Prof Olafunmilayo Fawole, University of Ibadan, Co-Chair Data Safety and Monitoring Board.
In the R-NEET project, we recognize the importance of handling, processing, and storing data responsibly. With a large, international, and interdisciplinary research team, we have developed a robust data protocol to ensure our data is accessible, secure, and beneficial for future research. At the core of our approach are the FAIR principles—Findability, Accessibility, Interoperability, and Reusability—which guide how we structure, document, and share our data.
The data protocol was co-developed across the team, and is comprehensive – in this blog post I would like to touch on some of the main ideas in the protocol.
What Does FAIR Mean for R-NEET?
The FAIR principles ensure that research data can be easily found, understood, and reused by others. Here’s how we apply them:
Findability: Our datasets include detailed metadata (data about data), making it easy for researchers to discover and understand the information.
Accessibility: While some of our data is sensitive and requires controlled access, we ensure that metadata remains openly available so researchers know how to request access under clear conditions.
Interoperability: We use standard formats and vocabularies, allowing different research teams and systems to integrate and compare data easily.
Reusability: We provide clear documentation about how data was collected and processed, including licensing terms that allow appropriate future use.
Ethical and Secure Data Handling
Since our project involves sensitive data, we take ethical and legal considerations seriously. We ensure compliance with international regulations on data privacy and intellectual property rights. Sensitive data is stored securely and, when necessary, anonymized or de-identified to protect participants’ identities while maintaining research integrity.
Data Documentation: Keeping Records Clear and Useful
To ensure clarity, all datasets in R-NEET include two levels of documentation:
- Data-level documentation explains the structure, type, and processing procedures for each dataset.
- Project-level documentation provides context on how and why the data was generated, ensuring transparency and reproducibility.
Storing and Sharing Data
To maintain long-term accessibility, our data is stored using platforms like Figshare and Arkivum. Sensitive data remains protected under permanent embargo, while other research outputs are openly accessible. Any software or analysis code developed within R-NEET follows open-source licensing, ensuring that future researchers can replicate and build on our findings.
Why does this Matters?
By following FAIR principles, the R-NEET project is not only ensuring compliance with best practices but also fostering collaboration, innovation, and scientific progress. Making data FAIR means making research more inclusive, transparent, and impactful for the global research community.
We are committed to ethical and open research, ensuring that our work contributes meaningfully to the field of youth mental health while safeguarding the rights and privacy of those involved. To help make this commitment, a reality we have produced a short infographic explaining our approach to using data.
