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The National Science Foundation Ice Core Facility (NSF-ICF, fka NICL) is in the process of building a new facility including freezer and scientist support space. The facility is being designed to minimize environmental impacts while maximizing ice core curation and science support. In preparation for the new facility, we are updating research equipment and integrating ice core data collection and processing by assigning International Generic Sample Numbers (IGSN) to advance the “FAIR”ness and establish clear provenance of samples, fostering the next generation of linked research data products. The NSF-ICF team, in collaboration with the US ice core science community, has established a metadata schema for the assignment of IGSNs to ice cores and samples. In addition, in close coordination with the US ice core community, we are adding equipment modules that expand traditional sets of physical property, visual stratigraphy, and electrical conductance ice core measurements. One such module is an ice core hyperspectral imaging (HSI) system. Adapted for the cold laboratory settings, the SPECIM SisuSCS HSI system can collect up to 224 bands using a continuous line-scanning mode in the visible and near-infrared (VNIR) 400-1000 nm spectral region. A modular system design allows expansion of spectral properties in the future. The second module is an updated multitrack electrical conductance system. These new data will guide real time optimization of sampling for planned analyses during ice core processing, especially for ice with deformed or highly compressed layering. The aim is to facilitate the collection of robust, long-term, and FAIR data archives for every future ice core section processed at NSF-ICF. The NSF-ICF is fully funded by the National Science Foundation and operated by the U.S. Geological Survey.
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GeospaceLAB: Python package for managing and visualizing data in space physics
In the space physics community, processing and combining observational and modeling data from various sources is a demanding task because they often have different formats and use different coordinate systems. The Python package GeospaceLAB has been developed to provide a unified, standardized framework to process data. The package is composed of six core modules, including DataHub as the data manager, Visualization for generating publication quality figures, Express for higher-level interfaces of DataHub and Visualization , SpaceCoordinateSystem for coordinate system transformations, Toolbox for various utilities, and Configuration for preferences. The core modules form a standardized framework for downloading, storing, post-processing and visualizing data in space physics. The object-oriented design makes the core modules of GeospaceLAB easy to modify and extend. So far, GeospaceLAB can process more than twenty kinds of data products from nine databases, and the number will increase in the future. The data sources include, e.g., measurements by EISCAT incoherent scatter radars, DMSP, SWARM, and Grace satellites, OMNI solar wind data, and GITM simulations. In addition, the package provides an interface for the users to add their own data products. Hence, researchers can easily collect, combine, and view multiple kinds of data for their work using GeospaceLAB. Combining data from different sources will lead to a better understanding of the physics of the studied phenomena and may lead to new discoveries. GeospaceLAB is an open source software, which is hosted on GitHub. We welcome everyone in the community to contribute to its future development.
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- PAR ID:
- 10386431
- Date Published:
- Journal Name:
- Frontiers in Astronomy and Space Sciences
- Volume:
- 9
- ISSN:
- 2296-987X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fspas.2022.1023163
