Container venv Overlay for Python Development
·10 mins
Demonstrates how to mount local code into stock or third-party containers and create lightweight venv overlays, bridging the container’s fixed environment with project-specific dependencies.
Reusable
The Reusable principle aims to ensure that data and metadata are well-described so they can be used and combined in future research. This requires:
- Describing data with a plurality of accurate and relevant attributes.
- Releasing data with a clear and accessible data usage license.
- Associating data with detailed provenance information.
- Meeting domain-relevant community standards for data and metadata.
STAMPED practices directly support reusability through comprehensive provenance tracking, machine-readable metadata, explicit licensing, and modular data organization. When every processing step is recorded and the full history of a dataset is available, other researchers can confidently reuse and build upon the work.
From Script to STAMPED Research Object
·9 mins
Four scenarios showing how to incrementally add STAMPED properties to a shell-based analysis using git, make, and singularity.
Ephemeral Shell Scripts for Reproducing Issues
·6 mins
Distills a common practice among open-source developers: writing throwaway shell scripts that set up a fresh environment, reproduce a problem, and can be shared as actionable bug reports or starting points for test cases.
Git Submodules for Modular Dataset Composition
·6 mins
Demonstrates how git submodules enable independent versioning and composition of dataset components.
Re-executing Computations with datalad rerun
·7 mins
Demonstrates how datalad rerun enables re-execution of previously recorded datalad run commands, turning provenance records into actionable recipes.
Recording Computational Provenance with datalad run
·6 mins
Shows how datalad run wraps arbitrary commands to record inputs, outputs, and the exact command in machine-reexecutable form.