A Data Curation, Interrogation, and Access System for the Texas Robotics DataVerse

Research in robot autonomy and human-robot interaction involves multidisciplinary perspectives including engineering, computer science, and social sciences. Datasets derived from robotics studies are large, multimodal, and uniquely structured. Open repositories are increasingly publishing robotics datasets. However, the lack of specific metadata standards hinders their understanding and usability. Additionally, current repository infrastructure does not provide support to interrogate and compare multiple datasets. This work introduces a system that leverages curated metadata from robotics datasets published in the DataVerse-based Texas Data Repository, to enable context-aware access through natural language interaction. A robotics-specific data model was implemented as a knowledge graph within the Texas Advanced Computing Center's open infrastructure. Descriptive and structural metadata from curated datasets in the Texas Data Repository are automatically harvested, mapped to the data model, and integrated into the knowledge graph. The datasets' metadata, selected data files, the knowledge graph schema, and related publications are used to train a ChatGPT-based chatbot, enabling users to query and retrieve data from the repository via natural language. The system's design, implementation, and evaluation are demonstrated, showcasing its potential to enhance open dataset's interoperability and accessibility. Through continuing research, the system can be applied to datasets from different domains in open repositories.