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- This course is aimed at advanced PhD students and post-doctoral researchers who are currently working with large-scale omics datasets with the aim of discerning biological function and processes. Ideal applicants should already have some experience (ideally 1-2 years) working with systems biology or related large-scale multi-omics data analyses. Applicants are expected to have a working knowledge of the Linux operating system and the ability to use the command line. Experience of using a programming language (i.e. Python) is highly desirable, and while the course will make use of simple coding or streamlined approaches such as Python notebooks, higher levels of competency will allow participants to focus on the scientific methodologies rather than the practical aspects of coding and how they can be applied in their own research. We recommend these free tutorials: Basic introduction to the Unix environment: www.ee.surrey.ac.uk/Teaching/Unix Introduction and exercises for Linux: https://training.linuxfoundation.org/free-linux-training Python turorial: https://www.w3schools.com/python/ R tutorial: https://www.datacamp.com/courses/free-introduction-to-r Regardless of your current knowledge we encourage successful participants to use these, and other materials, to prepare for attending the course and future work in this area.1
- This course is aimed at scientists working with biomage data across the life sciences. It is suitable for those involved in creating bioimages or taking their first steps in analysis. The content would also be suitable for those wanting to learn more about the BioImage Archive and gain experience with machine learning approaches for image analysis. The programme will be of particular interest to bioimage analysts with questions relating to the use of ‘big data’ and using the wealth of publically available data curated in the BioImage Archive. The course should be accessible to members of the bioimaging community and does not require prior experience with machine learning methods or use of the BioImage Archive. Applicants are encouraged to explore the resources below before starting their application. Applicants should be comfortable with basic programming tasks and have experience working with Python. Prerequisite reading: BioImage Archive: A call for public archives for biological image data ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy The BioStudies database - one stop shop for all data supporting a life sciences study EMPIAR: a public archive for raw electron microscopy image data Image Data Resource: a bioimage data integration and publication platform BioImage Model Zoo 1
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- Ugis Sarkans1
- Virginie Uhlmann1
- Wei Ouyang1
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