Related tools

checkHNMR is one of several efforts to check characterization data before and after publication. This page lists related tools and work, with short factual notes and links to the original publications; see each source for details. The list is not complete, and inclusion does not mean that the authors endorse checkHNMR or are affiliated with it.

Last updated: 3 November 2026.

Tools that check characterization data

• checkSI — Sakharov, P.; Elashkin, A.; Gandelman, M. ChemRxiv 2026. Checks compound names, molecular formulas, HRMS and elemental analysis data, ¹H integrals, heteronuclear (¹³C, ¹⁹F, ³¹P, ¹¹B, ²⁹Si) signal counts and reporting conventions in Supporting Information files and manuscripts (PDF or DOCX), with optional DP5q scoring of ¹³C data; runs locally. checkHNMR analyzes ¹H coupling constants, multiplicities and first-order notation, which checkSI does not, so the two tools complement each other. doi:10.26434/chemrxiv.15011545/v1
• Accurate mass (HRMS) data — Christmann, M. What I Learned from Analyzing Accurate Mass Data of 3000 Supporting Information Files. Org. Lett. 2025, 27, 4–7. doi:10.1021/acs.orglett.4c03458
• Experimental Data Checker — Adams, S. E. et al. Experimental Data Checker: Better Information for Organic Chemists. Org. Biomol. Chem. 2004, 2, 3067–3070. doi:10.1039/b411699m
• checkCIF (International Union of Crystallography) — validation of crystal structure reports, which inspired the form of the checkHNMR report. checkcif.iucr.org/

Structure verification from NMR data

• Kutateladze and co-workers test proposed structures against published chemical shifts and coupling constants by computation, for natural products and for the products of synthetic reactions: Novitskiy, I. M.; Kutateladze, A. G. J. Org. Chem. 2022, 87, 4818–4828. doi:10.1021/acs.joc.2c00169
• Beduru, S.; Kutateladze, A. G. Nat. Prod. Rep. 2026. doi:10.1039/D5NP00088B
• Beduru, S.; Asimbisa, E.; Novitskiy, I. M.; Kutateladze, A. G. J. Org. Chem. 2026, 91, 4130–4153. doi:10.1021/acs.joc.5c02863

Reporting ¹H NMR data

• Hoye, T. R.; Hanson, P. R.; Vyvyan, J. R. A Practical Guide to First-Order Multiplet Analysis in ¹H NMR Spectroscopy. J. Org. Chem. 1994, 59, 4096–4103. doi:10.1021/jo00094a018
• Tang, R.; Xu, Q.; Hoye, T. R. Molecules 2025, 30, 2610. Notation for non-first-order signals (“nfod”, “nfot”, “nfom” with J_app), which checkHNMR reads. doi:10.3390/molecules30122610
• Dickson, C. L. et al.; Butts, C. P. Analyst 2025, 150, 2901–2906. A validated reference set of experimental NMR parameters. doi:10.1039/D5AN00240K
• Bloodworth, S.; Willoughby, C.; Coles, S. J. Data Accessibility in the Chemical Sciences: An Analysis of Recent Practice in Organic Chemistry Journals. Beilstein J. Org. Chem. 2025, 21, 864–876. doi:10.3762/bjoc.21.70
• Eshtukov-Shcheglov, A. V. The Problem of NMR Data Analysis in Synthetic Chemistry: Why Is It Important to Understand Spectra? ChemRxiv 2026. The checklist that checkHNMR applies. doi:10.26434/chemrxiv.15010060/v1

NMR data and machine learning

• NMRexp — Wang, J.-J. et al.; Zhu, R. NMRexp: A Database of 3.3 Million Experimental NMR Spectra. Sci. Data 2025, 12, 1954. doi:10.1038/s41597-025-06245-5
• ChemBench — Mirza, A. et al.; Jablonka, K. M. A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists. Nat. Chem. 2025, 17, 1027–1034. doi:10.1038/s41557-025-01815-x