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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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Privacy and anonymity for multilayer networks: A reflection
AbstractÐPrivacy of data as well as providing anonymization of data for various kinds of analysis have been addressed in the context of tabular transactional data which was mainstream. With the advent of the Internet and social networks, there is an emphasis on using different kinds of graphs for modeling and analysis. In addition to single graphs, the use of MultiLayer Networks (or MLNs) for modeling and analysis is becoming popular for complex data having multiple types of entities and relationships. They provide a better understanding of data as well as flexibility and efficiency of analysis. In this article, we understand the provenance of data privacy and some of the thinking on extending it to graph data models. We will focus on the issues of data privacy for models that are different from traditional data models and discuss alternatives. We will also consider privacy from a visualization perspective as we have developed a community Dashboard for MLN generation, analysis, and visualization based on our research.
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- Award ID(s):
- 2120393
- PAR ID:
- 10447239
- Date Published:
- Journal Name:
- IEEEBigDataService
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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