CAML: In-Situ Cyanobacteria Cell Counts Across U.S. Water Bodies

23,570 in-situ measurements of cyanobacteria cell counts provide a ground-truth dataset for validating remote sensing models of harmful algal blooms. The CAML dataset aggregates manual labels from inland water bodies across the United States over a nine-year period from 2013 to 2021. Its primary purpose is to train algorithms for timely water quality monitoring and to investigate environmental factors linked to cyanobacteria proliferation.

Use Cases

• Training machine learning models for cyanobacteria detection based on satellite imagery.
• Classifying the severity of algal blooms using ground-truthed cell count data.
• Analyzing spatiotemporal trends in cyanobacteria incidence across U.S. inland waters.
• Investigating environmental and anthropogenic factors associated with bloom proliferation.
• Validating remote sensing data products for systematic water quality monitoring.

Strengths

• Contains 23,570 manually labeled data points, providing a substantial ground-truth corpus.
• Covers a wide temporal range from 2013 to 2021, enabling longitudinal analysis.
• Aggregates in-situ measurements from across the United States, offering broad spatial coverage.

Limitations

• Column names and a complete data dictionary are not provided in the available metadata.
• Conflicting license information exists between platforms ('other-license-specified' vs. 'None').
• The dataset's freshness is unclear due to conflicting last-updated dates (2026 vs. 2021).

Provenance

Source

National Aeronautics and Space Administration

Collection Method

Aggregated manual labels from in-situ sampling of cyanobacteria cell counts.

Time Range

2013 to 2021

Freshness

2026-04-10 04:06:35.034584 (from datagov, though this future date is anomalous)

Geography

Inland water bodies across the United States