Big Data and Libraries: Identifying Themes in the Literature

References

• Ackerman, W. (2018). Re: Position opening: Data services librarian. Retrieved from: [email protected]
   Google Scholar
• Affelt, M. (2015). The accidental data scientists: Big data applications and opportunities for librarians and information professionals. Medford, NJ: Information Today, Inc.
   Google Scholar
• Ahmed, W., & Ameen, K. (2017). Defining big data and measuring its associated trends in the field of information and library management. Library HI Tech News, 34(9), 21–24.
   Google Scholar
• Archambault, S. G., Helouvry, J., Strohl, B., & Williams, G. (2015). Data visualization as a communication tool. Library Hi Tech News, 32(2), 1–9.
   Google Scholar
• Bates, M. E. (2017). Big data ain’t so big. Online Searcher, 41(1):72.
   Google Scholar
• Bell, S. (2013). Promise and problems of big data. Library Journal. Available at: http://lj.libraryjournal.com/2013/03/opinion/steven-bell/promise-and-pro.
   Google Scholar
• Bertot, J. C., Butler, B. S., & Travis, D. (2014). Local big data: The role of libraries in building community data infrastructures. Paper presented at the 15th Annual International Conference on Digital Government Research, Aguascalientes, Mexico.
   Google Scholar
• Bieraugel, M. (2013). Keeping up with…big data. Association of College and Research Libraries. Available at: http://www.ala.org/acrl/publications/keeping_up_with/big_data Available at: http://works.bepress.com/mbieraug/3/
   Google Scholar
• Campbell, D. G., & Cowan, S. R. (2016). The paradox of privacy: Revisiting a core library value in an age of big data and linked data. Library Trends, 64(3), 492–511.
   Web of Science ®Google Scholar
• Catalano, M. M., Vaugh, P., & Been, J. (2017). Using maps to promote data-driven decision-making: One library’s experience in data visualization instruction. Medical Reference Services Quarterly, 36(4), 415–422.
   PubMedGoogle Scholar
• Cervone, H. F. (2016). Informatics and data science: An overview for the information professional. Digital Library Perspectives, 32(1), 7–10.
   Web of Science ®Google Scholar
• Cervone, H. F. (2017). Evaluating social media presence: A practical application of big data and analytics in information organizations. Digital Library Perspectives, 33(1), 2–7.
   Google Scholar
• Chant, I. (2014). More data for your dollar. Library Journal, 139(11), 44.
   Web of Science ®Google Scholar
• Chen, H. (2015, November). Library assessment and data analytics in the big data era: Practice and policies. Paper presented at ASIST St. Louis, MO.
   Google Scholar
• Chen, Z. (2017). Challenges to library information service and relevant coping strategies in the big data era based on regression method. Revista de la Facultad de Ingeniería U.C.V, 32(13), 123–127.
   Google Scholar
• Choudhury, G. S. (2016). Data management at scale. Information Services & Use, 36(1/2), 27–33.
   Google Scholar
• Cottrell, T., & Bell, B. (2016). When to say when: Using big data to support mobile communication. College & Undergraduate Libraries, 23(3), 315–322.
   Web of Science ®Google Scholar
• Crosas, M. (2012). A data sharing story. Journal of eScience Librarianship, 1(3), 173–179.
   Google Scholar
• Datig, I., & Whiting, P. (2018). Telling your library story: Tableau public for data visualization. Library Hi Tech News, 35(4), 1–3.
   Google Scholar
• De Mauro, A., Greco, M., & Grimaldi, M. (2016). A formal definition of big data based on its essential features. Library Review, 65(3), 122–135.
   Web of Science ®Google Scholar
• Deng, Z. (2017). Research on service innovation of library in big data age. Advances in Social Science Education and Humanities Research, 101 Paper presented at the 4th International Conference on Education Management and Computing Technology (ICEMCT 2017), Hangzhou, China.
   Google Scholar
• Enis, M. (2016). UM launches deep blue data. Library Journal November 1, 2017. Available at: https://lj.libraryjournal.com/2016/10/academic-libraries/university-of-michigan-launches-deep-blue-data-repository/#_
   Google Scholar
• Enis, M. (2017). Visualizing the future. Library Journal October, 4(2017), 30–32. Availabe at: https://lj.libraryjournal.com/2017/10/technology/visualizing-future-techknowledge/#_
   Google Scholar
• Fang, D., Yang, H., Gao, B., & Li, X. (2017). Discovering research topics from library electronic references using latent Dirichlet allocation. Library Hi Tech, 36(3), 400–410.
