e-Science: 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, don't 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.
- 26-29 October 2020
- Due to the restrictions imposed by the COVID pandemic, the course will be on-line using ZOOM
- Please register through the AMC Graduate School
- See course program here
Silvia Delgado Olabarriaga
Department of Epidemiology and Data Science
Amsterdam UMC, Location AMC
Email: s.d.olabarriaga @ amsterdamumc.nl
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 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, data management and collaboration. At the end of the course, the participant will
- be familiar with new concepts and state of the art,
- understand how these new approaches relate to his/her own research, and
- have used a few tools.
- Basic concepts, examples and state-of-the-art of research infrastructures and e-science.
- Introduction to high performance computing and national facilities.
- Introduction to advanced concepts and tools for (big data) research data management.
- Demonstrations and/or hands-on experience with selected facilities available for AMC researchers.
- Discussion about study cases from students.