• Governments: All departments, offices and other bodies which furnish those common services which cannot otherwise be conveniently and economically provided, as well as those that administer the state and the economic and social policy of the community. It includes NPO controlled and mainly financed by government. They are represented by a triangle in the graph.
• Universities: All universities, colleges of technology and other institutions of post-secondary education, whatever their source of finance or legal status. It also includes all research institutes, experimental stations and clinics operating under the direct control of or administered by higher education institutions. A circle is used to show the universities in the map.
• Firms: It includes all firms, organisations and institutions whose primary activity is the market production of goods or services for sale to the general public, and the private non-profit institutions which mainly works for them such as trade associations, chambers of commerce, or those who are mainly funded (more than the 50%) by their commercial activity. For example, the Pasteur Institute is classified in this category because although it is a non-profit organization it obtains their income mainly through selling vaccines. Firms are described in the graph as squares.
• Non-Profit Organizations (NPO): private non-profit institutions not included in the above categories, as well as private individuals. We used a diamond to show the NPO in the map.

     Results

     Descriptive analysis
     A previous descriptive analysis was performed to observe the most relevant organizations according to several indicators.

Rank	Country	Organization	Funding %	Partners	Projects
1	France	INSERM	2.51	956	164
2	Germany	Max Planck Society	2.39	546	92
3	Sweden	Karolinska Institute	1.99	802	110
4	France	CNRS	1.83	741	130
5	International	European Molecular Biology Laboratory	1.53	444	78
6	United Kingdom	University of Oxford	1.53	642	81
7	United Kingdom	Medical Research Council	1.38	518	69
8	France	Institute Pasteur	1.15	474	65
9	The Netherlands	Leiden University	1.05	597	63
10	United Kingdom	Imperial College London	.96	641	59

Table 1. The 10 first organizations by their percentage of funding

     Table 1 shows the first 10 organizations by the percentage of funding that they receive in this thematic area. The percentage of funding can be considered as a quality indicator that measures the strength and importance of their presence in the research programme. The top organizations are the Institut National de la Santé et de la Recherche Médicale (INSERM) with a funding ratio of 2.51%, followed by the Max Planck Society (2.39%) and the Karolinska Institute (1.99%). According to the number of projects, the most active organization is also the INSERM with 164 projects (27.3%), followed by the Centre National de la Recherche Scientifique (CNRS) (130 projects; 21.6%) and the Karolinska Institute (110 projects; 18.3%). These same organizations are the most collaborative as well, because they maintain the same position if we observe the number of their partners. However, if we normalized the percentage of funding with respect to the number of projects (Funding %/Projects), which will give us an idea of the weight of these organizations in each project, the Max Planck Society is the organization with the best ratio (.026), followed by the Medical Research Council (.02) and the European Molecular Biology Laboratory (.019). It is also interesting to note the strong presence of French and British organizations in the first positions, which is comparable to previous results (Gusmao, 2000; Roediger-Schulga & Barber, 2007).

     Network analysis
     The partners network (Figure 1) shows small-world properties as its clustering coefficient (C=.849). It is considerable higher than the expected for a random network (C=.0002) (Watts & Strogatz, 1998). Furthermore, its average path length (l=2.36) is also rather low. Visually, small-world properties can be seen through the traversal links that run across the network, connecting distant clusters. The degree frequency distributions follow a power law trend (trend coefficient= 1.64) which enables us to state that thisnetworkowns scale-free properties as well (Barabasi, Albert & Jeong, 2000).

Figure 1. Network of participant organization in the health thematic area (n=316, arcs= 5 partnerships)

Rank	Country	Organization	Betweenness
1	France	INSERM	.079
2	Sweden	Karolinska Institute	.052
3	France	CNRS	.050
4	United Kingdom	University of Oxford	.036
5	United Kingdom	Imperial College London	.032
6	Germany	University of Munich	.029
7	The Netherlands	Leiden University	.028
8	Sweden	Lund University	.025
9	Germany	Max Planck Society	.025
10	Czech Republic	Academy of Sceinces of the Czech Republic	.024

Table 3. The 10 first organizations by their centrality betweenness

Sample	Frequency	Mean	Standard deviation	Group
NPO	140	.000	.001	A
Company	1028	.000	.001	A
Government	406	.002	.005	B
University	548	.001	.005	C

Table 4. Sample grouping according to the centrality betwenness (Dunn’s post test)

