Partial Least Squares Regression Fits for Leaf Traits Across 12 Wine Grape Cultivars

Partial least squares regression model fits predicting leaf trait variation across 12 wine grape cultivars. The dataset, authored by Rachel O. Mariani, contains results from different modeling frameworks based on how data was split for calibration and validation. It was last updated on 2026-05-18 and is shared under a CC-BY-4.0 license.

Use Cases

• Compare model performance between 'Cultivar' and 'Random' data split strategies based on the described modeling frameworks.
• Validate partial least squares regression for predicting leaf traits in wine grapes based on the described calibration/validation approach.
• Benchmark predictive models for agricultural traits using a dataset focused on 12 different cultivars.

Strengths

• Model results are provided for two distinct data split strategies ('Cultivar' and 'Random'), allowing for methodological comparison.
• The dataset explicitly describes an 80%/20% split for calibration and validation datasets.

Limitations

• The dataset is very small at 9.5 KB, indicating limited scope.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

figshare

Collection Method

Likely contains statistical model outputs from an experimental study on grape cultivars.

Freshness

Last updated 2026-05-18 17:21:54; freshness should be verified.