The 2022 dataset contains 537,262 cleaned and streamlined Latin inscriptions from the Epigraphic Database Clauss Slaby (EDCS, http://www.manfredclauss.de/), aggregated on 2022/09/12, created for the purpose of a quantitative study of epigraphic trends by the Social Dynamics in the Ancient Mediterranean Project (SDAM, http://sdam.au.dk). The dataset contains 27 attributes with original and streamlined data. Compared to the 2021 dataset, there are 36,726 more inscriptions and 2 fewer attributes containing redundant legacy data, thus the entire (...)

The 2022 dataset contains 537,262 cleaned and streamlined Latin inscriptions from the Epigraphic Database Clauss Slaby (EDCS, http://www.manfredclauss.de/), aggregated on 2022/09/12, created for the purpose of a quantitative study of epigraphic trends by the Social Dynamics in the Ancient Mediterranean Project (SDAM, http://sdam.au.dk). The dataset contains 27 attributes with original and streamlined data. Compared to the 2021 dataset, there are 36,726 more inscriptions and 2 fewer attributes containing redundant legacy data, thus the entire dataset is approximately the same size but some of the attributes are streamlined (465.5 MB in 2022 compared to 451.5 MB MB in 2021.): some of the attribute names have changed for better consistency, e.g. `Material` > `material`, `Latitude` > `latitude`; some attributes are no longer available due to the improvements of the LatEpig tool, e.g. `start_yr`, `notes_dating`, `inscription_stripped_final`; and some new attributes were added due to the improvements of the cleaning process, e.g. `clean_text_conservative`. For a complete overview, see the `Metadata` section.

EDCS 2022 dataset metadata (https://github.com/sdam-au/EDCS_ETL/blob/master/EDCS_2022_dataset_metadata_SDAM.csv) with descriptions for all attributes. 

The full lifecycle of the transformation process, including programmatical access, modifications, and streamlining of the original dataset is documented by a sequence of Python and R scripts (https://github.com/sdam-au/EDCS_ETL). The dataset is stored as JSON file, ensuring compatibility both with Python and R.

The scripts used to generate the dataset and their metadata are available via GitHub: https://github.com/sdam-au/EDCS_ETL.