More Like this

1. Data for "Deep Nitrogen Fluxes and Sources Constrained by Arc Lava Phenocrysts"

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

   Hudak, Michael R; Barry, Peter H; Bekaert, David V; Turner, Stephen J; Broadley, Michael W; Walowski, Kristina; Tyne, Rebecca L; Li, Kan; Nielsen, Sune G; Curtice, Joshua M; et al (October 2024, Harvard Dataverse)

   These data correspond to the article “Deep Nitrogen Fluxes and Sources Constrained by Arc Lava Phenocrysts” by Hudak et al. submitted to Geophysical Research Letters. Table S1 includes N-He-Ar data for FIs in phenocrysts from mafic are lavas and tephras. Table S2 contains the corrected N2/3He data used for volcanic arc N flux calculations and the arc-averaged mean N arc flux. Table S3 summarizes previous literature estimates of N fluxes and the data used for those calculations. Table S4 provides the N concentrations, He concentrations, N isotope compositions of the mantle, sediments, and altered oceanic crust, as well as sediment thicknesses. Finally, Table S5 gives information about the sources of the mineral separates used for these analyses. 

   more » « less
2. GMAG: An open-source python package for ground-based magnetometers

   https://doi.org/10.3389/fspas.2022.1005061  

   Murphy, Kyle R.; Rae, I. Jonathan; Halford, Alexa J.; Engebretson, Mark; Russell, Christopher T.; Matzka, Jürgen; Johnsen, Magnar G.; Milling, David K.; Mann, Ian R.; Kale, Andy; et al (November 2022, Frontiers in Astronomy and Space Sciences)

   Magnetometers are a key component of heliophysics research providing valuable insight into the dynamics of electromagnetic field regimes and their coupling throughout the solar system. On satellites, magnetometers provide detailed observations of the extension of the solar magnetic field into interplanetary space and of planetary environments. At Earth, magnetometers are deployed on the ground in extensive arrays spanning the polar cap, auroral and sub-auroral zone, mid- and low-latitudes and equatorial electrojet with nearly global coverage in azimuth (longitude or magnetic local time—MLT). These multipoint observations are used to diagnose both ionospheric and magnetospheric processes as well as the coupling between the solar wind and these two regimes at a fraction of the cost of in-situ instruments. Despite their utility in research, ground-based magnetometer data can be difficult to use due to a variety of file formats, multiple points of access for the data, and limited software. In this short article we review the Open-Source Python library GMAG which provides rapid access to ground-based magnetometer data from a number of arrays in a Pandas DataFrame, a common data format used throughout scientific research. 

   more » « less
3. Sites M0081 and M0082 (Basin S1)

   Strasser, M.; Ikehara, K.; Everest, J.; Maeda, L.; Hochmuth, K.; Grant, H.; Stewart, M.; Okutsu, N.; Sakurai, N.; Yokoyama, T.; et al (November 2023, Proceedings of the International Ocean Discovery Program Expedition reports)

   During Expedition 386, a total of five Giant Piston Corer (GPC) system deployments in the southern Japan Trench focus area (Figure F1) resulted in the recovery of cores from six holes at Site M0081 and four at Site M0082 (Basin S1) (Figure F2). The water depth was between 7989 and 8023 meters below sea level (mbsl). A breakdown of operational time is reported weekly (see OPS in Supplementary material) due to decisions to move between sites based on weather and current conditions. Sites M0081 and M0082 were cored during Weeks 2 and 5 of the offshore phase. In total, 154.455 m of cores (Table T1) and 90 km of hydroacoustic profiles (see Hydroacoustics) were recovered and acquired, respectively, in this focus area. Further operations details, including winch log and inclinometer information, are found in Coring methodology in the Expedition 386 methods chapter (Strasser et al., 2023a) and associated files (see PALEOMAG and WINCH LOGS in Supplementary material). 

   more » « less
4. The Codon Statistics Database: A Database of Codon Usage Bias

   https://doi.org/10.1093/molbev/msac157  

   Subramanian, Krishnamurthy; Payne, Bryan; Feyertag, Felix; Alvarez-Ponce, David (August 2022, Molecular Biology and Evolution)

   Saitou, Naruya (Ed.)

   Abstract We present the Codon Statistics Database, an online database that contains codon usage statistics for all the species with reference or representative genomes in RefSeq (over 15,000). The user can search for any species and access two sets of tables. One set lists, for each codon, the frequency, the Relative Synonymous Codon Usage, and whether the codon is preferred. Another set of tables lists, for each gene, its GC content, Effective Number of Codons, Codon Adaptation Index, and frequency of optimal codons. Equivalent tables can be accessed for (1) all nuclear genes, (2) nuclear genes encoding ribosomal proteins, (3) mitochondrial genes, and (4) chloroplast genes (if available in the relevant assembly). The user can also search for any taxonomic group (e.g., “primates”) and obtain a table comparing all the species in the group. The database is free to access without registration at http://codonstatsdb.unr.edu. 

   more » « less
5. 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. 

   more » « less