Mapping the Open-Source Landscape: A Systematic Mapping and Ecosystem Analysis of Evidence Synthesis Software

Objective: The evidence synthesis software landscape is defined by the tension between the reproducibility of open-source software (OSS) and the usability of proprietary "black box" solutions. While OSS is critical for transparency, its structural dynamics remain unquantified. This study systematically mapped and analyzed the OSS ecosystem to characterize its growth, technological stratification, and structural gaps.Methods: A systematic mapping of Google Scholar, PubMed, CRAN, and GitHub (inception to February 2026) identified 277 OSS tools meeting strict criteria for public repositories and OSI-approved licenses. These were compared to 90 proprietary tools. Analysis utilized Quasi-Poisson regression to model innovation rates, Chi- square tests for workflow-language dependencies, and descriptive analysis for ecosystem architectures.Results: The market is dichotomous: proprietary tools predominantly featured "integrated platforms" (37.8%), compared to their near absence in OSS (0.7%). Quasi-Poisson regression (2000–2025) revealed significant OSS development acceleration (Incidence Rate Ratio [IRR] = 1.158, 95% CI 1.12–1.20), driven by Python-based machine learning tools. A significant association was found between programming language and workflow stage (χ2 = 122.4, df=48, p < 0.001), with Python dominating screening/extraction and R dominating analysis.Conclusion: The ecosystem is structurally split between monolithic proprietary suites and a modular, rapidly expanding OSS architecture. Although OSS is maturing, it suffers from fragmentation and a "modularity gap", a lack of integrated user interfaces. This study provides a quantitative framework for research infrastructure, highlighting the need for interoperability standards to bridge the OSS gap and support fully reproducible workflows.