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PmagPy: Software package for paleomagnetic data analysis and a bridge to the Magnetics Information Consortium (MagIC) Database

TitlePmagPy: Software package for paleomagnetic data analysis and a bridge to the Magnetics Information Consortium (MagIC) Database
Publication TypeJournal Article
Year of Publication2016
AuthorsTauxe L, Shaar R, Jonestrask L., Swanson-Hysell N.L, Minnett R., Koppers A.AP, Constable C.G, Jarboe N., Gaastra K., Fairchild L.
JournalGeochemistry, Geophysics, Geosystems
Date Published2016/07
ISBN Number1525-2027
Keywords0520 Data analysis: algorithms and implementation; 0525 Data management; 0530 Data presentation and visualization; 1594 Instruments and techniques; 1599 General or miscellaneous; MagIC database; Magnetics Information Consortium; paleomagnetic and rock magnetic database; PmagPy software package

The Magnetics Information Consortium (MagIC) database provides an archive with a flexible data model for paleomagnetic and rock magnetic 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. PmagPy facilitates thorough documentation of sampling, measurements, data sets, visualization, and interpretation of paleomagnetic and rock magnetic experimental data. Although not the only route into the MagIC database, PmagPy makes preparation of newly published data sets for contribution to MagIC as a byproduct of normal data analysis and allows manipulation as well as reanalysis of data sets downloaded from MagIC with a single software package. The graphical user interface (GUI), Pmag GUI enables use of much of PmagPy's functionality, but the full capabilities of PmagPy extend well beyond that. Over 400 programs and functions can be called from the command line interface mode, or from within the interactive Jupyter notebooks. Use of PmagPy within a notebook allows for documentation of the workflow from the laboratory to the production of each published figure or data table, making research results fully reproducible. The PmagPy design and its development using GitHub accommodates extensions to its capabilities through development of new tools by the user community. Here we describe the PmagPy software package and illustrate the power of data discovery and reuse through a reanalysis of published paleointensity data which illustrates how the effectiveness of selection criteria can be tested.

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