period: 2005-2009
• Weighted Scientific Production (WSP): It is the result of assessing each scientific paper by its Normalized Impact and then adding them up for an entire institution. In this way, each paper counts higher than 1 when cited above world average (in its field, type and year) and lower than 1 when cited below. Analysis Period: 2008
• Equivalent Full-Time Faculty Productivity (EFTF-P): Scientific Output by Equivalent Full-Time Faculty. Year 2008.
• Total Expenditure in Research (TER): Combination of the different Research Expenditure Items at a University. Year 2008.
• Expenditure per Paper (EP): Average expenditure per published scientific paper at a University (considering its Weighted Scientific Production). Year 2008.
• Total Equivalent Full-Time Faculty (EFTF): Number of Full-Time Academics by converting part-time to full-time equivalents. Year 2008.
• Expenditure per Equivalent Full-Time Faculty (TER/EFTF): Relationship between Total Expenditure in Research (TER) and Total Equivalent Full-Time Faculty (EFTF). Year 2008.

Overall thoughts about Normalized Impact

The evolution of the Normalized Impact of Spanish universities highlights the overall average impact improvement in Spanish system, as well as a slight decrease in top-notch universities. It remains true that the whole Public System is balanced and comparable with scientifically developed countries, specifically when compared with some English-speaking countries. More than 75% of Spanish public universities are above the world average impact, however none of them reach impact average levels higher than 50% of the world average.

The following chart relates the academics’ productivity average and the Normalized Impact of universities (columns EFTF-P and NI in the table) and shows that there exists a clear positive relationship between these variables. In other words, in those universities where academics’ quantitative output in terms of scientific publications is higher, their qualitative outcomes are higher too. This means that as faculty’s publication activity at universities becomes more intense, the average quality of its outcomes reach higher peaks, among other things as a consequence of the higher chances of scientific synergies that take place within the institutions.

Felix de Moya Anegon. Nowadays, georeferenced maps of science are becoming widely used in a number of ways in Research Analysis and Evaluation because of their ability to represent context information; see for instance Beauchesne’s captivating images in his post on Maps of scientific collaboration between researchers or Bornmann & Waltman’s geographical density maps. There are many more examples. However I will be posting on a different kind of map: a Scientific Collaboration Map that overtakes issues regarding the topographical rigidity exhibited by the former. The proposed maps represent the nearly 3,000 Research Institutions which account for about 80% of the world scientific output.

In the picture, institutions are linked one another based on Research Collaboration as stated on paper affiliations (the list of institutions in the chart is exactly the same to the one used to elaborate the Scimago Institutions Rankings World Report 2010) According to the algorithm used to make the picture, collaboration links act as gravitational forces in such a way that the closeness between nodes (and clusters) represents collaboration strength; each node represents an institution and colors symbolize world regions.

Scientific collaboration tends to take place among neighbors (preferably but not exclusively) so one might expect that a graph illustrating collaboration links among Research Institutions should group them into regions, as in the figure. This regional cluster formations, besides highlighting intra-regional vs inter-regional collaboration strength, helps us analyze the degree of centrality reached by the different regions of the world within the global network of Research Institutions. You can observe not only size differences in regional sub-networks but also how “central” are different regions within scientific knowledge generation and communication processes.

Reputed vs. Emerging Science

In these kind of representations, centered positions tends to reflect higher reputation levels while peripheral ones imply larger local collaboration patterns. As a consequence, researchers belonging to centered institutions are requested to collaborate by researchers from all around the world.

As it was to be expected Research Institutions from Northern America (USA and Canada) and Europe compete for central positions. These regions have intense research collaboration links as the wide contact front between institutions from both regions highlights. Meanwhile, the self-organizational system of World Science keeps on pushing outwards to traditionally peripheral regions (Asia, Latin America, Middle East and Africa) despite these regions currently exhibit larger Growth Rates than central ones.

In fact, despite the impressive increasingly important role played by Asia in Global Research Outputs, mainly due to Chinese Science grown, their institutions are still far from reaching the reputation levels achieved by some European and Northern American’s Research Institutions, hence the outlying position showed by the Asian Cluster. It is so, even though Asia and Northern America have strong collaborations links affecting many Asiatic Countries.

Latin America, Oceania and Middle East

These regions maintain priority collaboration links, at a regional scale, with at least two regions each. Latin America with Europe –mainly Spain and Portugal- and Northern America; in this case the picture shows that Latin America – Europe collaboration links are so intense that the boundary between both regions is not well defined in the map. Similarly, the deep links that Australian and New Zeeland Research Institution has with Northern American counterparts lead to the blurred boundaries observed in the map between Oceania and Northern American Regional Clusters.

