The Trend and Practice of Data Service in American Research Libraries

  • Zhang Ying
Expand
  • 1. School of information Resource Management, Renmin University of China, Beijing 100872;
    2. Library of Tianjin Foreign Studies University, Tianjin 300204

Received date: 2016-12-01

  Revised date: 2017-02-10

  Online published: 2017-05-05

Abstract

[Purpose/significance]Computers play an irreplaceable role in the ternary world formed by humans, machines and objects. And they are silent forces for social change. With the digital age evolving to the data age, scientific research has entered into the fourth paradigm. Both the scientific community and the individuals have a strong thirst for more in-depth, more convenient knowledge services. Under the urge of academic users, the research libraries are actively promoting the user experience of knowledge service, which will help users to expand knowledge discovery, and reveal the knowledge contained in the literature. Knowledge service can build the link among the knowledge and formulate the knowledge maps. Content-based knowledge service is the inevitable choice to break through the bottleneck of knowledge acquisition in the context of machine learning prevalence.[Method/process]This paper systematically reviewed the practice of knowledge services in American research libraries, and further analyzed the core content of the services. New contents of knowledge services given by the rise of research data were also clarified by this research. At the end of this paper, the author demonstrated the development trends of the knowledge services.[Result/conclusion]The content-based knowledge service is the inevitable choice of the library for the data age. Data turnover efficiency is the future determinant of the amount of scientific research output. Knowledge services will become increasingly fragmented, associated and mobile.

Cite this article

Zhang Ying . The Trend and Practice of Data Service in American Research Libraries[J]. Library and Information Service, 2017 , 61(9) : 33 -41 . DOI: 10.13266/j.issn.0252-3116.2017.09.004

References

[1] 齐曼. 真科学:它是什么,它指什么[M]. 曾国屏,匡辉,张成岗,译. 上海:上海科技教育出版社, 2008.
[2] TENOPIR C, BORCH B, ALLARD S. Academic libraries and research data services[EB/OL].[2017-01-05].http://www.ala.org/acrl/sites/ala.org.acrl/files/content/publications/whitepapers/Tenopir_Birch_Allard.pdf
[3] CURDT C, HOFFMEISTER D. Research data management services for a multidisciplinary, collaborative research project[J]. Program, 2015, 49(4):494-512.
[4] TAM W, FRY J, PROBETS S. The disciplinary shaping of research data management practices[EB/OL].[2017-01-05]. https://www.ideals.illinois.edu/bitstream/handle/2142/47276/338_ready.pdf?sequence=2&isAllowed=y.
[5] DREYER M, VOLLMER A. An integral approach to support research data management at the Humboldt-Universität zu Berlin[EB/OL].[2017-01-05]. http://www.eunis.org/eunis2016/wp-content/uploads/sites/8/2016/02/EUNIS2016_paper_66.pdf.
[6] MATTERN E, WEI J, HE D, et al. Using participatory design and visual narrative inquiry to investigate researchers' data challenges and recommendations for library research data services[J]. Program, 2015,49(4):408-423.
[7] WHITMIRE A, BOOCK M, SUTTON S. Variability in academic research data management practices[J]. Program, 2015, 49(4):382-407.
[8] KRAFT A, RAZUM M, POTTHOFF J, et al. The RADAR Project-a service for research data archival and publication[EB/OL].[2017-01-05]. http://www.mdpi.com/2220-9964/5/3/28/htm.
[9] MAKANI J. Knowledge management, research data management, and university scholarship[J]. Vine, 2015, 45(3):344-359.
[10] CANDELA L, ASSANTE M, TANI A, et al. Are scientific data repositories coping with research data publishing[J]. Data science journal, 2016, 15(6):1-24.
[11] 邓仲华, 李立睿. 面向科研的嵌入式知识推送服务研究(上)[J]. 图书馆杂志, 2015(7):19-23.
[12] 邓仲华, 李立睿. 面向科研的嵌入式知识推送服务研究(下)[J]. 图书馆杂志, 2015(8):15-20.
[13] 周晓燕, 尹亚丽. 国外高校图书馆科研数据服务人员知识结构分析——以IASSIST网站中2015年的招聘信息为例[J]. 图书情报工作, 2016,60(2):76-82.
[14] 王静,都平平,郭太敏,等. 基于四螺旋模型的高校图书馆科研数据知识服务风险管理研究[J]. 图书馆, 2015(3):79-85.
[15] 邵锐, 付先华. 面向科研的学科知识服务探索与实践——基于武汉理工大学图书馆学科知识平台实证研究[J]. 情报理论与实践, 2016(2):89-92.
[16] International Federation of Library Associations and Institutions. Big Data Special Interest Group[EB/OL].[2017-02-01]. http://www.ifla.org/about-big-data.
[17] ALA. Librarian and researcher knowledge space[EB/OL].[2017-02-01]. http://www.ala.org/research/larks.
[18] Association of Research Libraries. About SHARE[EB/OL].[2017-04-25]. http://www.share-research.org/about/about-share/.
[19] ACRL. 2016 top trends in academic libraries[EB/OL].[2016-10-18]. http://crln.acrl.org/content/77/6/274.full.
[20] Office of Science and Technology Policy. Promoting open data, open government, and open science[EB/OL].[2016-10-20].https://www.whitehouse.gov/administration/eop/ostp/initiatives#Openness.
[21] American Chemical Society. SciFinder Scholar[EB/OL].[2017-04-10]. http://www.cas.org/products/scifinder/content.
[22] University of Minnesota. AgEcon Search[EB/OL].[2017-04-10]. http://ageconsearch.umn.edu/.
[23] National Science Foundation. Dissemination and sharing of research results[EB/OL].[2016-10-22]. http://www.nsf.gov/bfa/dias/policy/dmp.jsp.
[24] National Institutes of Health. Policy for sharing of data obtained in NIH supported or conducted genome-wide association studies[EB/OL].[2016-10-22] http://grants.nih.gov/grants/guide/notice-files/NOT-OD-07-088.html.
[25] Princeton University Library. Data and statistical service[EB/OL].[2016-10-28].http://dss.princeton.edu/cgi-bin/dataresources/guides.cgi.
[26] OCLC. ArchiveGrid[EB/OL] [2016-10-28].http://www.oclc.org/research/activities/archivegrid.html.
[27] Cornell University Library. VIVO[EB/OL].[2016-10-29].http://vivo.cornell.edu/.
[28] National Agricultural Library. i5k Workspace[EB/OL].[2016-10-30]. https://i5k.nal.usda.gov/.
Options
Outlines

/

〈 〉