Libraries at University of Nebraska-Lincoln

 

Date of this Version

2020

Document Type

Article

Citation

Antell, K., Foote, J. B., Turner, J., & Shults, B. (2014). Dealing with data: Science librarians' participation in data management at the Association of Research Libraries institutions. College & Research Libraries, 75(4), 557-574.

Balachandran, K., & Kamalanathan, S. (2018). Data Management in Clinical Research. Thesis Writing for Master's and Ph. D. Program, 83-92.

Belzer, J. (1976). A concise survey of computer methods. Peter Naur. New York: Petrocelli Books, 397 p.(1975). Journal of the American Society for Information Science, 27(2), 125-126.

Burton, M., Lyon, L., Erdmann, C., & Tijerina, B. (2018). Shifting to Data Savvy: The Future of Data Science In Libraries.

Cai, L., & Zhu, Y. (2015). The challenges of data quality and data quality assessment in the big data era. Data Science Journal, 14.

Cao, L. (2018). Data Science Thinking: The Next Scientific, Technological, and Economic Revolution: Springer.

Carillo, K. D. A. (2017). Let’s stop trying to be “sexy”–preparing managers for the (big) data-driven business era. Business Process Management Journal, 23(3), 598-622.

Carlson, J., & Johnston, L. R. (2015). Data information literacy: Librarians, data, and the education of a new generation of researchers (Vol. 2): Purdue University Press.

Clement, R., Blau, A., Abbaspour, P., & Gandour-Rood, E. (2017). Team-based data management instruction at small liberal arts colleges. IFLA Journal, 43(1), 105-118.

Davenport, T. H., & Patil, D. (2012). Data scientist. Harvard business review, 90(5), 70-76.

Dzemyda, G. (2018). Data Science and Advanced Digital Technologies. Paper presented at the International Baltic Conference on Databases and Information Systems.

Federer, L. (2016). Research data management in the age of big data: Roles and opportunities for librarians. Information Services & Use, 36(1-2), 35-43.

Federer, L. (2018). Defining data librarianship: a survey of competencies, skills, and training. Journal of the Medical Library Association: JMLA, 106(3), 294.

Frederick, D. E. (2016). Data, Open Science and libraries–The Data Deluge Column. Library Hi Tech News, 33(8), 11-16.

Ge, M., Bangui, H., & Buhnova, B. (2018). Big data for internet of things: A survey. Future Generation Computer Systems, 87, 601-614.

Gilmore, R. O. (2016). From big data to deep insight in developmental science. Wiley Interdisciplinary Reviews: Cognitive Science, 7(2), 112-126.

Heidorn, P. B. (2011). The emerging role of libraries in data curation and e-science. Journal of Library Administration, 51(7-8), 662-672.

Henty, M. (2008). Dreaming of data: the library's role in supporting e-research and data management.

Koltay, T. (2017). Data literacy for researchers and data librarians. Journal of Librarianship and Information Science, 49(1), 3-14.

Lyon, L. (2012). The informatics transform: Re-engineering libraries for the data decade. International Journal of Digital Curation, 7(1), 126-138.

Lyon, L., & Mattern, E. (2017). Education for Real-World Data Science Roles (Part 2): A Translational Approach to Curriculum Development. International Journal of Digital Curation, 11(2), 13-26.

Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547-578.

Oyelude, A. A. (2017). Data, data everywhere, but little to use! Library Hi Tech News, 34(8), 24-25.

Peyne, B., & Chan, A. (2017). Data-driven decision making in Marketing: A theoretical approach.

Pinfield, S., Cox, A. M., & Smith, J. (2014). Research data management and libraries: relationships, activities, drivers and influences. PLoS One, 9(12), e114734.

Ramkumar, P. (2018). Initiatives of Research Data Literacy for Data Management in Libraries. Journal of Advances in Library and Information Science, 7(1), 128-131.

Shannon, C. E. (1948). A mathematical theory of communication. Bell system technical journal, 27(3), 379-423.

Tukey, J. W. (1962). The future of data analysis. The annals of mathematical statistics, 33(1), 1-67.

Tukey, J. W. (1977). Exploratory Data Analysis: Limited Preliminary Ed: Addison-Wesley Publishing Company.

Van Der Aalst, W. (2016). Data science in action Process Mining (pp. 3-23): Springer.

Walek, A. (2017). Is data management a new “digitisation”? A change of the role of librarians in the context of changing academic libraries’ tasks.

Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84.

Wang, L. (2018). Twinning data science with information science in schools of library and information science. Journal of Documentation, 74(6), 1243-1257.

Yoon, A., & Schultz, T. (2017). Research data management services in academic libraries in the US: a content analysis of libraries’ websites.

Zins, C. (2007). Conceptual approaches for defining data, information, and knowledge. Journal of the American society for information science and technology, 58(4), 479-493.

Zuo, Z., Zhao, K., & Eichmann, D. (2017). The state and evolution of US iSchools: From talent acquisitions to research outcome. Journal of the Association for Information Science and Technology, 68(5), 1266-1277.

Abstract

Data sciences usually involve data management, its utilization, distribution as well as its re-utilization. All these components need to be focused while targeting data science. Thus data puts a significant burden on research institutes because it is the authority that decides the responsible for the whole course of the procedure. It is of prime importance for data science librarians serving in data-centric age to know regarding LIS principles, theories, and other related skills that are mandatory for management and support of data science. This paper sums up the reviews of researchers regarding the data science era. Moreover, this paper includes diagnostic assessment of data science environment concerning recent advancements in data science and progress in duties of librarians, presentation of detailed data, the function of data science libraries as well as librarians concerning data users. It is supposed to be an exciting era to work in a library as its role is expanding with specific new challenges. It is the need of the current period to educate librarians, library science researchers, and students regarding understanding, utility, and management of data to meet the requirements of data science librarians.

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