Responsible Conduct of Research (RCR) in the BDSIL Program
The Importance of Responsible Conduct of Research (RCR) in the BDSIL Program
Responsible Conduct of Research (RCR) is a foundational element of professional scientific training, ensuring that investigators uphold the highest standards of integrity, transparency, and accountability in their work. For junior faculty and senior postdoctoral fellows—the primary participants of the BDSIL—the principles of RCR are especially critical, as they are at a formative career stage where habits, values, and professional norms are established. Training in RCR equips these emerging leaders not only to conduct their own research responsibly but also to serve as ethical mentors to the next generation of scientists.
The BDSIL is uniquely positioned to embed RCR instruction into its cross-disciplinary and collaborative framework. Participants in our program come from diverse scientific domains, spanning biomedical, clinical, and computational fields. This diversity necessitates explicit training in ethical and responsible practices that cut across disciplines, including issues of data privacy, authorship in team-based research, sharing of data and code, responsible application of machine learning and AI in biomedical contexts, and recognition of societal and environmental impacts of scientific work. By incorporating RCR into our structured workshops, mentorship, and ideation sessions, the BDSIL ensures that the innovative, grant-ready projects generated by participants are not only scientifically rigorous but also ethically grounded and societally responsible.
RCR is also directly tied to the goals of team science and collaborative innovation that lie at the heart of the BDSIL. When investigators work across disciplines, questions of authorship, credit, data ownership, intellectual property, and reproducibility are magnified. Without explicit attention to these issues, collaborations can falter. By foregrounding RCR, we give participants the tools to anticipate and manage these challenges constructively, thereby strengthening the durability and impact of the interdisciplinary teams and projects fostered by the BDSIL.
BDSIL-Relevant
RCR Lectures
Importantly, several seminars from our Foundations of Biomedical Data Science Seminar Series speak to the importance of research ethics, responsible data science, authorship, data sharing, and appropriate use of data in data science applications. Taking the time to watch these YouTube videos will help provide you with current context and examples of responsible data science.
Publicly Available Resources on RCR and Research Ethics
To complement our structured in-person training, participants will be directed to the following set of publicly available resources that provide continuing education and support in responsible conduct of research.
In addition, formal CITI training certification is expected of most university faculty working in the biosciences, especially where identifiable human subjects and any HIPAA-related data might be included. We encourage you to comply with your university’s policies on CITI training related to RCR.
-
https://ori.hhs.gov/education/products
Provides comprehensive modules and case studies on research misconduct, authorship, mentoring, peer review, and conflicts of interest.
-
https://grants.nih.gov/grants/research_integrity/
Outlines NIH expectations for RCR instruction and provides training resources tailored to NIH-funded investigators.
-
https://publicationethics.org/
Offers guidance on authorship, peer review, publication integrity, plagiarism, and research misconduct.
-
https://www.go-fair.org/fair-principles/
Provides international standards for making scientific data Findable, Accessible, Interoperable, and Reusable.
-
Offers clear expectations for sharing datasets, software, and methods in a reproducible manner.
-
http://www.icmje.org/recommendations/
Standards for authorship, disclosure of conflicts, data sharing, and publication ethics in biomedical sciences.
-
A widely used resource highlighting ethical issues in scientific practice, including mentorship, collaboration, and societal responsibility.
-