Topical Collection "Uncertainty in Digital Humanities"

A topical collection in Informatics (ISSN 2227-9709). This collection belongs to the section "Digital Humanities".

Editors

Assoc. Prof. Roberto Theron
Website SciProfiles
Collection Editor
Computer Science and Automation Department, University of Salamanca, 37008 Salamanca, Spain
Interests: visual analytics; information visualisation; human-computer interaction; digital humanities
Ms. Eveline Wandl-Vogt
Website1 Website2
Collection Editor
Austrian Academy of Sciences, Austrian Centre for Digital Humanities (ACDH-ÖAW), Vienna, Austria
Interests: open innovation; experimental humanities; knowledge design; standards and infrastructures; spatial humanities
Dr. Jennifer Cizik Edmond
Website
Collection Editor
Trinity College Dublin, Dublin, Ireland
Interests: digital humanities; research infrastructure; digital society
Dr. Cezary Mazurek
Website1 Website2
Collection Editor
Poznan Supercomputing and Networking Center, 61-888 Poznan, Poland
Interests: research infrastructures; software development; digital humanities; data and information analysis

Topical Collection Information

Dear Colleagues,

In recent years, with the pervasiveness of computers and a great variety of electronic devices connected to the Internet, Digital Humanities (DH), as a research field, has experienced a great transformation that has permitted the completion of very ambitious projects with a large impact on society beyond academia. This has resulted in a major economic impact in the cultural and creative industries. A number of new and powerful ICTs have made possible the exploitation of a wealth of data (either digitized or digitally born) that have enormously changed the practices of DH, and exposed novel challenges that must be faced in order to complete any of the said projects. From the creation to the consumption of digital resources, there are new stakeholders, contexts and tasks to consider. The amount of digital resources produced (or digitized), stored, explored, and analysed in any DH project is immensely vast (especially if we take into account the introduction of linked-data), so the traditional humanities tools have to be either substituted or aided with ancillary tools in the form of interactive visualizations or novel user interfaces.

Furthermore, during the whole lifecycle of any DH project—from the data preparation to the actual analysis or exploration phase—many decisions have to be made in order to yield the desired results, which depend on the uncertainty pertaining to both the datasets and the models behind them.

One result of these many adjustments, adaptations and migrations is that the sources, nature and role of uncertainty in humanities research, and the options researchers have to manage them, are changing. Debates, which previously could not be resolved in a satisfactory way, can now be argued statistically, but, at the same time, certain rich modes of information input, from the library shelf to the potsherd, have been deprecated in the shadow of their less contextualised digital surrogates. This Special Issue will feature a range of perspectives on how humanistic researchers’ relationship to uncertainty has changed in the digital age, how the risks might be managed and the opportunities exploited, and what digital research in other disciplines might learn from the lessons of uncertainty in DH.

Topics:

  • Concepts of uncertainty in various disciplines
  • Understanding all the sources of uncertainty that can affect the DH practice
  • Assessing the degree of uncertainty of data sources
  • Quantifying and Measurement of uncertainty in various disciplines
  • Uncertainty, risks and innovation
  • Uncertainty and digital transformation
  • Communication of uncertainty to the user/researcher
  • Uncertainty and teaching, communication of uncertainty to scholars
  • Uncertainty and the media, communication of uncertainty to non-scientists
  • Applications
  • Software and tools for uncertainty management
  • Technologies like semantics, linked data and language processing for data uncertainty
  • (Progressive) Visualization of uncertainty
  • History of discussion certainty and uncertainty in science

Assoc. Prof. Roberto Sánchez
Ms. Eveline Wandl-Vogt
Dr. Jennifer Cizik Edmond
Dr. Cezary Mazurek
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Informatics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Uncertainty
  • Uncertainty sources
  • Uncertainty modeling
  • Provenance
  • Uncertainty visualization
  • Perspectives
  • Narratives

Published Papers (5 papers)

