In the February 2025 episode of the Decision & Aging Insights podcast, Dr. Luke Stoeckel, SRNDNA’s NIA Program Officer, and Dr. David V. Smith, Associate Professor of Psychology and Neuroscience at Temple University, discuss the updated NIH Data Management and Sharing (DMS) Policy.
Dr. Stoeckel (with assistance from ChatGPT) shares 10 tips for those new to data management and sharing.
1. Start Early Treat your data management and sharing plan as a core part of your research design. Think about how you’ll collect, store, organize, and share your data from the outset. Starting early not only reduces stress but ensures your data is well-organized and reusable.
2. Leverage Institutional Resources Many institutions are stepping up to help researchers comply with the policy. This includes data management tools, repository access, templates for data management plans, and training sessions. Reach out to your institution’s library or data management office—they’re often an underused resource but can be incredibly helpful.
3. Use Established Repositories Depositing data into established repositories that adhere to FAIR principles simplifies the process. NIH has a list of recommended repositories, and these platforms often provide guidance and tools for making your data accessible and reusable.
4. Collaborate with Data Management Experts If managing data isn’t your strength, collaborate with data scientists, bioinformaticians, or other data management experts. They can help with best practices for organizing, annotating, and sharing data effectively. Investing in these partnerships can make a big difference.
5. Take Advantage of NIH Resources NIH provides an array of resources to help researchers understand and comply with the policy. From webinars and FAQs to tools for creating data management plans, these resources are designed to address common pain points. Familiarize yourself with what’s available and don’t hesitate to reach out for clarification.
6. Be Realistic and Transparent If there are limitations to how much data you can share—due to privacy concerns or intellectual property issues—be upfront about these challenges in your data management plan. NIH understands that not all data can be fully open, but they expect researchers to share as much as possible while protecting participants and respecting regulations.
7. Build Data Sharing into Your Lab Culture Create a culture of good data practices within your team. Train your students and staff on data management principles, and ensure your lab’s workflows are designed with data sharing in mind. This approach makes compliance easier and enhances the overall quality and impact of your research.
8. Think Long-Term It’s easy to view data management and sharing as extra work, but think of it as an investment in your research. Well-managed and accessible data increases the visibility, reproducibility, and impact of your work. It can also open up new collaborations and opportunities.
9. Don’t Be Afraid to Ask for Help If you’re stuck, reach out to your institution, NIH program officers, or colleagues who have successfully navigated these requirements. The research community is increasingly collaborative; you don’t have to solve these challenges alone.
10. Focus on the Bigger Picture Remember why this policy exists: to accelerate discovery, foster collaboration, and maximize the impact of public investment in research. By embracing data sharing, you’re contributing to a more open, equitable, and impactful scientific ecosystem.
Helpful resources/websites for psychology/neuroscience data sharing:
You must be logged in to post a comment.