Going Public with Your CPS Code and Data

Brief description

CPS code and data has its nature of embedding with specific software and hardware. This tutorial aims to address key challenges in CPS, including: (1) sharing, executing code and handling data across diverse software and hardware environments, and (2) accelerating testng and validation processes using virtual testbeds, eliminating the need for physical setups. In this hands-on tutorial, participants will learn, step by step, how to organize and document their code repository, make their data easily accessible, and ultimately share their code and data as a public resource for others to further accelerate research on AI-driven methods, testbeds and computational digital twins in the CPS field. The tutorial is divided into two sessions, the first (1) is “Making your code do the talking,” where participants learn to use tools for managing and documenting their code, running it effectively, enhancing its readability, enhancing its ability to be set up, and preparing it for going public; the second (2) session is focused on “Creating an accessible and active resource,” where participants learn to structure and present their data in ways that maximize accessibility, transparency, and usability. 

Motivation

The CPS community needs more access to code and data repositories, as convergence research continues to accelerate discovery. Data repositories in computer vision and pattern recognition communities have served as reliable benchmarks for new and updated algorithms, but it is important to note that such communities may have a common set of problems whose solutions may be measured with the same data. Making our CPS code and data to public enhances: 

1. Research equality via community-driven testbeds: Platforms supporting open resources like the Cyber-Physical Systems Virtual Organization (CPS-VO) can accelerate CPS discovery by providing equal access to shared testbeds, datasets and codebases to the community. This ensures that researchers, regardless of resources, can contribute to and validate findings, fostering inclusivity and accelerating innovation across the community.

2. Reproducibility in CPS research: This tutorial aims to support the CPS community by easing the challenges of achieving reproducibility. Participants can incorporate the tools and techniques learned in this tutorial into their regular workflows, streamlining their processes and improving reproducibility in their research and production activities.

3. Translatability across applications: CPS community members bridge multiple domains, making it essential to share validation results so experts across fields can explore them. Examples from related domains help demonstrate data-driven success, and as data becomes central for model training in CPS, datasets must offer a reliable ground truth.