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Context of academic domains and fields

Im Dokument Final Report (Seite 130-134)

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.

Im Dokument Final Report (Seite 130-134)