Digital Humanities at Edmon Low Library: Text & Data Mining

Data Resources

Library-Based Resources & Data for TDM

JSTOR Constellate
- Word Frequencies, Significant Terms, Topic Modeling
- For JSTOR resources, Chronicling America (historic newspapers), can use same open source tools on other resources
- Classes/Tutorials
HathiTrust Research Center
- Built-in algorithms: Topic Modeling, Name Entity Recognition, Token Count and Tag Cloud Creator
- Other algorithms/visualizations: almost anything you can think of (use python in data capsules)
- For HathiTrust Digital Library (~17.5 million volumes)
- Workshops
OSU Government Information
- Extensive Government Information resource by Documents Librarian Suzanne Reinman
OSU Maps & Spatial Data
- GIS Data Guide put together by Maps & Spatial Data Curator Kevin Dyke
OSU Carpentries
- Get coding skills for text and data mining. Regular workshops every semester.

Open Resources & Data for TDM

Other Fee-Based Resources & Data for TDM

Additional library resources (such as research databases) may be available for text and data mining depending on the license with OSU Libraries. Please contact libraryhelp@okstate.edu for more information and before mining.

Books

Working with Text by Emma Tonkin; Gregory J. L Tourte What is text mining, and how can it be used? What relevance do these methods have to everyday work in information science and the digital humanities? How does one develop competences in text mining? Working with Text provides a series of cross-disciplinary perspectives on text mining and its applications. As text mining raises legal and ethical issues, the legal background of text mining and the responsibilities of the engineer are discussed in this book. Chapters provide an introduction to the use of the popular GATE text mining package with data drawn from social media, the use of text mining to support semantic search, the development of an authority system to support content tagging, and recent techniques in automatic language evaluation. Focused studies describe text mining on historical texts, automated indexing using constrained vocabularies, and the use of natural language processing to explore the climate science literature. Interviews are included that offer a glimpse into the real-life experience of working within commercial and academic text mining. Introduces text analysis and text mining tools Provides a comprehensive overview of costs and benefits Introduces the topic, making it accessible to a general audience in a variety of fields, including examples from biology, chemistry, sociology, and criminology. Published 2016.
Learn Data Mining Through Excel by Hong Zhou Use popular data mining techniques in Microsoft Excel to better understand machine learning methods. Software tools and programming language packages take data input and deliver data mining results directly, presenting no insight on working mechanics and creating a chasm between input and output. This is where Excel can help. Excel allows you to work with data in a transparent manner. When you open an Excel file, data is visible immediately and you can work with it directly. Intermediate results can be examined while you are conducting your mining task, offering a deeper understanding of how data is manipulated and results are obtained. These are critical aspects of the model construction process that are hidden in software tools and programming language packages. This book teaches you data mining through Excel. You will learn how Excel has an advantage in data mining when the data sets are not too large. It can give you a visual representation of data mining, building confidence in your results. You will go through every step manually, which offers not only an active learning experience, but teaches you how the mining process works and how to find the internal hidden patterns inside the data. What You Will Learn Comprehend data mining using a visual step-by-step approach Build on a theoretical introduction of a data mining method, followed by an Excel implementation Unveil the mystery behind machine learning algorithms, making a complex topic accessible to everyone Become skilled in creative uses of Excel formulas and functions Obtain hands-on experience with data mining and Excel Who This Book Is For Anyone who is interested in learning data mining or machine learning, especially data science visual learners and people skilled in Excel, who would like to explore data science topics and/or expand their Excel skills. A basic or beginner level understanding of Excel is recommended. Published 2020.
Data Mining by Jiawei Han; Jian Pei; Hanghang Tong
ISBN: 0128117605
Publication Date: 2022-10-26
Practical Data Mining by Monte F. Hancock Jr. Used by corporations, industry, and government to inform and fuel everything from focused advertising to homeland security, data mining can be a very useful tool across a wide range of applications. Unfortunately, most books on the subject are designed for the computer scientist and statistical illuminati and leave the reader largely adrift in tech. Published 2011.
Knowledge Discovery in the Social Sciences by Xiaoling Shu Knowledge Discovery in the Social Sciences helps readers find valid, meaningful, and useful information. It is written for researchers and data analysts as well as students who have no prior experience in statistics or computer science. Suitable for a variety of classes--including upper-division courses for undergraduates, introductory courses for graduate students, and courses in data management and advanced statistical methods--the book guides readers in the application of data mining techniques and illustrates the significance of newly discovered knowledge.  Readers will learn to:  * appreciate the role of data mining in scientific research  * develop an understanding of fundamental concepts of data mining and knowledge discovery * use software to carry out data mining tasks * select and assess appropriate models to ensure findings are valid and meaningful * develop basic skills in data preparation, data mining, model selection, and validation * apply concepts with end-of-chapter exercises and review summaries   Published 2020.

Find more resources on Data Mining in the Library Catalog.