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1. A detrital zircon test of large-scale terrane displacement along the Arctic margin of North America

   https://doi.org/10.1130/G48336.1  

   Gibson, Timothy M. ; Faehnrich, Karol ; Busch, James F. ; McClelland, William C. ; Schmitz, Mark D. ; Strauss, Justin V. ( January 2021 , Geology)

   null (Ed.)

   Abstract Detrital zircon U-Pb geochronology is one of the most common methods used to constrain the provenance of ancient sedimentary systems. Yet, its efficacy for precisely constraining paleogeographic reconstructions is often complicated by geological, analytical, and statistical uncertainties. To test the utility of this technique for reconstructing complex, margin-parallel terrane displacements, we compiled new and previously published U-Pb detrital zircon data (n = 7924; 70 samples) from Neoproterozoic–Cambrian marine sandstone-bearing units across the Porcupine shear zone of northern Yukon and Alaska, which separates the North Slope subterrane of Arctic Alaska from northwestern Laurentia (Yukon block). Contrasting tectonic models for the North Slope subterrane indicate it originated either near its current position as an autochthonous continuation of the Yukon block or from a position adjacent to the northeastern Laurentian margin prior to >1000 km of Paleozoic–Mesozoic translation. Our statistical results demonstrate that zircon U-Pb age distributions from the North Slope subterrane are consistently distinct from the Yukon block, thereby supporting a model of continent-scale strike-slip displacement along the Arctic margin of North America. Further examination of this dataset highlights important pitfalls associated with common methodological approaches using small sample sizes and reveals challenges in relying solely on detrital zircon age spectra for testing models of terranes displaced along the same continental margin from which they originated. Nevertheless, large-n detrital zircon datasets interpreted within a robust geologic framework can be effective for evaluating translation across complex tectonic boundaries. 

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2. Replication Data for: Protein Secondary Structure in Spider Silk Nanofibrils

   https://doi.org/10.7910/DVN/GMEEYL  

   Schniepp, Hannes C. ; Wang, Qijue ; McArdle, Patrick ; Wang, Stephanie L. ; Wilmington, Ryan L. ; Xing, Zhen ; Greenwood, Alexander ; Cotten, Myrian L. ; Qazilbash, M. Mumtaz ( January 2022 , Harvard Dataverse)

   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. 

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3. Late Jurassic paleogeography of the U.S. Cordillera from detrital zircon age and hafnium analysis of the Galice Formation, Klamath Mountains, Oregon and California, USA

   https://doi.org/10.1130/B36810.1  

   Surpless, Kathleen D. ; Alford, Ryan W. ; Barnes, Calvin ; Yoshinobu, Aaron ; Weis, Natalee E. ( August 2023 , Geological Society of America Bulletin)

   The Upper Jurassic Galice Formation, a metasedimentary unit in the Western Klamath Mountains, formed within an intra-arc basin prior to and during the Nevadan orogeny. New detrital zircon U-Pb age analyses (N = 11; n = 2792) yield maximum depositional ages (MDA) ranging from ca. 160 Ma to 151 Ma, which span Oxfordian to Kimmeridgian time and overlap Nevadan contractional deformation that began by ca. 157 Ma. Zircon ages indicate a significant North American continental provenance component that is consistent with tectonic models placing the Western Klamath terrane on the continental margin in Late Jurassic time. Hf isotopic analysis of Mesozoic detrital zircon (n = 603) from Galice samples reveals wide-ranging εHf values for Jurassic and Triassic grains, many of which cannot be explained by a proximal source in the Klamath Mountains, thus indicating a complex provenance. New U-Pb ages and Hf data from Jurassic plutons within the Klamath Mountains match some of the Galice Formation detrital zircon, but these data cannot account for the most non-radiogenic Jurassic detrital grains. In fact, the in situ Cordilleran arc record does not provide a clear match for the wide-ranging isotopic signature of Triassic and Jurassic grains. When compiled, Galice samples indicate sources in the Sierra Nevada pre-batholithic framework and retroarc region, older Klamath terranes, and possibly overlap strata from the Blue Mountains and the Insular superterrane. Detrital zircon age spectra from strata of the Upper Jurassic Great Valley Group and Mariposa Formation contain similar age modes, which suggests shared sediment sources. Inferred Galice provenance within the Klamath Mountains and more distal sources suggest that the Galice basin received siliciclastic turbidites fed by rivers that traversed the Klamath-Sierran arc from headwaters in the retroarc region. Thus, the Galice Formation contains a record of active Jurassic magmatism in the continental arc, with significant detrital input from continental sediment sources within and east of the active arc. These westward-flowing river systems remained active throughout the shift in Cordilleran arc tectonics from a transtensional system to the Nevadan contractional system, which is characterized by sediment sourced in uplifts within and east of the arc and the thrusting of older Galice sediments beneath older Klamath terranes to the east.

