DataSHIELD is open-source software that enables researchers to analyse data held across multiple institutions without any individual-level data ever leaving the organisation that holds it. Instead of moving data around, analysis commands are sent from a central R session to each remote data server, which runs the computation locally and returns only non-disclosive summary statistics. The results are then combined into a single pooled estimate — equivalent to a joint analysis of all the data -- but without the practical, legal and ethical challenges of data transfer.
You can read a full technical explanation of how the platform works here.
We have structured information in the wiki around user journeys, i.e. how and why you are using DataSHIELD. We have identified five main types of user:
- Systems operator - Installing and maintaining DataSHIELD on your server.
- Data Manager - Managing your data and providing access to researchers.
- Researcher - Applying for data access, conducting research and disseminating results.
- Developer - Writing or contributing to DataSHIELD packages.
- Community contributor - Contributing to the wiki or the broader DataSHIELD community.
We also have a selection of tutorials for doing analyses:
- Analysis tutorials - Detailed tutorials on conducting analysis with DataSHIELD
In addition to the wiki, there are two main sources of help:
- The DataSHIELD forum
- The DataSHIELD Slack channel maintained as part of the MOLGENIS open source project for scientific software. If you would like to join, please email support@molgenis.org and request access.
Through both of these channels you can ask questions and receive updates about DataSHIELD.
Anyone can edit the wiki, but to do so you'll need to request a wiki account (detailed on the community participation page), then you'll be able to make any changes you like. If there is something missing or you think something could be improved, feel free to add or edit it! You don't need to ask permission (all changes are stored, so it's easy to revert to a previous version if something is accidentally deleted).