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his directory contains model code, input, output, and scripts from a hosing (freshwater forcing in the North Atlantic) simulation with the OSU-UVic climate model (version 2.9.10) to investigate the effect of changes in the Atlantic Meridional Overturning Circulation (AMOC) on carbon and carbon-13 components in the ocean as described in Schmittner and Boling (2025) and Schmittner (2025). Model code is in the code/ subdirectory. Model input data is in the data/ subdirectory and in the control.in and mk.in files. Model output data is in the tavg*nc and tsi*nc files. Ferret scripts used to produce the figures are in the ferret/ subdirectory. Andreas Schmittner (andreas.schmittner@oregonstate.edu) References: Schmittner, A. and M. Boling (2025) Impact of Atlantic Meridional Overturning Circulation Collapse on Carbon Components in the Ocean, Global Biogeochemical Cycles, 39, e2025GB008526 doi: 10.1029/2025GB008526. Schmittner, A. (2025) Impact of Atlantic Meridional Overturning Circulation Collapse on Carbon-13 Components in the Ocean, Global Biogeochemical Cycles, 39, e2025GB008527 doi: 10.1029/2025GB008527.
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Data complementing the publication: "Does total column ozone change during a solar eclipse?"
Data published in this zip file complement the publication "Does total column ozone change during a solar eclipse?" by Germar H. Bernhard, George T. Janson, Scott Simpson, Raúl R. Cordero, Edgardo I. Sepúlveda Araya, Jose Jorquera, Juan A. Rayas, and Randall N. Lind, which will be published in the journal "Atmospheric Chemistry and Physics". A DOI of the publication will be added to this meta data description when available. The DOI of the publication's pre-print (paper under review) is: https://doi.org/10.5194/egusphere-2024-2659 The contents of the zip file are organized in the following four subdirectories: - Figures: This directory contains the figures of the paper in PDF and PNG format plus the data used for plotting the figures. - GUVis-3511 Data Processor: This directory contains the software for processing the raw data collected during the solar eclipses described in the publication as well as ancillary data used for processing and manuals describing the software. - Limb darkening functions: This directory contains the functions expressing the change in the spectral irradiance during the eclipses discussed in the publication as a function of time and wavelength. - Raw data: This directory contains the raw data measured during the eclipses discussed in the publication. Each subdirectory and subdirectories nested therein contains "readme.txt" (in English) and "léeme_Espanol.txt" (in Spanish) files with further information of the contents of each subdirectory.
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- Award ID(s):
- 2328210
- PAR ID:
- 10558915
- Publisher / Repository:
- Zenodo
- Date Published:
- Edition / Version:
- 1
- ISSN:
- 1680-7316
- Subject(s) / Keyword(s):
- Solar eclipse Total column ozone Solar limb darkening Stratospheric ozone Gravity waves Solar spectral irradiance Aerosol optical depth
- Format(s):
- Medium: X Size: 209MB Other: zip
- Size(s):
- 209MB
- Right(s):
- Creative Commons Attribution 4.0 International
- Institution:
- Biospherical Instruments, Inc.
