Abstract
Different types of uncertainties occur in almost all datasets and are an inherent property of data across different academic disciplines, including digital humanities (DH). In this paper, we address, demonstrate and analyse spatio-temporal uncertainties in a non-standard German legacy dataset in a DH context. Although the data collection is primarily a linguistic resource, it contains a wealth of additional, comprehensive information, such as location and temporal detail. The addressed uncertainties have manifested because of a variety of reasons, and partly also because of decades of data transformation processes. We here propose our own taxonomy for capturing and classifying the various uncertainties, and show with numerous examples how the remedying but also re-introduction of uncertainties affects DH practices.
Highlights
Uncertainty is an inherent aspect of humanity, of our daily activities, actions and interactions.It is typically associated with unknown or lacking information, imprecise or incomplete knowledge, inaccurate measurements and risk
We provide an overview of the exploreAT! project and exploration space as a general context and framework for the scenario described in this study, introducing the indigenous language data collection and discussing sources of uncertainty, focusing on spatio-temporal dimensions in our linguistic dataset
Uncertainty in DBÖ—Examples first established our own taxonomy of uncertainties, we present concrete examples for the Having most relevant cases from to exemplify and illustrate category
Summary
Uncertainty is an inherent aspect of humanity, of our daily activities, actions and interactions. It is typically associated with unknown or lacking information, imprecise or incomplete knowledge, inaccurate measurements and risk. As uncertainty permeates our lives in various forms and ways, it has become a topic of discussion in policy making and entrepreneurship, and has been taken up in the scholarly and scientific discourses. As a consequence, embracing uncertainty, creating a culture of learning from errors and allowing for the creation and acknowledgement of serendipitous discovery conditions are key; yet they lie at the centre of the ongoing discussion around scientific innovation and progress, at the policy level. Across academic and scholarly discourses, uncertainty—for decades—has been a fundamental topic of interest across scientific disciplines, including philosophy [4], psychology [5], physics [6], information science [7], economics [8], law [9]
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