* estimated completion time 5-15 minutes *

Cultural heritage organizations are developing collections as data for a variety of purposes, including for use in artificial intelligence (AI) systems. The potential for community-wide benefit is significant, yet it is challenging to move forward in part because data are difficult to find.
 
In an effort to make finding and reusing cultural heritage data for AI easier, Thomas Padilla (Center for Research Libraries), Elizabeth Lorang (University of Nebraska–Lincoln), and Harish Maringanti (University of Utah) are surveying the cultural heritage community. This survey is part of Ground Truths, a new project which seeks to (1) advance conversations about machine learning in cultural heritage organizations by centralizing and sharing information about data development efforts, (2) pose questions about data and process that support responsible operationalization of machine learning in cultural heritage organizations, and (3) encourage reuse of cultural heritage data in AI contexts.
 
We expect that cultural heritage organizations are in varying stages of data development. An organization may provide bulk access to data via direct download or API for scholarly use. An organization may have created ground truth data in order to embed machine learning in its own collection processing or description workflows. An organization may have partnered with disciplinary researchers on creation of derivative datasets or statements of ground truth to meet a range of disciplinary and professional needs.
 
In whatever ways your organization is developing data for AI, we are interested in learning more. We will centralize information from this survey and make it openly available. It is our hope this work will provide collective benefit.

Thank you in advance for contributing to this effort. Please direct questions to Thomas Padilla (tpadilla@archive.org).