DOI: 10.5281/zenodo.17177597
title: Augmented and Real Microalgae Datasets for Biomass and Biochemical Composition Prediction

description: This project provides an interactive Algae Yield Predictor that estimates biomass, lipid, protein, and carbohydrate yields for selected microalgae species under defined culture conditions. The system integrates multiple machine learning models (XGBoost, LightGBM, CatBoost, MLP, and a stacked ensemble) trained on augmented and validated datasets. Key features include:





Model selection: Compare predictions across single models or use the stacked ensemble (R² ≈ 0.89).




Uncertainty quantification: Local 80% prediction intervals based on KNN neighborhood analysis.




Species × medium clamping: Predictions are constrained to literature-reported ranges for biological plausibility.




DOI reference matching: Closest culture conditions from published studies (via DOI database) are suggested.




Visualization: Interactive response plots showing predicted trends across input variables with confidence bands.



This repository contains two complementary datasets designed for the development and validation of predictive models of microalgal biomass and biochemical composition (lipid, protein, and carbohydrate fractions):





Augmented Dataset (200,000 samples)





Generated through controlled augmentation of experimental ranges reported in literature.




Includes multiple species (e.g., Arthrospira platensis, Chlorella spp., Scenedesmus sp., Haematococcus pluvialis, Porphyridium purpureum).




Covers diverse media formulations (BG-11, BBM, Zarrouk, TAP, artificial seawater, etc.).




Features culture variables such as light intensity, photoperiod (day/night exposure), pH, temperature, and cultivation duration.




Used primarily for model training.






Real Dataset (Experimental Validation)





Extracted and curated from actual cultivation experiments.




Provides biomass yield (g/L) and proximate composition (lipid, protein, carbohydrate %) measured under specific species × medium × culture condition combinations.




Used to benchmark and validate predictive models trained on the augmented dataset.





Both datasets are harmonized with consistent feature naming, include categorical encodings for species/media, and are ready for direct use in machine learning pipelines (CSV format).

These datasets have been applied in the Algae Yield Predictor project (Tiwari et al., 2025), enabling stacked ensemble modeling (XGB, LGBM, CatBoost, MLP) with literature-range clamping and DOI reference matching.

Developed by Ashutosh Tiwari (Lead) and Siddhant Dubey ( Co-Lead) with contributions from Yamini Sumathi, this tool aims to support researchers in algal biotechnology, biofuel production, and bioprocess optimization by providing reproducible, literature-aware predictive analytics
description: Technical Information





File Formats: CSV (comma-separated values), UTF-8 encoded.




Dataset Structure:





Columns (features):





species (categorical) — microalgae strain (e.g., Arthrospira platensis, Chlorella zofingiensis).




media (categorical) — cultivation medium (e.g., BG-11, Zarrouk, BBM).




light (float) — incident photon flux, μmol·m⁻²·s⁻¹ (cycle-first format resolved).




expo_day (float) — day exposure duration, hours.




expo_night (float) — night exposure duration, hours.




_c (float) — cultivation temperature, °C.




ph (float) — medium pH.




days (int) — total cultivation duration, days.




biomass (float) — dry cell weight, g/L.




lipid (float) — lipid content, % of dry weight.




protein (float) — protein content, % of dry weight.




carb (float) — carbohydrate content, % of dry weight.








Real Dataset:





Source: curated from experimental cultivation records.




Size: 188 rows across 5+ species × 10+ media.




Purpose: benchmarking and model validation.






Augmented Dataset:





Size: 200,000 rows (stream-generated, saved in ~20k row chunks).




Generation Method:





Random resampling of categorical features (species, media).




Gaussian perturbations around observed mean ± σ for continuous features.




Random Forest (200 trees) regression used to synthesize target values (biomass, lipid, protein, carb) with added Gaussian noise (σ = 0.1 × observed STD).






Purpose: robust machine learning training and uncertainty estimation.






Preprocessing:





Missing values imputed with median.




Label encoding for categorical features.




Optional StandardScaler for MLP models.




Literature-range clamping for species × medium combinations.






Model Compatibility:





Directly consumable by scikit-learn, XGBoost, LightGBM, CatBoost, and TensorFlow/Keras pipelines.




Aligned with the Algae Yield Predictor ensemble workflow.


 




Species Code Map species_code_map.csv

Format: CSV (UTF-8 encoded)Columns:





label — microalgae species name (canonicalized, lowercase or abbreviated, e.g., c. pyrenoidosa, a. platensis).




code — unique integer ID assigned by the label encoder.




count_ai_al — number of rows in the real dataset (ai_al.csv) where this species occurs.



Purpose: Provides a consistent mapping between species names and encoder codes, ensuring compatibility with ML models trained on label-encoded inputs.

 

Media Code Map media_code_map.csv

Format: CSV (UTF-8 encoded)Columns:





label — culture medium name (curated + canonicalized, e.g., BG-11, Zarrouk, BBM, F/2).




code — unique integer ID assigned by the label encoder.



Purpose: Ensures reproducibility and transparency in how cultivation media were encoded for modeling pipelines.

 

Usage in Workflow





Both maps are automatically used in label encoding steps for the features species and media.




Guarantees one-to-one mapping:





Same code always corresponds to the same biological entity (species or medium).




Prevents “previously unseen labels” errors during model inference.






Combined with augmentation, ensures scalable training for ensemble and deep learning models.