Introduce an Auditing Layer to Web Science

Scientific discoveries increasingly depend on data and data processing, and Web Science is no exception.As an established practice, data-intensive research typically uses scientific workflows and provenance to facilitate data and method sharing while automatically preserving processing history.Prior research has reported the possibility of ex-post policy-based compliance checking from provenance data.Based on these works, in this paper, we present the conceptual design of a framework of data-harvesting Web Science practices, especially by introducing a common auditing layer.We discuss the framework's practical, scientific, and ethical advantages, including its applicability in the period of large language model (LLM), autonomous agent, and artificial intelligence (AI) explosion.We hope this framework design can incubate a new norm for research practice to be transparent, ethical, and lightweight. CCS Concepts Information systems Semantic web description languages; Security and privacy Human and societal aspects of security and privacy