   Google Scholar
• 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.
   Google Scholar
• Fister, B. (2015). Big data or big brother? Data, ethics, and academic libraries. Library Issues: Briefings for Faculty and Administrators, 35(4) Retrieved from https://barbarafister.net/LIbigdata.pdf
   Google Scholar
• Fishleigh, J. (2014). A non-technical journal into the world of big data: An introduction. Legal Information Management, 14(02), 149–151.
   Google Scholar
• Fox, R. (2013). The art and science of data curation. OCLC Systems & Services: International Digital Library Perspectives, 29(4), 195–199.
   Google Scholar
• Fridsma, D. B. (2015). A new vision for the National Library of Medicine. Journal of the American Medical Informatics Association, 22(5), 1111.
   PubMed Web of Science ®Google Scholar
• Gerrard, D. M., Mooney, J. E., & Thompson, D. (2018). Digital preservation at big data scales: Proposing a step-change in preservation system architectures. Library Hi Tech, 36(3), 524–538.
   Web of Science ®Google Scholar
• Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. New York, NY: Aldine Publishing Co.
   Google Scholar
• Goldberg, B. (2012). What’s new in LIS schools: A snapshot of librarianship’s never-ending metamorphosis. American Libraries, 2012, 24–26. Available at: https://americanlibrariesmagazine.org/2012/08/21/whats-new-in-lis-schools/
   Google Scholar
• Goldberg, M., Olivares, M., Li, Z., & Klein, A. G. (2014). Maps & GIS data libraries in the era of big data and cloud computing. Journal of Map & Geography Libraries, 10(1), 100–122.
   Google Scholar
• Gordon-Murnane, L. (1999). Big data: A big opportunity for librarians. Online, 36 (7), 36-4074–34.
   Google Scholar
• Harper, L. M., & Oltmann, S. M. (2017). Big data’s impact on privacy for librarians and information professionals. Bulletin of the Association for Information Science & Technology, 43(4), 19–23.
   Google Scholar
• Hoy, M. B. (2014). Big data: An introduction for librarians. Medical Reference Services Quarterly, 33(3), 320–326.
   PubMedGoogle Scholar
• Jantti, M. (2016). Libraries and big data: A new view on impact and affect. In A. Jeremy (Ed.). Quality and the academic library: Reviewing, assessing and enhancing service provision (pp. 267–273) Cambridge: Chandos Publishing.
   Google Scholar
• Jantti, M., & Heath, J. (2016). What role for libraries in learning analytics?. Performance Measurement and Metrics, 17(2), 203–210.
   Web of Science ®Google Scholar
• Jin, W., & He, S. (2017). Research on management of libraries in universities and colleges based on K-means clustering algorithm under big data environment. Revista de la Facultad de Ingeniería U.C.V, 32(8), 177–181.
   Google Scholar
• Johnson, N. (2015). Sophomores and statistics: An outreach initiative of a special library. Public Services Quarterly, 11(1), 74–78.
   Google Scholar
• Johnson, V. (2017). Leveraging technical library expertise for big data management. Journal of the Australian Library and Information Association, 66(3), 1–286.
   Google Scholar
• Kanarkard, W., Seemajaruek, C., Pongsuwan, T., & Inlam, T. (2017). Predictive analytic of library patron behavior (pp. 1–5). Paper presented at ICCIP’17, Tokyo, Japan.
   Google Scholar
• Keil, D. E. (2014). Research data needs from academic libraries: The perspective of a faculty researcher. Journal of Library Administration, 54(3), 233–240.
   Google Scholar
• Kilb, M., & Jansen, M. (2016). Visualizing collections data: Why pie charts aren’t always the answer. Serials Review, 42(3), 192–200.
   Web of Science ®Google Scholar
• Kim, J. (2016). Who Is teaching data: Meeting the demand for data professionals. Journal of Education for Library and Information Science, 57(2), 161–173.
   Google Scholar
• Kim, Y., & Cooke, L. (2017). Big data analysis of public library operations and services by using the Chernoff face method. Journal of Documentation, 73(3), 466–480.
   Web of Science ®Google Scholar
• Knapp, M. (2013). Big data. Journal of Electronic Resources in Medical Libraries, 10(4), 215–222.
   Google Scholar
• Kumbhar, R. M. (2014). Academic library's responses to the emerging trends in higher education. DESIDOC Journal of Library & Information Technology, 34(6), 477–485.
   Google Scholar
• Koltay, T. (2016). Data governance, data literacy and the management of data quality. IFLA Journal, 42(4), 303–312.
