This dataset was created within the framework of the Digital Roofs project (2020–2025), a research initiative of the German Archaeological Institute (DAI). The project focuses on the development and evaluation of methods and technologies for the high-quality digital documentation and publication of large quantities of archaeological roof tile finds.

Ancient roof tiles, particularly from Greek and Roman contexts, represent a challenging category of material due to their size, weight, and the large number of fragments typically recovered during excavations and surveys. Despite their research potential, especially unornamented tiles are often insufficiently documented and rarely published in a form suitable for comparative analysis.

Within the project, standardized workflows were developed that integrate database structures, documentation methodologies, and digital recording techniques. These workflows aim to ensure consistent, efficient, and high-quality documentation, facilitating reuse, comparability, and publication.

The data were recorded using a customized configuration of iDAI.field developed within the Digital Roofs project. The development of this configuration formed a central part of the project’s methodological work, with Annalize Rheeder playing a key role in its design and implementation. This configuration, which reflects the project’s approach to structuring and documenting roof tile assemblages, is provided as part of the dataset for reuse in other projects.

The configuration was created prior to the introduction of the dedicated “Find collection” category in iDAI.field; consequently, some structural elements represent earlier strategies for handling large groups of finds. Parts of the configuration can nevertheless be reused and imported into newer iDAI.field projects.

The dataset further includes accompanying documentation in the form of methodological guidelines and manuals, provided in English and German, each in both high- and low-resolution PDF versions. These materials document the workflows and technologies applied and support the reuse and adaptation of the dataset.