About
EDDI is the annual conference for users of DDI Alliance metadata specifications for the social, demographic, economic, and health sciences.
The conference is run by a Program Committee of volunteers from the DDI community, under the auspices of the DDI Alliance, and host organisation(s), supported by GESIS.
EDDI is designed to a provide forum where DDI users from Europe and the world can gather to showcase their work and their progress toward DDI adoption, as well as discuss any questions or challenges they may have about the standards.
EDDI includes presentations, poster sessions, and discussion sessions. The philosophy of EDDI is to be an open and inclusive DDI community-building activity. The conference including related meetings usually spans a week in early December in a different European country each year.
The counterpart of EDDI in North America is NADDI, the North American DDI User Conference, modelled on the successful EDDI.
EDDI Conference Code of Conduct
EDDI is dedicated to a positive, safe and harassment-free conference experience in which diverse participants may learn, network and enjoy the company of colleagues in an environment of mutual human respect and regardless of gender, sexual orientation, gender identity, gender expression, disability, physical appearance, ethnicity, religion or other group identity or political beliefs. Our Code of Conduct provides more details on how we seek to do that.
Hosting EDDI
We are always interested in new and interesting places to hold EDDI. We have created the EDDI Manual which gives more information on the running of the conference. If you are interested, please contact the Program Committee at eddi-2025 (at) googlegroups.com.
DDI Metadata Standards
The DDI Alliance is an international membership organization that creates and maintains technical standards for describing research data in the social, demographic, economic, and health sciences.
The DDI Alliance supports a suite of products that address the evolving needs of data producers and users. Documenting data with our open standards improves consistency, integration, and quality, producing FAIR data, realizing its full potential for people, software, and machines.
These include: