Exploring Big Historical Data
The Historian's Macroscope
Nonfiction, Computers, Database Management, General Computing
| Author: | Shawn Graham, Ian Milligan, Scott Weingart | ISBN: | 9781783266111 |
| Publisher: | World Scientific Publishing Company | Publication: | September 16, 2015 |
| Imprint: | ICP | Language: | English |
| Author: | Shawn Graham, Ian Milligan, Scott Weingart |
| ISBN: | 9781783266111 |
| Publisher: | World Scientific Publishing Company |
| Publication: | September 16, 2015 |
| Imprint: | ICP |
| Language: | English |
The Digital Humanities have arrived at a moment when digital Big Data is becoming more readily available, opening exciting new avenues of inquiry but also new challenges. This pioneering book describes and demonstrates the ways these data can be explored to construct cultural heritage knowledge, for research and in teaching and learning. It helps humanities scholars to grasp Big Data in order to do their work, whether that means understanding the underlying algorithms at work in search engines, or designing and using their own tools to process large amounts of information.
Demonstrating what digital tools have to offer and also what 'digital' does to how we understand the past, the authors introduce the many different tools and developing approaches in Big Data for historical and humanistic scholarship, show how to use them, what to be wary of, and discuss the kinds of questions and new perspectives this new macroscopic perspective opens up. Authored 'live' online with ongoing feedback from the wider digital history community, Exploring Big Historical Data breaks new ground and sets the direction for the conversation into the future. It represents the current state-of-the-art thinking in the field and exemplifies the way that digital work can enhance public engagement in the humanities.
Exploring Big Historical Data should be the go-to resource for undergraduate and graduate students confronted by a vast corpus of data, and researchers encountering these methods for the first time. It will also offer a helping hand to the interested individual seeking to make sense of genealogical data or digitized newspapers, and even the local historical society who are trying to see the value in digitizing their holdings.
The companion website to Exploring Big Historical Data can be found at http://www.themacroscope.org/. On this site you will find code, a discussion forum, essays, and datafiles that accompany this book.
Contents:
- The Joys of Big Data for Historians
- The DH Moment
- Text Mining Tools: Techniques and Visualizations
- Topic Modeling: A Hands-on Adventure in Big Data
- Making Your Data Legible: A Basic Introduction to Visualizations
- Network Analysis
- Networks in Practice
Readership: Researchers, graduate and undergraduate students in the field of Digital Humanities, and people looking to digitize historical archives.
Key Features:
- Combines clear explanation of theory and background with detailed practical instruction
- Approaches Big Data and the digital humanities from a historical perspective
- Each section builds upon the previous, allowing readers to build their skills, knowledge, and to reflect critically on what these skills are doing to the writing of history
- Readers will develop an ability to collect and interrogate big data in its many forms
The Digital Humanities have arrived at a moment when digital Big Data is becoming more readily available, opening exciting new avenues of inquiry but also new challenges. This pioneering book describes and demonstrates the ways these data can be explored to construct cultural heritage knowledge, for research and in teaching and learning. It helps humanities scholars to grasp Big Data in order to do their work, whether that means understanding the underlying algorithms at work in search engines, or designing and using their own tools to process large amounts of information.
Demonstrating what digital tools have to offer and also what 'digital' does to how we understand the past, the authors introduce the many different tools and developing approaches in Big Data for historical and humanistic scholarship, show how to use them, what to be wary of, and discuss the kinds of questions and new perspectives this new macroscopic perspective opens up. Authored 'live' online with ongoing feedback from the wider digital history community, Exploring Big Historical Data breaks new ground and sets the direction for the conversation into the future. It represents the current state-of-the-art thinking in the field and exemplifies the way that digital work can enhance public engagement in the humanities.
Exploring Big Historical Data should be the go-to resource for undergraduate and graduate students confronted by a vast corpus of data, and researchers encountering these methods for the first time. It will also offer a helping hand to the interested individual seeking to make sense of genealogical data or digitized newspapers, and even the local historical society who are trying to see the value in digitizing their holdings.
The companion website to Exploring Big Historical Data can be found at http://www.themacroscope.org/. On this site you will find code, a discussion forum, essays, and datafiles that accompany this book.
Contents:
- The Joys of Big Data for Historians
- The DH Moment
- Text Mining Tools: Techniques and Visualizations
- Topic Modeling: A Hands-on Adventure in Big Data
- Making Your Data Legible: A Basic Introduction to Visualizations
- Network Analysis
- Networks in Practice
Readership: Researchers, graduate and undergraduate students in the field of Digital Humanities, and people looking to digitize historical archives.
Key Features:
- Combines clear explanation of theory and background with detailed practical instruction
- Approaches Big Data and the digital humanities from a historical perspective
- Each section builds upon the previous, allowing readers to build their skills, knowledge, and to reflect critically on what these skills are doing to the writing of history
- Readers will develop an ability to collect and interrogate big data in its many forms
