Reproducible analyses: Glossary

Workflow is the new data.

Data + Code + Environment + Workflow = Reproducible Analyses

Before reproducibility comes preproducibility

Use reana-client rich command-line client to run containerised workflows from your laptop on remote compute clouds

Before running analysis remotely, check locally its correctness via validate command

As always, when it doubt, use the --help command-line argument

Develop workflows progressively; add steps as needed

When developing a workflow, stay on the same workspace

When developing a bytecode-interpreted code, stay on the same container

Use smaller test data before scaling out

Use workflows as Continuous Integration; make atomic commits that always work

Writing serial workflows is like chaining shell script commands

Refresh your mind

Discuss your experience

Computational analysis is a graph of inter-dependent steps

Fully declare inputs and outputs for each step

Use Scatter/Gather or Map/Reduce to avoid copy-paste coding

Use step dependencies to express main analysis stages

Use scatter-gather paradigm in staged to massively parallelise DAG workflow execution

REANA usage scenarios remain the same regardless of workflow language details

Workflow specification uses hints to hide implementation complexity

Use kerberos: true clause to automatically trigger Kerberos token initalisation

Use resources clause to access CVMFS repositories

Use compute_backend hint in your workflow steps to dispatch jobs to various HPC/HTC backends

Use open/close commands to open and close interactive sessions on your workspace

Enable REANA application on GitLab to run long-standing tasks that would time out in GitLab CI

Experiment with containerised workflows to advance scientific reproducibility in your research