Featured Neighborhoods

The Middle East region deserves an isolated view. It is the unique region divided into two separated areas in the map. Arabic countries are placed at the bottom of the map primarily connected to Europe, mainly East Europe (white nodes), and a lesser extent to Asia and Northern America; while Israel sets itself in the junction of Europe, Northern America and Latin America.
The overlapping nodes from Western and Eastern Europe depict only one research cluster for Europe; suggesting there is not Western Europe separated from Eastern Europe in terms of Research Collaboration.
African Institutions share their collaboration links between Europe and Middle East.
Japanese Institutions fit their institutional links among the Northern American collaboration web.

Further Analyses

It turns out obvious that the relative weight of Biomedicine and Health Sciences is very important in this kind of representation given the skewed thematic distribution shown by the world research output and the fact that all those papers with authors belonging to the same institutions are excluded. In the future will be appropriate to carry out more thorough analyses devoted to concrete scientific fields where possible collaboration pattern differences are highlighted, meanwhile this post can serve as a discussion provoking contribution.

Felix de Moya Anegon. Research is not the only activity carried out at universities, but we know Higher Education Institutions’ (HEIs) ability to generate scientific knowledge is an obvious symptom of general performance. The existence of renowned researchers in universities is not by itself a sign of global quality, however nowadays it is hard to think about advanced human capital training unconnected to high quality knowledge generation processes.

In this regard, bibliometric indicators measuring the average quality of HEIs research outputs are valuable reference marks of research capability when it comes time to disseminate the acquired knowledge and spread it to the students. For this reason, it makes sense to put HEIs through benchmarking processes using the Normalized Impacts (NI)* reached by its research outputs. And it will make sense as well to compare the impact distributions of National Higher Education Systems to assess aspects such as the level of heterogeneity of universities within a country or its compared average level, so that policymakers consider whether is better to sacrifice homogeneity to promote top-class institutions, or otherwise the excellence can be reached for the system itself without having to give up equity.

The following chart generated from the World Report SIR 2010 (based on Scopus data) shows, in a comparative way, the Normalized Impact Distributions for all the universities belonging to the world’s 50 most productive countries on scientific knowledge.

[Download data: Microsoft Excel | Open Office]

Universities within countries are distributed in quartiles ranging form higher to lower NI. As can be seen on the chart, the 50 countries can be divided into two groups: on one hand, those which have more than 75% of its universities over the world average and; on the other, those which have the same percentage underneath. We find 24 countries in the first group featuring USA, UK, Germany, France, Canada, Italy and Spain and 26 in the second one including China, Japan, Korea, India, Brazil and Russia.

It might be surprising the presence of Japan or Korea in the second group, but it is not. We must consider that NI distributions show the average value of the international visibility reached by universities research outputs in those countries. Therefore the fact of being technologically developed countries is not incompatible with the relatively low research visibility showed by its academic institutions. The reasons for this are varied and this may not be the place to deal with them, but it can be pointed out briefly that according to the SIR World Report 2010 while one can easily find large Japanese and Korean Companies carrying out high-profile research amongst the world’s Top 100 ordered by NI (i.e. Toyota, Nippon, Samsung, Toshiba, Fujitsu, Hitachi and Mitsubishi) it is otherwise hard to find a Japanese or Korean university in the same ranking Top 400.

It also worth analyzing the level of homogeneity shown by different National Higher Education Systems. The chart shows some countries having similar NI values for all their universities, these countries, which belong either to the first or second before-mentioned groups, show small differences between extreme NI values, i.e. Belgium and Ukrania. On the contrary, the most heterogeneous countries, from an NI perspective, are those whose universities manage to reach very high impact rates jointly coexisting with other ones presenting very low NI rates. This phenomena is not only related to the size or complexity of educational systems, but also to Public vs. Private balance and the level of openness to the globalized market of the different National Higher Education Systems.

To conclude I would mention that the systems’ ability to attract talent is one of the most supporting factors in rising research reputation and, attached to it, the scientific visibility reached by universities around the world.

* Normalized Impact scores indicate the scientific impact that institutions have over the scientific community. In order to obtain a fair measurement of such impact, its calculation removes the influence due to institutions’ size and research profile making it ideal for comparing research performance. [More]

We are starting up Scimago Lab’s blog with this post. The blog aims at set itself up as the main channel to show our particular vision about our work as well as important issues relating to research evaluation, quantitative analysis of scientific information, scientometrics news and so on. We will also inform and discuss here about Scimago Lab‘s product schedule, development, and strategic directions.

We are confident this blog will be as useful to you as exciting to us and encourage everyone interested to participate and set your ideas out.

The Scimago Lab staff