The Rescue of Magnetotellurics (MT) Time Series Datasets
Abstract
It is very common for a magnetotellurics (MT) geophysicist to have filing cabinets full of unique and unpublished MT datasets on data storage media that are no longer maintained or considered safe, with critical metadata recorded completely independently. Such datasets are potentially valuable to the wider MT community as they contain important information over large geographical areas that would otherwise require expensive re-acquisition. The raw time series are typically not released by the MT scientist who worked on the data and are routinely stored in private spaces on tapes, CDs, hard drives or local network drives. The metadata associated with the raw time series, which are critical for undertaking subsequent analysis, are often stored on separate PDF documents or sometimes on paper in folders or workbooks. By rescuing old MT datasets and processing and securing along with new data in modern archival formats, the MT community can build on the many diverse collection programs from the last thirty years. Vintage MT data is still valuable today and there is no reason why today's data will not also be valuable into the future.
As part of the 2017-2018 AuScope-Australian Research Data Commons (ARDC) funded Geoscience Data-enhanced Virtual Laboratory (DeVL) project, the National Computational Infrastructure (NCI) has been working with The University of Adelaide to rescue their high quality and valuable collection of historic raw MT time series data, transfer functions, model outputs and survey metadata dating back to 1993. To realise their full potential, significant time was expended linking the associated survey metadata to the rescued raw time series and then made accessible as file downloads in their pre-existing EDI and text formats. However, in order to make these vintage datasets comply with modern data repository needs of Findable, Accessible, Interoperable and Reusable (FAIR), NCI have been investigating the value of converting the data to modern open scientific self-describing formats, with a view of demonstrating better accessibility through data services such as OPeNDAP. The investigation has shown the value of aggregating data from multiple historical surveys and enabling reuse for continental scale analysis and/or use of a much wider range of interoperable scientific software from other domains.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFMIN41C..37D
- Keywords:
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- 1912 Data management;
- preservation;
- rescue;
- INFORMATICSDE: 1946 Metadata;
- INFORMATICSDE: 6699 General or miscellaneous;
- PUBLIC ISSUES