Notes: Reproducible Science IG Call Sept 21

Main talking points

Challenges in Reproducible Science:

  • Difficulties in iterating on different model types and scaling up for various configurations.
  • The need for tools that abstract away complexities and promote rapid prototyping.
  • The challenge of deciding how long we want to reproduce old code versus finding modern libraries that achieve the same results, given the rapid pace of technological change.
  • The idea of tracking future libraries that can run older code, especially from the perspective of the “map of open source science.”

Tools and Solutions:

  • Introduction of Covalence, a workflow orchestration platform for quantum and classical computing.
  • Emphasis on Covalence’s ease of use and relevance to reproducible science.
  • Discussion on the importance of data integration for machine learning models, especially in compliance-heavy industries.
  • Tools in the Python ecosystem that help record versions used in pipelines.

Best Practices in Code Development:

  • The significance of adhering to best practices for long-lasting and stable code pipelines.
  • Challenges with versioning libraries in Python and the importance of reproducibility.
  • Experience with using Docker for pipelines and issues with Conda channels.
  • The importance of replication and the challenges associated with it.
  • The significance of credit for researchers and software developers in open-source research software.