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1. Software and Data for “Variations in Tropical Cyclone Size and Rainfall Patterns based on Synoptic-Scale Moisture Environments in the North Atlantic”

   https://doi.org/10.7294/27994187.v2  

   Zick, Stephanie; Addington, Kayleigh; Wood, Kimberly M (January 2025, University Libraries, Virginia Tech)

   {"Abstract":["This is software and data to support the manuscript "Variations in Tropical Cyclone Size and Rainfall Patterns based on Synoptic-Scale Moisture Environments in the North Atlantic," which we are submitting to the journal, Journal of Geophysical Research Atmospheres.The MIT license applies to all source code and scripts published in this dataset.The software includes all code that is necessary to follow and evaluate the work. Public datasets include (1) the Atlantic hurricane database HURDAT2 (https://www.nhc.noaa.gov/data/#hurdat), (2) NASA’s Global Precipitation Measurement IMERG final precipitation (https://catalog.data.gov/dataset/gpm-imerg-final-precipitation-l3-half-hourly-0-1-degree-x-0-1-degree-v07-gpm-3imerghh-at-g), (3) the Tropical Cyclone Extended Best Track Dataset (https://rammb2.cira.colostate.edu/research/tropical-cyclones/tc_extended_best_track_dataset/), (4) the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5), and (5) the Statistical Hurricane Intensity Prediction Scheme (SHIPS) dataset (https://rammb.cira.colostate.edu/research/tropical_cyclones/ships/data/). We are also including four datasets generated by the code that will be helpful in evaluating the work. Lastly, we used the eofs software package, a python package for computing empirical orthogonal functions (EOFs), available publicly here: https://doi.org/10.5334/jors.122.All figures and tables in the manuscript are generated using Python, ArcGIS Pro, and GraphPad/Prism 10 Software:ArcGIS Pro used to make Figures 5GraphPad/Prism 10 Software used to make box plots in Figures 6-9Python used to make Figures 1-4, 10-11, and Tables 1-5Public Datasets:HURDAT2: Landsea, C. and Beven, J., 2019: The revised Atlantic hurricane database (HURDAT2). March 2022, https://www.aoml.noaa.gov/hrd/hurdat/hurdat2-format.pdfIMERG:NASA EarthData: GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06. 9 December 2024, https://catalog.data.gov/dataset/gpm-imerg-final-precipitation-l3-half-hourly-0-1-degree-x-0-1-degree-v07-gpm-3imerghh-at-g. Note that this dataset is not longer publicly available, as it has been replaced with IMERG version 7: https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGHH_07/summary?keywords="IMERG final"Extended Best Track:Regional and Mesoscale Meteorology Branch, 2022: The Tropical Cyclone Extended Best Track Dataset (EBTRK). March 2022, https://rammb2.cira.colostate.edu/research/tropical-cyclones/tc_extended_best_track_dataset/ERA5: Guillory, A. (2022). ERA5. Ecmwf [Dataset]. https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. (Accessed March 2, 2023). Also: Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049, https://doi.org/10.1002/qj.3803SHIPS:Ships Predictor Files - Colorado State University (2022). Statistical Tropical Cyclone Intensity Forecast Technique Development. https://rammb.cira.colostate.edu/research/tropical_cyclones/ships/data/ships_predictor_file_2022.pdf. Also: DeMaria, M., and J. Kaplan, 1994: A Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic Basin. Weather and Forecasting, 9, 209–220, https://doi.org/10.1175/1520-0434(1994)009<0209:ASHIPS>2.0.CO;2.Public Software: Dawson, A., 2016: eofs: A Library for EOF Analysis of Meteorological, Oceanographic, and Climate Data. JORS, 4, 14, https://doi.org/10.5334/jors.122.van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., et al. (2014). Scikit-image: Image processing in Python [Software]. PeerJ, 2, e453. https://doi.org/10.7717/peerj.453"]} 

