地学数据产品的开发、发布与共享

DEVELOPMENT, PUBLISHING AND SHARING OF DATA PRODUCTS FOR GEO-SCIENCES

数据是科学研究的基础,数据共享可以最大程度地发挥数据的使用价值,数据是实现数据共享最基本的要素。地学数据具有空间性、综合性、时间性、海量性、多源性等特点。属性数据、遥感数据、矢量数据是加工生产地学数据产品的重要数据源,属性数据空间化是加工、生产地学数据产品的重要技术手段。地学数据共享发布平台应当具备用户管理、数据目录查询、元数据管理、数据查询与浏览、数据下载等基本功能。推进科学数据共享,必须要有相应的政策措施保证,必须建立公正、合理的数据工作评价体系。

Abstract: Data is the foundation of scientific research, and in the mean time, it is the result of scientific research. Data sharing can make data more usefully valuable to a great extent. Data sharing is a systematic engineering which includes not only data itself but also software platform for data sharing, technical standards, sharing policy, administrative mechanism and data service. Data is the most basic and important element in data sharing engineering. Geo-science data have attributes of space, time, integration, great capacity and multi-sources. It is costly work to capture geo-science data. Therefore, it is more necessary to share them among different organizations or scientists. Tabular data, remotely sensed data and vector data are most important and often used types of geo-science data. Because of limitations of various kinds of original data, such as statistical data, remotely sensed spectral data(e.g. TM, NOAA-AVHRR,MODIS), map data and so on, besides them, series of data sets should be developed to meet the need of basic and forward research for geo-science. By means of spatialisation technique, observed data from observation stations can be used to calculate data in areas without observation stations and statistical data from administrative divisions can be re-assigned to regular grid, for example, one kilometer by one kilometer. Through various algorithms, remotely sensed spectral data can be used to produce data sets with obvious geo-science sense, for example, Vegetation Index (VI) and Net Primary Production(NPP). The software platform is important technological support for geo-science data sharing. It should be easy to use and include the following fundamental functions: (a) users management; (b) data catalogue query; (c) metadata management; (d) data query and view; and (e) data download. In order to promote scientific data sharing, relevant policies, measures and standards should be drawn up. Reasonable appraising system for people who are engaged in work of data should be set up to encourage they devote themselves to data sharing.