Country	Country code	Company %	NPO %	University %	Government %	Subvention %
Germany	DE	20.73	.35	44.1	34.82	18.54
United Kingdom	UK	17.24	.78	64.81	17.17	16.58
France	FR	34.41	1.06	15.15	49.38	13.06
Italy	IT	23.18	12.8	39.93	24.09	8.62
The Netherlands	NL	13.21	1.57	71.18	14.04	7.76
Sweden	SE	19.04	1.39	76.74	2.83	5.67
Belgium	BE	16.38	5.02	71.8	6.81	4.28
Switzerland	CH	19.64	.75	71.4	8.21	4.2
Spain	ES	17.68	3.3	33.19	45.83	3.97
Denmark	DK	29.83	1.24	55.29	13.65	2.96
Austria	AT	37.32	1.64	51.93	9.12	2.16
Finland	FI	18.35	1.49	71.34	8.82	1.91
Israel	IL	23.45		71.35	5.19	1.66
Norway	NO	12.89	1.15	52.96	33.01	1.06
Hungary	HU	25.38	.33	28.55	45.73	.88
Greece	GR	7.51	10.87	28.85	52.77	.83
Poland	PL	.81	.09	39.46	59.64	.7
Czech Republic	CZ	9.93	.1	30.07	59.9	.68
Ireland	IE	17.26		82.63	.11	.61
Portugal	PT	11.46	1.92	29.65	56.97	.42
Estonia	EE	24.32	.35	46.09	29.24	.34
Iceland	IS	84.47	3.47	9.73	2.32	.34
Slovenia	SI	31.64		25.9	42.47	.29
Russia	RU	14.05		14.11	71.84	.26
United States	US	58.98		38.5	2.52	.17
Slovakia	SK	1.7		36.89	61.41	.1
Latvia	LV	18.99	2.38	58.46	20.17	.1
South Africa	ZA	1.26		97.62	1.12	.09
Croatia	HR	47		32.95	20.05	.07
Turkey	TR	19.98		68.56	11.45	.06
Australia	AU	6.82		44.06	49.12	.06
China	CN			24.03	75.97	.06
Canada	CA			94.85	5.15	.05
Romania	RO	3.11	61.25	34.15	1.49	.04
Argentina	AR		21.16	45.03	33.81	.03
Lithuania	LT	44.14		5.2	50.65	.03
Bulgaria	BG		6.04	80.41	13.55	.02
Cyprus	CY	15.03		10.5	74.47	.02
India	IN	24.11		60.3	15.59	.02
Brazil	BR			77.96	22.04	.01
Luxemburg	LU	17.2	3.26		79.54	.01
TOTAL		21.31	2.28	49.85	25.33	98.77

Table 1. Percentage of subvention of each type of organization by country

     PCA Results
     PCA was applied to four variables: percentage of subvention obtained by the universities, companies, governments and NPO’s distributed by each country. Two components were obtained with a correlation of 43.25% to the first one and 31.28% to the second one, being a cumulate of 74.52%. If we observe the matrix correlation of the variables we found low correlations between them. The highest is -.74 between University and Government and the lowest is -.1 between University and NPO. Notice that all the correlations are negative because we calculate the proportion of subvention, so when one increases the other ones decrease. This low correlation causes that we found only two components with 74.52% of the variance. A third factor will explain the 100%, but does not allow us to plot the observations in a two dimension plane. So we have decided to use only the two first components because they are enough to visually describe the relationships between variables and observations.

Variables	F1 (Public)	F2 (Private)
Company	0.014	0.952
NPO	-0.041	-0.484
University	-0.914	-0.266
Government	0.945	-0.197

Table 2. Correlation between factors and variables (Varimax rotation)

     Table 2 shows the Pearson’s correlation between the variables and the principal components obtained from those variables. First component (F1) may be labelled as a “public component” because it is highly correlated with University (-.91) and Government (.95). Although there are private universities, the public universities are majority, so we have decided to label that factor with that tag. Contrarily, the second component (F2) is defined as “private component” because is correlated with NPO (-.48) and Company (.95). NPOs may include private or public entities, but its correlation with F2 component suggest us to consider that the majority of these entities have a private profile.
     It is interesting to observe that both components are correlated with different sign with the variables. Thus, University is inversely and Government is directly correlated with the “public component”, while NPO is negatively correlated and Company is positively correlated with the “private component”. This is due to the already mentioned reason, because the variables were quantified as percentages, so when one increases the other ones decreases. Thus we may observe on one hand a “public” axe (x) in which the positive values show countries which participation is highly supported by government institutions, and the negative ones display countries in which the universities contribute more actively. On the other hand there is a “private” axe (y) where the positive values identify countries with a high business activity, while the negatives show an important presence of NPO.

