Exploring Big Historical Data: The Historian's Macroscope

Every day, more and more kinds of historical data become available, opening exciting new avenues of inquiry but also new challenges. This updated and expanded 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. Originally 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.

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.

Product details

• Authors: Shawn Graham, Graham Shawn, Ian Milligan, Kimberley Martin, Ian Milligan, Milligan Ian, Scott B Weingart, Shawn Graham, Ian Milligan Scott B Wein Shawn Graham, Scott Weingart, Scott B. Weingart
• Publisher: Ingram Publishers Services
• Languages: English
• Product format: Hardback
• Released: 31.03.2022
• EAN: 9789811243035
• ISBN: 978-981-1243-03-5
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; COMPUTERS / Data Science / Data Modeling & Design, COMPUTERS / Data Science / Machine Learning, Data capture and analysis, Data Capture & Analysis