    

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4. Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning Dataset

   https://doi.org/10.5281/zenodo.6304194  

   Jacovic, Marko ; Rivas, Xaime Rey ( January 2022 , Zenodo)

   Data files were used in support of the research paper titled “Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning" which has been submitted to the IET Communications journal.

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   All 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/:'

   • 'ModeSelect.py': class used to defined the antenna state selection algorithm
   • 'standalone-radio.py': SDR implementation for normal radio operation with reconfigurable antenna
   • 'standalone-radio-tuning.py': SDR implementation for hyperparameter tunning
   • 'standalone-radio-onmi.py': SDR implementation for omnidirectional mode only

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   Authors: Marko Jacovic, Xaime Rivas Rey, Geoffrey Mainland, Kapil R. Dandekar
   Contact: krd26@drexel.edu

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   Top-level directories and content will be described below. Detailed descriptions of experiments performed are provided in the paper.

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   classifier_training: files used for training classifiers that are integrated into SDR platform

   • 'logs-8-18' directory contains OTA SDR collected log files for each jammer type and under normal operation (including congested and weaklink states)
   • 'classTrain.py' is the main parser for training the classifiers
   • 'trainedClassifiers' contains the output classifiers generated by 'classTrain.py'

   post_processing_classifier: contains logs of online classifier outputs and processing script

   • 'class' directory contains .csv logs of each RTE and OTA experiment for each jamming and operation scenario
   • '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'

   post_processing_mgen: contains MGEN receiver logs and parser

   • 'configs' contains JSON files to be used with parser for each experiment
   • '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'

   ray_tracing_emulation: contains files related to Drexel area, Art Museum, and UAV Drexel area validation RTE studies.

   • Directory contains detailed 'readme.txt' for understanding.
   • 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. 
     • S. 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.

   results: contains results obtained from study

   • 'classifier_performance' contains .txt files summarizing binary and multi-class performance of online SDR system. Files obtained using 'post_processing_classifier.'
   • 'mitigation_performance' contains figures generated by 'post_processing_mgen.'
   • 'validation' contains RTE and OTA performance comparison obtained by 'ray_tracing_emulation->validation->matlab->outdoor_hover_plots.m'

   tuning_parameter_study: contains the OTA log files for antenna state selection hyperparameter study

   • '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.
   • 'data_summary.txt'this file contains the summaries from all the CSV files for convenience.

    

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5. Supporting data for "Linearity of the climate system response to raising and lowering West Antarctic and coastal Antarctic topography" by Andrew G. Pauling, Cecilia M. Bitz and Eric J. Steig

   https://doi.org/10.5281/zenodo.8115374  

   Pauling, Andrew G. ( January 2023 , Zenodo)

   Contains the model output and topography files necessary to reproduce the results of "Linearity of the climate system response to raising and lowering West Antarctic and coastal Antarctic topography" by Andrew G. Pauling, Cecilia M. Bitz and Eric J. Steig. Published in Journal of Climate, https://doi.org/10.1175/JCLI-D-22-0416.1.

   Please download and extract the data from each of the tar.gz.files. A description of the directories, run names, and use of the topography files is given in the file readme.txt within the dataset.

    

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