Figure 1. PCA map with the two main components, variables and observations. Variance (74.52%) with varimax rotation

     Figure 1 shows the bi-dimensional projection of the variables (lines) and observations (dots) according to the two principal components. To increase the visualization added value of the map we have represented the size of the dot according to the total percentage of subvention of each country. We also have coloured each dot according to a geographical classification in which blue is used for the EU-15 countries, red is for the EU-25 ones, yellow represent the European countries non-EU members and green is reserved for non-European countries.
     To understand Figure 1 we firstly have to observe the projection of the variables. As we have seen before (Table 1), “public” component (horizontal axe) mainly contains information from the University and Government variables, while the “private” component (vertical axe) shows information of Company and NPO variables. The vectors length shows the contribution of the variables to their components, thus we observe that the contribution of Company (72.49%), Government (51.58%) and University (48.31%) to their components is very high, but NPO (18.75%) is quite low. This causes that the observations are scarcely affected by this last variable, and the observations are mainly distributed along the horizontal axe.
     Figure 1 allows us to group the observations by their relationship with the components. Thus the upper-half part shows the countries which participation in this research programme is mainly supported by companies. In this group we can highlight Iceland (IS), the United States (US) and Croatia (HR). Contrarily, the lower-half part shows countries in which the weight of the NPO is more important such as Romania (RO) and Argentina (AR).
     If we observe the “public” axe we locate in it the major proportion of countries. In the right-half part there are the countries with participation carried out profusely by universities. In this homogenous group are the United Kingdom (UK), the Netherlands (NL), Belgium (BE) and Sweden (SE). In the left-half are located countries which governments play a central role in their activity in the health thematic area, like France (FR), Spain (ES) and Greece (GR).
     Finally, we can observe countries located in the centre of the map, these are countries that do not have a specific pattern, because the weight of the four types of organizations is similar. Those countries are Germany (DE), Italy (IT) and Norway (NO).

     Discussion
     Obviously, the obtained results may not be generalized and they are not representatives of the Science and Technology system of a country at all. Due to the type of organizations distribution could vary according to different programmes and it should be taken into account that not every research organizations of a country participate in the European research programmes. ROEDIGER-SCHULGA and DACHS (2006) found significant differences in two EU programmes. They detected that while the telecommunication programme had more industrial partners and require greater funding; the agricultural one was dominated by public research institutions and attract less income. Our results show that the universities are the sector most active, while the presence of firms and government is rather balanced. We have observed that the firms’ presence might be due to the emergence of biotechnology sector [GRAVALOS, GARCIA & BARNES, 2002], while the government presence is related with the social aspect of the health. Although HUGHES-WILSON [2004] claims that the business presence was lower than the expected in this thematic area.
     In spite of these limitations, the obtained results allow to map the principal characteristic of the Science and Technology system in each country, at least in the health thematic area. Hence, one can observe that the countries closer to the “government” variable are countries with strong government research councils such as the CNRS in France, the CSIC in Spain, and the MPG in Germany. Together with these countries, we also detect other ones that come from command economies and where they still have an important presence of government organizations. In this set we identify eastern countries such Hungary, Poland, Czech Republic or Russia and others still with command economy such as China. On the other side, close to the “university” variable, we find countries where the basic research is mainly supported by universities because their research councils are basically oriented to the research investment. The United Kingdom, the Netherlands, Belgium and Sweden are countries in this set. It is interesting to notice that important countries such as Germany, Italy and Norway are located in the centre of the plot. This suggests that these countries share out the research performance between government research centres, university and private laboratories in a balanced way. For example, Germany has a strong private sector whose performance amounts for around two-thirds of the German R&D activity, while the government research councils (MPG, Fraunhofer, etc.) and universities are responsible of the rest of the research [FEDERAL MINISTRY OF EDUCATION AND RESEACH, 2002]. This balanced distribution, in which each sector fairly participates in the R&D system, suggests that these countries go toward the Triple Helix model [ETZKOWITZ & LEYDESDORFF, 2000]. This theoretical model suggests that an intense participation of the Government, University and Industry organizations in a coordinate way increases the innovation in a regional system. So we understand that that equilibrated position of Germany, Italy and Norway could be is a good indicator of the joint role of these agents in the biomedical research of those countries.