  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 provider
can employ ML models developed by third-party vendors through the acquisition
of key performance indicators (KPIs) from geographically distant base stations
or user equipment (UE). Yet, the development of ML models hinges on the
availability of realistic and robust datasets. In this study, we embark on a
two-fold journey. First, we collect a comprehensive 5G dataset, harnessing
real-world cell phones across diverse applications, locations, and mobility
scenarios. Next, we replicate this traffic within a full-stack srsRAN-based
O-RAN framework on Colosseum, the world's largest radio frequency (RF)
emulator. This process yields a robust and O-RAN compliant KPI dataset
mirroring real-world conditions. We illustrate how such a dataset can fuel the
training of ML models and facilitate the deployment of xApps for traffic slice
classification by introducing a CNN based classifier that achieves accuracy
$>95\%$ offline and $92\%$ online. To accelerate research in this domain, we
provide open-source access to our toolchain and supplementary utilities,
empowering the broader research community to expedite the creation of realistic
and O-RAN compliant datasets.
