Maize Genotype Traits with Cluster-wise Means and Standard Deviations

Cluster-wise mean and standard deviation values for grain yield, phenological, agronomic, and plant architectural traits of maize genotypes. The data is derived from K-means clustering applied to standardized multi-trait phenotypic data. It was authored by Goshime Muluneh Mekasha and last updated on June 3, 2026.

Use Cases

• Compare trait performance across genotype clusters based on the reported cluster-wise means.
• Identify genotypes with high yield potential based on grain yield trait data.
• Analyze correlations between plant architectural and agronomic traits using the standardized multi-trait data.
• Validate clustering algorithms by comparing results with this K-means derived dataset.

Strengths

• Data is openly licensed under CC-BY-4.0.
• The dataset provides standardized multi-trait phenotypic data, which facilitates comparative analysis.
• The file size is 9.5 KB, indicating a compact and focused dataset.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

K-means clustering using standardized multi-trait phenotypic data.

Freshness

Last updated 2026-06-03 17:38:29; freshness should be verified.