openalexZenodo (CERN European Organization for Nuclear Research)2026-07-23Cited by 0

Chemical Identity Lost in Regulation: code and data

Geert Van Haute, Stijn Goedertier, Pieter Fannes, Maxim Van de Wynckel

Code and data for "Chemical Identity Lost in Regulation: A Study of Semantic Interoperability in European Chemical Substance Data" (Van Haute, Goedertier, Fannes, Van de Wynckel), Poster & Demo track, SEMANTiCS 2026, Ghent. European regulatory datasets represent chemical substances heterogeneously — as single molecules, mixtures, UVCBs, substance groups, or analytical sum parameters — identified only by CAS numbers and free-text names rather than structure-based identifiers. This repository integrates 18 ECHA-based datasets (41,813 records) under a seven-tier linkability taxonomy: only 62.5% of 33,452 unique entries link to a defined molecular structure via InChIKey, and CAS numbers prove unreliable as cross-dataset identifiers (70% of structure-resolved InChIKeys occur in only one source). Sentence-embedding models fail to substitute for structure in ChemOnt classification (Hit@1 ≤ 5.75%); Claude Sonnet 4.6 reaches 39.2% but still misclassifies the majority. Structure-defined substances are integrated into an RDF knowledge graph (SKOS, XKOS, ChEBI, ~48,400 cross-domain annotation links), with a separate LLM-based scope-mapping step yielding 304 validated SKOS crosswalk triples between regulatory groups and ChemOnt classes. Contents: the R/Python analysis pipeline, the SPARQL/ROBOT-based RDF integration scripts, the resulting knowledge graph (data/processed/rdf/), and the poster/paper sources. Licensed CC BY 4.0.

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Deciphering the Host-Range Grammar of Orthoflaviviruses Using Foundation Model Embeddings: A Leakage-Aware Evaluation Framework — Data and Code

Brhanu F. Znabu, Qiuming Yao, Nicole R. Sexton

Data and code for a leakage-aware evaluation of machine-learning predictors of orthoflavivirus host range. Contains the full analysis pipeline, DNABERT-2 embeddings, window-level sequence data, results, and figure-generation scripts to reproduce every figure and result. verify_re…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-23

Interpretable Machine Learning Recovers Transferable Gamma-Ray Attenuation Laws from A Priori Material Descriptors: Code and Data

Nassar N. Asemi, Abdullah Al Mazrooei, Hanan Akhdar

Code and dataset accompanying the manuscript "Interpretable Machine Learning Recovers Transferable Gamma-Ray Attenuation Laws from A Priori Material Descriptors." Includes the symbolic-regression scripts (PySR) for discovering closed-form mass-attenuation laws of lead-free PEI/me…

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openalexZenodo (CERN European Organization for Nuclear Research)

Data and Code to reproduce results in paper "A Systematic Literature Review on Graph-Based Models in Credit Risk Assessment"

Lennart John Baals, Yiting Liu, Joerg Osterrieder, Branka Hadji Misheva

Data and Code to reproduce results in paper "A Systematic Literature Review on Graph-Based Models in Credit Risk Assessment" This repository contains the necessary codes to reproduce results in the paper: Baals, L. J., Liu, Y., Osterrieder, J., & Hadji-Misheva, B. (2025). A Syste…

Also available via: European Organization for Nuclear Research

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-24

Code and data for: Leakage-audited machine learning versus ETAS for earthquake forecasting in the Sea of Marmara

Basri Kerem Alhan, Kenessary Khabat

Code, processed data products, configuration, and results artifacts for "Machine learning versus ETAS for earthquake forecasting in the Sea of Marmara: a leakage-audited negative result and a closed-form scoring artifact" (Alhan & Khabat, submitted to Seismica). Version 1.2.0 acc…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-24

Code and data for "Dynamic evaluation of uncertainty quantification under distribution shift in materials property prediction"

Wenbin Wan, Kexin Liu, Shanlin Tong, Wu Lu, Liu Y, Xingwen Jiang, et al.

This record contains the supplementary code and data supporting the manuscript “Dynamic evaluation of uncertainty quantification under distribution shift in materials property prediction.” The archive contains raw and processed data tables, crystallographic structures, Materials…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-23

Data and code for "Spatial but not temporal predictability of Korean extreme-rainfall change: limits of covariate machine learning and a climate-factor implementation of the Clausius-Clapeyron / nonstationary-GEV alternative Manuscript TypeResearch Article"

Seokhwan Hwang

This repository archives the data and code accompanying the manuscript: "Spatial but not temporal predictability of Korean extreme-rainfall change: limits of covariate machine learning and a climate-factor implementation of the Clausius-Clapeyron / nonstationary-GEV alternative."

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Chemical Identity Lost in Regulation: code and data

Related papers

Deciphering the Host-Range Grammar of Orthoflaviviruses Using Foundation Model Embeddings: A Leakage-Aware Evaluation Framework — Data and Code

Interpretable Machine Learning Recovers Transferable Gamma-Ray Attenuation Laws from A Priori Material Descriptors: Code and Data

Data and Code to reproduce results in paper "A Systematic Literature Review on Graph-Based Models in Credit Risk Assessment"

Code and data for: Leakage-audited machine learning versus ETAS for earthquake forecasting in the Sea of Marmara

Code and data for "Dynamic evaluation of uncertainty quantification under distribution shift in materials property prediction"

Data and code for "Spatial but not temporal predictability of Korean extreme-rainfall change: limits of covariate machine learning and a climate-factor implementation of the Clausius-Clapeyron / nonstationary-GEV alternative Manuscript TypeResearch Article"