地球科学进展 ›› 2005, Vol. 20 ›› Issue (2): 166 -172. doi: 10.11867/j.issn.1001-8166.2005.02.0166

研究论文 上一篇    下一篇

地学数据产品的开发、发布与共享
廖顺宝,孙九林,李泽辉,马 琳,彭 梅   
  1. 中国科学院地理科学与资源研究所,北京 100101
  • 收稿日期:2003-11-10 修回日期:2004-06-03 出版日期:2005-02-25
  • 通讯作者: 廖顺宝 E-mail:liaosb@igsnrr.ac.cn
  • 基金资助:

    国家科学数据共享工程项目“中国地球系统科学数据共享服务网建设”(编号:2003DEA2C010);中国科学院知识创新工程项目“中国自然资源数据库”(编号:INF105-SDB-1-18)资助.

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

LIAO Shunbao; SUN Jiulin; LI Ze-hui; MA Lin; PENG Mei   

  1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2003-11-10 Revised:2004-06-03 Online:2005-02-25 Published:2005-02-25

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。

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. 

中图分类号: 

[1]Liao Shunbao.Scientific data sharing and its solutions based on internet[J].Science & Technology Review,2002:(1):25-27.[廖顺宝.科学数据共享及其在互联网上实现的技术途径[J].科技导报,2002,(1):25-27.]
[2]Sun Shu.Learn new things by reviewing the past, carry on the past and open a way for future-A special issue of review and forecast on subjects related to geoscience(preface)[J] .Advances in Earth Science,2001,16(5):597-598.[孙枢.温故知新继往开来——地球科学有关学科回顾与展望专辑(序)[J].地球科学进展,2001,16(5):597-598.]
[3]Liao Shunbao, Sun Jiulin.GIS based spatialisation of population census data in Qinghai-Tibet Plateau[J].Acta Geographica Sinica,2003,58(1):25-33.[廖顺宝,孙九林.基于GIS的青藏高原人口统计数据空间化[J].地理学报,2003,58(1):25-33.]
[4]Liao Shunbao, Li Zehui.Study on spatialisation of population census data based on relationship between population distribution and land use-Taking Tibet as an example[J].Journal of Natural Resources,2003,(6):659-665.[廖顺宝,李泽辉.基于人口分布与土地利用关系的人口数据空间化研究——以西藏自治区为例[J].自然资源学报,2003,(6):659-665.]
[5]Liao Shunbao, Li Zehui .A methodology of spatialisation of observed data based on GIS[J].Progress in Geography,2003,22(1):87-93.[廖顺宝,李泽辉.基于GIS的定位观测数据空间化[J].地理科学进展,2003,22(1):87-93.]
[6]Sun Jiabing, Shu Ning, Guan Zequn.Fundamentals, Methods and Application of Remote Sensing [M].Beijing: Publishing House of Survey and Mapping,1997.[孙家柄,舒宁,关泽群.遥感原理、方法和应用[M].北京:测绘出版社,1997.]
[7]Zheng Du.Forming Environment and Development of Qinghai-Tibet Plateau[M] .Shijiazhuang: Hebei Publishing House of Science and Technology,2003.[郑度.青藏高原形成环境与发展[M].石家庄:河北科学技术出版社,2003.]
[8]Liu Chuang, Ge Chenghui.Characteristics and application of remote sensed data from U.S. EOS-MODIS[J].Remete Sensing Information,2000,(3):45-48.[刘闯,葛成辉.美国对地观测系统(EOS)中分辨率成像光谱仪(MODIS)遥感数据的特点与应用[J].遥感信息,2000,(3):45-48.]
[9]Yin Gongbai, Wang Jiayao,Tian Desen, et al.Tian Desen,An Introduction to Map[M].Beijing: Publishing House of Survey and Mapping,1991.[尹贡白,王家耀,田德森,等.地图概论[M].北京:测绘出版社,1991.]
[10]Zhao Junxi, Liu Honglin.Usually used theories and methods for study on spatial data quality[J].Map,2001,(4):16-18 .[赵军喜,刘宏林.研究空间数据质量的常用理论和方法[J].地图,2001,(4):16-18 .]

[1] 马鹏飞,刘志飞,拓守廷,蒋璟鑫,许艺炜,胡修棉. 国际大洋钻探科学数据的现状、特征及其汇编的科学意义[J]. 地球科学进展, 2021, 36(6): 643-662.
[2] 储著银, 许继峰. 铼—锇同位素和铂族元素分析方法及地学应用进展[J]. 地球科学进展, 2021, 36(3): 245-264.
[3] 常明恒, 左洪超, 摆玉龙, 段济开. 两种耦合模糊控制的局地化方法研究[J]. 地球科学进展, 2021, 36(2): 185-197.
[4] 吴佳梅,彭秋志,黄义忠,黄亮. 中国植被覆盖变化研究遥感数据源及研究区域时空热度分析[J]. 地球科学进展, 2020, 35(9): 978-989.
[5] 王卷乐,石蕾,王玉洁,高孟绪,徐波,王超,王明明,王艳杰,周业智. 科学数据汇聚的模式分析及对我国的发展建议[J]. 地球科学进展, 2020, 35(8): 839-847.
[6] 刘元波, 吴桂平, 赵晓松, 范兴旺, 潘鑫, 甘国靖, 刘永伟, 郭瑞芳, 周晗, 王颖, 王若男, 崔逸凡. 流域水文遥感的科学问题与挑战[J]. 地球科学进展, 2020, 35(5): 488-496.
[7] 张凌, 王平, 陈玺赟, 殷勇. 碎屑锆石 U-Pb年代学数据获取、分析与比较[J]. 地球科学进展, 2020, 35(4): 414-430.
[8] 陆大道. 地理国情与国家战略[J]. 地球科学进展, 2020, 35(3): 221-230.
[9] 张菁,路紫,杜欣儒,杜晓辉,高玉健. 京津石多机场系统航空流运行结构及其对比研究[J]. 地球科学进展, 2020, 35(12): 1281-1291.
[10] 魏勇,许强,王卓,李骅锦,李松林. 动态摄影测量在物理模型实验全过程地形数据获取中的应用[J]. 地球科学进展, 2020, 35(10): 1087-1098.
[11] 张一诺,路紫,杜欣儒,董雅晴,张菁. 时空连续数据支持下的空域资源配置研究:评述与展望[J]. 地球科学进展, 2019, 34(9): 912-921.
[12] 杨福强,陈科贵,黄长兵,陈愿愿,李进,马小林. PSO-LIBSVM在钾盐矿层识别中的应用研究[J]. 地球科学进展, 2019, 34(7): 757-764.
[13] 时连强,郭俊丽,刘海江,叶清华. Argus系统在我国海滩研究中的应用进展与展望[J]. 地球科学进展, 2019, 34(5): 552-560.
[14] 张春灌,李想,袁炳强,宋立军. 地球磁异常( EMAG2)数据中海域资料质量评估[J]. 地球科学进展, 2019, 34(3): 288-294.
[15] 高峰,赵雪雁,宋晓谕,王宝,王鹏龙,牛艺博,王伟军,黄春林. 面向 SDGs的美丽中国内涵与评价指标体系[J]. 地球科学进展, 2019, 34(3): 295-305.
阅读次数
全文


摘要