地球系统科学数据共享国家平台构建、关键技术与应用服务

地球系统科学数据是支撑地球系统科学和全球变化创新研究的重要基础,也是社会经济发展决策的科学依据。长期以来,我国缺乏可真正运行的地球系统科学数据共享平台,造成了数据资源的重复投资和浪费,严重影响了我国地学创新研究水平,减缓了地学大国向地学强国迈进的步伐。由于地球系统科学数据具有分散、海量、多源、异构和时空特征明显等特点,其共享尤其复杂和困难,已经成为地学研究领域重要的国际前沿。项目通过地球科学、计算机科学技术、天文学等领域的40多个单位400多名学者10多年的协同研究,突破了分散科学数据持续共享的机制,攻克了分散、多源、异构地球系统科学数据的集成方法、标准规范和关键技术,建成并运行我国地球系统科学数据共享国家平台,提供了持续的数据共享服务。     

中国基础科学·2003·总目录·索引

中国科学数据共享学术讨论会专辑编者按 4(1 )………………………………………………………………实施科学数据共享　增强国家科技竞争力徐冠华　 5(1 )……………科学数据的共享管理———创建共享新秩序程津培 1 0 (1 )……………国际科学技术数据前沿领域发展研究孙鸿烈 ,刘　闯 1 3(1 )…………地球数据是地球科学创新的重要源泉　　———从地球科学谈科学数据共享孙　枢 1 9(1 )…………………科学数据共享 :国家资源有效利用的必然选择　　———以中国气象局资料共享工作为例秦大河 2 4 (1 )……………科学数据资源与共享孙九林 30 …     

科技基础性工作

科学数据共享工程第二届专家委员会成立科学数据共享工程第二届专家委员会于2006年10月23日成立。新一届的专家委员会由地球物理等众多领域的国内资深专家组成,中国科学院院士秦大河担任主任。该专家委员会主要负责我国科学数据共享工程的规划、实施和发展中的咨询、评议等工作,在我国科学数据共享工作的深化和发展中肩负着重大职责。中国地震数据共享网站开通中国地震数据共享网站于2006年9月28日正式开通。中国地震数据共享网站建设是国家科学数据共享工程的重要组成部分,2002年纳入科技部科学数据共享工程的首批试点项目,历时3年多建设完…     

地球系统科学数据共享网

地球系统科学数据共享网是国家科技基础条件平台中以整合共享分散科学数据为重点的项目。作为国家“科学数据共享工程”前批9个试点之一,该项目于2003年启动,2005年纳入国家科技基础条件平台进入建设阶段,2009年通过国家科技单位评估和验收,开始进入运行服务阶段。通过6年多的研究与实践,地球系统科学数据共享网（以下简称“共享网”）为我国科技创新、国家宏观战略决策和社会经济发展提供了大量的数据服务,引领和推动了我国的科学数据共享,提升了我国科学数据共享在国际上的地位。     

科学数据共享法规体系建设的若干思考

本文试图通过法理分析 ,探讨科学数据共享法规体系建设的指导思想、原则、工作思路 ,提出了科学数据共享过程中需要法律调整的 4个关键问题 ,即 :从法律上协调科学数据同时作为社会公有物品和市场商品两者之间的关系 ;协调和界定知识产权保护范围和社会公共功能范围 ;确定和调整共享过程中当事各方之间、以权利和义务为内容的各种社会关系 ;从法律上确立科学数据共享技术的地位     

科学数据共享平台之数据联盟模式初探

数据资源建设是科学数据共享平台建设的重要内容之一,也是其难点之一。本文比较了我国科学数据共享平台资源建设的三种主要模式,指出数据交换模式更适合于当前目标定位,即建设开放共享的科学数据共享平台;进一步提出了科学数据联盟模式,由此构建的科学数据共享平台可灵活地解决数据资源建设和整合问题,从而提高数据的利用率,提升服务质量;并提出了当前构建科学数据联盟模式的科学数据共享平台的两点建议;最后以地球系统科学数据共享网的建设为例进行了实例说明。本研究将为我国科学数据共享平台建设提供参考。     

