Published February 25, 2023
| Version v1
Dataset
Open
Precipitation
Authors/Creators
Description
This dataset represents binary measurements of precipitation at 92 weather stations across four states in the United States of America. These four states are Illinois, Indiana, Iowa, and Missouri.
Task: The dataset can be used to study causal discovery algorithms as in Foygel Barber and Drton (2015).
Summary:
- Size of dataset: 371 x 93
- Task: Causal Discovery
- Data Type: Binary
- Dataset Scope: Standalone
- Ground Truth: Known
- Temporal Structure: Time Series (irregular)
- License: TBD
- Missing Values: No
Missingness Statement: There are no missing values.
Features: The first column indicates the record time, wheras the following 92 columns represent the postal codes of the respective weather stations.
Files:
- precipitation_dataset.csv: dataset
- GroundTruth_delaunay.csv: Undirected graph obtained by applying Delaunay triangulation to the geographical layout via the postcodes.
Files
GroundTruth_delauney.csv
Additional details
Related works
- Is derived from
- Data paper: 10.1214/15-EJS1012 (DOI)
- Is published in
- Journal article: 10.3334/cdiac/cli.ndp070 (DOI)
References
- Menne, M. J., Williams Jr., C. N. and Vose, R. S. (2011). United States Historical Climatology Network Daily Temperature, Precipitation, and Snow Data
- Rina Foygel Barber. Mathias Drton. "High-dimensional Ising model selection with Bayesian information criteria." Electron. J. Statist. 9 (1) 567 - 607, 2015. https://doi.org/10.1214/15-EJS1012