2019

Open AccessArticle
Strategies and Recommendations for the Management of Uncertainty in Research Tools and Environments for Digital History
Informatics 2019, 6(3), 36; https://doi.org/10.3390/informatics6030036 - 01 Sep 2019
Cited by 1
Abstract
This paper takes a high-level view of both the sources and status of uncertainty in historical research and the manners in which possible negative effects of this omnipresent characteristic might be managed and mitigated. It draws upon both the experience of a number [...] Read more.
This paper takes a high-level view of both the sources and status of uncertainty in historical research and the manners in which possible negative effects of this omnipresent characteristic might be managed and mitigated. It draws upon both the experience of a number of digital projects and research into the many-faceted concept of uncertainty in data, and in particular, it explores the conflicting strategies for the management of uncertainty in historical research processes that are reflected in the historiographical and digital humanities literature. Its intention is to support a dialogue between the humanities and computer science, able to realise the promise of digital humanities without a reversion to a new positivism in disciplines such as history and literary studies and it therefore concludes with recommendations for the developers of research tools and environments for digital history. Full article
Open AccessArticle
Towards A Taxonomy of Uncertainties: Analysing Sources of Spatio-Temporal Uncertainty on the Example of Non-Standard German Corpora
Informatics 2019, 6(3), 34; https://doi.org/10.3390/informatics6030034 - 01 Sep 2019
Cited by 2
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 [...] Read more.
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. Full article
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Open AccessArticle
Towards an Uncertainty-Aware Visualization in the Digital Humanities
Informatics 2019, 6(3), 31; https://doi.org/10.3390/informatics6030031 - 10 Aug 2019
Cited by 1
Abstract
As visualization becomes widespread in a broad range of cross-disciplinary academic domains, such as the digital humanities (DH), critical voices have been raised on the perils of neglecting the uncertain character of data in the visualization design process. Visualizations that, purposely or not, [...] Read more.
As visualization becomes widespread in a broad range of cross-disciplinary academic domains, such as the digital humanities (DH), critical voices have been raised on the perils of neglecting the uncertain character of data in the visualization design process. Visualizations that, purposely or not, obscure or remove uncertainty in its different forms from the scholars’ vision may negatively affect the manner in which humanities scholars regard computational methods as useful tools in their daily work. In this paper, we address the issue of uncertainty representation in the context of the humanities from a theoretical perspective, in an attempt to provide the foundations of a framework that allows for the construction of ecological interface designs which are able to expose the computational power of the algorithms at play while, at the same time, respecting the particularities and needs of humanistic research. To this end, we review past uncertainty taxonomies in other domains typically related to the humanities and visualization, such as cartography and GIScience. From this review, we select an uncertainty taxonomy related to the humanities that we link to recent research in visualization for the DH. Finally, we bring a novel analytics method developed by other authors (Progressive Visual Analytics) into question, which we argue can be a good candidate to resolve the aforementioned difficulties in DH practice. Full article
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Open AccessArticle
Exhibiting Uncertainty: Visualizing Data Quality Indicators for Cultural Collections
Informatics 2019, 6(3), 29; https://doi.org/10.3390/informatics6030029 - 31 Jul 2019
Cited by 3
Abstract
Uncertainty is a standard condition under which large parts of art-historical and curatorial knowledge creation and communication are operating. In contrast to standard levels of data quality in non-historical research domains, historical object and knowledge collections contain substantial amounts of uncertain, ambiguous, contested, [...] Read more.
Uncertainty is a standard condition under which large parts of art-historical and curatorial knowledge creation and communication are operating. In contrast to standard levels of data quality in non-historical research domains, historical object and knowledge collections contain substantial amounts of uncertain, ambiguous, contested, or plainly missing data. Visualization approaches and interfaces to cultural collections have started to represent data quality and uncertainty metrics, yet all existing work is limited to representations for isolated metadata dimensions only. With this article, we advocate for a more systematic, synoptic and self-conscious approach to uncertainty visualization for cultural collections. We introduce omnipresent types of data uncertainty and discuss reasons for their frequent omission by interfaces for galleries, libraries, archives and museums. On this basis we argue for a coordinated counter strategy for uncertainty visualization in this field, which will also raise the efforts going into complex interface design and conceptualization. Building on the PolyCube framework for collection visualization, we showcase how multiple uncertainty representation techniques can be assessed and coordinated in a multi-perspective environment. As for an outlook, we reflect on both the strengths and limitations of making the actual wealth of data quality questions transparent with regard to different target and user groups. Full article
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Open AccessArticle
Conceptualization and Non-Relational Implementation of Ontological and Epistemic Vagueness of Information in Digital Humanities
Informatics 2019, 6(2), 20; https://doi.org/10.3390/informatics6020020 - 06 May 2019
Cited by 2
Abstract
Research in the digital humanities often involves vague information, either because our objects of study lack clearly defined boundaries, or because our knowledge about them is incomplete or hypothetical, which is especially true in disciplines about our past (such as history, archaeology, and [...] Read more.
Research in the digital humanities often involves vague information, either because our objects of study lack clearly defined boundaries, or because our knowledge about them is incomplete or hypothetical, which is especially true in disciplines about our past (such as history, archaeology, and classical studies). Most techniques used to represent data vagueness emerged from natural sciences, and lack the expressiveness that would be ideal for humanistic contexts. Building on previous work, we present here a conceptual framework based on the ConML modelling language for the expression of information vagueness in digital humanities. In addition, we propose an implementation on non-relational data stores, which are becoming popular within the digital humanities. Having clear implementation guidelines allow us to employ search engines or big data systems (commonly implemented using non-relational approaches) to handle the vague aspects of information. The proposed implementation guidelines have been validated in practice, and show how we can query a vagueness-aware system without a large penalty in analytical and processing power. Full article
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