Phytoplankton Cell Density and Biovolume from Flow Cytometry

Data from 2014 supports a study quantifying phytoplankton community structure using scanning flow cytometry and unsupervised clustering. The dataset contains raw flow cytometry measurements linked to environmental metadata like date, time, and sampling depth. It is intended for developing and validating machine learning methods to analyze aquatic microbial communities.

Use Cases

• Training unsupervised clustering models on raw flow cytometry signals.
• Validating algorithms for estimating phytoplankton cell densities from cytometric data.
• Analyzing temporal patterns in phytoplankton communities based on timestamped samples.
• Correlating phytoplankton functional group biovolumes with sampling depth metadata.
• Adapting the provided analysis code for other flow cytometry datasets.

Strengths

• Data is associated with a submitted manuscript, indicating research-grade curation.
• Includes raw flow cytometry files with detailed naming conventions encoding date, time, and depth.
• Analysis code is publicly available on Zenodo, facilitating reproducibility.

Limitations

• Key metadata like row count, column names, and dataset size are unknown across all sources.
• The manuscript status is 'in submission', so the data may be preliminary.
• No information on the geographic location of the sampling is provided.

Provenance

Source

Mridul K. Thomas, Technical University of Denmark

Collection Method

Scanning flow cytometry measurements of phytoplankton samples.

Time Range

2014 (inferred from example filename: Allparameters_2014-07-31)