PART 1 – The Empirical Picture
6.3 Characteristics of the virtual research community involved in an e-infrastructure
In a first set of variables the respondents were asked about the extent of involvement in their field in the same e-infrastructure. We see this group of peers from the same field who use the same e-infrastructure (or participate in the development of the same e-infrastructure in the case of developers) as a good approximation of a virtual community that has formed around an e-infrastructure. The questions assessed the number of other individuals involved, their geographic distribution and last but not least their affiliation by sector (academic versus non-academic).
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6.3.1 Size of the virtual research community
Likely because estimating general involvement in the field requires comprehensive outlook, only about 75% of the respondents had an idea of how many other individuals from the same field participated in the selected e-infrastructure (see Table 6-9). 36% of those who answered the question (N=388) pointed to a small community and in another 28% to a medium-sized community of 21-100 people. Just about 16% work with larger e-infrastructure-based communities of more than a 100 participants.
Table 6-9: Number of other individuals working in the field that are using/participating in the e-Infrastructure
Frequency Percent Valid Percent Cumulative Percent
Valid None 9 2.2 2.3 2.3
1-5 58 14.3 14.9 17.3
6-10 71 17.4 18.3 35.6
21-100 109 26.8 28.1 63.7
101-500 30 7.4 7.7 71.4
More than 500 33 8.1 8.5 79.9
Don't know 78 19.2 20.1 100.0
Total 388 95.3 100.0
Missing System 19 4.7
Total 407 100.0
Next, the data permits us to assess the correlates of community size. We find that the e-infrastructure that respondents use is one of the most remarkable correlates (see Table 6-10):
participants to DEISA and EELA-2 point most often to a small number of peers working with the e-infrastructure, whereas those participating in EGEE and US NVO point to mid-size and large communities. Classifying the e-infrastructures into computing respectively data infrastructures (see annex Table 2–13), we also obtain an interesting pattern. Respondents involved in computing infrastructures point out more often than those involved in data infrastructures, that only few peers from the same field work with the e-infrastructure (see Figure 6-4: Size of the community from the same field using/participating in the
e-Infrastructure by type of e-infrastructure (in %)
Figure 6-4). This suggests that data infrastructures tend to involve a larger number of people in the same manner and with similar needs, whereas computing infrastructures rather serve small groups in different ways. A similar pattern appears if we differentiate between community- and developer-driven e-infrastructures (see again
Figure 6-4). Those that are community-driven are considerably more often backed by larger communities.
Page 145 Table 6-10: Number of other individuals from the same field using/participating in the
e-Infrastructure by e-infrastructure (in %) Selected e-infrastructure
DEISA EELA-2 EGEE US NVO Other Total
None 10.8% 1.4% 0.0% 0.0% 2.0% 2.3%
1-5 18.9% 28.2% 11.8% 4.0% 11.8% 14.9%
6-10 32.4% 22.5% 9.8% 12.0% 17.2% 18.3%
21-100 13.5% 23.9% 25.5% 36.0% 31.9% 28.1%
101-500 0.0% 2.8% 11.8% 8.0% 9.8% 7.7%
More than 500 0.0% .0% 13.7% 12.0% 11.3% 8.5%
Don’t know 24.3% 21.1% 27.5% 28.0% 16.2% 20.1%
Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
Figure 6-4: Size of the community from the same field using/participating in the e-Infrastructure by type of e-infrastructure (in %)
0% 20% 40% 60% 80% 100%
Computing
Data
Developer-driven
Community-driven
None Small (<10) Mid-size (20-100) Large (>100) Driv er
Type of se rvice
Data for this figure in table 2-12 in the annex.
These results are partially due to differences between the fields involved in the projects. We see that in particular astronomers and social scientists point more often to large numbers of other e-infrastructure users, whereas the other fields indicate smaller numbers of users (see table 2-11 in the annex). The field characteristics also relate to the number of individuals involved in the e-infrastructure: in established low collaboration fields there are less other people from the same field involved, in novel dynamic collaborative and dynamic competitive fields there are more other people involved (see Table 6-11).
