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E-raamat: Data Science for Librarians

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This unique textbook intersects traditional library science with data science principles that readers will find useful in implementing or improving data services within their libraries.

Data Science for Librarians introduces data science to students and practitioners in library services. Writing for academic, public, and school library managers; library science students; and library and information science educators, authors Yunfei Du and Hammad Rauf Khan provide a thorough overview of conceptual and practical tools for data librarian practice.

Partially due to how quickly data science evolves, libraries have yet to recognize core competencies and skills required to perform the job duties of a data librarian. As society transitions from the information age into the era of big data, librarians and information professionals require new knowledge and skills to stay current and take on new job roles, such as data librarianship. Skills such as data curation, research data management, statistical analysis, business analytics, visualization, smart city data, and learning analytics are relevant in library services today and will become increasingly so in the near future. This text serves as a tool for library and information science students and educators working on data science curriculum design.


  • Reviews fundamental concepts and principles of data science

  • Offers a practical overview of tools and software

  • Highlights skills and services needed in the 21st-century academic library

  • Covers the entire research data life cycle and the librarian's role at each stage

    • Provides insight into how library science and data science intersect



  • "This unique textbook intersects traditional library science with data science principles that readers will find useful in implementing or improving data services within their libraries"--

    Arvustused

    A worthy contribution to the dynamic field of data science, suited for library professionals in all types of libraries. * Library Journal *

