Efficient Large Instrument and Scientific Data Sharing Platform Based on the Mobile Internet

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  • College of Animal Science & Technology, Nanjing Agricultural University, Nanjing 210095

Received date: 2019-08-10

  Online published: 2019-11-28

Abstract

The current efficiency of scientific data acquisition is low. Management and projects in Chinese universities lack an overall standard for management and coordination, and large amounts of scientific data are at risk of damage and permanent loss. Hence, measures to improve research data acquisition and platform management efficiency are urgently needed to ensure the value of scientific data is fully exploited. Instruments and equipment are the material basis for obtaining scientific data. To improve the utilization of large instruments and equipment, most universities in China have created sharing management systems for them. As the world enters the era of the mobile Internet, the daily life and work activities of humans are undergoing profound changes. In particular, mobile communication is now widely used on university campuses. It is hence imperative to use mobile Internet technology to improve the level of open sharing of large instruments and equipment. The National Experimental Teaching Demonstration Center for Animal Science at Nanjing Agricultural University has actively investigated and implemented the extension of its existing large instrument reservation management system to personal mobile phone clients. In this system, new modes of micro-reservation, micro-use, and micro-management have been integrated. Instruments reservations may now be made and scientific data may be accessed anytime and anywhere. This system has improved the efficiency of the large-scale instrument sharing platform, its management, and the ability of scientific data to be acquired and stored safely.

Cite this article

Yixuan Fan,Honglin Liu,Yanli Zhang,Fanxing Meng . Efficient Large Instrument and Scientific Data Sharing Platform Based on the Mobile Internet[J]. Journal of Agricultural Big Data, 2019 , 1(3) : 82 -90 . DOI: 10.19788/j.issn.2096-6369.190308

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