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关键词： 数据管理视角下   人事档案   信息资源   建设研究  

摘要： 随着我国现代信息科技的发展,社会中的很多工作都需要用到数据的搜集和管理,它有利于提高我们的办事效率,增强我们办事的准确性和精准度,同时还可以帮助我们查找到一些以人力资源无法完成的细节,因此,在如今社会上,越来越多的公司和企业开始尝试通过运用大数据的管理来提高自身的综合发展水平,提高自身的市场竞争力和工作完成水平。

标准号: GB/T 40208-2021

摘要： 本文件给出了物流信息资源分类，规定了核心元数据模式、元数据描述方法、核心元数据描述以及核心元数据扩展的一般要求，并给出了核心元数据代码表。本文件适用于物流信息资源进行编目、建库、发布，以及与共享有关的数据交换和网络查询服务等。

关键词： 公共图书馆   科技创新   信息资源   保障体系  

摘要： 科技创新信息资源保障体系建设是公共图书馆面向科研主体用户提供信息服务的前提和基础,具有十分重要的意义。公共图书馆应当以分析科技创新信息用户需求特征为切入点,慎重把握保障体系构架的基本层次,从优化信息资源结构、推动馆际信息资源共享、深度挖掘现有信息资源价值以及加强对信息资源的知识产权保护4个角度,来逐步推动公共图书馆科技创新信息资源保障体系的完善与可持续发展。

关键词： 信息资源管理   文献计量   信息资源  

摘要： 通过采用文献计量的方式,将“信息资源管理”为篇名的文献作为研究对象,检索库为中国知网(CNKI)。分别从文献的研究年份、来源、研究的层次类别、著者和机构研究情况、基金和被引状况以及词频分布等方面进行了分析,将此阶段信息资源管理研究在不同领域的研究热点进行展示,并总结分析了其发展规律及演变。

关键词： Statistical tests  

摘要： With the advancements in biomedical imaging applications, it becomes more important to provide potential results for searching the biomedical imaging data. During the health emergency, tremors require efficient results at rapid speed to provide results to spatial queries using the Web. An efficient biomedical search engine can obtain the significant search intention and return additional important contents in which users have already indicated some interest. The development of biomedical search engines is still an open area of research. Recently, many researchers have utilized various deep-learning models to improve the performance of biomedical search engines. However, the existing deep-learning-based biomedical search engines suffer from the overfitting and hyperparameter tuning problems. Therefore, in this paper, a nondominated-sorting-genetic-algorithm-III- (NSGA-III-) based deep-learning model is proposed for biomedical search engines. Initially, the hyperparameters of the proposed deep-learning model are obtained using the NSGA-III. Thereafter, the proposed deep-learning model is trained by using the tuned parameters. Finally, the proposed model is validated on the testing dataset. Comparative analysis reveals that the proposed model outperforms the competitive biomedical search engine models.

关键词： tandem mass spectrometry   cross-linking   bioinformatics   search engine   MS/MS   XL-MS   protein-protein-interaction   PPI  

摘要： Cross-linking mass spectrometry (XL-MS) has become a powerful technique that enables insights into protein structures and protein interactions. The development of cleavable cross-linkers has further promoted XL-MS through search space reduction, thereby allowing for proteome-wide studies. These new analysis possibilities foster the development of new cross-linkers, which not every search engine can deal with out of the box. In addition, some search engines for XL-MS data also struggle with the validation of identified cross-linked peptides, that is, false discovery rate (FDR) estimation, as FDR calculation is hampered by the fact that not only one but two peptides in a single spectrum have to be correct. We here present our new search engine, MS Annika, which can identify cross-linked peptides in MS2 spectra from a wide variety of cleavable cross-linkers. We show that MS Annika provides realistic estimates of FDRs without the need of arbitrary score cutoffs, being able to provide on average 44% more identifications at a similar or better true FDR than comparable tools. In addition, MS Annika can be used on proteome-wide studies due to fast, parallelized processing and provides a way to visualize the identified cross-links in protein 3D structures.