Soxhlet oil-yield screening benchmark: data and code

Data and computational materials accompanying 'How much do published data add to five local measurements? A publication-held-out benchmark for Soxhlet oil-yield screening'. The deposit contains 197 literature condition-yield records from 22 publications and five feedstocks, with bibliographic mappings, model code, fixed settings, reference predictions and verified results. ESM_2_Model_Reproduction.zip reproduces the eleven-method comparison in Table 2 using eleven publication holdouts, five calibration measurements per holdout and fifteen seeds. ESM_7_Benchmark_Analysis.zip reproduces the calibration-label subgroups, Figure 2 and paired publication-bootstrap intervals from the saved predictions. Standalone CSV files duplicate the input data and source inventory in ESM_2. Each archive includes execution instructions and dependency versions. The separate five laboratory measurements and 27-condition design accompany the manuscript and are not included in this computational deposit. Version v1.1.0 standardises publication and record identifiers, removes the unused relative-class column, updates manuscript references and includes the subgroup analysis. Code is organised by computational role. All 197 observations, feature values, class boundaries, model settings and predictions are unchanged. The earlier version is retained in the Zenodo version history. Original publications retain their respective rights; source article PDFs are not redistributed. No additional reuse licence is granted; see RIGHTS.md. Funding acknowledgment (as supplied by the authors):This work was supported by Zhejiang Provincial Natural Science Foundation of China (LQN26B060001), 2025 Yongjiang Talent Introduction Program funded by the Ningbo Education Bureau, Ningbo Commonweal Programme (2022S122), Ningbo Youth Science and Technology Programme (2024QL007) funded by the Ningbo Science and Technology Bureau, China, Research Fund for International Excellent Young Scientists (W2532017) from the Natural Science Foundation of China (NSFC), Zhejiang Provincial Key Laboratory for Carbonaceous Wastes Processing and Process Intensification Research funded by the Zhejiang Provincial Department of Science and Technology (2020E10018), and Ningbo Municipal Key Laboratory on Clean Energy Conversion Technologies (2014A22010)