Supplementary Graph Data for 31P NMR Signal Prediction via GNN

Supplementary material 6 for a research paper on predicting 31P Nuclear Magnetic Resonance signals using a light-weight Graph Neural Network. The 161.1 MB file, published on figshare by Dimitri Domnjuk under a CC-BY-4.0 license, was last updated on 2026-04-27. The specific content and structure of the data require verification after download.

Use Cases

• Training or benchmarking a Graph Neural Network for molecular property prediction (inferred from domain, verify after download)
• Validating computational chemistry methods for NMR signal estimation (inferred from domain, verify after download)
• Developing feature representations for phosphorus-containing molecules (inferred from domain, verify after download)

Strengths

• Published on the figshare platform with a clear CC-BY-4.0 license.
• File size of 161.1 MB suggests a non-trivial amount of data.
• Last update timestamp is 2026-04-27 03:51:03.

Limitations

• Metadata is minimal; actual content requires verification after download.
• Row count and column definitions are unknown, limiting suitability assessment.
• The description 'Supplementary material 6' provides no specifics on data composition.

Provenance

Source

figshare

Collection Method

Likely generated as part of computational chemistry research.

Time Range

null

Freshness

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

Geography

null