     Regression analysis
     A regression analysis was done to know which and in what manner these structural variables (degree centrality and betweenness centrality) would affect to the amount of funding that each organization gain in this research thematic area. Three variables were used in the model: total number of partners, number of coordinated projects and total number of projects. Betweenness centrality was rejected by the model because this variable showed strong collinearity with the variable total number of partners (degree centrality). The rest of variables showed acceptable levels of Tolerance and VIF. Then we can accept the absence of collinearity in the model. 
     Table 5 shows the obtained results form the regression model with an explanation of 50%. It means that the funding of an organization in the 6PM is determined for 50% by the number of projects, partners and coordinated projects. Multiple regression models assess in which proportion these variables explain the funding and what the contribution of each one of them is? 
     The obtained model interprets that if the number of projects increases 10% -maintaining constant the other variables- could cause upward of 10.3% the incomes, while a similar increase in coordinated projects would go up 16% the funding. However, if the number of partners increases then the funding would go down. It seems contradictory because a simple regression model shows that the increase in partners could raise the funding (6.7%). This is due to a strong correlation between partners and projects (R2= .59),which affects to the estimation of the funding in the multiple regression model. Therefore, results suggest that the increase in partners is positive only if the number of projects increases as well. So, the rise of partners favours the participation in new projects, and therefore helps to get more funding.

  Table 5. Multiple regression analysis model of the percentage of funding (Adjusted R2=.50)

     Discussion and Conclusions

     The analysis of the participation in the health thematic area of the 6FP has allowed to describe the principal actors in this research programme according to several indicators. INSERM, the Max Planck Society, the CNRS and the Karolinska Institute are the most highlighted organizations in the thematic area, because they obtain the largest proportion of funding and participate in the largest number of projects. These organizations are, in the great majority of the cases, central institutions in the biomedicine research system of their countries. Thus, INSERM and the CNRS are the main research centres in France, Max Planck Society in Germany and the Karolinska Institute in Sweden. The K-Core allowed us to identify the nucleus of the network, which is mainly set up by universities and government organizations such as research councils and public research bodies. These organizations are the most qualified partners to develop a research project, because they have gained extensive experience and knowledge participating in previous research projects. However, studies on different thematic areas (Cabo, 1999; Roediger-Schluga & Dachs, 2006) have shown that the core changes according to the research field. Thus in technical areas there is a higher proportion of large companies in the core, maybe because these sectors are more interested in development-related projects with a strong business profit orientation. Nevertheless, the health area is supported by basic research, which is developed by universities and research centres probably due to the social relevance of the health. This causes that the most interested agent in performing health research are public bodies (McMillan et al, 2000). 
     The observed role of companies and NPOs in the health thematic area is rather peripheral. Although Company is the largest set of organizations (48.44%), it is almost not found in the core of the network and it has the lowest partners and betweeness centrality mean. This lets us to suppose that the companies participate in few and specific projects oriented to their business line, and look for the support of universities and research centres, which are located in the core of the programme, to develop those projects. This also may be due to the fact that most of these companies are small-size bio companies -10% of the participants are small and medium enterprises (European Comission, 2008)-, born from the university (spin-offs) (George et al, 2002) and with an intensive activity in specialized areas (Biotechnology Industry Organization, 2008). Maybe these particular characteristics of the biotechnology companies explain their peripheral situation.
     The obtained results lead us to speculate an explanation of how the collaboration network of the health thematic area of the 6FP is formed. The network is constituted by a dense core of government research organizations and universities. They are the most outstanding research actors in the system. This causes a cumulative process (Price, 1960; Barabasi & Albert, 1999) in which these principal entities participate in more and more European research projects, gaining technical and knowledge resources. Most of them act as large hubs that attract new partners to the network. Their prestige and experience make possible that these new partners contact with them in order to develop a research project. Most of these new members are companies and NPO. They have a low participation degree because they are small size companies which are centred in a specific business line and they are focused in specific projects with defined partners.
     The multiple regression model showed that it is more profitable to be a coordinator (16%) than not (10%), because the income is larger if an institution participates with that role. On the opposite, the number of partners causes the contrary effect reducing to 4% of income. This opposite result is because the more partners we have the less money we share, being the number of projects constant. Hence, the partners affect both the income and the projects. This strong relationship between projects and partners suggests that the increase of partners would raise the likelihood to participate in new projects and therefore to obtain more funding from the programme. So we may conclude that to contact with new partners helps to improve the income as long as that involves more projects.

     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