This updated labeled dataset builds upon the initial systematic review by van de Schoot et al. (2018; DOI: 10.1080/00273171.2017.1412293), which included studies on post-traumatic stress symptom (PTSS) trajectories up to 2016, sourced from the Open Science Framework (OSF). As part of the FORAS project - Framework for PTSS trajectORies: Analysis and Synthesis (funded by the Dutch Research Council, grant no. 406.22.GO.048 and pre-registered at PROSPERO under ID CRD42023494027), we extended this dataset to include publications between 2016 and 2023.

In total, the search identified 10,594 de-duplicated records obtained via different search methods, each published with their own search query and result:

Exact replication of the initial search: OSF.IO/QABW3 Comprehensive database search: OSF.IO/D3UV5 Snowballing: OSF.IO/M32TS Full-text search via Dimensions data: OSF.IO/7EXC5 Semantic search via OpenAlex: OSF.IO/M32TS

Humans (BC, RN) and AI (Bron et al., 2024) have screened the records, and disagreements have been solved (MvZ, BG, RvdS). Each record was screened separately for Title, Abstract, and Full-text inclusion and per inclusion criteria. A detailed screening logbook is available at OSF.IO/B9GD3, and the entire process is described in https://doi.org/10.31234/osf.io/p4xm5.

A description of all columns/variables and full methodological details is available in the accompanying codebook.

Important Notes:

Duplicates: To maintain consistency and transparency, duplicates are left in the dataset and are labeled with the same classification as the original records. A filter is provided to allow users to exclude these duplicates as needed. Anonymized Data: The dataset "...._anonymous" excludes DOIs, OpenAlex IDs, titles, and abstracts to ensure data anonymization during the review process. The complete dataset, including all identifiers, is uploaded under embargo and will be publicly available on 01-10-2025.

This dataset serves not only as a valuable resource for researchers interested in systematic reviews of PTSS trajectories and facilitates reproducibility and transparency in the research process but also for data scientists who would like to mimic the screening process using different machine learning and AI models.