   Google Scholar
• Kong, N. (2015). Exploring best management practices for geospatial data in academic Libraries. Journal of Map and Geography Libraries, 11(2), 207–225.
   Web of Science ®Google Scholar
• Lawlor, B. (2016). An overview of the NFAIS 2016 Annual Conference: Data sparks discovery of tomorrow’s global knowledge. Information Services & Use, 36 (1/2), 3–21.
   Google Scholar
• Leetaru, K. (2017). Reimagining our world at planetary scale: The big data future of our libraries. Paper presented at the 2016 Charleston Library Conference, Charleston, S.C. Retrieved from: https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1936&context=charleston
   Google Scholar
• Lemieux, V. L., Gormly, B., & Rowledge, L. (2014). Meeting big data challenges with visual analytics: The ole of records management. Records Management Journal, 24(2), 122–141.
   Google Scholar
• Li, J., Lu, M., Dou, G., & Wang, S. (2017). Big data application framework and its feasibility analysis in library. Information Discovery and Delivery, 45(4), 161–168.
   Web of Science ®Google Scholar
• Little, G. (2012). Managing the data deluge. The Journal of Academic Librarianship, 38(5), 263–264.
   Web of Science ®Google Scholar
• Lyon, L. (2012). The informatics transform: Re-engineering libraries of the data decade. International Journal of Digital Curation, 7(1), 126–138.
   Google Scholar
• Lyon, L., & Brenner, A. (2015). Bridging the data talent gap: Positioning the iSchool as an agent for change. International Journal of Digital Curation, 10 (1), 111–122.
   Google Scholar
• Mai, J.-E. (2016). Big data privacy: The datafication of personal information. The Information Society, 32(3), 192–199.
   Web of Science ®Google Scholar
• Marciano, R. J., Allen, R. C., Hou, C.-Y., & Lach, P. R. (2013). Big historical data feature extraction. Journal of Map and Geography Libraries, 9(1/2), 69–80.
   Google Scholar
• Marvin, H. (2014). Big data in libraries: Content and policies for librarians. Government Information Quarterly, 31(4), 681.
   Web of Science ®Google Scholar
• Massis, B. (2016). Data-driven decision-making in the library. New Library World, 117(1/2), 131–134.
   Google Scholar
• McKibbon, K. A. (2006). Systematic reviews and librarians. Library Trends, 55(1), 202–215.
   Web of Science ®Google Scholar
• Murray, T. E. (2015). Trends in special libraries: Observations based on conference content. Journal of Library Administration, 55(8), 658–666.
   Web of Science ®Google Scholar
• Mutula, S. (2016). Editorial feature—Big data industry: Implications for the library and information sciences. African Journal of Library, Archives and Information Science, 26(2), 93–96.
   Google Scholar
• Noh, Y. (2015). Imagining library 4.0: Creating a model for future libraries. The Journal of Academic Librarianship, 41(6), 786–797.
   Web of Science ®Google Scholar
• Norton, H. G., Tennant, M. R., Botero, C., & Garcia-Milian, R. (2016). Assessment of and response to data needs of clinical and translational science researchers and beyond. Journal of eScience Librarianship, 5(1), e1090–e1017.
   Google Scholar
• Ojala, A. M. (2012). Value, vision, and big data. Online, 5.
   Google Scholar
• Ovadia, S. (2013). The role of big data in the social sciences. Behavioral & Social Sciences Librarian, 32(2), 130–134.
   Google Scholar
• Plale, B. (2013). Big data opportunities and challenges for IR, text mining and NLP. In Proceedings of the 2013 International Workshop on Mining Unstructured Big Data Using Natural Language Processing (UnstructureNLP '13) (pp. 1–2). New York, NY: ACM.
   Google Scholar
• Poole, A. H., & Garwood, D. A. (2018). “Natural allies” librarians, archivists, and big data in international digital humanities project work. Journal of Documentation, 74(4), 804–826.
   Web of Science ®Google Scholar
• Powell, J., Collins, L., Eberhardt, A., Izraelevitz, D., Roman, J., Dufresne, T., … Grider, G. (2012). "At scale" author name matching with Hadoop/MapReduce. Library Hi Tech News, 29 (4), 6–12.
   Google Scholar
• Prescott, A. (2013). Bibliographic records as humanities big data. In X. Hu (Eds.), Proceedings of 2013 IEEE International Conference on Big Data (pp. 55–58). Santa Clara, CA: IEEE.
   Google Scholar
• Reilly, B. F. (2016). Toward a rational and sustainable division of labor for the preservation of knowledge. Library Management, 37(4/5), 166–169.
