GraphMZ-benchmark

GraphMZ SRFA cross-tool benchmark Paper-supporting reproducibility dataset for GraphMZ — a Neo4j graph-database application written in Python for high-resolution mass-spectrometry analysis (organic-contaminant detection and natural-organic-matter characterisation).This record provides a frozen snapshot of the SRFA cross-tool consensus analysis used to validate GraphMZ's formula-assignment performance. The companion GraphMZ-demo record (concept DOI: `10.5281/zenodo.20351944`) holds tutorial-only inputs and uses an always-latest concept DOI.All raw acquisitions are 21T FT-ICR negative-ESI on Suwannee River Fulvic Acid (SRFA — IHSS reference NOM), at the National High Magnetic Field Laboratory (MagLab, Tallahassee FL, USA). Cross-tool formula assignments were produced externally by CoreMS and MFAssignR. Files are uploaded flat; the filename prefix identifies the logical group. Cross-tool formula assignments — CoreMS, MFAssignR, Hawkes Per-tool, per-acquisition formula tables that feed the two-tier consensus pipeline. CoreMS and MFAssignR each produced four assignment tables: one for SRFA_1 and three replicates for SRFA_2. Filename Source CoreMS_MagLab_NegESI_SRFA_1.csv CoreMS, SRFA_1 acquisition CoreMS_MagLab_NegESI_SRFA_2_rep1.csv CoreMS, SRFA_2 replicate 1 CoreMS_MagLab_NegESI_SRFA_2_rep2.csv CoreMS, SRFA_2 replicate 2 CoreMS_MagLab_NegESI_SRFA_2_rep3.csv CoreMS, SRFA_2 replicate 3 MFAssignR_MagLab_NegESI_SRFA_1.csv MFAssignR, SRFA_1 acquisition MFAssignR_MagLab_NegESI_SRFA_2_rep1.csv MFAssignR, SRFA_2 replicate 1 MFAssignR_MagLab_NegESI_SRFA_2_rep2.csv MFAssignR, SRFA_2 replicate 2 MFAssignR_MagLab_NegESI_SRFA_2_rep3.csv MFAssignR, SRFA_2 replicate 3 Hawkes_common_SRFA_neg.csv Hawkes 2020 inter-lab SRFA consensus (1,125 formulas) — historical recall benchmark Raw `.pks` acquisitions Upstream of the formula-assignment tables — the original peak-list exports that CoreMS and MFAssignR ran on. Filename Sample MagLab_NegESI_SRFA_1.pks SRFA_1 (byte-identical to the file of the same name in the demo record, where it's used by NOM_demo1) MagLab_NegESI_SRFA_2_rep1.pks SRFA_2 replicate 1 MagLab_NegESI_SRFA_2_rep2.pks SRFA_2 replicate 2 MagLab_NegESI_SRFA_2_rep3.pks SRFA_2 replicate 3 Consensus ground truth — pipeline output Frozen output of the two-tier consensus pipeline (`graphmz.validation.consensus_ground_truth`), applied to the formula-assignment tables above. Shipped as a snapshot so paper reviewers can verify GraphMZ's claims against the canonical reference without re-running the pipeline. Filename Content Consensus_GT.csv Headline ground truth: Tier-1 ∩ Tier-2 Tier1_GT.csv SRFA_2 triplicate-per-tool ≥2-of-3 intersect, then cross-tool intersect Tier2_GT_SRFA1.csv Tier-2: cross-tool intersect on SRFA_1 Tier1_classification.csv Per-formula classification trace from Tier-1 Tier2_classification.csv Per-formula classification trace from Tier-2 hawkes_recall.csv Recall of the Hawkes 2020 formula set against each tier and the consensus subset Methods documentation Filename Content coverage_gap_analysis.md Coverage-gap analysis of