Understanding how people navigate digital media environments requires behavioral data beyond survey self-reports. However, existing infrastructures for collecting such data reproducibly, sustainably, and with strong privacy guarantees remain limited: commercial solutions lack transparency and data governance, while academic tools are typically designed for narrow, short-term use cases and do not support general-purpose academic panel data collections. We address this gap with GESIS Surf, a research infrastructure for large-scale longitudinal collection of web behavioral data in naturalistic environments. The system consists of a browser extension (Chrome and Firefox), a backend with a REST application programming interface (API), and a relational database. It passively captures user interactions, page metadata, and full HTML snapshots of each rendered page, embedding each observation within a hierarchical session model that preserves the structure of browsing behavior. GESIS Surf implements privacy-by-design through strict opt-in participation, per-domain collection rules, and client-side data minimization, ensuring that sensitive data are controlled at the point of collection. The system has been deployed with more than 1,000 participants in a large-scale academic panel infrastructure that includes surveys of participants, and supports reproducible, content-level, and structurally rich analysis of digital behavior. By combining hierarchical session modeling, content-level capture, and integrated privacy enforcement within a unified system, GESIS Surf provides a general-purpose infrastructure for studying digital behavior in a reproducible manner over time.

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