Shakra Mehak ^1* Ines F. Ramos ^2* Keerthi Sagar ³ Aswin Ramasubramanian ³ John D. Kelleher ⁴ Michael Guilfoyle ¹ Gabriele Gianini ⁵ Ernesto Damiani ⁶ Maria C. Leva ^7*

• ¹ Pilz GmbH & Co. KG, Cork, Ireland
• ² Secure Service-oriented Architectures Research Lab, Department of Computer Science, Universit `a degli Studi di Milano, Milan, Italy, Milan, Italy
• ³ Robotics and Automation Group from the Irish Manufacturing Research Centre (IMR), Ireland, Mullingar, Ireland
• ⁴ Trinity College Dublin, Dublin, County Dublin, Ireland
• ⁵ Department of Informatics, Systems and Communication (DISCo), Università degli Studi di Milano-Bicocca, Italy, Milan, Italy
• ⁶ Department of Computer Science, Università degli Studi di Milano, Italy, Milan, Italy
• ⁷ Technological University Dublin, Dublin, Ireland

Collaborative intelligence (CI) involves human-machine interactions and is deemed safety-critical because their reliable interactions are crucial in preventing severe injuries and environmental damage. As these applications become increasingly data-driven, the reliability of CI applications depends on the quality of data, shaping the system's ability to interpret and respond in diverse and often unpredictable environments. In this regard, it is important to adhere to data quality standards and guidelines, thus facilitating the advancement of these collaborative systems in industry. This study presents the challenges of data quality in CI applications within industrial environments, with two use cases that focus on the collection of data in Human-Robot Interaction (HRI). The first use case involves a framework for quantifying human and robot performance within the context of naturalistic robot learning, wherein humans teach robots using intuitive programming methods within the domain of HRI. The second use case presents real-time user state monitoring for adaptive multi-modal teleoperation, that allows for a dynamic adaptation of the system's interface, interaction modality and automation level based on user needs. The article proposes a hybrid standardization derived from established data quality-related ISO standards and addresses the unique challenges associated with multi-modal HRI data acquisition. The use cases presented in this study were carried out as part of an EU-funded project, Collaborative Intelligence for Safety-Critical Systems (CISC).• The paper describes the key data quality challenges specific to multimodal HRI data acquisition for CI application, dissecting the complexities of data collection from various sensors and input types in industrial scenarios, also considering data obtained from interactions between humans and robots as well as, metrics related to physiological measures indicative of human cognitive and physical state, and subjective responses.• Review of existing ISO standards and guidelines specifically addressing data quality for multimodal HRI data acquisition and the gaps identified for their applicability and their capacity to cover all data quality issues in multimodal HRI data.