This website compiles all necessary information about the workshop contents, organisation, registration information and people involved in the first edition of the Amsterdam Science Park Study Group Summer School.
This Summer School will consist of a series of two-day workshops designed to teach basics as well as more advanced topics in scientific programming. Whether you’re just getting started in programming or feel already competent, this training event should help you to enhance the quality and efficiency of your research. Learning basic and domain-specific programming skills will not only improve the quality of your research findings, it will also promote a more Open Science as well as offering you new career perspectives in the long run.
- Week 1: Monday 21 to Friday 25 June 2021.
- Week 2: Monday 28 to Friday 2 July 2021.
- Week 1: programming in R and Python: from absolute beginner to competent practitioner.
- Week 2, several parallel tracks:
- Parallel track 1: microbiome analysis, amplicon metagenomics
- Parallel track 2: Google Earth Engine for geoscientists and ecologists
- Parallel track 3: RNA-seq analysis
You do not have to participate in all modules but, rather you can register for one or more two-day training modules independently.
Free for staff from the SILS and IBED institutes (FNWI, UvA).
Small costs (10-50E) to be determined for external participants.
Number of participants
30 participants maximum per workshop will be allowed.
Links to register
- Week 1
- Introduction to Open Data Science with R workshop (Monday and Tuesday 21-22 June)
- Introduction to programming in Python, the Shell and version control with Git workshop (Thursday and Friday 24-25 June)
- Week 2
- Metabarcoding analysis using QIIME 2 workshop (Monday and Tuesday 28-29 June)
- Introduction to Google Earth Engine (Thursday 1 July)
- Introduction to RNA-seq workshop (Thursday and Friday 1-2 July)
- Shotgun metagenomics and geospatial data in R workshops have been postponed until October or November 2021.
Week 1: Monday 21 to Friday 25 June
|Monday||Introduction to Open Data Science with R - day 1||Marc Galland||tba|
|Tuesday||Introduction to Open Data Science with R - day 2||Marc Galland||tba|
|Thursday||Introduction to programming in Python, the Shell and and Git version control - day 1||Marc Galland||tba|
|Friday||Introduction to programming in Python, the Shell and and Git version control - day 2||Marc Galland||tba|
Week 2: Monday 28 to Friday 2 July
Parallel track 1: the microbiome analyst
|Monday||Metabarcoding analysis using QIIME 2 - day 1||Evelien Jongepier||Fred White|
|Tuesday||Metabarcoding analysis using QIIME2 - day 2||Evelien Jongepier||Fred White|
Parallel track 2: the geoscientist and ecologist
|Thursday||Introduction to Google Earth Engine||Johannes De Groeve||Stacy Shinneman|
Parallel track 3: the RNA-seq specialist
|Thursday||Introduction to RNA-seq - day 1||Tijs Bliek, Marc Galland||tba|
|Friday||Introduction to RNA-seq - day 2||Tijs Bliek, Marc Galland||tba|
For absolute beginners
If you’re an absolute beginner, we advise you to follow either the Open Data Science with R or the Python programming workshops. This will get you going and you will be able to import, transform and curate a dataset as well as creating plots to generate insights on your data.
If you are interested in RNA sequencing analysis, please consider following:
- The Open Data Science with R workshop.
- Followed by the Introduction to RNA-seq workshop.
If you are interested in amplicon-based and/or shotgun sequencing for microbiome analyses, please consider the following:
- The Programming with Python workshop.
- Followed by the Metabarcoding analysis using QIIME 2 workshop.
- And/or followed by the Data processing and visualization for metagenomics workshop.
If you are interested in geospatial data, please consider the following:
- The Open Data Science with R workshop.
- Followed by the Google Earth Engine workshop (no R skills required for this workshop).
Amsterdam Science Park Study Group
A vibrant community that fosters Open Science practices related to data analysis and programming for the natural sciences.
Goals and values
- Support students (undergraduate to post-graduate) and early career researchers in the Faculty of Science
- Empower and provide a safe environment for learning data analysis and programming
- Share good data science practices
- Promote Open Science