# Supporting data for climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics

## Abstract

This data supports the conclusions found in "climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics". Included here are (i) the binarized geolocation vectors used for exhaustive vector comparisons, (ii) the resulting climatic networks, (iii) the results of applying Markov clustering to the climatic networks, and (iv) the results of applying Correlation-of-Correlations (cor-cor) to the climatic networks. The set of binarized geolocation vectors that are used as inputs for the Combinatorial Metrics library (CoMet) are of the form "comet-UUUUUxVVVVV-XXXX-YYYY.shuffled.tped" where "UUUUU" is the number of vectors, "VVVVV" is the length of each vector, "XXXX" is the starting year, and "YYYY" is the ending year. Each line corresponds to a geolocation vector of binary elements A (i.e., 0) and T (i.e., 1). The set of climatic networks that are used for downstream network analysis are of the form "network-U-way-XXXX-YYYY.parsed.txt" where "U" is the order of the comparison (2-way or 3-way), "XXXX" is the starting year, and "YYYY" is the ending year. Each line corresponds to an edge linking two geolocations (defined by latitude and longitude) with its corresponding edge weight (i.e., DUO score). The set of cluster results are of the form "clusters-U-way-XXXX-YYYY-thresh-VVVV-inflation-WWW.clustered.txt" where "U" is the ordermore »

- Authors:

- Publication Date:

- DOE Contract Number:
- DE-AC05-00OR22725

- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); University of Tennessee, Knoxville, TN; University of Missouri, St. Louis, MO

- Sponsoring Org.:
- Office of Science (SC), Biological and Environmental Research (BER) (SC-23); National Institutes of Health (NIH)

- Subject:
- 54 ENVIRONMENTAL SCIENCES

- Keywords:
- Exhaustive vector comparison; climatic clustering; longitudinal network analysis; high-performance computing; exascale computing; predictive modeling

- OSTI Identifier:
- 1828678

- DOI:
- https://doi.org/10.13139/ORNLNCCS/1828678

### Citation Formats

```
Lagergren, John, Cashman, Mikaela, Melesse Vergara, Veronica, Eller, Paul, Gazolla, Joao, Chhetri, Hari, Streich, Jared, Climer, Sharlee, Thornton, Peter, Joubert, Wayne, and Jacobson, Daniel.
```*Supporting data for climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics*. United States: N. p., 2021.
Web. doi:10.13139/ORNLNCCS/1828678.

```
Lagergren, John, Cashman, Mikaela, Melesse Vergara, Veronica, Eller, Paul, Gazolla, Joao, Chhetri, Hari, Streich, Jared, Climer, Sharlee, Thornton, Peter, Joubert, Wayne, & Jacobson, Daniel.
```*Supporting data for climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics*. United States. doi:https://doi.org/10.13139/ORNLNCCS/1828678

```
Lagergren, John, Cashman, Mikaela, Melesse Vergara, Veronica, Eller, Paul, Gazolla, Joao, Chhetri, Hari, Streich, Jared, Climer, Sharlee, Thornton, Peter, Joubert, Wayne, and Jacobson, Daniel. 2021.
"Supporting data for climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics". United States. doi:https://doi.org/10.13139/ORNLNCCS/1828678. https://www.osti.gov/servlets/purl/1828678. Pub date:Thu Nov 18 00:00:00 EST 2021
```

```
@article{osti_1828678,
```

title = {Supporting data for climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics},

author = {Lagergren, John and Cashman, Mikaela and Melesse Vergara, Veronica and Eller, Paul and Gazolla, Joao and Chhetri, Hari and Streich, Jared and Climer, Sharlee and Thornton, Peter and Joubert, Wayne and Jacobson, Daniel},

abstractNote = {This data supports the conclusions found in "climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics". Included here are (i) the binarized geolocation vectors used for exhaustive vector comparisons, (ii) the resulting climatic networks, (iii) the results of applying Markov clustering to the climatic networks, and (iv) the results of applying Correlation-of-Correlations (cor-cor) to the climatic networks. The set of binarized geolocation vectors that are used as inputs for the Combinatorial Metrics library (CoMet) are of the form "comet-UUUUUxVVVVV-XXXX-YYYY.shuffled.tped" where "UUUUU" is the number of vectors, "VVVVV" is the length of each vector, "XXXX" is the starting year, and "YYYY" is the ending year. Each line corresponds to a geolocation vector of binary elements A (i.e., 0) and T (i.e., 1). The set of climatic networks that are used for downstream network analysis are of the form "network-U-way-XXXX-YYYY.parsed.txt" where "U" is the order of the comparison (2-way or 3-way), "XXXX" is the starting year, and "YYYY" is the ending year. Each line corresponds to an edge linking two geolocations (defined by latitude and longitude) with its corresponding edge weight (i.e., DUO score). The set of cluster results are of the form "clusters-U-way-XXXX-YYYY-thresh-VVVV-inflation-WWW.clustered.txt" where "U" is the order of the comparison (2-way or 3-way), "XXXX" is the starting year, "YYYY" is the ending year, "VVVV" is the similarity threshold, and "WWW" is the Markov clustering inflation rate. Each line corresponds to a single cluster and is composed of a number of corresponding geolocations (defined by latitude and longitude). The set of cor-cor results are of the form "corcor-U-way-XXXX-YYYY.cumulative.txt" where "U" is the order of the comparison (2-way or 3-way), "XXXX" is the starting year, and "YYYY" is the ending year. Each line corresponds to a single geolocation with it's corresponding cor-cor value.},

doi = {10.13139/ORNLNCCS/1828678},

journal = {},

number = ,

volume = ,

place = {United States},

year = {2021},

month = {11}

}