Dataset for Systematic Review: Computational Strategies for Depression Detection and Treatment

Systematic review metadata and supplementary materials for the study: Computational Strategies for Depression Detection and Treatment: The Role of Behavioral Activation and Neurobiological Insights – A Systematic Review This dataset contains structured metadata for the 61 studies included in the PRISMA 2020-compliant systematic review. It supports the analysis of artificial intelligence (AI), machine learning, behavioral activation (BA), physical activity monitoring, and neurobiological mechanisms in depression detection and treatment. Contents:- `dataset.csv`: Full bibliographic details (Study ID, first author, year, full title, journal, AI technique, accuracy/outcome metrics, population, DOI/URL) for all 59 included studies.- `S1_File.docx`: Retrospective review protocol, including PICO framework, search strategy, and inclusion/exclusion criteria.- `S1_Table.pdf`: Complete search strings used across PubMed, Scopus, ACM Digital Library, and Web of Science.- `S2_File.docx`: Completed PRISMA 2020 Checklist.- `README.markdown`: Dataset overview and usage instructions.- `data_availability_statement.md`: Final data availability statement for inclusion in the manuscript.Keywords: depression, artificial intelligence, behavioral activation, machine learning, EEG, neuroimaging, digital mental health, PRISMA -'Appendix_S2_AI_ML_reporting_checklist.docx': Compliance with CLAIM and IJMI AI/ML reporting guidelines