v6.19.0

Supporting datasets and ensemble members/submembers for the PASTEL model.Brief description of individual entries:v0_1_5_Awakens.csv — A merged dataset combining multiple airborne campaigns with supplementary 24-hour backward trajectory information. Represents the input samples used to train PASTEL.Koppen_npy_files.zip — Numpy arrays containing merged land (Beck et al. 2023) and ocean (Walterscheid 2011) Köppen climate classifications at 0.5° x 0.5° global resolution. Includes a Matplotlib colormap (Python .pkl), following Beck et al. (2023), along with alternative Köppen representations.worldcities.zip — Simplemaps basic dataset (see attribution and license within).df_preprocessed.csv — A preprocessed version of v0_1_5_Awakens.csv containing additional derived features and statistics. Can be used to bypass preprocessing steps in the main PASTEL notebook.AllTrajectories.zip — All 24-hour backward HYSPLIT trajectories generated for each sample in v0_1_5_Awakens.csv and df_preprocessed.csv, with varying meteorological inputs (see associated publication for details).ne_10m_land.zip — Natural Earth shapefile containing 10-meter resolution land boundaries.ERA5_32yr_monthly_avg.nc — NetCDF file containing 32-year monthly averages of ERA5 data (ozone, specific humidity, relative humidity, temperature) over the study period.ensemble.zip — Ensemble members and submembers contributing to PASTEL predictions, along with derived statistics and plots (≈27 GB uncompressed).LicenseCode (not included here, see linked repository): GNU General Public License v3.0 (GPLv3).Data: Creative Commons Attribution–ShareAlike 4.0 International (CC-BY-SA 4.0).Third-party data (Simplemaps, Natural Earth) is redistributed under their respective licenses (see included attributions).CitationIf you use this dataset, please cite:Geiser, Victor (2025). Supplementary data for the Predictive model for Atmospheric Substances and Trace pollutants in the Environment using machine Learning (PASTEL). Zenodo. https://doi.org/10.5281/zenodo.17204569How to UseThese datasets are intended for use with the PASTEL model, but may also be of independent value for climate classification, atmospheric transport analysis, or ensemble modeling.Size Warning"ensemble.zip" is roughly 27GB uncompressed as statistics/plotting information for all members/submembers is included!ContactFor questions regarding this dataset or publication please contact victor.w.geiser[at]gmail.com

Supporting datasets and ensemble members/submembers for the PASTEL model.Brief description of individual entries:v0_1_5_Awakens.csv — A merged dataset combining multiple airborne campaigns with supplementary 24-hour backward trajectory information. Represents the input samples used to train PASTEL.Koppen_npy_files.zip — Numpy arrays containing merged land (Beck et al. 2023) and ocean (Walterscheid 2011) Köppen climate classifications at 0.5° x 0.5° global resolution. Includes a Matplotlib colormap (Python .pkl), following Beck et al. (2023), along with alternative Köppen representations.worldcities.zip — Simplemaps basic dataset (see attribution and license within).df_preprocessed.csv — A preprocessed version of v0_1_5_Awakens.csv containing additional derived features and statistics. Can be used to bypass preprocessing steps in the main PASTEL notebook.AllTrajectories.zip — All 24-hour backward HYSPLIT trajectories generated for each sample in v0_1_5_Awakens.csv and df_preprocessed.csv, with varying meteorological inputs (see associated publication for details).ne_10m_land.zip — Natural Earth shapefile containing 10-meter resolution land boundaries.ERA5_32yr_monthly_avg.nc — NetCDF file containing 32-year monthly averages of ERA5 data (ozone, specific humidity, relative humidity, temperature) over the study period.ensemble.zip — Ensemble members and submembers contributing to PASTEL predictions, along with derived statistics and plots (≈27 GB uncompressed).LicenseCode (not included here, see linked repository): GNU General Public License v3.0 (GPLv3).Data: Creative Commons Attribution–ShareAlike 4.0 International (CC-BY-SA 4.0).Third-party data (Simplemaps, Natural Earth) is redistributed under their respective licenses (see included attributions).CitationIf you use this dataset, please cite:Geiser, Victor (2025). Supplementary data for the Predictive model for Atmospheric Substances and Trace pollutants in the Environment using machine Learning (PASTEL). Zenodo. https://doi.org/10.5281/zenodo.17204569How to UseThese datasets are intended for use with the PASTEL model, but may also be of independent value for climate classification, atmospheric transport analysis, or ensemble modeling.Size Warning"ensemble.zip" is roughly 27GB uncompressed as statistics/plotting information for all members/submembers is included!ContactFor questions regarding this dataset or publication please contact victor.w.geiser[at]gmail.com

Data were acquired during a greenhouse experiment in which plants in large tree pots were watered from either the soil line ("top-watered") or from below using watering trays ("bottom-watered"). Plants represented a backcrossed F1 hybrid Silphium integrifolium x Silphium perfoliatum population. Aboveground biomass, root distribution, and root morphology (e.g., specific root length) were measured destructively. "silphiumGH_rootStrandScans_SUBMISSION.zip" contains three images that each represent a root strand from a plant in the experiment. Images were acquired using a flatbed scanner and used to calculate specific root length in the Rhizovision software interface. "silphiumGreenhouseDataSubmission.csv" contains all of the data used for this manuscript, including manually entered measurements and the output of Rhizovision software. "submissionAnalysisAndFigures_Ch2.R" includes all of the code necessary to analyze the data in the csv file (e.g., t-tests and robust regressions) and produce all of the data-related figures in the manuscript (excluding data tables).

Data were acquired during a greenhouse experiment in which plants in large tree pots were watered from either the soil line ("top-watered") or from below using watering trays ("bottom-watered"). Plants represented a backcrossed F1 hybrid Silphium integrifolium x Silphium perfoliatum population. Aboveground biomass, root distribution, and root morphology (e.g., specific root length) were measured destructively. "silphiumGH_rootStrandScans_SUBMISSION.zip" contains three images that each represent a root strand from a plant in the experiment. Images were acquired using a flatbed scanner and used to calculate specific root length in the Rhizovision software interface. "silphiumGreenhouseDataSubmission.csv" contains all of the data used for this manuscript, including manually entered measurements and the output of Rhizovision software. "submissionAnalysisAndFigures_Ch2.R" includes all of the code necessary to analyze the data in the csv file (e.g., t-tests and robust regressions) and produce all of the data-related figures in the manuscript (excluding data tables).

v6.11.0

v6.10.0

v6.9.0

Data for plots in Topological state permutations in time-modulated non-Hermitian multiqubit systems with suppressed non-adiabatic transitions (Physical Review Research, in press (2025))preprint: arXiv:2501.16160 (2025)Further details can be found in the accompanying README.txt file.

Data for plots in Topological state permutations in time-modulated non-Hermitian multiqubit systems with suppressed non-adiabatic transitions (Physical Review Research, in press (2025))preprint: arXiv:2501.16160 (2025)Further details can be found in the accompanying README.txt file.