This dataset has been specifically designed to evaluate the performance and effectiveness of BCubed (Blockchain-based Black Box) and ACOLYTE (dAta Curation fOr traceabiLity sYsTEm) in ensuring traceability and accountability in autonomous service robots. The data was collected by executing the Robot Inspection Task of the RoboCup@Home competition, using a ROS 2-based service robot. The dataset is composed of four different test scenarios, each of which has been executed three times to ensure consistency and variability analysis. Test Scenarios Test Base: Baseline execution of the Robot Inspection. ACOLYTE disable. Test 1: Normal execution. ACOLYTE is enabled. Test 2: Execution with a USB speaker disconnection, preventing the robot from producing speech. ACOLYTE is enabled. Test 3: Execution under a Denial-of-Service (DoS) attack targeting the LiDAR sensor, resulting in degraded navigation and a collision with a person. ACOLYTE is enabled. Data Contents Each test scenario contains: A ROS bag file (.mcap) that contains time-stamped ROS 2 messages recorded during each run, capturing sensor data (e.g., LiDAR, cameras), robot pose and actuator commands. A CSV file containing system performance metrics (e.g., CPU usage, memory usage, I/O network statistics) recorded during the task. Tests involving the accountability system (Test 1, 2 and 3) also include: The logs from the retrieval of records by ACOLYTE. GitHub ACOLYTE: https://github.com/uleroboticsgroup/ACOLYTE BCubed: https://github.com/uleroboticsgroup/BCubed Acknowledgements This research is part of the project TESCAC, financed by “European Union NextGeneration-EU, the Recovery Plan, Transformation and Resilience, through INCIBE".