Paths · RCC ClusterDocs
Data analysis path
Use this path when your main goal is to explore research data, run statistical analyses, train or evaluate models, create figures, or build a reproducible result pipeline.
Service status: RCC workers and Slurm analysis are ready now. Project vhosts and RCC-to-Coscine transfer are not yet released; the linked classes prepare those future publication and archive steps.
1. Build the shared foundation
| Step | Learn | Why it matters |
|---|---|---|
| Class 1 | Safe SSH, VS Code, and file access | Establish an attributable, verified connection |
| Class 3 | CPU, RAM, GPU, and efficient I/O | Avoid slow or disruptive analysis patterns |
| Class 5 | Slurm jobs and resource requests | Run computation on managed workers |
| Class 6 | Ready-now managed Snakemake | Submit reproducible rule jobs through Slurm |
| Class 7 | Planned Nextflow and nf-core | Prepare for the not-yet-released managed service |
| Class 13 | Biomedical-data governance | Confirm the project and data are suitable for RCC |
| Class 14 | Local staging and safe publication | Keep active I/O off shared storage when measurement supports it |
| Class 16 | Wet-lab instrument handoff | Preserve authoritative acquisition data and verify transfer before analysis |
| Class 17 | Instrument-to-Coscine data lifecycle | Keep data in governed projects, stage analysis correctly, and archive an approved set |
2. Choose your analysis environment
Read the optional account setup patterns if you want guidance for a reviewable Conda data-science environment, an inexpensive prompt, or a bounded Shiny setup.
- Class 9: Python notebooks for pandas, Polars, DuckDB, Arrow, numerical analysis, visualization, machine learning, and AI exploration.
- Class 10: R analysis for statistical workflows, larger tables, reporting, and reproducible R environments.
- AI and data science for technique selection, validation, training, inference, GPUs, and distributed processing.
3. Make the analysis reproducible
Use Class 2 to organise the project, then Class 6 for the ready managed workflow path. Use Class 7 when a reviewed community workflow uses Nextflow or nf-core. Use Class 4 when an immutable runtime is more appropriate than an environment containing many small files.
Keep durable inputs and final outputs in approved project storage. Stage high-I/O intermediates into job-local scratch and retain code, environments, parameters, checksums, logs, benchmarks, and Slurm job IDs.
Use Class 15 when you need to diagnose metadata, object-storage, network, or cache behavior in more detail.
4. Share an approved result
- Use Class 11 for a bounded Shiny development workflow.
- Use Class 12 when converting a notebook, model, or analysis into a governed service.
- Use Class 8 to plan a protected project website. Project vhosts are not yet released.
An analysis result, model, or AI prediction remains a research output unless the applicable clinical validation and governance processes explicitly approve another use.