These variables and attributes were analysed and processed with SPSS 15 and XLStat 2008 statistical packages.

    Results

     Country network

Figure 1. Country collaboration graph of the health thematic area of the 6FP

     Figure 1 shows that the EU-15 countries are the most frequent participant countries in the health thematic area of the 6FP. This maybe due to their large experience in those research programmes. The main countries in the network according to their centrality degree are United Kingdom and France (k=30) followed by Germany (k=29). However, we appreciate that there are non EU-15 countries with a centred position such as Switzerland (k= 28), Hungary (k=23) and Czech Republic (k= 22). Switzerland isan example of the traditional scientific collaboration, which participates in those research programmes as a full member [SWISS STATE SECRETARIAT FOR EDUCATION AND RESEARCH, 2007]. Hungary and Czech Republic are emergingcountries inthe new EU-27 framework which wish to increasetheir position in the EU research programmes. On the contrary, we observe EU-15 countries with a low centrality degree such as Portugal (k= 15), Ireland (k= 17) and Greece (k= 21) which although being older EU members show a low degreeof research collaboration.

Countries	% Funds	Organizations	% Organizations	% Funds/Organizations
United Kingdom	16.58	247	11.59	.0671
Sweden	5.67	86	4.03	.0659
The Netherlands	7.76	118	5.53	.0657
Finland	1.91	33	1.55	.058
Germany	18.54	322	15.1	.0576
Switzerland	4.2	78	3.66	.0538
France	13.06	248	11.63	.0527
Italy	8.62	187	8.77	.0461
Denmark	2.96	68	3.19	.0436
Belgium	4.28	100	4.69	.0428
Israel	1.66	41	1.92	0.404
Spain	3.97	106	4.97	0.0375
Greece	.83	24	1.13	.0347
Austria	2.16	63	2.95	.0342
Norway	1.06	31	1.45	.0341
Czech Republic	.68	21	.98	.0324
Ireland	.61	19	.89	.032
Hungary	.88	34	1.59	.026
Estonia	.34	14	.66	.0241
Poland	.7	32	1.5	.0218
TOTAL	100	2132	100	.0469

Table 1. 20 top countries ranked by fund percentage per number of participant organizations.

     Table 1 shows the most important countries by the percentage of funds received per number of participant organizations. Germany receives the largest proportion of funds (18.54%) followed by United Kingdom (16.58%) and France (13.06%). However, there is a size factor that influences on this appreciation, because these same countries contribute with the largest number of organizations to the health programmes as well. Thus, 15.1% of the participant organizations are German, 11.63% French and 11.59% British. We have normalized the percentage of funding by the number of organizations in each country (%F/Org) in order to avoid this effect and to show a new indicator more closely related to the effort of each country in achieving research funds. According to this indicator, United Kingdom is the country that most percentage of funds achieves by organization (%F/Org=.067), followed by Sweden (%F/Org=.066) and The Netherlands (%F/Org= .066). It is interesting to notice the emergence of Sweden, The Netherlands and Finland, while Germany (%F/Org= .058) or France (%F/Org= .053) moveto average positions. It is also interesting to observe that non EU countries such as Switzerland (%F/Org= .054) or Israel (%F/Org= .04) have better performance than important EU countries such a Spain (%F/Org=.038) and Austria (%F/Org= .034).

Country	Partners	Cooordinators	Coordinator/Partners	Coordinators %
Germany	438	103	23.52%	17.14
France	357	83	23.25%	13.81
United Kingdom	380	80	21.05%	13.31
Italy	288	64	22.22%	10.65
The Netherlands	267	49	18.35%	8.15
Belgium	176	41	23.3%	6.82
Sweden	217	40	18.43%	6.66
Austria	115	23	20%	3.83
Spain	215	23	10.7%	3.83
Denmark	137	19	13.87%	3.16
Switzerland	193	17	8.81%	2.83
Norway	42	14	33.33%	2.33
Greece	51	10	19.61%	1.66
Finland	94	10	10.64%	1.66
Israel	95	7	7.37%	1.16
Hungary	76	5	6.58%	.83
Poland	80	4	5%	.67
Portugal	41	3	7.32%	.5
Island	5	3	60%	.5
Ireland	47	2	4.26%	.33
Slovenia	28	1	3.57%	.17
TOTAL	3719	601	16.16%	100

Table 2. Countries ranked by the percentage of Coordinators.

     We assume that a country which research teams participate in the 6FP more as coordinators than as partners, they are leaders of that research issue. At the country level, we may observe which country holds more leader institutions or research groups in an EU research programme. Table 2 shows the participant countries ranked by the percentage of projects coordinated/total projects in each country. It shows that Iceland (60%) and Norway (33.33%) are the countries with the highest coordinator/partner percentages, followed by Germany (23.51%) and Belgium (23.29%). However, Iceland and Norway have a scarce participation, so we think that these percentages are not representative due to their small weight in the EU research programmes. So, we have calculated the percentage of coordinators over the total number of granted projects in the health thematic area, giving largest weights to the most participant countries. This indicator was already utilized in previous national studies [BMBF, 2009]. Germany (17.14%), France (13.81%) and United Kingdom (13.31%) are the countries with a highest leadership, while the countries with lowest leadership are Slovenia (.17%), Ireland (.33%) and Island (.5%). It is interesting to notice the low position of Switzerland (2.83%), which even though it has a great participation, in fact it leaders few projects. Or Norway (2.33%), although not belonging to the EU, it has a good leadership score.

