This dataset archived with the Magnetics Information Consortium contains rock-magnetic data for rhyolitic pumice and obsidian from Glass Mountain, Medicine Lake, California, USA. Data were generated at Montclair State University and include magnetic susceptibility measured at 976Hz and 3904Hz, magnetic susceptibility vs. temperature, anhysteretic remanent magnetization (ARM), and magnetic hysteresis measurements. This dataset accompanies the publication Brachfeld, S., McCartney, K., Hammer, J.E., Shea, T., Giachetti, T., Evaluating the role of titanomagnetite in bubble nucleation: Rock magnetic detection and characterization of nanolites and ultra-nanolites in rhyolite pumice and obsidian from Glass Mountain, California, Geochemistry Geophysics Geosystems, https://doi.org/10.1029/2023GC011336.
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Evaluating the Role of Titanomagnetite in Bubble Nucleation: Novel Applications of Low Temperature Magnetic Analysis and Textural Characterization of Rhyolite Pumice and Obsidian From Glass Mountain, California (Dataset)
This dataset archived with the Earthref Magnetics Information Consortium contains low-temperature remanent magnetization data generated at the Institute for Rock Magnetism, University of Minnesota. This dataset accompanies the publication McCartney, K., Hammer, J.E., Shea, T., Brachfeld, S., Giachetti, T., 2024. Investigating the role of nanoscale titanomagnetite in bubble nucleation via novel applications of magnetic analyses (Dataset), Magnetics Information Consortium (MagIC), doi:10.7288/V4/MAGIC/20019.
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- Award ID(s):
- 1839313
- NSF-PAR ID:
- 10513801
- Publisher / Repository:
- Earthref Magnetics Information Consortium
- Date Published:
- Subject(s) / Keyword(s):
- Glass Mountain pumice obsidian titanomagnetite bubble nucleation
- Format(s):
- Medium: X
- Location:
- earthref.org/MagIC/20019
- Institution:
- Montclair State University
- Sponsoring Org:
- National Science Foundation
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PmagPy Online: Jupyter Notebooks, the PmagPy Software Package and the Magnetics Information Consortium (MagIC) Database Lisa Tauxe$^1$, Rupert Minnett$^2$, Nick Jarboe$^1$, Catherine Constable$^1$, Anthony Koppers$^2$, Lori Jonestrask$^1$, Nick Swanson-Hysell$^3$ $^1$Scripps Institution of Oceanography, United States of America; $^2$ Oregon State University; $^3$ University of California, Berkely; ltauxe@ucsd.edu The Magnetics Information Consortium (MagIC), hosted at http://earthref.org/MagIC is a database that serves as a Findable, Accessible, Interoperable, Reusable (FAIR) archive for paleomagnetic and rock magnetic data. It has a flexible, comprehensive data model that can accomodate most kinds of paleomagnetic data. The PmagPy software package is a cross-platform and open-source set of tools written in Python for the analysis of paleomagnetic data that serves as one interface to MagIC, accommodating various levels of user expertise. It is available through github.com/PmagPy. Because PmagPy requires installation of Python, several non-standard Python modules, and the PmagPy software package, there is a speed bump for many practitioners on beginning to use the software. In order to make the software and MagIC more accessible to the broad spectrum of scientists interested in paleo and rock magnetism, we have prepared a set of Jupyter notebooks, hosted on jupyterhub.earthref.org which serve a set of purposes. 1) There is a complete course in Python for Earth Scientists, 2) a set of notebooks that introduce PmagPy (pulling the software package from the github repository) and illustrate how it can be used to create data products and figures for typical papers, and 3) show how to prepare data from the laboratory to upload into the MagIC database. The latter will satisfy expectations from NSF for data archiving and for example the AGU publication data archiving requirements. Getting started To use the PmagPy notebooks online, go to website at https://jupyterhub.earthref.org/. Create an Earthref account using your ORCID and log on. [This allows you to keep files in a private work space.] Open the PmagPy Online - Setup notebook and execute the two cells. Then click on File = > Open and click on the PmagPy_Online folder. Open the PmagPy_online notebook and work through the examples. There are other notebooks that are useful for the working paleomagnetist. Alternatively, you can install Python and the PmagPy software package on your computer (see https://earthref.org/PmagPy/cookbook for instructions). Follow the instructions for "Full PmagPy install and update" through section 1.4 (Quickstart with PmagPy notebooks). This notebook is in the collection of PmagPy notebooks. Overview of MagIC The Magnetics Information Consortium (MagIC), hosted at http://earthref.org/MagIC is a database that serves as a Findable, Accessible, Interoperable, Reusable (FAIR) archive for paleomagnetic and rock magnetic data. Its datamodel is fully described here: https://www2.earthref.org/MagIC/data-models/3.0. Each contribution is associated with a publication via the DOI. There are nine data tables: contribution: metadata of the associated