Subjects: Other Disciplines >> Synthetic discipline submitted time 2023-03-19 Cooperative journals: 《中国科学院院刊》
Abstract： Space science is a discipline with high innovation orientation and frontier intersection. Countries all over the world attach great importance to it and have promoted a series of strategic planning and major programs. The age of big data in space science has arrived. In this study, the main trends of big data development in space science are expounded. Specifically, space scientific data volumes are exploding, data storage and management are valued, the scientific research paradigm is shifting, big data technology and tools are booming, the intelligent application is budding and a benign research ecosystem of big data has been formed. Based on the development requirements and national strategic planning, this study analyzes the specific challenges and development opportunities of big data in space science. An all-out efforts should be made, from the perspectives of data sharing, data long-term storage, big data infrastructure construction, disruptive technologies breakthrough and research ecosystem construction, to promote the open and sharing of scientific data, to expand intellectual innovation and scientific and technological output, and to create a new era for the development of space science.
Subjects: Geosciences >> Space Physics submitted time 2016-04-22
Abstract： Drawing the magnetic lines as streamlines is a general method of visualizing geomagnetic field. A key factor to evaluate the effect of the geomagnetic field visualization is whether the space distribution of geomagnetic field lines is consistent with that of magnetic field intensity, while the distribution of geomagnetic field lines is determined by seed point selection. The traditional algorithms that select seed points with uniform angles on magnetic meridian circles cannot objectively reflect the space distributions of magnetic field intensity. This paper proposes an algorithm of selecting seed points with equal line integral of magnetic field intensity. The algorithm is applied to draw the geomagnetic field lines with the data from T96 model and IGRF model. The redundant magnetic field lines existing in the result are removed. Statistical analysis and comparison between the space distribution of magnetic field lines and the geomagnetic field intensity reveals that this algorithm can effectively visualize the geomagnetic field.
Peer Review Status:
Awaiting Review
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