PART 1 – The Empirical Picture
6.4 Involvement of respondents in e-infrastructures
6.4.3 Use or development of services and resources
Each e-infrastructure offers a specific set of services and resources that may also correlate to the impact it has on a served community. In the questionnaire we therefore asked the
respondents, whether they used or developed 14 services or resources within the previous six months and – if yes – how often this was the case (see for instance questions 21-23 in the annexed questionnaire). More than half of our respondents were involved with grid computing resources (see Figure 6-17).
Page 157 Figure 6-17: Respondents by services and resources used or developed (in %)
52.8%
36.7%
30.7%
29.9%
29.1%
27.8%
27.5%
22.6%
21.6%
20.2%
20.1%
17.3%
16.4%
11.3%
0% 10% 20% 30% 40% 50% 60%
Grid computing Data management tools Data collections Data analysis tools My own applications ported on the infrastructure Online storage Collaboration tools Simulation Supercomputing Individual support/advice Online digital materials for research Visualization Remote access to research instruments Other
At least 30% of the respondents dealt with data related services and resources (data management tools, data analysis tools, data collections). Several other tools and resources were called upon by 20-30% of the respondents: Above all their own application ported on the e-infrastructure, next online storage and collaboration tools, simulation applications and supercomputing resources.
We also assessed how frequently respondents worked with any of the services and resources in the previous six months; we distinguish between irregular use (just once, quarterly, or
monthly), regular use (weekly) and intensive use (daily). However, the pattern that appears is not very clear (see Figure 6-18).
Page 158 Figure 6-18: Services and resources used or developed by frequency of use (in %)
16%
Data m anagem ent tools
Data analysis tools
Data collection
Online storage
Collaboration tools Rem ote access to research
instrum ents
Irregular use Regular use Intensive use
Respondents’ affiliation correlates with the resources with which they are involved (see Table 6-21). Respondents from governments and international organizations less often rely on computing and simulation resources, and more often on data-related tools and online storage.
Private sector responses were not analyzed due to the low case numbers. Activity profiles also correlate to some extent with the services and resources and confirm this finding (see table 2-19 in the annex). The most interesting group in this case are professionals, i.e. respondents who spend a large share of their time on professional (and not academic) work. This group is a lot less involved with computing resources – in particular supercomputing – and the services and resources which support analytical tasks (simulations, remote access to research
instruments, or own applications ported on the e-infrastructure). However, they particularly often refer to data management tools, online digital material and data collections.
Page 159 Table 6-21: Respondents by services and resources used or developed and affiliation (in %) Services and resources used or
developed
Academia Government and international org.
Private sector Total
Grid computing 50.0% 36.0% 50.0% 48.2%
Supercomputing 21.9% 16.0% 4.5% 20.2%
Visualization 15.8% 18.0% 13.6% 16.0%
Simulation 22.6% 14.0% 18.2% 21.2%
Data management tools 32.6% 44.0% 22.7% 33.5%
Data analysis tools 27.1% 38.0% 9.1% 27.5%
Data collections 26.8% 40.0% 13.6% 27.7%
Online storage 24.5% 38.0% 13.6% 25.7%
Collaboration tools 24.8% 30.0% 9.1% 24.6%
Remote access to research
instruments 16.1% 16.0% 0.0% 15.2%
Individual support/advice 18.1% 26.0% 4.5% 18.3%
Other 8.1% 14.0% 22.7% 9.7%
My own applications ported on the
e-infrastructure 28.4% 32.0% 13.6% 28.0%
Online digital materials for
research 18.6% 33.3% 33.3% 20.3%
Note: The number of responses from the private sector is only 20.
As we would expect, we also find quite different profiles of service and resource usage, depending on the users’ field of research (see table 2-20 in the annex, note however the small case numbers). For instance astronomers and social scientists use more than most others data-related tools, including visualization applications. Grid computing is more common among computer scientists and biologists and supercomputing among chemists and material scientists. Physicists and earth scientists use both to similar extent. Collaboration tools are particularly commonly used among social scientists and computer scientists. Categorizing the fields according to how respondents assess competition, collaboration, maturity and pace of change (see 0 above) we also obtain some interesting patterns (see Table 6-22). Respondents from fields characterized as “novel dynamic collaborative”, i.e. fields which are not yet established, experience a fast pace of change of research problems, paradigms and approaches and rely to large extent on collaboration, use distributed computing and collaboration tools more than respondents from the other two categories of fields. Data-related tools and online storage are most often used in established low collaboration fields with low levels of competition and dynamics. In dynamic competitive fields with high intensity of competition, fast pace of change, and collaboration mostly in small groups of collaborators, several services are less common –exceptions are supercomputing, simulation applications, and the respondents’ own applications ported on the e-infrastructure. As others before us have already put forth (Fry, 2004, 2006; Kling & McKim, 2000; Talja et al., 2007; Wouters &
Beaulieu, 2006; Wouters et al., 2008), this finding suggests that field characteristics influence the types of services and resources needed and used. In other word, a one size fits all
approach is destined to fail. In highly competitive fields in particular, Grid proponents are well advised to enable scientists to reliably use their own applications on the Grid.
Page 160 Table 6-22: Respondents by services and resources used or developed and field
characteristics (in %)
Field characteristics
Services and resources used or developed
Established low collaboration
Novel dynamic collaborative
Dynamic competitiv
e
Total
Grid computing 49.0% 63.5% 47.9% 52.5%
Supercomputing 23.1% 23.8% 30.1% 25.4%
Visualization 21.2% 19.0% 19.2% 20.0%
Simulation 20.2% 23.8% 30.1% 24.2%
Data management tools 42.3% 34.9% 37.0% 38.8%
Data analysis tools 33.7% 31.7% 26.0% 30.8%
Data collections 31.7% 33.3% 26.0% 30.4%
Online storage 33.7% 27.0% 23.3% 28.8%
Collaboration tools 25.0% 44.4% 19.2% 28.3%
Remote access to research instruments 12.5% 25.4% 16.4% 17.1%
Individual support/advice 22.1% 25.4% 21.9% 22.9%
Other 4.8% 22.2% 5.5% 9.6%
My own applications ported on the
e-infrastructure 35.6% 27.0% 38.4% 34.2%
Online digital materials for research 24.5% 20.0% 12.1% 20.4%