CaDENCE: a large-scale call disconnection event dataset from consumer Android devices (Mar–Jun 2024)

CaDENCE is a curated event-level dataset of call disconnection records collected from consumer Android smartphones. The dataset was prepared to support studies on call termination outcomes, dropped-call characterization, radio access technology transitions, signal-quality conditions, and machine learning methods applied to event-level mobile network data. The source records were generated by event-driven device-level software instrumentation and were curated through filtering, standardization, pseudonymization, and binary labeling.

The dataset contains 125,532,358 records collected over 98 days, from 2024-03-09 to 2024-06-14. Each row corresponds to one call disconnection event and includes temporal attributes, pseudonymous device and software identifiers, mobile network codes, radio access technology indicators, radio band and channel information, signal-quality measurements (RSRP and RSRQ), service-state flags, and the binary label is_drop, which distinguishes dropped-call events from other non-drop call termination outcomes.

The complete release is provided as a compressed archive containing Hive-partitioned Parquet files under data_by_date/, metadata files under metadata/, and root-level documentation files README.md, CITATION.cff, and DATA_LICENSE.txt. The metadata files describe the schema, missingness, value ranges, and daily and network-level aggregates.

CaDENCE_call_disconnection_dataset_release_v1/
├── data_by_date/
├── metadata/
├── README.md
├── CITATION.cff
└── DATA_LICENSE.txt

Funding/context: This dataset was produced within the PD&I SWPERFI Project, “Artificial Intelligence Techniques for Analysis and Optimization of Software Performance,” conducted in partnership with UFAM and Motorola Mobility under Agreement No. 004/2021 with ICOMP/UFAM and Brazilian Federal Law No. 8.387/1991.