CPS-IoT Week 2022 will be fully virtual
This tutorial session will be held as a fully virtual event. We appreciate the hard work that the CPSWeek organizing committee displayed to permit an in-person event, but the tail of the pandemic prevents an in-person component for CPS-IoT Week. Detailed information, including registration options, will be available shortly on the website of CPS-IoT Week and of all its conferences and related events.
In this tutorial, participants will learn how to existing datasets can be posted to the Cyber-Physical Systems Virtual Organization (CPS-VO), and how to acquire a DOI for that dataset in order to broadly disseminate those results to community. Tutorial elements will include issues of reproducibility, and walk participants through approaches they can take to maximize the impact of data they have gathered through the course of research in cyber-physical systems. At the end of the tutorial, participants will either have requested a DOI for their data set, or have the means to do so after finalizing issues of license and reproducibility requirements.
The Cyber Physical Systems Virtual Organization (https://cps-vo.org/) is a broad community of interest for CPS researchers and developers. The CPS-VO includes institutions from academy and industry, and people who work on a wide range of related disciplines with different approaches, methods, tools and experimental platforms. Through this tutorial, the CPS-VO will empower researchers to more effectively disseminate the data that drive their discoveries, and software the reproduces their research results.
The CPS Community needs more access to 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. There are three important motivations that explain the CPS community-driven need for data.
Reproducibility in model-based and data-driven CPS research: CPS architectures increasingly in- corporate Learning Enabled Components (LEC). Consequently, their models, the datasets used for their training, as well as the models of their training processes are essential for the validation, evaluation and exploitation of research results.
Translatability is key: Members of the CPS community bridge application domains, and it is important to be able to disseminate the results of validation experiments such that the experts in another application domain can explore them. However, it is also important to see from the examples of other application domains relevant to CPS, how the data from those validation experiments provide evidence of success As more researchers depend on data for their CPS applications to train models, datasets must provide some kind of ground truth.
Ease of publication will facilitate dissemination: A key goal of the CPS-VO data architecture is to support several use cases for researchers. This tutorial supports researchers who simply want to archive data on the VO as proof of their validation experiment, and paves the way for more interactive data exploration tools in future tutorials.