Systematic Literature Review Dataset: Model Transformations in Early IT Project Phases

This dataset contains the structured analysis data underlying the systematic literature review published in the journal Computers (2025), titled “Model Transformations Used in IT Project Initial Phases: A Systematic Literature Review”. The dataset includes the full list of reviewed publications, screening results, inclusion/exclusion decisions, extracted metadata, identified transformation types, artefact categories, and classification matrices. It provides the empirical basis for analyzing how model transformations are applied in early IT project phases and supports reproducibility and secondary analysis of the reported findings. The dataset supports: replication of the SLR selection and analysis process, secondary studies on model-driven engineering in early IT project phases, quantitative and qualitative analysis of transformation chains and automation levels. The Excel file contains three worksheets, each representing a different stage of the review and analysis workflow. Worksheet "Papers_selected_full_data" contains the final set of primary studies included in the SLR scope (54 papers) with enriched and normalized data. Column Description Paper ID Unique identifier assigned within the dataset. Title of the paper Full title of the primary study. Real transformation Confirms the presence of a model transformation. Transformation suits paper focus Confirms inclusion in the final SLR scope. Transformation domain (target) Target lifecycle activity. Transformation (formal, semi-formal, manual) Transformation type. Source model essence – for which artefact the model is created Source artifact type. SM-Notation-Short Normalized short code for source notation (used for statistics and charts). Source model notation Full source notation name. Target model essence – for which artefact this model is created Target artifact type. TM-Notation-Short Normalized short code for target notation. Target model notation Full target notation name. Meta-model Presence and type of metamodel. Support Tool Tool implementing the transformation. paper analysed Full-text analysis confirmation. Transformation comment Analytical notes on the transformation. Comment Additional remarks. DOI Digital Object Identifier. Link Direct access URL. ID within SLR Reference number used in the published article. This research has been supported by research and development grant No. PA-2024/1-0015 “Model-Based Methodology for Development of IT Project Management Plan and Scope Using Artificial Intelligence Algorithms” under the EU Recovery and Resilience Facility funded project No. 5.2.1.1.i.0/2/24/I/CFLA/003 “Implementation of consolidation and management changes at Riga Technical University, Liepaja University, Rezekne Academy of Technology, Latvian Maritime Academy and Liepaja Maritime College for the progress towards excellence in higher education, science, and innovation”.