科学数据的共享管理—创建共享新秩序

鉴于科学数据共享已经成为我们的共识,那么如何顺利实现科学数据的共享管理,如何创建共享新秩序,实现科学数据的最大价值,就需要我们认真思考,并提出解决办法。 综观科学数据管理的历史,经历了漫长的手抄和印刷时代,在此期间,所产生科学数据的量相对不大,人们对科学数据的管理主要是进行立卷和归档     

实现科学数据共享的基石语言——XML的理论与应用

本文介绍了逐渐成为新世纪网络“国际语言”的XML;阐述了其在标记语言设计、数据交换与整合、内容管理、Web服务、语义网等应用领域中的核心地位;分析了其在学科标记语言设计、元数据技术系统、通用科学数据描述与发布、互操作能力、元数据目录服务、精确数据服务与个性化功能服务、数据网格等科学数据共享管理诸方面的基石作用,指出了XML对于科学数据共享技术平台构架建设的重要意义;最后展望了基于XML的科学数据共享实施策略。     

科学数据共享的价值及其表征——基于主体性的分析视角

从科研主体间际关系的视角阐释科学数据共享价值的内涵，依据主体尺度和主体关系质态论述科学数据共享价值的属性，剖析科学数据共享价值表征。由于科学数据共享本身无法直接满足主体目的，需要主体通过数据共享关系建构和挖掘数据资源才能实现相应的目的，主要体现的是手段性价值，本文从主体身份认同寻求、信任关系建立、共享管理自为秩序生成、学术资本积累、嵌入行为建构等方面来阐释科学数据共享价值的多维表征。     

科学数据共享标准体系框架

一、科学数据共享的内涵 科学数据共享,旨在通过应用现代信息技术手段,从国家宏观层次上营造一种科学数据共享氛围,使政府部门、科技教育工作者和社会公众可以广泛、有效、便捷地获取所需要的科学数据,对科学数据进行有序管理,实现科学数据拥有者为政府部门和广     

Ordination in the presence of group structure,for general multivariate data

A low-dimensional representation of multivariate data is often sought when the individuals belong to a set ofa-priori groups and the objective is to highlight between-group variation relative to that within groups. If all the data are continuous then this objective can be achieved by means of canonical variate analysis, but no corresponding technique exists when the data are categorical or mixed continuous and categorical. On the other hand, if there is noa-priori grouping of the individuals, then ordination of any form of data can be achieved by use of metric scaling (principal coordinate analysis). In this paper we consider a simple extension of the latter approach to incorporate grouped data, and discuss to what extent this method can be viewed as a generalization of canonical variate analysis. Some illustrative examples are also provided.     

我国医药卫生领域科学数据资源共享现状分析

科学数据的作用日益得到学术界的重视,其开放与共享是推动科技创新的重要举措,我国在科学数据共享领域已经有了近10年的实践经验,构建了一批领域科学数据共享平台。然而,对这些平台数据资源的组织、规模、类型、开放程度等客观情况的分析比较少。鉴于掌握数据资源的现状有着十分重要的意义,本文以国家人口与健康科学数据共享平台这一医药卫生领域的国家级平台为研究对象,从其网站上获取公开的已共享数据资源信息,以第三方的视角对平台数据资源现状进行深度挖掘和分析研究。对整体把握我国特定领域的科学数据资源情况提供思路,指导平台的进一步建设和发展。     

公益性科技名词数据资源共享的原则和方法

公益性科技名词数据是一种重要的科技数据资源,也是一种重要的公共资源,应当被全社会平等共享。通过分析公益性科技名词数据的性质和特点,探讨公益性科技名词数据共享的原则和方法,希望以此建立科技名词数据共享的体制机制,促进科技创新发展。     

论技术创新与文化产业制度创新悖论

从理论视域来看,技术创新与文化产业制度创新具有契合点,两者是对立统一的矛盾体。从产业发展的实际来看,一方面,技术创新推动了文化产业制度的创新,但技术创新宽泛性为制度创新设置更高壁垒;另一方面,制度创新也推动了技术创新的进程,但文化产业制度创新滞后性又成为技术创新的内在障碍。于是,形成了技术创新与文化产业制度创新的二元悖论。而要走出两者关系的悖论,需基于技术创新对文化产业制度进行重构。     

