food-ai-nexus/fecal-indicator-water-nys

Predicting Agricultural Water Quality (PAWQ) Project Datasets

Predicting Agricultural Water Quality (PAWQ) Project Datasets is a tabular dataset containing water quality, weather, land use, livestock, and microbial source tracking (MST) markers for predicting foodborne bacterial contamination in New York streams used to source water for produce production.

With this dataset, researchers can train machine learning models to predict the presence of E. coli and specific microbial source tracking markers (human, ruminant, avian, canid) in surface waters based on environmental, geographical, and weather predictors.

This dataset accompanies the publications:

• Green H, Wilder M, Wiedmann M and Weller D (2021). Integrative Survey of 68 Non-overlapping Upstate New York Watersheds Reveals Stream Features Associated With Aquatic Fecal Contamination. Front. Microbiol. 12:684533.
• Weller, D., A. Belias, H. Green, S. Roof, and M. Wiedmann (2020). Landscape, water quality, and weather factors associated with an increased likelihood of foodborne pathogen contamination of New York streams used to source water for produce production. Frontiers in Sustainable Food Systems. (3) 124.

Content

• The dataset contains 196 unique records collected across 68 non-overlapping Upstate New York watersheds.
• It spans a variety of features including water quality metrics, weather conditions, land cover, and proximity to livestock operations and infrastructure.
• The dataset is imbalanced for binary classification of MST markers, containing 49 positive samples for HF183 (human) and 34 positive samples for Rum2Bac (ruminant).
• The target variables are derived from the ecoli, HF183_pa, Rum2Bac_pa, DG3_pa, and GFD_pa columns.

Data Fields

The dataset contains 95 columns.

Key Features

Column	Type	Description
ecoli	float64	Log10 E. coli concentration in the waterway (MPN/100 mL)
ph	float64	pH
cond	float64	Conductivity (Log10 uS/cm)
do	float64	Dissolved oxygen levels (mg/L)
flow	float64	Flow rate measured 3-6” below the surface (m/s)
a_t	float64	Air temperature measured at the sampling site at the time of sample collection (°C)
w_t	float64	Water temperature (°C)
turb	float64	Turbidity (Log10 NTU)
perc_SAV	float64	Description not available
SAV_pa	object	Description not available
precip_1d	float64	Description not available
avg_solar_1d	float64	Description not available
area_10km	float64	Total area of upstream watershed (10-km2)

Target Variables (Microbial Source Tracking Markers)

Column	Type	Description
HF183_pa	object	Microbial source tracking (MST) marker that indicates human fecal contamination
Rum2Bac_pa	object	MST marker that indicates ruminant fecal contamination
DG3_pa	object	MST marker that indicates canid fecal contamination
GFD_pa	object	MST marker that indicates avian fecal contamination

Uses

The dataset was originally used to train machine learning models to predict E. coli levels and the presence of specific microbial source tracking markers in agricultural water. It can also be used in research areas such as environmental microbiology, agricultural safety, and spatial ecology.

Use the "Use this dataset" button at the top of the page to load the dataset into your preferred library. To load and prepare the data:

import pandas as pd
from datasets import load_dataset

# Load the dataset
ds = load_dataset("food-ai-nexus/fecal-indicator-water-nys")
df = ds["train"].to_pandas()

# Example: Create a binary label for human fecal contamination
# df['target_present'] = (df['HF183_pa'] == 'P').astype(int)

License

This dataset is licensed under the MIT License. It is intended for research and educational use.

Citation

@article{green2021integrative,
  title={Integrative Survey of 68 Non-overlapping Upstate New York Watersheds Reveals Stream Features Associated With Aquatic Fecal Contamination},
  author={Green, H. and Wilder, M. and Wiedmann, M. and Weller, D.},
  journal={Frontiers in Microbiology},
  volume={12},
  pages={684533},
  year={2021},
  doi={10.3389/fmicb.2021.684533}
}

@article{weller2020landscape,
  title={Landscape, water quality, and weather factors associated with an increased likelihood of foodborne pathogen contamination of New York streams used to source water for produce production},
  author={Weller, D. and Belias, A. and Green, H. and Roof, S. and Wiedmann, M.},
  journal={Frontiers in Sustainable Food Systems},
  volume={3},
  pages={124},
  year={2020},
  doi={10.3389/fsufs.2019.00124}
}

Source

Original dataset: Zenodo 10.5281/zenodo.18500867
Code repository: wellerd2/Green-et-al.-2021-Datasets