     Regression models
     Several regression analyses were made to solve the previous question about the relationship between the percentage of funds and the centrality measures such as centrality degree and betweenness centrality. Our aim is to know if a good collaboration degree in the 6FP network makes possible to achieve more funds than other countries as well as to quantify those relationships. A contingency table was obtained in order to calculate the Pearson's chi-square and to test if the response variable (%Funds) is independent from the explanatory variables (centrality degree and betweenness centrality). The chi-square test shows that the explanatory variables are independent from the %Funds (chi-square=.558), them they are suitable predictors of the %Funds variable.

Figure 2. Scatter plot (log-normal) between percentage of funds and centrality degree of each country

aaaaa Figure 2 shows the relationship between the percentage of funds and the centrality degree. This relationship follows a logarithmic equation in which the percentage of funds has been log- transformed while the centrality degree has not. Figure 2 shows that there is a strong relationship between both variables and the centrality degree may explain the 77% of the percentage of funds that a country may raise in the 6FP. Then, we can make an estimation in which an increase of one centrality degree may cause an increase of the 4.9% of the percentage of funds of a country.

Figure 3. Scatter plot (log-normal) between percentage of funds and Betweenness centrality of each country

    Figure 3 shows the relationship between the percentage of funds and the Betweenness centrality. In this case, the relationship also follows a logarithmic equation (R2=.73). This good fit allows us to state that the Betweenness -interpreted as the capacity to attract new organizations to the research programmes- is also an important variable to raise funds from the 6FP. However, the estimation is lower than the one obtained from centrality degree because an increase of one betweenness centrality may only cause an increase of the 0.75% of the percentage of funds of a country.

     Conclussions and Discussion

     The structural analysis of the participant countries in the health programme of the 6FP has made possible to know the research relationships of the participant organizations at the country level, showing a picture of the principal countries in the network, their relationships and their collaboration degree in this research programme. Social network measures (centrality degree and betweenness centrality) not only have assessed the collaboration performance of these countries, but also have estimated and quantified the funds that a country may receive participating in the EU research programmes. The most important conclusion from a methodological point of view is that the social network analysis is a very suitable tool to analyse the research collaboration in research programmes and put in relation R&D indicators, such as funds and participation degree, with network indicators such as centrality degree and betweenness centrality.
     Although these indicators were already used or proposed in previous studies [BMBF, 2009; BRAUN et al., 2009], the availability of this rich data has made possible to apply these R&D indicators in order to describe and assess the research activity of the participant countries. The proportion of funds by organization (%F/Org) has shown the effort of the organizations of each country in achieving research funds, while the percentage of coordinators has ranked the capacity of their organizations to coordinate projects. These indicators may be used to measure qualitative aspects such as leadership and effort, being very useful to value the role of a country in the new ERA.
     According to the results, we may conclude that the United Kingdom, France and Germany are the best located countries in the network of the health research programme and the best ranked ones according to the calculated indicators. These results can be compared with GUSMAO [2001], which found similar country rank distribution in the 3rd and 4th Framework Programme. AVEDAS et al. [2009, p. 53] also showed a similar distribution in the country participation of the whole 6PM.
     We also conclude that Switzerland is a key partner in the EU research activity due to its high collaboration degree and its weight in the health research programme, while Czech Republic and Hungary are potential members which are taking an important part in the EU research programmes. These results are similar to the ones observed by AVEDAS et al. [2009, figure 32], mainly identifying the central position of United Kingdom, France and Germany; and the detection of Czech Republic, Poland and Hungary as emergent countries in the 6FP. However, they omit the important contribution of Non-EU countries such as Switzerland or Norway and they only show the coordinate collaborations, causing the absence of the whole interactions between countries. KUITUNEN et al. [2008] also present network graphs of the participation in the 6FP but limited to those organizations and countries that collaborate with Finland.
     Results obtained in the regression models have shown that the increase of the collaboration degree between countries may cause an increase of 5% of the funds that a country may receive, while to attract new countries to the network may increase .75% these same funds. Obviously, the most actives countries, making new contacts and attracting new partners to the network, are the countries that more funds attract. However, our results allow to quantify and estimate those relationships, as well as to compare the performance of both structural indicators. Thus, the regression model shows that to collaborate with countries inside the network is more beneficial (5%) than attract new members to the programme (.75%). We think that these results are important because they could help to define research policies, which promote the collaboration inter-country as an effective way of achieve more funds form the research programmes and to increase the scientific performance of a country.

     Acknowledgements

     We wish to thank the R&D Framework Programmes Department of the Centre for the Development of Industrial Technology (CDTI) of Spain for their support and the supply of 6th EU Framework Programme data.

     References aaaa