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Abstract As the need to tackle complex clinical and societal problems rises, researchers are increasingly taking on a translational approach. This approach, which seeks to integrate theories, methodologies, and frameworks from various disciplines across a team of researchers, places emphasis on translation of findings in order to offer practical solutions to real-world problems. While translational research leads to a number of positive outcomes, there are also a multitude of barriers to conducting effective team science, such as effective coordination and communication across the organizational, disciplinary, and even geographic boundaries of science teams. Given these barriers to success, there is a significant need to establish team interventions that increase science team effectiveness as translational research becomes the new face of science. This review is intended to provide translational scientists with an understanding of barriers to effective team science and equip them with the necessary tools to overcome such barriers. We provide an overview of translational science teams, discuss barriers to science team effectiveness, demonstrate the lacking state of current interventions, and present recommendations for improving interventions in science teams by applying best practices from the teams and groups literature across the four phases of transdisciplinary research.
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This content will become publicly available on May 2, 2026
Behavior Matters: An Alternative Perspective on Promoting Responsible Data Science
Data science pipelines inform and influence many daily decisions, from what we buy to who we work for and even where we live. When designed incorrectly, these pipelines can easily propagate social inequity and harm. Traditional solutions are technical in nature; e.g., mitigating biased algorithms. In this vision paper, we introduce a novel lens for promoting responsible data science using theories of behavior change that emphasize not only technical solutions but also the behavioral responsibility of practitioners. By integrating behavior change theories from cognitive psychology with data science workflow knowledge and ethics guidelines, we present a new perspective on responsible data science. We present example data science interventions in machine learning and visual data analysis, contextualized in behavior change theories that could be implemented to interrupt and redirect potentially suboptimal or negligent practices while reinforcing ethically conscious behaviors. We conclude with a call to action to our community to explore this new research area of behavior change interventions for responsible data science.
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- Award ID(s):
- 2141506
- PAR ID:
- 10625414
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 9
- Issue:
- 2
- ISSN:
- 2573-0142
- Page Range / eLocation ID:
- 1 to 23
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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