Page 146 Table 6-11: Number of other individuals from the same field using/participating in the
e-Infrastructure by fields of professional work and development areas (in %)
Field characteristics
Established low
collaboration
Novel dynamic collaborative
Dynamic
competitive Total
None 2.9% 1.6% 0.0% 1.7%
1-5 22.3% 9.7% 15.3% 16.9%
6-10 15.5% 16.1% 19.4% 16.9%
21-100 24.3% 37.1% 36.1% 31.2%
101-500 6.8% 12.9% 9.7% 9.3%
More than 500 6.8% 9.7% 6.9% 7.6%
Don’t know. 21.4% 12.9% 12.5% 16.5%
Total 100.0% 100.0% 100.0% 100.0%
These assessments of the size of the communities of users and developers from the same field participating in an e-infrastructure are interesting, but they have to be interpreted with caution: there is obviously a learning effect taking place: The longer respondents have worked with an infrastructure, the more often they can answer the question and the higher the number of peers of which they have become aware (see table 2-12 in the annex).
6.3.2 Geographic distribution of the virtual research community
The next questions in the questionnaire modules for research users, other users and developers asked for the geographical distribution of the communities participating in the specified e-infrastructure. Local communities, i.e. those confined to a single region within a country, are not common. Approximately one third of the communities are national and two third are international (see ).
Table 6-12: Geographic distribution of other individuals in the field that are using/participating in the e-Infrastructure
Geographic distribution of peers Frequency Percent Valid Percent Cumulative Percent
Valid In a single region 39 9.6 10.7 10.7
In multiple regions within
a country 78 19.2 21.3 32.0
Across multiple countries
within a continent 115 28.3 31.4 63.4
Across continents 134 32.9 36.6 100.0
Total 366 89.9 100.0
Missing System 41 10.1
Total 407 100.0
An important correlate of the geographic distribution of the peers from the same field is again the e-infrastructure in question (see Table 6-13): e-infrastructures either involve national communities with strong international links (NVO), international communities with a strong focus on Europe (DEISA), or extend on more than one continent (EELA-2, EGEE). It will come as little surprise that national infrastructures involve mostly peers from the same country, whereas international infrastructures cater to international communities (see table 2-14 in the annex).
Page 147 Table 6-13: Geographic distribution of other individuals in the field that are
using/participating in the e-Infrastructure by e-infrastructure (in %) Selected e-Infrastructure Geographic distribution of peers
DEISA EELA-2 EGEE US NVO Other Total
In a single region 3.2% 15.9% 6.1% 4.5% 11.8% 10.7%
In multiple regions within a country 12.9% 14.5% 8.2% 40.9% 26.2% 21.3%
Across multiple countries within a
continent 74.2% 13.0% 38.8% 4.5% 32.3% 31.4%
Across continents 9.7% 56.5% 46.9% 50.0% 29.7% 36.6%
Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
The geographic distribution again relates to the disciplines which e-infrastructures serve (see table 2-15 in the annex). Astronomers, physicists, chemists and material scientists and life scientists are more distributed at international level, whereas engineering communities (including computer science), earth scientists and social scientists and humanists are more bounded to national level (Note: Case numbers for most fields are rather small).
Another correlate of the geographic distribution of other community members involved in the selected e-infrastructure is the continent of the respondent (see Figure 6-5). European respondents have the smallest shares of national communities and the largest shares of communities limited to one continent (Europe, of course). North-American and other (mostly Australian) respondents point to either national or global peer communities. Collaboration with other countries on the American/Australian continent is in both cases negligible.
Respondents from developing countries22 state more often that their communities are national or even bounded to a single region than respondents from developed countries (see Figure 6-6).
Figure 6-5: Geographic distribution of other individuals in the field that are using/participating in the e-Infrastructure by continent of respondent (in %)
0% 20% 40% 60% 80% 100%
Europe North-America Latin America Asia Other Total
In a single region In multiple regions within a country Across multiple countries within a continent Across continents
Note: Case numbers Europe: 224; North-America: 34; Latin-America: 80; Asia: 20; Other: 8; Total: 366
22 It should be noted that whenever we mention “respondents from developing countries” this represents the sample of respondents. It includes a large share of Latin American respondents, not least because of the inclusion of EELA into our selection of projects covered. Several regions of the world are not or not adequately represented, including especially Africa.
