PART 1 – The Empirical Picture
5.4 Context of academic domains and fields
This section takes a closer look at the academic fields and non-academic communities which are involved in the cases with a focus on both, the developer fields as well as the user fields.
It describes different characteristics of these fields with a perspective on their influence on the uptake of e-infrastructure.
As our sample of e-infrastructure projects is purposive and by no means representative of any larger population of projects, it cannot give a general overview of the fields involved in e-infrastructure activities. It comes as no surprise that Grid computing and supercomputing predominate among the fields from which the developers come, with some contributions by high energy physicists and other fields of computer science (networking, scientific
visualization) and neighbouring fields (bioinformatics, computational linguistics). Among the user fields are biosciences, HEP and other fields of physics, earth and environmental sciences, computer science, astronomy and astrophysics the most prominent fields in our sample. Social sciences, arts & humanities, materials science, chemistry and medicine are also involved in some of the projects.
Table 5-7: Developer and user fields ESFRI category Developer fields User fields
C3-Grid Environmental
Sciences Grid computing
− Climatology
− Geophysics
− Biogeography
− Hydrology
− Oceanography
− Other earth system sciences
Page 107 ESFRI category Developer fields User fields
CineGrid e-Infrastructure
− Computer networking
− Scientific visualization
− Media science
CLARIN Social Sciences and Humanities
− Computational Linguistics
− Literature D4science Environmental
Sciences Grid computing
− Environmental Monitoring
− Fisheries and Aquaculture Resources Management
DARIAH Social Sciences and Humanities
DEISA e-Infrastructure Supercomputing
− Nuclear fusion
− Climate/earth system research
− Astrophysics/cosmology
− Computational Neuro Sciences
− Plasma Physics
− Computational Bio Sciences
− Materials sciences DRIVER e-Infrastructure
− Library science
− Computer
science N/A (any)
EELA-2 e-Infrastructure Grid computing
− High-energy physics (HEP)
− Biomedicine and bioinformatics
− Earth sciences
− Artificial intelligence and optimization
− Chemistry
− Civil protection
− Engineering
− Environmental science
EGEE e-Infrastructure
− Astronomy & Astrophysics
− Civil Protection
− Computational Chemistry
− Computational Fluid Dynamics
− Computer Science/Tools
− Condensed Matter Physics
− Earth Sciences
− Finance (through the Industry Task Force)
− Fusion
− Geophysics
− High-Energy Physics
− Life Sciences
− Multimedia
− Material Sciences
ETSF
Materials and Analytical Facilities
Theoretical physics
− Condensed matter physics
− Chemistry
− Biology
− Material science
− Nanotechnology GÉANT e-Infrastructure Computer
networking N/A (any)
MediGrid Biological and Grid computing − (Clinical) Medicine
Page 108 ESFRI category Developer fields User fields
Medical Sciences
− Biomedicine
− Biomedical informatics NVO
Physical Sciences and Engineering
Grid computing Astronomy OGF e-Infrastructure Grid computing Grid computing
OSG e-Infrastructure − Grid computing
− HEP
− HEP (~90%)
− Others (10%), such as theoretical physics, astrophysics, industrial engineering, computer science and natural language processing, chemistry, biochemistry, computational biology, genetics, struc-tural biology and economics
Swedish Nat.
Data Service
Social Sciences
and Humanities − Grid computing
− Humanities
− Grid computing − Biological Sciences
− Pharmaceutical research
TeraGrid e-Infrastructure − Supercomputing
− Grid computing
− Molecular Biosciences
− Physics
− Chemistry
− Astronomical Sciences
− Materials Research
− Earth Sciences
− Advanced scientific computing
− Chemical, thermal systems
− Atmospheric Sciences
− 19 other fields (<3% used NUs)
Next we assessed several characteristics of the case studies’ user fields. We can distinguish between cases that were developed for and often also in close interaction with a rather narrow community of users and those that were developed as general purpose infrastructures for any interested community. Only for those of the former is an assessment of the field characteristics possible.
Collaboration is an important element in all user fields involved in the e-infrastructure cases.
However, there is usually an intricate mix of collaboration and competition; OSG may serve as an example: the HEP community collaborates in developing the technology for running its competitive experiments. Also there are strong incentives to using e-infrastructure services in all of the cases: the fields are confronted with an increasing necessity of using large amounts of heterogeneous data from different sources and they require fast network connections and high-performance computing power to transmit and process it. The dynamics could only be assessed for half of the included cases; however we see that the need for e-infrastructure does not necessarily go in parallel with a fast pace of change in regard to problems, paradigms and approaches. Rather to the opposite, some projects - C3-Grid, OSG and this certainly also applies to DEISA, EGEE, TeraGrid for which this question was not answered at general level due to the many user fields to which they cater - serve fields needing the infrastructure to move forward on big challenges which they have been addressing for some time already, e.g. the search of the Higgs boson (HEP), better climate modelling and identification of human influences on climatic change (environmental sciences), computer-based or in silico screening of compounds for drug discovery (biomedicine/-informatics). And even if there is a strong need, for example, in joining heterogeneous datasets in health, biological and social science research, it is not clear if the demand is starting to be met, or if
Page 109 there is a large demand which is going unmet (only interviews with domain scientists could answer this). of data and demand for data management
Rather low dynamics, persistent work on big challenges
CLARIN Few Increasing prevalence of collaboration
DARIAH Many Likely in some fields, very unlikely in others
Drive for collation of fragmented data sets to improve access for researchers
ETSF Few Collaboration in small
teams dating back to the 1920s 2. Growing practice of using data from other sub-fields computing is the main objective
Highly dynamic, project tries to bring more coherence into Grid development
Page 110 infrastruc-ture is essential for HEP research
Rather low dynamics, persistent work on big challenges
Swedish Nat.
Data Service Many
Collaboration with the primary focus, no research collaboration
a User disciplines: Number of user fields to which the e-infrastructure caters; Collaboration and competition: Between fields, roles of theoreticians, empiricists, method/tool developers;
Infrastructure/facilities: Importance of infrastructure/facilities, computing, data; Dynamics: Pace of change in regard to problems, paradigms and approaches in the fields.