Supplementary data for: A systems thinking governance framework for large-scale sustainability synthesishttps://doi.org/10.18710/NZHGW3Barahmand, ZahirDataverseNO2026-02-262026-04-22T06:26:58ZThis dataset supports the paper “A Governance-Aware Systems Thinking Architecture for Sustainability at Scale.” The study addresses a key challenge in large-scale sustainability research: how to ensure transparency, consistency, and auditability when integrating heterogeneous evidence and systems thinking approaches. The paper introduces STAI³RS, a governance framework designed to ensure rigor and reproducibility in sustainability synthesis. STAI³RS stands for Scalable, Transparent, Analytical, Interpretable, Reliable, Reproducible, Robust, and Systematic, and provides cross-cutting principles and procedural cues for applying systems thinking methods at scale. Building on this governance layer, the paper presents SEEDS (Systems Evidence Extraction and Decision Support), a six-component operational model that structures sustainability synthesis from problem framing to decision support. SEEDS integrates boundary governance, evidence extraction, harmonization, synthesis, and iterative learning into a transparent and auditable workflow. The dataset includes materials that document the governance diagnostics and operational implementation of these frameworks. In particular, Table S2 presents a case study demonstrating the SEEDS workflow in practice through a large-scale feedstock evidence mapping study spanning over 130,000 peer-reviewed studies across biomass- and waste-to-X conversion technologies. This case study illustrates how governance rules, audit trails, and harmonization procedures enable reproducible synthesis across three large scientific corpora. SEEDS_components_in_practice These materials support reuse, verification, and extension of governance-aware systems thinking approaches in sustainability research and decision support.Engineeringsystems thinking modelsustainabilitylarge-scale studystrategic decision-makingEnglishSubmitted for review2026-02-26Barahmand, ZahirEikeland, MarianneWang, LiangHeirati, Nima2026-02-252026-01-152026-02-20Barahmand Zahir; Eikeland Marianne Sørflaten, 2025, "Dataset and code supplement: Mapping gasification technologies and feedstocks with a dual validated large-scale literature-derived dataset", https://doi.org/10.23642/USN.30546347, DataverseNO, V1Barahmand Zahir, 2025, "Supplementary code and curated data for 1,863 experimental gasification studies (laboratory to commercial scale)", https://doi.org/10.23642/USN.30702092, DataverseNO, V1Barahmand, Zahir, 2026, "Label system, dictionaries, and audit evidence for harmonised over 133,000 feedstock items across major conversion technologies", https://doi.org/10.18710/WEZMJE, DataverseNO, V1Barahmand, Zahir, 2026, "Supplementary dataset and reproducible codes for LLM-assisted mapping feedstocks of eight conversion technologies from over 121,000 studies", https://doi.org/10.18710/JM6U7B, DataverseNO, V1diagnostic matrixUsed systems thinking model referencesSEEDS components in a case study21 systems thinking models are collected narratively from literatureCC BY 4.0