Page 148 Figure 6-6: Geographic distribution of other individuals in the field that are
using/participating in the e-Infrastructure by development status of the of respondent’s country (in %)
0% 20% 40% 60% 80% 100%
Least developed, low and middle income
countries Developed and high
income countries
In a single region In multiple regions within a country Across multiple countries within a continent Across continents
6.3.3 Affiliation of the virtual community members
Last but not least respondents were asked about the institutional affiliations of their peers, differentiating between academic and non-academic organizations. Approximately 41% of the respondents stated that their community consists exclusively of academics and another 54%
point out that their peers are both academics and academics (see Figure 6-7). Purely non-academic communities are infrequently reported. As we would expect, there is some bias resulting from the affiliation of the respondents themselves (see Figure 6-8): Those who are affiliated to an academic organization more often see their peer affiliations also as academic.
Respondents involved in non-academic organizations, governments, international
organizations or the private sector, point more often to peers with non-academic affiliations.
Figure 6-7: Affiliation of other individuals in the field that are using/participating in the e-Infrastructure (in %)
41.1
4.9
54.0
0 10 20 30 40 50 60
Purely academic
Purely non-academic
Academic and non-academic
Page 149 Figure 6-8: Affiliation of other individuals in the field that are using/participating in the
e-Infrastructure by affiliation of the respondent (in %)
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Academia Government and international org.
Private sector
Total
Purely academic Purely non-academic Academic and non-academic
We find notable differences between the e-infrastructures also for this issue of institutional affiliation of community members (see Table 6-14). DEISA is strongly characterized by academia and US NVO by mixed communities; EGEE, too, but not to the same extent as US NVO. Non-academic communities were mostly mentioned by respondents involved in the other e-infrastructures. Distinguishing the affiliation of participants in an e-infrastructure by the type of the infrastructure we get a few striking differences (see Table 7-16). Disciplinary e-infrastructures serve more often mixed than purely academic communities whereas in multidisciplinary e-infrastructures both community types are equally important.
Infrastructures offering computing services cater more to academic communities, non-academic communities are not that important. Data infrastructures, on the other hand, are more often dealing with non-academic communities.
Table 6-14: Affiliation of other individuals in the field that are using/participating in the e-Infrastructure by e-infrastructure (in %)
Selected e-Infrastructure Affiliation of other individuals
in the field DEISA EELA-2 EGEE US NVO Other Total
Purely academic 79.4% 44.3% 31.3% 18.2% 38.2% 41.1%
Purely non-academic 0.0% 0.0% 2.1% 4.5% 8.4% 4.9%
Academic and non-academic 20.6% 55.7% 66.7% 77.3% 53.4% 54.0%
Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
Table 6-15: Affiliation of other individuals in the field that are using/participating in the e-Infrastructure by type of e-infrastructure (in %)
Geographic scope Disciplinary scope Type of service Driver Affiliation of other
individuals in the field
National Inter-national
Disci-plinary
Multi-disciplin
ary
Computi ng
Data Devel-oper
Com-munity Purely academic 35.8% 40.9% 22.5% 47.2% 44.6% 28.6% 46.1% 30.0%
Purely non-academic 5.7% 2.2% 8.5% 1.0% 1.0% 7.1% 2.0% 5.0%
Academic and
non-academic 58.5% 57.0% 69.0% 51.8% 54.4% 64.3% 52.0% 65.0%
Last but not least, responses on the affiliation of the other people involved in an
e-infrastructure also correlate with respondents’ continent (see Figure 6-9). European, Latin
Page 150 American and other (mostly Australian) respondents are more often aware of colleagues with academic affiliation. North-American and Asian respondents perceive a larger importance of non-academics, from the governmental or private sector, in the communities participating in an e-infrastructure.
Figure 6-9: Affiliation of other individuals in the field that are using/participating in the e-Infrastructure by continent of the respondent (in %)
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Europe North-America Latin America Asia Other Total
Purely academic Purely non-academic Academic and non-academic