OSF repository for: **"A Deep Learning Approach for Automated Scoring of the Rey-Osterrieth Complex Figure"** ------------------------------------------------------------------------ published in ELife: https://doi.org/10.7554/eLife.96017.2 **Data availability statement:** -------------------------------- The clinical dataset cannot be shared publicly due to the absence of consent from patients for data sharing. The Prolific dataset, deidentified raw data (i.e. image of the ROCF) can be accessed upon request and after signing a data use agreement. Access to the Prolific dataset is generally allowed for non-commercial academic research. Interested researchers must contact the corresponding author (n.langer@psychologie.uzh.ch) and may need to provide a brief description of their research objectives. In this repository you can find the unidentified raw data (i.e. ROCF drawings) from the prospective validation study (Prolific). A Deep Learning Approach for Automated Scoring of the Rey-Osterrieth Complex Figure © 2024 by Nicolas Langer is licensed under **CC BY-NC-ND 4.0** (**Attribution-NonCommercial-NoDerivatives 4.0 International**). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/ **Processed Data:** ------------------- Processed data used in analyses, such as summary statistics and numbers used to plot figures in the manuscript, are available as source data. **Code and Software Availability:** ----------------------------------- All preprocessing and analysis scripts used in this study are available on GitHub (https://github.com/methlabUZH/rey-figure). Researchers interested in accessing any data or materials should contact the corresponding author for further instructions and to discuss the appropriate access procedures. **License:** ------------ **CC BY-NC-ND 4.0 Attribution-NonCommercial-NoDerivatives 4.0 International** You are free to: Share — copy and redistribute the material in any medium or format The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. NonCommercial — You may not use the material for commercial purposes . NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.