Hello all,
In 2024, the LD4 Discovery Affinity Group shared the Linked Data In Systems For Discovery spreadsheet identifying discovery systems with linked open data related features and asked for community review. Laura Akerman from Emory University and Allison Bailund from San Diego State University originally compiled this spreadsheet to list both systems in production as well as those in prototype stage. In 2025, we used the feedback and suggestions we received to update the spreadsheet and made the spreadsheet publicly available through our website at ld4.github.io/discovery.
We are now asking for the community to review these entries and add systems which use linked open data and address discovery needs. We also encourage people to add corrections to URLs or update the existing entries with more current information. Furthermore, we would like to learn about any entries that should no longer be in this list due to being sunset as projects or inaccessible for other reasons. Please use this form to submit any corrections or additions by April 15th, 2026. Please forward this email to others who would be interested in contributing.
We are now asking for the community to review these entries, make suggestions for new projects not already listed, and inform us of any projects that should no longer be in this list. We hope this spreadsheet can serve as an inventory of current and past work and also inspire future efforts.
Thanking you in advance,
Astrid Usong and Huda Khan, co-chairs of the LD4 Discovery Affinity Group
About the LD4 Discovery Affinity Group
The LD4 Discovery Affinity Group meets regularly to discuss topics at the intersection of discovery and linked data, exploring how linked data can enhance discovery from a variety of perspectives including user experience, design, metadata, and technical implementation. You can review more about the affinity group at our wiki at ld4.github.io/discovery.The affinity group operates as part of the larger LD4 community.
Previously, members of the affinity group co-authored a white paper, the “LD4 Knowledge Panel Recipe”. This paper outlines practical considerations and approaches to implementing knowledge panels in a technology-agnostic way. Additionally, the paper discusses common example uses and potential complications.
If you have any questions, please feel free to reach out via email.