- Sponsoring Org:
- National Science Foundation
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This directory contains model code, input, output, and scripts from a hosing (freshwater forcing in the North Atlantic) simulation with the OSU-UVic climate model (version 2.9.10) to investigate the effect of changes in the Atlantic Meridional Overturning Circulation (AMOC) on carbon and carbon-13 components in the ocean as described in Schmittner and Boling (2025) and Schmittner (2025). Model code is in the code/ subdirectory. Model input data is in the data/ subdirectory and in the control.in and mk.in files. Model output data is in the tavg*nc and tsi*nc files. Ferret scripts used to produce the figures are in the ferret/ subdirectory. A more detailed description about the OSU-UVic climate model is available at https://github.com/OSU-CEOAS-Schmittner/UVic2.9 and https://doi.org/10.5281/zenodo.11224826. Andreas Schmittner (andreas.schmittner@oregonstate.edu) References: Schmittner, A. and M. Boling (2025) Impact of Atlantic Meridional Overturning Circulation Collapse on Carbon Components in the Ocean, Global Biogeochemical Cycles, 39, e2025GB008526 doi: 10.1029/2025GB008526. Schmittner, A. (2025) Impact of Atlantic Meridional Overturning Circulation Collapse on Carbon-13 Components in the Ocean, Global Biogeochemical Cycles, 39, e2025GB008527 doi: 10.1029/2025GB008527.more » « less
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This repository includes all data used in the writing of "Characteristics of long-track tropopause polar vortices," published in Weather and Climate Dynamics in March 2022 with doi https://doi.org/10.5194/wcd-3-251-2022. These files, along with the ERA-Interim atmospheric reanalysis, should be sufficient to replicate all figures and tables produced in that publication. Data formats include csv, txt, and npy/nzp files that can be opened with the Numpy Python package.more » « less
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{"Abstract":["Data files were used in support of the research paper titled \u201cMitigating RF Jamming Attacks at the Physical Layer with Machine Learning<\/em>" which has been submitted to the IET Communications journal.<\/p>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nAll data was collected using the SDR implementation shown here: https://github.com/mainland/dragonradio/tree/iet-paper. Particularly for antenna state selection, the files developed for this paper are located in 'dragonradio/scripts/:'<\/p>\n\n'ModeSelect.py': class used to defined the antenna state selection algorithm<\/li>'standalone-radio.py': SDR implementation for normal radio operation with reconfigurable antenna<\/li>'standalone-radio-tuning.py': SDR implementation for hyperparameter tunning<\/li>'standalone-radio-onmi.py': SDR implementation for omnidirectional mode only<\/li><\/ul>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nAuthors: Marko Jacovic, Xaime Rivas Rey, Geoffrey Mainland, Kapil R. Dandekar\nContact: krd26@drexel.edu<\/p>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nTop-level directories and content will be described below. Detailed descriptions of experiments performed are provided in the paper.<\/p>\n\n---------------------------------------------------------------------------------------------<\/p>\n\nclassifier_training: files used for training classifiers that are integrated into SDR platform<\/p>\n\n'logs-8-18' directory contains OTA SDR collected log files for each jammer type and under normal operation (including congested and weaklink states)<\/li>'classTrain.py' is the main parser for training the classifiers<\/li>'trainedClassifiers' contains the output classifiers generated by 'classTrain.py'<\/li><\/ul>\n\npost_processing_classifier: contains logs of online classifier outputs and processing script<\/p>\n\n'class' directory contains .csv logs of each RTE and OTA experiment for each jamming and operation scenario<\/li>'classProcess.py' parses the log files and provides classification report and confusion matrix for each multi-class and binary classifiers for each observed scenario - found in 'results->classifier_performance'<\/li><\/ul>\n\npost_processing_mgen: contains MGEN receiver logs and parser<\/p>\n\n'configs' contains JSON files to be used with parser for each experiment<\/li>'mgenLogs' contains MGEN receiver logs for each OTA and RTE experiment described. Within each experiment logs are separated by 'mit' for mitigation used, 'nj' for no jammer, and 'noMit' for no mitigation technique used. File names take the form *_cj_* for constant jammer, *_pj_* for periodic jammer, *_rj_* for reactive jammer, and *_nj_* for no jammer. Performance figures are found in 'results->mitigation_performance'<\/li><\/ul>\n\nray_tracing_emulation: contains files related to Drexel area, Art Museum, and UAV Drexel area validation RTE studies.<\/p>\n\nDirectory contains detailed 'readme.txt' for understanding.<\/li>Please note: the processing files and data logs present in 'validation' folder were developed by Wolfe et al. and should be cited as such, unless explicitly stated differently. \n\tS. Wolfe, S. Begashaw, Y. Liu and K. R. Dandekar, "Adaptive Link Optimization for 802.11 UAV Uplink Using a Reconfigurable Antenna," MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM), 2018, pp. 1-6, doi: 10.1109/MILCOM.2018.8599696.