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2. Software and Data For "Evaluating the Skillfulness of Experimental High Resolution Model Forecasts of Tropical Cyclone Precipitation using an Object-Based Methodology"

   https://doi.org/10.7294/21737039  

   Zick, Stephanie; Stackhouse, Shakira (August 2023, University Libraries, Virginia Tech)

   {"Abstract":["This is software and data to support the manuscript "Evaluating the Skillfulness of Experimental High Resolution Model Forecasts of Tropical Cyclone Precipitation using an Object-Based Methodology," which we are submitting to the journal Weather and Forecasting. The software includes all code that is necessary to follow and evaluate the work. We are also including some of the HAFS and HWRF-B model output for testing the code. Additional model output is available upon request. Public datasets include the Atlnatic hurricane database HURDAT2 (https://www.nhc.noaa.gov/data/#hurdat) and Stage IV precipitation (https://data.eol.ucar.edu/dataset/21.093)."]} 

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3. Training Data for the NeonTreeEvaluation Benchmark

   https://doi.org/10.5281/zenodo.3459803  

   Weinstein, Ben; Marconi, Sergio; White, Ethan (January 2020, Zenodo)

   This dataset is the large training data files for the NeonTreeEvaluation Benchmark for individual tree detection from airborne imagery. For each geographic site, given by the NEON four letter code (e.g HARV -> Harvard Forest), there are three files: a RGB image, a LiDAR tile, and a 426 band hyperpspectral file. For more information on the benchmark, and the corresponding R package, see https://github.com/weecology/NeonTreeEvaluation_package</p> 

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4. Local structural features elucidate crystallization of complex structures

   https://doi.org/10.18126/wy01-4e11  

   Martirossyan, Maya M; Spellings, Matthew; Pan, Hillary; Dshemuchadse, Julia (January 2024, Materials Data Facility)

   This dataset accompanies the “Local structural features elucidate crystallization of complex structures” preprint (https://arxiv.org/abs/2401.13765) by M. M. Martirossyan, M. Spellings, H. Pan, and J. Dshemuchadse. This dataset is built to be used in conjunction with the GitHub code (https://github.com/capecrystal/local-structural-features) for training order metrics with machine learning methods. In this work, we show that this method can distinguish different crystallographic sites in highly complex structures of varying complexity and coordination number, and it can be used to study the growth trajectories of such structures. The dataset includes self-assembly trajectories from 10 different crystal structures and 2 trajectories of the same structure assembling via different crystallization pathways. A README.txt file is included for parsing the data. 

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5. StarCraftImage: A Dataset for Prototyping Spatial Reasoning Methods for Multi-Agent Environments

   https://doi.org/10.1109/CVPR52729.2023.02107  

   Kulinski, Sean; Waytowich, Nicholas R.; Hare, James Z.; Inouye, David I. (January 2023, IEEE Conference on Computer Vision and Pattern Recognition)

   Spatial reasoning tasks in multi-agent environments such as event prediction, agent type identification, or missing data imputation are important for multiple applications (e.g., autonomous surveillance over sensor networks and subtasks for reinforcement learning (RL)). StarCraft II game replays encode intelligent (and adversarial) multiagent behavior and could provide a testbed for these tasks; however, extracting simple and standardized representations for prototyping these tasks is laborious and hinders reproducibility. In contrast, MNIST and CIFAR10, despite their extreme simplicity, have enabled rapid prototyping and reproducibility of ML methods. Following the simplicity of these datasets, we construct a benchmark spatial reasoning dataset based on StarCraft II replays that exhibit complex multi-agent behaviors, while still being as easy to use as MNIST and CIFAR10. Specifically, we carefully summarize a window of 255 consecutive game states to create 3.6 million summary images from 60,000 replays, including all relevant metadata such as game outcome and player races. We develop three formats of decreasing complexity: Hyperspectral images that include one channel for every unit type (similar to multispectral geospatial images), RGB images that mimic CIFAR10, and grayscale images that mimic MNIST. We show how this dataset can be used for prototyping spatial reasoning methods. All datasets, code for extraction, and code for dataset loading can be found at https://starcraftdata.davidinouye.com/. 

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