Microbiome-Based Models for Southern Oscillation Index Prediction

5.5 KB of model performance metrics for predicting the Southern Oscillation Index (SOI) using school-level log10-transformed abundances of five bacterial families. The dataset, authored by Yufei Zhou and last updated on 2026-04-27, contains mean squared error (MSE) and R-squared values from Generalized Additive and Random Forest models, validated on two independent test datasets (GMB and TGM).

Use Cases

• Benchmarking predictive models for climate indices based on microbial abundance data.
• Comparing the performance of Generalized Additive Models (GAM) and Random Forest Models (RFM) for ecological forecasting.
• Conducting external validation of environmental models using independent datasets (GMB and TGM).
• Analyzing the relationship between specific bacterial families (e.g., Fusobacteriaceae, Bacillaceae) and the Southern Oscillation Index.

Strengths

• Includes external validation results on two independent datasets (GMB and TGM), which suggests a check for model generalizability.
• Provides specific performance metrics (MSE and R-squared) for two distinct modeling approaches (GAM and RFM).
• Released under a permissive CC-BY-4.0 license, facilitating reuse.

Limitations

• The dataset is very small (5.5 KB), indicating it likely contains only summary statistics, not the underlying training or validation data.
• Row count and column-level documentation are unknown, limiting detailed assessment of the data structure.
• The description metadata is limited; actual data quality and context require manual inspection after download.

Provenance

Source

figshare

Collection Method

Likely derived from research modeling school-level microbial abundance data to predict a climate index.

Time Range

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Freshness

Last updated 2026-04-27 17:27:12; freshness should be verified.

Geography

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