Welcome to the Amsterdam Science Park Study Group Github repository!
The Amsterdam Science Park Study Group is meant to be a small organisation composed of scientists that program. Most of people that join now are Life Scientists but every all fields are welcome! We are officially affiliated to the Mozilla Science Lab Study Group initiative.
Wait, What's a 'Mozilla Study Group'?
Mozilla Study Groups are fun, informal meetups of your friends and colleagues from around your local institution or town to share skills, stories and ideas on using code for research, share good practises, learn together and explore open research practices. The goal is to create a friendly, no-pressure environment where people can share their work, ask for help on a coding problem, and learn and work together with their peers. Anyone can start and join a Study Group-- you don't have to be an expert coder to do so!
What do other Study Groups look like? Check out the Boston University Study Group's website, and the University of Toronto Coders website. You can also watch a few short videos from Study Group Leads in our Orientation Guide.
Some great topics adressed by the Study Group
Based on the publication "Bioinformatics curriculum guidelines: toward a definition of core competencies." Some core skill sets include:
- General skills: project management, independence, curiosity, self-motivation, leadership, etc.
- Computational skills: software engineering, programming, system administration, machine learning, database design and management, statistical softwares, etc.
- Biology: molecular biology, genomics, genetics, systems biology, Next-Generation Sequencing, etc.
- Statistics: application of statistics, experimental design, NGS analysis using R and Bioconductor, etc.
- Bioinformatics: scientific data management, analysis of large datasets, broad knowledge of bioinformatics, functional genetics and genomics, etc.
To help you to acquire these skills, the Amsterdam Science Park Study Group will cover:
Data Science
Building analytical pipelines
- Workflow management engines:
- Snakemake
- Galaxy
Data Science and Statistics
- Data tidyness: keeping your data clean
- Fitting regression models
- Machine Learning applied to biological data.
FAIR data
How to make your data (more) FAIR = Findable, Accessible, Interoperable, Reusable.
Adding informative meta-data ('data about the data') about your dataset
- Using ontologies and controlled vocabularies:
- The OntoMaton tool combined with Google Spreadsheet
- Using controlled formats:
- The ISA-TAB format: The Investigation/Study/Assay (ISA) tab-delimited (TAB) format is a general purpose framework with which to collect and communicate complex metadata (i.e. sample characteristics, technologies used, type of measurements made) from 'omics-based' experiments employing a combination of technologies. metadata standard