{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T02:04:44Z","timestamp":1784167484476,"version":"3.55.0"},"reference-count":26,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Fundamental Resources Investigation Program","award":["2022FY101200"],"award-info":[{"award-number":["2022FY101200"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Liquid chromatography\u2013tandem mass spectrometry (LC-MS\/MS) is widely employed in pesticide residue analysis. Machine learning methods for automated spectral interpretation depend on large, well-curated training datasets; however, publicly available pesticide mass spectrometry data are fragmented across heterogeneous repositories, lack standardized preprocessing, and suffer from incomplete metadata. We introduce PAID (Pesticide AI-ready Dataset), comprising two curated LC-MS\/MS spectral collections derived from 15 public sources (GNPS, MassIVE, MoNA, MassBank). Starting from 91,420 raw spectra, after initial pesticide-directed screening, a seven-step reproducible pipeline\u2014spanning multi-source integration, spectral cleaning, deduplication, metadata standardization, quality scoring, stratified splitting, and feature engineering\u2014yields PAID-Strict (7527 spectra; 3197 compounds) and PAID-Extended (21,292 spectra; 3224 compounds). Both versions cover eight pesticide categories across QTOF and Orbitrap platforms, with core metadata fields (SMILES, InChIKey, molecular formula) exceeding 98% completeness. A feature suite of 32 chemoinformatic descriptors, 2214 molecular fingerprints, and 30 spectral features is provided alongside the spectra. Benchmark classification of the eight pesticide categories using XGBoost and LightGBM achieved 81.5% accuracy. The dataset, code, and pre-computed features are publicly available under CC BY 4.0 and MIT licenses.<\/jats:p>","DOI":"10.3390\/data11070177","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T01:05:07Z","timestamp":1784163907000},"page":"177","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PAID: An AI-Ready LC-MS\/MS Dataset for Pesticide Residue Analysis"],"prefix":"10.3390","volume":"11","author":[{"given":"Jihang","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianjin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Center for Metrology Scientific Data, National Institute of Metrology, Beijing 100029, China"},{"name":"National Metrology Data Center, Beijing 100029, China"},{"name":"Key Laboratory of Metrology Digitalization and Digital Metrology, State Administration for Market Regulation, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5059-102X","authenticated-orcid":false,"given":"Lin","family":"Guo","sequence":"additional","affiliation":[{"name":"Center for Metrology Scientific Data, National Institute of Metrology, Beijing 100029, China"},{"name":"National Metrology Data Center, Beijing 100029, China"},{"name":"Key Laboratory of Metrology Digitalization and Digital Metrology, State Administration for Market Regulation, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4629-8626","authenticated-orcid":false,"given":"Xingchuang","family":"Xiong","sequence":"additional","affiliation":[{"name":"Center for Metrology Scientific Data, National Institute of Metrology, Beijing 100029, China"},{"name":"National Metrology Data Center, Beijing 100029, China"},{"name":"Key Laboratory of Metrology Digitalization and Digital Metrology, State Administration for Market Regulation, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1023\/A:1013713905833","article-title":"Metabolomics\u2014The link between genotypes and phenotypes","volume":"48","author":"Fiehn","year":"2002","journal-title":"Plant Mol. 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