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As these applications become increasingly data-driven, the reliability of CI applications depends on the quality of data, shaping the system\u2019s 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\u2019s 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).<\/jats:p>","DOI":"10.3389\/frobt.2024.1434351","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T05:11:29Z","timestamp":1733980289000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["A roadmap for improving data quality through standards for collaborative intelligence in human-robot applications"],"prefix":"10.3389","volume":"11","author":[{"given":"Shakra","family":"Mehak","sequence":"first","affiliation":[]},{"given":"In\u00eas F.","family":"Ramos","sequence":"additional","affiliation":[]},{"given":"Keerthi","family":"Sagar","sequence":"additional","affiliation":[]},{"given":"Aswin","family":"Ramasubramanian","sequence":"additional","affiliation":[]},{"given":"John D.","family":"Kelleher","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Guilfoyle","sequence":"additional","affiliation":[]},{"given":"Gabriele","family":"Gianini","sequence":"additional","affiliation":[]},{"given":"Ernesto","family":"Damiani","sequence":"additional","affiliation":[]},{"given":"Maria Chiara","family":"Leva","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,12,12]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1145\/2663204.2663263","article-title":"Data-driven model of nonverbal behavior for socially assistive human-robot interactions","volume-title":"Proceedings of the 16th international conference on multimodal interaction","author":"Admoni","year":"2014"},{"key":"B2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3290605.3300233","article-title":"Guidelines for human-ai interaction","volume-title":"Proceedings of the 2019 chi conference on human factors in computing systems","author":"Amershi","year":"2019"},{"key":"B3","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1109\/tcss.2019.2956481","article-title":"A lightweight blockchain-based model for data quality assessment in crowdsensing","volume":"7","author":"An","year":"2020","journal-title":"IEEE Trans. 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