Data policy with respect to person-specific datasets¶
A priority of the Sandbox is to guide health data science learning using real-world-similar datasets. A major component is addressing how to analyze and leverage person-specific data, such as electronic health records, without invading personal privacy or straying from GDPR guidelines on sensitive data use. We are therefore focused on using either publicly accessible datasets (that are generally well anonymized to enable such release) or we are using/creating synthetic datasets that mimic real-world datasets without replicating real people's data such that they can be identified. In either case, it is essential for Sandbox users to treat person-specific data respectfully and be aware of the additional responsibility and limitations of working with this type of data as part of their career in health data science.
We recommend that users interested in this type of data complete an ethics course on research using health datasets before digging into any analysis. A well regarded course that is also often required for using public databases that contain person-specific data is the Human Subject and Data Research Ethics course designed by the Massachusetts Institute of Technology. The course is hosted at CITI, the Collaborative Institutional Training Initiative. Completing the course is free of charge and provides you with a certificate which you may need to upload to certain databases to gain access. Set up an account at CITI, add an Institutional affiliation with 'Massachusetts Institute of Technology Affiliates', and then find and complete the course titled 'Data or Specimens Only Research' to obtain a certificate (in pdf form).
Public domain data¶
The intended scope of the Sandbox is broad, and we will be pulling from many different public access databases in our development of teaching modules. There are classical datasets that serve as benchmark resources for teaching and comparing new methods with old, and also brand new datasets that will support modules on emerging technologies (such as spatial single cell RNA-seq analysis). Databases can be topically broad giant repositories or field-specific, and each may have its own usage rules. We plan to provide our own copies of publically available datasets where allowed to ensure compatibility with the linked module is preserved, but some datasets may need to be downloaded by users themselves under specific access / distribution restrictions.
The Sandbox is focused on supporting Danish health data science education and research. Via our collaborators and broader network, we have the opportunity to simulate/synthesize data resembling different databases and registries from the Danish health sector in addition to using traditional data simulation techniques to replicate general datasets. We are exploring methods of creating useful synthetic datasets with local access guidelines/GDPR restrictions in mind, while developing initial datasets using published data from Danish studies and publically available resources.