图书馆大数据知识服务生态体系及其动力机制研究*

大数据技术为图书馆知识服务带来了发展机遇。基于信息生态链理论,阐述大数据管理系统和大数据服务体系,提出"服务3.0"的大数据服务理念,并构建图书馆大数据知识服务生态体系模型,在此基础上探讨体系构成要素及其动力机制,以期为图书馆更好地开展知识服务提供理论依据与实践参考。图4。表1。参考文献28。

《科学数据管理办法》落实现状、影响因素及推进策略研究

[目的/意义]对《科学数据管理办法》出台以来的相关研究、落实情况及落实的影响因素等相关问题进行探讨,以期为《科学数据管理办法》的进一步落实提供参考与借鉴。[方法/过程]首先,系统梳理《科学数据管理办法》的相关研究成果;然后,调研科学数据管理利益相关机构对《科学数据管理办法》的落实情况;其次,从积极和消极两个方面分析《科学数据管理办法》落实的影响因素;最后,提出《科学数据管理办法》落实的几点推进策略。[结果/结论]研究发现,目前《科学数据管理办法》的落实推进以各级政府职能部门为主,总体落实情况尚不理想;推动《科学数据管理办法》落实的积极因素包括数据价值凸显、数据设施完善、国家高度重视、政府推进明显、相关研究丰富等,阻碍《科学数据管理办法》落实的消极因素包括利益相关者意识淡薄、相关机构管理失位、落实规划定位模糊、监督激励机制缺失、实践研究数量不足等。可以从提高认识加强实践研究、推动机制体制建设、提高利益相关者合作强度等方面推进《科学数据管理办法》的落实。

Metadata Hierarchy in Integrated Geoscientific Database for Regional Mineral Prospecting

One of the core developments in geomathematics in now days is the use of digital data processing in mineral prospecting and assessment. The information discovery is based on multidisciplinary geoscientific data and an integrated management approach is crucial. The lack of a standard description hinders interoperations in database search and discovery. Metadata hierarchy aims to provide a standard view of the geoscientific data, and facilitate data description and discovery. In the research of integrated geoscientific database, the metadata hierarchy used a standardized description for each collection in the content structure and realized in semantic structure. It recorded both dataset identification and inner structures and relationships of objects, thus differed from many other applications. There were four tiers in the content structure and three levels in the semantic structure. With its help, database users could determine how applicable a dataset is to a project, and improve their queries to the database. Effectiveness of data accessing is significantly enhanced through the rich, consistent metadata.

Fundamental Theories of Spatial Similarity Relations in Multi-scale Map Spaces

Similarity relation is one of the spatial relations in the community of geographic information science and cartography.It is widely used in the retrieval of spatial databases, the recognition of spatial objects from images, and the description of spatial features on maps.However, little achievements have been made for it by far.In this paper, spatial similarity relation was put forward with the introduction of automated map generalization in the construction of multi-scale map databases;then the definition of spatial similarity relations was presented based on set theory, the concept of spatial similarity degree was given, and the characteristics of spatial similarity were discussed in detail, in-cluding reflexivity, symmetry, non-transitivity, self-similarity in multi-scale spaces, and scale-dependence.Finally a classification system for spatial similarity relations in multi-scale map spaces was addressed.This research may be useful to automated map generalization, spatial similarity retrieval and spatial reasoning.

The development history and enlightenment of the scientific data services of American university libraries

In the era of the big data. the national strategies and the rapid development of computers and storage technologies bring opportunities and challenges to the library＇s data services. Based on the investigation literature of the scientific data services in the university libraries in the United States, the development process of the scientific data is analyzed from three aspects of the service types, the service mode and the service contents. The author of this paper also proposes opportunities and challenges from 5 aspects of the policy support. strengthening the publicity, the self learning, the self positioning and relying on the embedded subject librarians, to promote the development of the library scientific data services.

Research on the Model of Linear Data Fitting Method

By using the method of least square linear fitting to analyze data do not exist errors under certain conditions, in order to make the linear data fitting method that can more accurately solve the relationship expression between the volume and quantity in scientific experiments and engineering practice, this article analyzed data error by commonly linear data fitting method, and proposed improved process of the least distance squ^re method based on least squares method. Finally, the paper discussed the advantages and disadvantages through the example analysis of two kinds of linear data fitting method, and given reasonable control conditions for its application.

Neural substrates of data-driven scientific discovery:An fMRI study during performance of number series completion task

Although much has been known about how humans psychologically perform data-driven scientific discovery,less has been known about its brain mechanism.The number series completion is a typical data-driven scientific discovery task,and has been demonstrated to possess the priming effect,which is attributed to the regularity identification and its subsequent extrapolation.In order to reduce the heterogeneities and make the experimental task proper for a brain imaging study,the number magnitude and arithmetic operation involved in number series completion tasks are further restricted.Behavioral performance in Experiment 1 shows the reliable priming effect for targets as expected.Then,a factorial design (the priming effect:prime vs.target;the period length:simple vs.complex) of event-related functional magnetic resonance imaging (fMRI) is used in Experiment 2 to examine the neural basis of data-driven scientific discovery.The fMRI results reveal a double dissociation of the left DLPFC (dorsolateral prefrontal cortex) and the left APFC (anterior prefrontal cortex) between the simple (period length=1) and the complex (period length=2) number series completion task.The priming effect in the left DLPFC is more significant for the simple task than for the complex task,while the priming effect in the left APFC is more significant for the complex task than for the simple task.The reliable double dissociation may suggest the different roles of the left DLPFC and left APFC in data-driven scientific discovery.The left DLPFC (BA 46) may play a crucial role in rule identification,while the left APFC (BA 10) may be related to mental set maintenance needed during rule identification and extrapolation.

Model-free feature screening for high-dimensional survival data

With the rapid-growth-in-size scientific data in various disciplines, feature screening plays an important role to reduce the high-dimensionality to a moderate scale in many scientific fields. In this paper, we introduce a unified and robust model-free feature screening approach for high-dimensional survival data with censoring, which has several advantages: it is a model-free approach under a general model framework, and hence avoids the complication to specify an actual model form with huge number of candidate variables; under mild conditions without requiring the existence of any moment of the response, it enjoys the ranking consistency and sure screening properties in ultra-high dimension. In particular, we impose a conditional independence assumption of the response and the censoring variable given each covariate, instead of assuming the censoring variable is independent of the response and the covariates. Moreover, we also propose a more robust variant to the new procedure, which possesses desirable theoretical properties without any finite moment condition of the predictors and the response. The computation of the newly proposed methods does not require any complicated numerical optimization and it is fast and easy to implement. Extensive numerical studies demonstrate that the proposed methods perform competitively for various configurations. Application is illustrated with an analysis of a genetic data set.