Data Data Everywhere, but Not A Drop to Reuse: Why is Secondary Data Analysis Still So Rare?
Despite the prevalence of open data, reusing existing datasets for new studies remains uncommon. Reasons include poor documentation, narrow datasets, and lower prestige compared to primary data, but greater uptake in secondary data analysis could save the field millions.
This hackathon aims to address this issue by creating a strategy document in three parts:
- Barriers: Identify obstacles inhibiting wider adoption of secondary data analysis, such as data access, licensing, documentation, training, and incentives.
- Solutions: Brainstorm practical solutions to overcome these challenges, including OSF features, training curricula, publishing and funding instruments, and team science for higher-value data.
- Actions: Translate solutions into actionable steps. What can researchers, lab groups, or institutions do to normalize secondary data analysis as both sharer and reuser?
Our output will be an action plan in Google Docs for making secondary data analysis more accessible and rewarding, improving the resource efficiency of psychological research.