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Digital Methods for Complex Datasets (IJHAC Volume 10, Issue 1)
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Product Details
Author:
Jennifer Guiliano, Mia Ridge
Format:
Paperback
Pages:
138
Publisher:
Edinburgh University Press (March 15, 2016)
Language:
English
Audience:
Professional and scholarly
ISBN-13:
9781474417426
ISBN-10:
1474417426
Dimensions:
6.14" x 9.21"
File:
TWO RIVERS-PERSEUS-Metadata_Only_Perseus_Distribution_Customer_Group_Metadata_20260108163228-20260108.xml
Folder:
TWO RIVERS
List Price:
$29.95
Series:
International Journal of Humanities and Arts Computing Special Issues
As low as:
$23.06
Publisher Identifier:
P-PER
Discount Code:
A
Weight:
8oz
Imprint:
Edinburgh University Press
Country of Origin:
United States
Overview
Seeking to challenge the focus on 'big data' by understanding it outside of the computational power required to process it, this volume explores the role of digital methods in the future of digital humanities research. The essays are united by the theme of complexity - but manifest that complexity across an unusual spectrum. The methods included rise out of fields of study including library and information science, informatics, literary studies, English, and computer science. Sources explored include traditional national archives, international web archives, medieval musical scores, digitised books, early modern network ontologies and educational data/learning analytics. These essays discuss the practical implications of web scraping, the implications of creating new scholarly objects, the importance of documentation and the intricacies of applying topic modelling and linked open data methods. Together, the volume suggests that the humanities comfort with multiplicities, contingency, and uncertainty in sources may lend itself to resisting the reductionism that makes technical projects easier to manage, flattening messy, human data into neat binaries. These essays remind us that their results must be contextualised through scholars' knowledge of the sources and the methods by which they came to be constructed not just as ‘big data’ datasets.








