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Before you start
Before you start with the REANA software development itself, it is important to familiarise yourself with the topics of open science, data preservation and reuse, scientific replicability and computational reproducibility.
Amongst good overview papers to check out are:
- Reproducibility and Replicability in Science (2019), National Academic Press, DOI 10.17226/25303, 256 pages.
- Reproducibility of scientific results in the EU (2020), European Commission, DOI 10.2777/341654, 30 pages.
The next step is to familiarise yourself with REANA from the point of view of a researcher. You would consider REANA as a black box where you run computational analyses, without especially caring about knowing what is "inside" the box.
Please do the following:
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Install REANA on your laptop in a researcher-friendly manner. This will require to set up Docker, Kubernetes, Kind and Helm, and allow you to develop basic familiarity with those technologies.
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Run several REANA analysis examples on your local instance. Try to understand the general flow. Observe Kubernetes pods starting and stopping as the workflow runs.
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Skim briefly through CWL tutorial or Snakemake tutorial to get a feeling for how a researcher would express the analysis computations in a declarative way. The reading doesn't have to be detailed if you are not going to work on CWL or Snakemake; just to get the basic ideas.
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Explore
reana-client
command-line client options. Use--help
to discover the functionality and try out various commands. -
Explore REANA web user interface on your local installation at https://localhost:40334.
Once you are comfortable using REANA as a black box on your laptop, run and modify example analyses as researcher would, we can proceed with exploring what's inside the box.
Please continue to Setting up your system in order to start with REANA developments.
REANA reproducible analysis platform
blog.reana.io | docs.reana.io | forum.reana.io | www.reana.io |
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Introduction
Getting started
- Setting up your system
- Cloning sources
- Using production-like development mode
- Using live-code-reload and debug mode
Issue lifecycle
Understanding code base
Technology tips and tricks
- Tips for Docker
- Tips for Git
- Tips for GitLab
- Tips for Keycloak
- Tips for Kind
- Tips for Kubernetes
- Tips for OpenAPI
- Tips for PostgreSQL
- Tips for Python
- Tips for RabbitMQ
- Tips for SQLAlchemy