Universitätsklinikum Essen RCCResearch Compute Cluster
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Overview · RCC ClusterDocs

RCC team members installing and checking equipment inside the cluster racks
RCC is built by scientists for other scientists. Meet the team.

RCC ClusterDocs NG

Welcome to RCC guidance for medical professionals, biomedical researchers, research software developers, and technical project staff.

RCC is designed for two access styles:

  • advanced command-line/developer access, using SSH, VS Code, and direct Slurm tools — this is how you use RCC today; and
  • browser-first research, centered on Files and the planned RCC Analysis Notebook/Workflow experience — not yet released.

Start with RCC Expedition Light, the required first-use path, to set up SSH access on Windows 11 or macOS. If you prefer a guided local course, use RCC Expedition.

The course is designed so that a new user can progress without needing an administrator beside them. Each class has a small practical exercise and a gate that checks readiness without exposing credentials or generating significant cluster load.

RCC in plain language

Think of RCC as a set of project workrooms rather than one large shared disk. For definitions used throughout the documentation, see the RCC terminology reference.

Your primary group records where you belong; it is not how cross-department research data is shared. The project is the access and collaboration boundary. Start with the complete plain-language TL;DR for the important limits and links.

The underlying compute model remains Slurm-backed, but users should not all have to interact with the scheduler directly. The planned (not yet released) user-facing model is:

Files -> RCC Analysis
           |      |
           |      +-> Workflow: repeat / scale / reproduce
           +--------> Notebook: explore / visualize / prototype
        -> Files: durable results

The interactive session machinery previously described as RCC Workbench is now treated as an implementation/advanced execution layer behind Analysis Notebook mode rather than a separate primary research product.

RCC supports statistics, visualization, Python and R data science, machine learning, GPU-accelerated AI, and distributed data processing. These techniques remain part of a reproducible research workflow: computation runs through Slurm, data stays within its project governance, and model evaluation includes validation, uncertainty, bias, and scientific limitations.

Not sure which surface to use? Read RCC services: where should I go?. It also records which newer browser surfaces are documented before release.

Choose your path

01

Data analysis

Move from research data to a reproducible result

Use notebooks for bounded exploration and governed workflows for repeated or scalable analysis; learn Python, R, statistics, AI, efficient I/O, GPUs, validation, and result sharing.

Follow the data analysis path →
02

Software development

Build reviewable workflows and protected services

Use advanced SSH/VS Code tools when needed; learn Git, Snakemake, Slurm, Conda, Apptainer, Python/R applications, Shiny, APIs, and governed deployment.

Follow the software development path →

Shared foundation

A genome connected to Python, R, GPUs, Shiny, Jupyter notebooks, and biomedical data-science tools
One research environment for statistics, data science, reproducible AI, distributed computation, visualization, and governed sharing.