E-science in the Netherlands Maria Heijne TU Delft Library Director / Chair Consortium of University Libraries and National Library

Perspectives for e-science challenges and risks* Challenges Accelerate knowledge development Higher productivity More interdisciplinarity More detailed knowledge of processes in science, biology, social science Better access to and better use of knowledge Risks Higher costs (investments and research) Replacement of other research methods *Based on conference ‘E-science in Action’, July 2008

Virtual Laboratory for e- Science (VL-e) Aim: to bridge the gap between the technology push of the high performance networking plus the Grid and the application pull of a wide range of scientific experimental application domains.  Create scientific prototypes of application specific e-Science environments.  Develop a methodology for re-usable ICT components.  Scale up to and validate real-life applications.  Build up and transfer knowledge on effectively realising application specific e-Science environments.

VL-e Six application domains involved:  Data-Intensive Sciences  Food Informatics  Medical Diagnosis & Imaging  Biodiversity  Bioinformatics  Telescience

Project BIG Grid BIG Grid  Aim is to set up an e-science infrastructure based on the national Grid (SURFnet). This e- Science Grid will enlarge the existing storage and data processing capacity that is required for storage, access, archiving, linking and exchange of growing streams of data.

DAS-3: The Next Generation Grid Infrastructure in the Netherlands  The goal of DAS-3 is to provide a common computational infrastructure for researchers, who work on various aspects of parallel, distributed, and grid computing, and applications like large- scale multimedia content analysis.

Example of e-science project* Bird Avoidance Models  Huge amount of data  Data cleaning  Data originate from research and non research (birdwatchers) Next steps  From national to international scale  Software needs attention  Knowledge about mathematical models  Visualisation techniques  Software engineering *Based on conference ‘E-science in Action’, July 2008

Business model for e-science?*  E-science can/will lead to different methods of knowledge exchange  Not the traditional model through publication  New services for peer review and access to knowledge add value to knowledge knowledge ‘exploitation’ * Based on conference ‘E-science in action’, July 2008

Role for libraries?  Teach enhanced academic skills  Data and research management Storage, preservation, metadata creation, access provision  Active role in knowledge exchange  Methods to maintain connections Publication – data Data- scientific workflow

SURF Share programme (follow-up from SURF DARE)  Projects based on tenders by researchers and libraries  Support for collaboratories  Enhanced publications  New models for peer review  Data collection, storage and re-use DANS, 3 TU datarepository/datastudio