    Muu info

    This unique textbook intersects traditional library science with data science principles that readers will find useful in implementing or improving data services within their libraries.
    1 More Data, More Problems
    		1(16)
    What Is Data?
    		2(3)
    Quantitative vs. Qualitative Data
    		3(1)
    Digital vs. Nondigital Data
    		3(1)
    What Is Big Data?
    		4(1)
    How Big Data Works
    		5(1)
    Problems with Having Too Much Data
    		5(1)
    Data and Information Are Different
    		5(1)
    Data Saturation
    		6(1)
    Confirmation Bias and Signal Error
    		6(1)
    Effects on Society
    		6(2)
    Impact on Health Services
    		6(1)
    Government Planning
    		7(1)
    News and Media Consumption
    		7(1)
    Sports
    		7(1)
    Big Data and the Data Deluge
    		7(1)
    Open Data
    		8(2)
    Open Government Data
    		9(1)
    Data.gov
    		9(1)
    Principles of Open Government Data
    		10(1)
    Research Data in Academic Libraries
    		10(2)
    Data Literacy Concepts
    		11(1)
    Data Life Cycle
    		11(1)
    Era of Big Data
    		12(2)
    Looking Ahead
    		14(1)
    References
    		14(3)
    2 A New Strand of Librarianship
    		17(14)
    Data-Driven Decision Making
    		17(2)
    History of Data in Academic Libraries
    		19(1)
    What Does Big Data Mean for Libraries?
    		20(1)
    Data Librarianship
    		21(2)
    Research Data Services
    		23(5)
    Data Management Plans
    		24(2)
    Management: GIS
    		26(2)
    Conclusion
    		28(1)
    References
    		28(3)
    3 Data Creation and Collection
    		31(12)
    Surveys
    		31(1)
    Online Tools
    		32(1)
    Social Media Data
    		33(1)
    Data Noise
    		33(2)
    Data Acquisitions
    		35(1)
    Disadvantages of Big Data Collection
    		36(1)
    Big Data Analytics
    		37(2)
    Conclusion
    		39(1)
    References
    		40(3)
    4 Data for the Academic Librarian
    		43(12)
    E-Science and E-Research
    		44(1)
    Data Reference Interview
    		45(3)
    Data Storage and Archiving
    		48(1)
    Data Repositories
    		49(2)
    References
    		51(4)
    5 Research Data Services and the Library Ecosystem
    		55(10)
    What Is RDS?
    		56(1)
    How Much of the Research Data Life Cycle is Represented within RDS?
    		56(3)
    Who Works in RDS?
    		59(1)
    Data Literacy
    		60(3)
    References
    		63(2)
    6 Data Sources
    		65(16)
    Data and the Library Professional
    		65(1)
    Open Government Data
    		66(1)
    Data Repositories
    		67(1)
    Metadata
    		68(1)
    Data Citation
    		69(1)
    Data Collection and Harvesting
    		70(1)
    Data Extraction, Transformation, and Loading
    		71(1)
    Data Mining
    		72(4)
    Data Cleaning
    		72(1)
    Data Mining and Analysis for Librarians
    		73(2)
    Data Mining: Techniques
    		75(1)
    Data Mining: Advantages and Disadvantages
    		75(1)
    Data Analysis and Librarians: An Overview
    		76(1)
    Conclusion
    		77(1)
    References
    		78(3)
    7 Data Curation (Archiving/Preservation)
    		81(10)
    Data Curation Process
    		81(1)
    Data Stewardship
    		82(1)
    Metadata
    		83(2)
    Data Access and Reuse
    		85(1)
    Data Sharing
    		86(1)
    Data Quality
    		86(2)
    Conclusion
    		88(1)
    References
    		88(3)
    8 Data Storage, Management, and Retrieval
    		91(14)
    Big Data Storage Solutions
    		91(4)
    High-Performance Computing
    		92(1)
    Variety of Big Data Storage Patterns
    		93(1)
    Social Networking Data
    		94(1)
    Cloud Computing
    		95(3)
    Apache Hadoop
    		96(1)
    Common Cloud Storage Solutions
    		96(1)
    Privacy Concerns on Cloud Computing
    		97(1)
    Big Data Management
    		98(2)
    Data Cleaning
    		98(1)
    Big Data Security and Policies
    		99(1)
    Managing the Velocity of Big Data
    		100(1)
    Conclusion
    		100(1)
    References
    		101(4)
    9 Data Analysis and Visualization
    		105(16)
    Big Data Analysis
    		105(2)
    Descriptive Analytics
    		105(1)
    Diagnostic Analytics
    		106(1)
    Predictive Analytics
    		106(1)
    Prescriptive Analytics
    		107(1)
    Statistics for Data Science
    		107(4)
    Hypothesis Testing and Statistical Significance
    		107(1)
    Probability Distributions
    		108(1)
    Correlation
    		109(1)
    Regression
    		110(1)
    Data Visualization
    		111(6)
    Brief History of Data Visualization
    		111(1)
    Data Visualization Methods and Tools
    		112(2)
    Text Visualization
    		114(1)
    Data Visualization Applications
    		115(2)
    Conclusion
    		117(1)
    References
    		117(4)
    10 Data Ethics and Policies
    		121(10)
    Data Security
    		122(1)
    User Privacy and Data Retention
    		123(2)
    Data Privacy
    		125(1)
    Data Ethics
    		126(1)
    Copyright and Ownership
    		127(1)
    Personal Information Data in Libraries
    		128(1)
    Conclusion
    		129(1)
    References
    		130(1)
    11 Data for Public Libraries and Special Libraries
    		131(14)
    Smart Cities Initiatives
    		131(2)
    Open Government Initiatives
    		133(1)
    Internet of Things and Privacy Concerns
    		134(1)
    Internet of Things
    		134(1)
    Challenges
    		135(1)
    Census Data
    		135(1)
    Role of Public Libraries in the Era of Big Data
    		136(2)
    Public Libraries Can Use Big Data to Address Local Needs
    		136(1)
    Librarians Are Advocates for Privacy of Citizens
    		137(1)
    Data Librarians in Public Libraries
    		137(1)
    Public Libraries as Learning Centers for Teens
    		137(1)
    Role of Special Libraries in the Era of Big Data
    		138(3)
    Law Librarians
    		139(1)
    Corporate Libraries
    		140(1)
    Medical Librarians
    		140(1)
    Conclusion
    		141(1)
    References
    		141(4)
    12 Conclusion: Library, Information, and Data Science
    		145(12)
    Data as an Infrastructure for Society
    		145(2)
    Data and Information
    		146(1)
    Data as Public Good
    		146(1)
    Data as the Driving Force for the Economy
    		146(1)
    Data for Governance
    		147(1)
    Librarians and Data Life Cycle
    		147(2)
    New Job Titles for Librarians
    		148(1)
    Librarians in Data Life Cycle
    		148(1)
    Data Analysis Skill Sets for Librarians
    		149(2)
    Data Ingestion
    		149(1)
    Data Curation
    		149(1)
    Data Visualization
    		149(1)
    Data Analytics
    		150(1)
    Data Literacy for Library Users
    		151(2)
    Data Literacy in Academic Settings
    		151(1)
    Data Literacy for Public Library Users
    		151(2)
    Conclusion
    		153(1)
    References
    		153(4)
    Glossary 157(8)
    Index 165
    Yunfei Du is professor of library science and associate dean in the College of Information at the University of North Texas. He has worked as a systems librarian and published in many academic journals.

    Hammad Rauf Khan is the director of library services at the Columbus College of Art & Design.
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