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5G and beyond cellular networks promise remarkable advancements in bandwidth,latency, and connectivity. The emergence of Open Radio Access Network (O-RAN)represents a pivotal direction for the evolution of cellular networks,inherently supporting machine learning (ML) for network operation control.Within this framework, RAN Intelligence Controllers (RICs) from one providercan employ ML models developed by third-party vendors through the acquisitionof key performance indicators (KPIs) from geographically distant base stationsor user equipment (UE). Yet, the development of ML models hinges on theavailability of realistic and robust datasets. In this study, we embark on atwo-fold journey. First, we collect a comprehensive 5G dataset, harnessingreal-world cell phones across diverse applications, locations, and mobilityscenarios. Next, we replicate this traffic within a full-stack srsRAN-basedO-RAN framework on Colosseum, the world's largest radio frequency (RF)emulator. This process yields a robust and O-RAN compliant KPI datasetmirroring real-world conditions. We illustrate how such a dataset can fuel thetraining of ML models and facilitate the deployment of xApps for traffic sliceclassification by introducing a CNN based classifier that achieves accuracy$>95\%$ offline and $92\%$ online. To accelerate research in this domain, weprovide open-source access to our toolchain and supplementary utilities,empowering the broader research community to expedite the creation of realisticand O-RAN compliant datasets.

TRACTOR: Traffic Analysis and Classification Tool for Open RAN