Supplementary Graph Data for 31P NMR Signal Prediction with GNN

Supplementary material 3 for a research paper on a light-weight Graph Neural Network for predicting 31P Nuclear Magnetic Resonance signals. The dataset is published on figshare by author Dimitri Domnjuk under a CC-BY-4.0 license and was last updated on 2026-04-27. The file is 1.6 MB in size and is provided in an IPYNB (Jupyter Notebook) format.

Use Cases

• Training a Graph Neural Network to predict 31P NMR chemical shifts (inferred from domain, verify after download)
• Benchmarking machine learning models for molecular spectroscopy tasks (inferred from domain, verify after download)
• Analyzing molecular graph representations for chemical property inference (inferred from domain, verify after download)

Strengths

• Published on figshare under a permissive CC-BY-4.0 license
• File size is 1.6 MB, indicating a focused and potentially lightweight dataset

Limitations

• Metadata is minimal; actual content requires verification after download
• Row count and column definitions are unknown, which may limit suitability assessment
• The description 'Supplementary material 3' provides no detail on the data's structure or contents

Provenance

Source

figshare

Freshness

Last updated 2026-04-27 03:51:02