Christopher Holder of Johns Hopkins University provides the dataset and scripts for the manuscript "Can machine learning extract the mechanisms controlling phytoplankton growth from large-scale observations? – A proof of concept study." The data is associated with research linking intrinsic and apparent relationships between phytoplankton and environmental forcings using machine learning. The dataset is released under an Open Access (green) license.
Use Cases
- Developing machine learning models to predict phytoplankton growth based on environmental forcings.
- Analyzing the relationship between phytoplankton dynamics and large-scale environmental observations.
- Reproducing the proof-of-concept study results from the associated manuscript.
- Testing the challenges of linking intrinsic and apparent ecological relationships using computational methods.
Strengths
- Dataset is directly linked to a peer-reviewed manuscript, providing a clear research context.
- Includes accompanying scripts and functions, facilitating reproducibility of the study.
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
Provenance
- Source
- Johns Hopkins University
- Collection Method
- Likely contains observational data on phytoplankton and environmental variables.