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Editors' Note

Editors' Note Complex systems attract considerable attention from computer and information scientists, and for good reason. Much of what concerns us in our everyday lives is in fact embedded in a complex environment filled with heterogeneous data. Mining these data has become the stock-in-trade of e-commerce giants such as Amazon and cutting-edge e-conglomerates such as Alphabet (aka Google). The humanities and social sciences also are turning to Big Data, but as guest editors Jennifer Guiliano and Mia Ridge argue in this special issue of IJHAC, we too often have identified homogeneous datasets as targets of our mining efforts. What about datasets that cross typological boundaries? Our research questions, after all, reflect a recognition that we live in an endlessly varied world, and the way we write about problems reveals a keen understanding that context cannot be well viewed through one lens alone. Yet rarely do we create or exploit datasets that represent multiple types of data, with text mining linked to quantitative data and video or audio materials, for example. This special issue, aptly titled Digital Methods for Complex Datasets, explores what it means to pursue research strategies that straddle data categories. Guiliano and Ridge solicited contributors who already were http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Humanities and Arts Computing Edinburgh University Press

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Publisher
Edinburgh University Press
Copyright
© Edinburgh University Press 2016
Subject
Special Issue: The Future of Digital Methods for Complex Datasets; Historical Studies
ISSN
1753-8548
eISSN
1755-1706
DOI
10.3366/ijhac.2016.0153
Publisher site
See Article on Publisher Site

Abstract

Complex systems attract considerable attention from computer and information scientists, and for good reason. Much of what concerns us in our everyday lives is in fact embedded in a complex environment filled with heterogeneous data. Mining these data has become the stock-in-trade of e-commerce giants such as Amazon and cutting-edge e-conglomerates such as Alphabet (aka Google). The humanities and social sciences also are turning to Big Data, but as guest editors Jennifer Guiliano and Mia Ridge argue in this special issue of IJHAC, we too often have identified homogeneous datasets as targets of our mining efforts. What about datasets that cross typological boundaries? Our research questions, after all, reflect a recognition that we live in an endlessly varied world, and the way we write about problems reveals a keen understanding that context cannot be well viewed through one lens alone. Yet rarely do we create or exploit datasets that represent multiple types of data, with text mining linked to quantitative data and video or audio materials, for example. This special issue, aptly titled Digital Methods for Complex Datasets, explores what it means to pursue research strategies that straddle data categories. Guiliano and Ridge solicited contributors who already were

Journal

International Journal of Humanities and Arts ComputingEdinburgh University Press

Published: Mar 1, 2016

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