GraphMZ vs. the consensus ground truth cross_tool_spec.md 16-column unified schema spec for the cross-tool ingestion How to fetch programmatically The files above are exposed via the GraphMZ data loader (`graphmz.data.load_benchmark`). The loader downloads from this record on first call, caches locally, and reconstructs the project's expected layout from filename prefixes (e.g. `CoreMS_*` / `MFAssignR_*` / `Hawkes_*` under `SRFA_benchmark/formula_assignments/`, `*.pks` under `SRFA_benchmark/pks_lists/`, `*_GT.csv` / `*_classification.csv` / `hawkes_recall.csv` under `SRFA_benchmark/consensus_gt/`). SHA256 checksums are verified against values baked into the GraphMZ package. See the GraphMZ project README and `benchmark_consensus_gt.ipynb` (or `benchmark_nom_ground_truth.ipynb`) for the analysis walkthrough.GraphMZ SRFA跨工具基准测试集 本数据集为GraphMZ配套的可复现论文支持数据集——GraphMZ是一款基于Python编写的Neo4j图数据库应用，用于高分辨质谱分析（有机污染物检测与天然有机物表征）。本记录为SRFA跨工具一致性分析的冻结快照，用于验证GraphMZ的分子式指派性能。配套的GraphMZ演示数据集（概念DOI：`10.5281/zenodo.20351944`）仅包含教程使用的输入数据，采用始终更新的概念DOI。 所有原始采集数据均为美国国家高磁场实验室（美国佛罗里达州塔拉哈西市MagLab）获取的21T傅里叶变换离子回旋共振（FT-ICR）负电喷雾电离（NegESI）的Suwannee河腐殖酸（SRFA——IHSS参考天然有机物）样本数据。跨工具分子式指派由CoreMS与MFAssignR外部完成。所有文件以扁平结构上传，文件名前缀用于标识逻辑分组。 跨工具分子式指派——CoreMS、MFAssignR、Hawkes 各工具针对各采集任务生成的分子式表格，用于支撑两级一致性分析流程。CoreMS与MFAssignR各生成四张分子式指派表格：一张对应SRFA_1样本，三张对应SRFA_2的重复样本。 文件名 来源 CoreMS_MagLab_NegESI_SRFA_1.csv CoreMS，SRFA_1采集任务 CoreMS_MagLab_NegESI_SRFA_2_rep1.csv CoreMS，SRFA_2重复样本1 CoreMS_MagLab_NegESI_SRFA_2_rep2.csv CoreMS，SRFA_2重复样本2 CoreMS_MagLab_NegESI_SRFA_2_rep3.csv CoreMS，SRFA_2重复样本3 MFAssignR_MagLab_NegESI_SRFA_1.csv MFAssignR，SRFA_1采集任务 MFAssignR_MagLab_NegESI_SRFA_2_rep1.csv MFAssignR，SRFA_2重复样本1 MFAssignR_MagLab_NegESI_SRFA_2_rep2.csv MFAssignR，SRFA_2重复样本2 MFAssignR_MagLab_NegESI_SRFA_2_rep3.csv MFAssignR，SRFA_2重复样本3 Hawkes_common_SRFA_neg.csv Hawkes 2020年跨实验室SRFA一致性结果（1125个分子式）——历史召回基准测试集 原始`.pks`采集文件 位于分子式指派表格的上游——即CoreMS与MFAssignR所处理的原始峰列表导出文件。 文件名 样本 MagLab_NegESI_SRFA_1.pks SRFA_1（与演示数据集中同名文件字节完全一致，该文件被NOM_demo1使用） MagLab_NegESI_SRFA_2_rep1.pks SRFA_2重复样本1 MagLab_NegESI_SRFA_2_rep2.pks SRFA_2重复样本2 MagLab_NegESI_SRFA_2_rep3.pks SRFA_2重复样本3 一致性基准真值——分析流程输出结果 本部分为两级一致性分析流程（`graphmz.validation.consensus_ground_truth`）的冻结输出结果，应用于上文的分子式指派表格。本快照已预先打包，以便论文审稿人可直接对照标准参考结果验证GraphMZ的结论，无需重新运行分析流程。 文件名 内容 Consensus_GT.csv 核心基准真值：一级∩二级结果 Tier1_GT.csv SRFA_2三次重复样本的工具内≥2/3交集，再进行跨工具交集 Tier2_GT_SRFA1.csv 二级结果：SRFA_1的跨工具交集 Tier1_classification.csv 一级流程中每个分子式的分类轨迹 Tier2_classification.csv 二级流程中每个分子式的分类轨迹 hawkes_recall.csv Hawkes 2020分子式集合针对各层级及一致性子集的召回率 方法文档 文件名 内容 coverage_gap_analysis.md GraphMZ与一致性基准真值的覆盖差距分析 cross_tool_spec.md 跨工具导入所用的16列统一架构规范 程序化获取方式 上述文件可通过GraphMZ数据加载器（`graphmz.data.load_benchmark`）获取。该加载器会在首次调用时从此记录下载数据并本地缓存，并根据文件名前缀重构项目预期的目录结构：例如`CoreMS_*`/`MFAssignR_*`/`Hawkes_*`文件存放于`SRFA_benchmark/formula_assignments/`目录，`*.pks`文件存放于`SRFA_benchmark/pks_lists/`目录，`*_GT.csv`/`*_classification.csv`/`hawkes_recall.csv`文件存放于`SRFA_benchmark/consensus_gt/`目录。加载器会验证SHA256校验和与GraphMZ包中内置的校验值是否一致。 有关完整的分析流程说明，请参阅GraphMZ项目README文档以及`benchmark_consensus_gt.ipynb`（或`benchmark_nom_ground_truth.ipynb`）。