loading . . . Reaction Database for Catalysis and Organometallics via Freely Available Supplementary Information Chemical reaction databases have become core scientific infrastructure. Most prominent datasets focus on or- ganic reactions, or only include reactants and product rather than full reaction pathways, leaving organometallic chemistry particularly underserved despite its centrality to homogeneous catalysis. This gap limits the develop- ment of machine learning models for organometallic reactions and limits applications in mechanism discovery, selectivity prediction, and catalyst design. This work introduces an open, reaction-centric resource derived from XXX SI across 50+ journals from seven publishers through 2025 using the Gold-DIGR (Gold-Data Integration for Generalized Reactions) workflow. Reported organometallic reactions are aggregated and reaction properties extracted or recalculated, including reactant, product, and transition-state geometries, intrinsic reaction coor- dinate (IRC) traces, reaction classes, and ligand/metal descriptors (coordination, valence-electron counts, bond orders). Bondâelectron matrices enable electron-flow analyses along reaction coordinates, visualized as Sankey diagrams connecting local electron rearrangements to class-level patterns. The resulting corpus spans canoni- cal classesâoxidative addition, reductive elimination, migratory insertion, β-hydride elimination, CâH activation, transmetalation, and Ď-bond metathesisâenabling quantitative mechanistic analyses at scale. As a demonstration of the meta-analyses enabled by this broad-based data generation, the relationship between bond-breaking/forming events and the transition states are studied to investigate concerted versus sequential scenarios. Class-specific tim- ing asymmetries emerge, with reductive elimination and β-atom elimination events skewed pre-transition-state, oxidative addition and migratory insertion skewed post-transition-state, and transmetallation showing the broad- est dispersion. By releasing both tooling and data, this work provides a foundation for mechanistic benchmarking and data-driven catalyst design. https://chemrxiv.org/engage/chemrxiv/article-details/69065ddfa482cba12279e023