<\/li><\/ul>\n\t<\/li><\/ul>\n\nresults: contains results obtained from study<\/p>\n\n'classifier_performance' contains .txt files summarizing binary and multi-class performance of online SDR system. Files obtained using 'post_processing_classifier.'<\/li>'mitigation_performance' contains figures generated by 'post_processing_mgen.'<\/li>'validation' contains RTE and OTA performance comparison obtained by 'ray_tracing_emulation->validation->matlab->outdoor_hover_plots.m'<\/li><\/ul>\n\ntuning_parameter_study: contains the OTA log files for antenna state selection hyperparameter study<\/p>\n\n'dataCollect' contains a folder for each jammer considered in the study, and inside each folder there is a CSV file corresponding to a different configuration of the learning parameters of the reconfigurable antenna. The configuration selected was the one that performed the best across all these experiments and is described in the paper.<\/li>'data_summary.txt'this file contains the summaries from all the CSV files for convenience.<\/li><\/ul>"]}more » « less
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This dataset contains raw data, processed data, and the codes used for data processing in our manuscript from our Fourier-transform infrared (FTIR) spectroscopy, Nuclear magnetic resonance (NMR), Raman spectroscopy, and X-ray diffraction (XRD) experiments. The data and codes for the fits of our unpolarized Raman spectra to polypeptide spectra is also included. The following explains the folder structure of the data provided in this dataset, which is also explained in the file ReadMe.txt. Browsing the data in Tree view is recommended. Folder contents Codes Raman Data Processing: The MATLAB script file RamanDecomposition.m contains the code to decompose the sub-peaks across different polarized Raman spectra (XX, XZ, ZX, ZZ, and YY), considering a set of pre-determined restrictions. The helper functions used in RamanDecomposition.m are included in the Helpers folder. RamanDecomposition.pdf is a PDF printout of the MATLAB code and output. P Value Simulation: 31_helix.ipynb and a_helix.ipynb: These two Jupyter Notebook files contain the intrinsic P value simulation for the 31-helix and alpha-helix structures. The simulation results were used to prepare Supplementary Table 4. See more details in the comments contained. Vector.py, Atom.py, Amino.py, and Helpers.py: These python files contains the class definitions used in 31_helix.ipynb and a_helix.ipynb. See more details in the comments contained. FTIR FTIR Raw Transmission.opj: This Origin data file contains the raw transmission data measured on single silk strand and used for FTIR spectra analysis. FTIR Deconvoluted Oscillators.opj: This Origin data file was generated from the data contained in the previous file using W-VASE software from J. A. Woollam, Inc. FTIR Unpolarized MultiStrand Raw Transmission.opj: This Origin data file contains the raw transmission data measured on multiple silk strands. The datasets contained in the first two files above were used to plot Figure 2a-b and the FTIR data points in Figure 4a, and Supplementary Figure 6. The datasets contained in the third file above were used to plot Supplementary Figure 3a. The datasets contained in the first two files above were used to plot Figure 2a-b, FTIR data points in Figure 4a, and Supplementary Figure 6. NMR Raw data files of the 13C MAS NMR spectra: ascii-spec_CP.txt: cross-polarized spectrum ascii-spec_DP.txt: direct-polarized spectrum Data is in ASCII format (comma separated values) using the following columns: Data point number Intensity Frequency [Hz] Frequency [ppm] Polypeptide Spectrum Fits MATLAB scripts (.m files) and Helpers: The MATLAB script file Raman_Fitting_Process_Part_1.m and Raman_Fitting_Process_Part_2.m contains the step-by-step instructions to perform the fitting process of our calculated unpolarized Raman spectrum, using digitized model polypeptide Raman spectra. The Helper folder contains two helper functions used by the above scripts. See the scripts for further instruction and information. Data aPA.csv, bPA.csv, GlyI.csv, GlyII.csv files: These csv files contain the digitized Raman spectra of poly-alanine, beta-alanine, poly-glycine-I, and poly-glycine-II. Raman_Exp_Data.mat: This MATLAB data file contains the processed, polarized Raman spectra obtained from our experiments. Variable freq is the wavenumber information of each collected spectrum. The variables xx, yy, zz, xz, zx represent the polarized Raman spectra collected. These variables are used to calculate the unpolarized Raman spectrum in Raman_Fitting_Process_Part_2.m. See the scripts for further instruction and information. Raman Raman Raw Data.mat: This MATLAB data file contains all the raw data used for Raman spectra analysis. All variables are of MATLAB structure data type. Each variable has fields called Freq and Raw, with Freq contains the wavenumber information of the measured spectra and Raw contains 5 measured Raman signal strengths. Variable XX, XZ, ZX, ZZ, and YY were used to plot and sub-peak analysis for Figure 2c-d, Raman data points in Figure 4a, Figure 5b, Supplementary Figure 2, and Supplementary Figure 7. Variable WideRange was used to plot and identify the peaks for Supplementary Figure 3b. X-Ray X-Ray.mat: This MATLAB data file contains the raw X-ray data used for the diffraction analysis in Supplementary Figure 5.more » « less
