e-Science 2014: Computing for Biomedical Research

Have you ever thought that your computer has become too small for your data processing needs?
Do you feel overflowed by a tsunami of data?
Do you have the impression that you spend more time arranging computers to run data analysis than actually doing the research that the data is supposed to support in the first place?

If this sounds familiar, dont’ worry. You are not alone. Come and join the e-science course, where you can learn how others are addressing the modern challenges of data analysis for biomedical research.

NOTE: this course has been completed in 2014. If you are interested in following a future edition of this course, please contact the coordinator.

Silvia Delgado Olabarriaga
Department of Clinical Epidemiology, Biostatistics and Bioinformatics, J1B-206
Tel. +31 (0)20-5664660
Email: s.d.olabarriaga @ amc.uva.nl

See also:


Computing infrastructures have become an essential ingredient of biomedical sciences. The variety and amount of data has significantly increased, and the typical desktop is not longer sufficient. Large collaborations are necessary to carry out research, involving complex logistics for handling distributed data collection, analysis and management. New approaches (computers, software and methodology) are needed to tackle challenges regarding compute power, storage space, and collaboration. These new approaches enable and enhance science (e-science), and are increasingly used in (AMC) biomedical research.

The aim of this course is to bring attention to new approaches that can be used for handling large-scale data in biomedical research, including processing, storage and collaboration. At the end of the course, the participant will (1) be familiar with new concepts and state-of-the-art, (2) understand how these new approaches relate to his/her own research, and (3) be able to apply some of these technologies and tools in practice.


  • Basic concepts of research infrastructures and e-science
  • Introduction to distributed computing (grids, clouds, clusters)
  • Scientific workflow management (concepts and examples)
  • Hands-on experience with the AMC e-science platform

An optional project can also be developed, please contact the coordinator if you are interested in this activity.

Schedule and Location

  • 31 March - 4 April 2014, from 1pm to 5pm
  • Lecture room: K01-208
  • Practice room: L0-211

Program and Contents

Course syllabus

Topic revision: r38 - 11 Apr 2014, SilviaOlabarriaga

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