   Web of Science ®Google Scholar
• Reinhalter, L., & Wittman, R. J. (2014). The library: Big data’s boomtown. The Serials Librarian, 67(4), 363–372.
   Google Scholar
• Shen, Y., & Varvel, V. E. Jr. (2013). Developing data management services at the Johns Hopkins University. The Journal of Academic Librarianship, 39(6), 552–557.
   Web of Science ®Google Scholar
• Sherman, C. (2014). “What’s the big deal about big data?”. Online Searcher, 38(2), 10–16.
   Google Scholar
• Singh, P. K., & Singh, A. P. (2017). Diffusion of big data in Indian scientific literature: Study of research productivity and scientific collaboration. Library Philosophy and Practice (e-journal). 1599. Available at: https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=4613&context=libphilprac
   Google Scholar
• Smith, A. (2016). Big data technology, evolving knowledge skills and emerging roles. Legal Information Management, 16(04), 219–224.
   Google Scholar
• Steeleworthy, M. (2014). Research data management and the Canadian academic library: An organizational consideration of data management and data stewardship. Partnership: The Canadian Journal of Library and Information Practice and Research, 9(1), 1–11.
   Google Scholar
• Sun, Z. (2017). Research on college archives resources information in the background of big data. Revista de la Facultad de Ingenieria, 32(11), 268–273.
   Google Scholar
• Teets, M., & Goldner, M. (2013). Libraries’ role in curating and exposing big data. Future Internet, 5(3), 429–438.
   Google Scholar
• Tonta, Y. (2016). Developments in education for information: Will “data" trigger the next wave of curriculum changes in LIS schools? Pakistan Journal of Information Management & Libraries, 17, 2–12. Retrieved from: http://yunus.hacettepe.edu.tr/∼tonta/yayinlar/tonta-pakistan-jiml-2016-888-2369-1-SM.pdf
   Google Scholar
• Toops, M., & Hughes, M. (2013). When data curation isn’t: A redefinition for liberal arts universities. Journal of Library Administration, 53(4), 223–233.
   Google Scholar
• Tuppen, S., Rose, S., & Drosopoulou, L. (2016). Library catalogue records as a research resource: Introducing “A big data history of music”. Fontes Artis Musicae, 63(2), 67–88.
   Web of Science ®Google Scholar
• University of Maryland Libraries. (2018). Interdisciplinary dialogue: Big Data. Retrieved from https://www.lib.umd.edu/rc/big-data
   Google Scholar
• Vaidhyanathan, S., & Bulock, C. (2014). Knowledge and dignity in the era of “Big Data.” The Serials Librarian, 66(1–4), 49–64.
   Google Scholar
• Vassilakaki, E. (2014). Mobile information services in libraries: A review of current trends in delivering information. Interlending & Document Supply, 42(4), 176–186.
   Google Scholar
• Wang, D.-X. (2016). Information service in the big data era and development strategies for university libraries. Paper presented at the 2nd Annual International Conference on Social Science and Contemporary Humanity Development (SSCHD 2016), Wuhan, Hubei, China.
   Google Scholar
• Weber, N. M., Palmer, C. L., & Chao, T. C. (2012). Current trends and future directions in data curation research and education. Journal of Web Librarianship, 6(4), 305–320.
   Google Scholar
• Xie, M., Zhang, Y., Wang, J., & Wang, Y. (2017). Framework research on the personalized recommendation system of university library based on data mining technology in the background of big data. Revista de la Facultad de Ingeniería U.C.V, 32(15), 625–631.
   Google Scholar
• Yi, K., Chen, T., & Cong, G. (2018). Library personalized recommendation service method based on improved association rules. Library Hi Tech, 36(3), 443–457.
   Web of Science ®Google Scholar
• Yu, H. H. (2017). The role of academic libraries in research data service (RDS) provision: Opportunities and challenges. The Electronic Library, 35(4), 783–797.
   Web of Science ®Google Scholar
• Yuvaraj, M. (2017). Infographics tools for designing, visualizing data and storytelling in libraries. Library Hi Tech News, 34(5), 6–9.
   Google Scholar
• Zeng, Y. (2015). On the analysis of library information ethics and the standard construction in the era of big data. Studies in Literature and Language, 11(4), 25–28.
   Google Scholar
• Zhan, M., & Widén, G. (2017). Understanding big data in librarianship. Journal of Librarianship and Information Science, 1, 16.
   Google Scholar
• Zhan, M., & Widén, G. (2018). Public libraries: Roles in big data. The Electronic Library, 36(1), 133–145.
   Web of Science ®Google Scholar