Data sources by subject area:
Other guides related to research data:
More Data Resources:
- GIS & Digital Mapping Sarah Watson's introductory guide offering resources for projects involving GIS, digital mapping, and geospatial technologies.
- ICPSR: In-Depth Research Guide Margie Ruppel's detailed guide to ICPSR (Inter-University Consortium for Political and Social Research): a data resource for University of Kentucky faculty, students and staff.
- Research Data Services at UK UK Libraries' guide on Resources and information to help you get started with data management, data preservation, and data sharing.
Research Methods & Protocols
- Research Methods & Protocols Guide covers research methods, research design, statistical methods, research ethics and research protocols for a vast range of disciplines.
Research & Teaching Resources:
- Open Educational Resources Adrian Ho's guide about UK Libraries' Alternative Textbook Program. It also provides an overview of free online peer-reviewed textbooks and offers assistance with finding and adapting them for courses.
- Publishing, Research, & Scholarly Communication Adrian Ho's guide on resources supporting the research process. Brings together apps, data tools, library collections, and scholarly communication resources.
- Resources & Support for Digital Scholarship Jennifer Hootman's guide with broad coverage of technologies, methodologies, resources, and scholarship in the digital humanities
Data management -- Library Resources (starter list)
Data Management for Researchers by
A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management. Data Management for Researchers includes sections on: * The data problem - an introduction to the growing importance and challenges of using digital data in research. Covers both the inherent problems with managing digital information, as well as how the research landscape is changing to give more value to research datasets and code. * The data lifecycle - a framework for data's place within the research process and how data's role is changing. Greater emphasis on data sharing and data reuse will not only change the way we conduct research but also how we manage research data. * Planning for data management - covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans. * Documenting your data - an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable. * Organizing your data - explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content. * Improving data analysis - covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems. * Managing secure and private data - many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure. * Short-term storage - deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost. * Preserving and archiving your data - digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project. * Sharing/publishing your data - addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data. * Reusing data - as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it. This book is designed for active scientific researchers but it is useful for anyone who wants to get more from their data: academics, educators, professionals or anyone who teaches data management, sharing and preservation. "An excellent practical treatise on the art and practice of data management, this book is essential to any researcher, regardless of subject or discipline." --Robert Buntrock, Chemical Information Bulletin
Call Number: Education Library. Book Stacks Q180.55.E4 B75 2015ISBN: 9781784270124Publication Date: 2015
Research Data Management by
It has become increasingly accepted that important digital data must be retained and shared in order to preserve and promote knowledge, advance research in and across all disciplines of scholarly endeavor, and maximize the return on investment of public funds. To meet this challenge, colleges and universities are adding data services to existing infrastructures by drawing on the expertise of information professionals who are already involved in the acquisition, management and preservation of data in their daily jobs. Data services include planning and implementing good data management practices, thereby increasing researchers' ability to compete for grant funding and ensuring that data collections with continuing value are preserved for reuse. This volume provides a framework to guide information professionals in academic libraries, presses, and data centers through the process of managing research data from the planning stages through the life of a grant project and beyond. It illustrates principles of good practice with use-case examples and illuminates promising data service models through case studies of innovative, successful projects and collaborations.
Call Number: Young Library Books - 5th Floor Z667 .R47 2014ISBN: 9781557536648Publication Date: 2014
Data Mining -- Library Resources (starter list):
Big Data: a Very Short Introduction by
Since long before computers were even thought of, data has been collected and organized by diverse cultures across the world. Once access to the Internet became a reality for large swathes of the world's population, the amount of data generated each day became huge, and continues to growexponentially. It includes all our uploaded documents, video, and photos, all our social media traffic, our online shopping, even the GPS data from our cars."Big Data" represents a qualitative change, not simply a quantitative one. The term refers both to the new technologies involved, and to the way it can be used by business and government. Dawn E. Holmes uses a variety of case studies to explain how data is stored, analysed, and exploited by avariety of bodies from big companies to organizations concerned with disease control. Big data is transforming the way businesses operate, and the way medical research can be carried out. At the same time, it raises important ethical issues; Holmes discusses cases such as the Snowden affair, datasecurity, and domestic smart devices which can be hijacked by hackers.ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, andenthusiasm to make interesting and challenging topics highly readable.
Call Number: Young Library, Books - 5th Floor QA76.9.B45 H66 2017ISBN: 9780198779575Publication Date: 2018
Data Mining by
This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. Praise for Data Mining: The Textbook - "As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It's a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology "This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners." -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago
Call Number: Online access -- ebookISBN: 3319141422Publication Date: 2015-04-13
Data Analytics -- Software Available on Campus
- QualtricsOnline tool (linkblue login): Survey design and data collection with built-in data analysis, reporting, and visualizations.Compatible across all web and mobile browsers.
Campus users: Please visit our campus software page or contact UKITS about the below-listed software.
- MATLABProgramming language for analyzing data, developing algorithms, and creating models.
- Microsoft Office 365 Pro
- Access: Relational database software with powerful tools for custom queries, reports, and input forms for database management and data analysis.
- Excel: Spreadsheet software with customizable field concatenation, built-in statistical analysis, and visual charting tools.
- SASPowerful data analysis software with built-in visualization tools.
- SPSS : IBM SPSS StatisticsStatistical software with built-in visualization tools.
Text mining - Library Resources (starter list):
Deep text : using text analytics to conquer information overload, get real value from social media, and add big(ger) text to big data by
Introduction -- Text analytics basics -- Getting started in text analytics -- Text analytics development -- Text analytics applications -- Enterprise text analytics as a platform.
Deep text is an approach to text analytics that adds depth and intelligence to our ability to utilize a growing mass of unstructured text the world is drowning in. Here, author Tom Reamy explains what deep text is and surveys its many uses and benefits. He describes applications and development best practices, discusses business issues including ROI, provides how-to advice and instruction, and offers guidance on selecting software and building a text analytics capability within an organization.
This is an important book for anyone who needs to be on the text analytics cutting edge from developers and information professionals who create, manage, and curate text-based and Big Data projects to entrepreneurs and business managers looking to cut costs and create new revenue streams. Whether you want to harness a flood of social media content or turn a mountain of business information into an organized and useful asset, Deep Text will supply the insights and examples you'll need to do it effectively.
Call Number: Young Library. Books - 5th Floor QA76.9.D343 R422 2016ISBN: 9781573875295Publication Date: 2016
Find more library resources -- InfoKat topic searches:
- Computational statistics
- Data management -- also:
- Data management plan* (picks up plan, plans, planning)
- Research data management
- Data mining
- Text mining