Spectral analysis of phylogenetic data

The spectral analysis of sequence and distance data is a new approach to phylogenetic analysis. For two-state character sequences, the character values at a given site split the set of taxa into two subsets, a bipartition of the taxa set. The vector which counts the relative numbers of each of these bipartitions over all sites is called a sequence spectrum. Applying a transformation called a Hadamard conjugation, the sequence spectrum is transformed to the conjugate spectrum. This conjugation corrects for unobserved changes in the data, independently from the choice of phylogenetic tree. For any given phylogenetic tree with edge weights (probabilities of state change), we define a corresponding tree spectrum. The selection of a weighted phylogenetic tree from the given sequence data is made by matching the conjugate spectrum with a tree spectrum. We develop an optimality selection procedure using a least squares best fit, to find the phylogenetic tree whose tree spectrum most closely matches the conjugate spectrum. An inferred sequence spectrum can be derived from the selected tree spectrum using the inverse Hadamard conjugation to allow a comparison with the original sequence spectrum. A possible adaptation for the analysis of four-state character sequences with unequal frequencies is considered. A corresponding spectral analysis for distance data is also introduced. These analyses are illustrated with biological examples for both distance and sequence data. Spectral analysis using the Fast Hadamard transform allows optimal trees to be found for at least 20 taxa and perhaps for up to 30 taxa. The development presented here is self contained, although some mathematical proofs available elsewhere have been omitted. The analysis of sequence data is based on methods reported earlier, but the terminology and the application to distance data are new.     

The mixture method of clustering applied to three-way data

Clustering or classifying individuals into groups such that there is relative homogeneity within the groups and heterogeneity between the groups is a problem which has been considered for many years. Most available clustering techniques are applicable only to a two-way data set, where one of the modes is to be partitioned into groups on the basis of the other mode. Suppose, however, that the data set is three-way. Then what is needed is a multivariate technique which will cluster one of the modes on the basis of both of the other modes simultaneously. It is shown that by appropriate specification of the underlying model, the mixture maximum likelihood approach to clustering can be applied in the context of a three-way table. It is illustrated using a soybean data set which consists of multiattribute measurements on a number of genotypes each grown in several environments. Although the problem is set in the framework of clustering genotypes, the technique is applicable to other types of three-way data sets.     

Subspace Projection for Multivariate Selection Problems

A new set of derived variables is proposed for exhibiting grouped multivariate data in a small number of dimensions, in such a way as to highlight `extremeness' of one or more groups relative to the rest of the data. Such display can provide a useful exploratory tool in multivariate ranking and selection problems. We explore four possible measures of `extremeness', and suggest which one is best for practical application. We show that the technique can be used to derive either orthogonal or uncorrelated dimensions for any type of input data, and we give an illustrative example of its use.     

A mathematical programming approach to fitting general graphs

We present an algorithm for fitting general graphs to proximity data. The algorithm utilizes a mathematical programming procedure based on a penalty function approach to impose additivity constraints upon parameters. For a user-specified number of links, the algorithm seeks to provide the connected network that gives the least-squares approximation to the proximity data with the specified number of links, allowing for linear transformations of the data. The network distance is the minimum-path-length metric for connected graphs. As a limiting case, the algorithm provides a tree where each node corresponds to an object, if the number of links is set equal to the number of objects minus one. A Monte Carlo investigation indicates that the resulting networks tend to fall within one percentage point of the least-squares solution in terms of the variance accounted for, but do not always attain this global optimum. The network model is discussed in relation to ordinal network representations (Klauer 1989) and NETSCAL (Hutchinson 1989), and applied to several well-known data sets.     

The Kohonen self-organizing map method: An assessment

The “self-organizing map” method, due to Kohonen, is a well-known neural network method. It is closely related to cluster analysis (partitioning) and other methods of data analysis. In this article, we explore some of these close relationships. A number of properties of the technique are discussed. Comparisons with various methods of data analysis (principal components analysis, k-means clustering, and others) are presented. This work has been partially supported for M. Hernández-Pajares by the DGCICIT of Spain under grant No. PB90-0478 and by a CESCA-1993 computer-time grant. Fionn Murtagh is affiliated to the Astrophysics Division, Space Science Department, European Space Agency.