Welcome to the Curious Containers project and its RED file format for reproducible experiments.

Computational Reproducibility

RED (Reproducible Experiment Description) is a JSON or YAML based file format to describe data-driven experiments. A RED file allows researchers to share or publish their computational experiments, such that others can reproduce the results or customize the experiments. A minimal RED file consists of an application’s commandline interface (CLI) description in Common Workflow Language (CWL) syntax, a reference to a container image, as well as CLI arguments and input file references.

Curious Containers provides a reference implementation of RED in Python (cc-core). Experiments can be executed on a local Linux host using the Docker container runtime via the FAICE tool suite (cc-faice). For a more advanced usage, Curious Containers Agency (cc-agency) can distribute experiments in a Docker cluster across multiple hosts.

Together, RED and Curious Containers support the FAIR principles for reproducible research. If you are new to the project, we advise you to work through the RED Beginner’s Guide.

As an introduction (in german), watch the following talk at the deRSE 2019 conference for research software engineering (PDF Slides, Video).

The Road to RED 10

In the past, Curious Containers was mainly a research project to develop ideas and try new concepts in the context of computational reproducibility. Our existing file formats and software components are considered BETA releases.

It is now time to stabilize the ecosystem as a major step towards long-term reproducibility. RED 10 will be the first stable release, such that experiments defined in the RED 10 format will be supported in all future Curious Containers software releases.

RED 10 Pre-Releases

We have released RED 8, that includes the RED Connector CLI 1 specification. Therefore container images and their installed RED connectors, that are prepared to work with RED 8, will be compatible with all future RED versions.

With RED 9, we will move from the CWL 1.0 to the CWL 1.1 standard. RED 9 will be tested extensively without many additional changes, hopefully leading to a rock solid RED 10 release.

Machine Learning Workloads

We are improving CC’s capablities in the realm of machine learning and other high performance workloads. CC ships with support for CUDA via nvidia-docker. Large data directories (e.g. CAMELYON image database) can be mounted via FUSE based network connectors.


If you want to open an issue, please go to the curious-containers meta project on Github. Issue trackers of every other repository are closed.


The Curious Containers software is developed at CBMI (HTW Berlin - University of Applied Sciences). The work is supported by the German Federal Ministry of Economic Affairs and Energy (ZIM project BeCRF, grant number KF3470401BZ4), the German Federal Ministry of Education and Research (project deep.TEACHING, grant number 01IS17056 and project deep.HEALTH, grant number 13FH770IX6) and HTW Berlin Booster.