publication. locations: metadata for locations, which are groups of sites (e.g., stratigraphic section, region, etc.) sites: metadata and derived data at the site level (units with a common expectation) samples: metadata and derived data at the sample level. specimens: metadata and derived data at the specimen level. criteria: criteria by which data are deemed acceptable ages: ages and metadata for sites/samples/specimens images: associated images and plots. Overview of PmagPy The functionality of PmagPy is demonstrated within notebooks in the PmagPy repository: PmagPy_online.ipynb: serves as an introdution to PmagPy and MagIC (this conference). It highlights the link between PmagPy and the Findable Accessible Interoperable Reusabe (FAIR) database maintained by the Magnetics Information Consortium (MagIC) at https://earthref.org/MagIC. Other notebooks of interest are: PmagPy_calculations.ipynb: demonstrates many of the PmagPy calculation functions such as those that rotate directions, return statistical parameters, and simulate data from specified distributions. PmagPy_plots_analysis.ipynb: demonstrates PmagPy functions that can be used to visual data as well as those that conduct statistical tests that have associated visualizations. PmagPy_MagIC.ipynb: demonstrates how PmagPy can be used to read and write data to and from the MagIC database format including conversion from many individual lab measurement file formats. Please see also our YouTube channel with more presentations from the 2020 MagIC workshop here: https://www.youtube.com/playlist?list=PLirL2unikKCgUkHQ3m8nT29tMCJNBj4kjmore » « less
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This dataset uses trajectory data from a large set of drifters to extract and analyze displacement signals associated with coherent eddies in the Gulf of Mexico, using a multivariate wavelet ridge analysis as presented in Lilly and Pérez-Brunius (2021). The data includes eddy displacement signals for all ridges, as well as the time-varying ellipse parameters and estimated ellipse center location. The instantaneous frequency is also included, as is the instantaneous bias estimate derived by Lilly and Olhede (2012). The data are organized as appended trajectory data that can be readily separated through the use of the "ids" field. The ridge length (\(L\)),and ridge-averaged circularity (\(\overline{\xi}\)) are also included, as is measure of statistical significance denoted by (\(\rho\)). The dataset is available for download as a NetCDF file.
Lilly, J. M. and P. Pérez-Brunius (2021). Extracting statistically significant eddy signals from large Lagrangian datasets using wavelet ridge analysis, with application to the Gulf of Mexico. Nonlinear Processes in Geophysics, 28: 181–212. https://doi.org/10.5194/npg-28-181-2021.
Lilly, J. M. and Olhede, S. C.: Analysis of modulated multivariate oscillations, IEEE T. Signal Proces., 60, 600–612, 2012. 10.1109/TSP.2011.2173681
The GOMED database is a product of the Gulf of Mexico Research Consortium (CIGoM) and was partially funded by the CONACYT-SENER-Hydrocarbons Sector Fund, Mexico, project 201441.See database webpage with additional information, as well as request for download form (https://giola.cicese.mx/database/GOMED) {"references": ["Lilly, J. M. and Olhede, S. C.: Higher-order properties of analytic wavelets, IEEE T. Signal Proces., 57, 146\u2013160,\u00a0https://doi.org/10.1109/TSP.2008.2007607, 2009.", "Lilly, J. M. and Olhede, S. C.: Analysis of modulated multivariate oscillations, IEEE T. Signal Proces., 60, 600\u2013612, 2012."]} -
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Abstract Initiated by the University Consortium of Geographic Information Science (UCGIS), the GIS&T Body of Knowledge (BoK) is a community‐driven endeavor to define, develop, and document geospatial topics related to geographic information science and technologies (GIS&T). In recent years, GIS&T BoK has undergone rigorous development in terms of its topic re‐organization and content updating, resulting in a new digital version of the project. While the BoK topics provide useful materials for researchers and students to learn about GIS, the semantic relationships among the topics, such as semantic similarity, should also be identified so that a better and automated topic navigation can be achieved. Currently, the related topics are either defined manually by editors or authors, which may result in an incomplete assessment of topic relationships. To address this challenge, our research evaluates the effectiveness of multiple natural language processing (NLP) techniques in extracting semantics from text, including both deep neural networks and traditional machine learning approaches. Besides, a novel text summarization—KACERS (Keyword‐Aware Cross‐Encoder‐Ranking Summarizer)—is proposed to generate a semantic summary of scientific publications. By identifying the semantic linkages among key topics, this work guides the future development and content organization of the GIS&T BoK project. It also offers a new perspective on the use of machine learning techniques for analyzing scientific publications and demonstrates the potential of the KACERS summarizer in semantic understanding of long text documents.
