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The shape of data in the digital humanities

Jan 23, 20256 min read

The shape of data in the digital humanities

Bibliography

Flanders, J., & Jannidis, F. (Eds.). (2019). The shape of data in the digital humanities. Routledge, Taylor & Francis Group.

Abstract

“Data and its technologies now play a large and growing role in humanities research and teaching. This book addresses the needs of humanities scholars who seek deeper expertise in the area of data modeling and representation. The authors, all experts in digital humanities, offer a clear explanation of key technical principles, a grounded discussion of case studies, and an exploration of important theoretical concerns. The book opens with an orientation, giving the reader a history of data modeling in the humanities and a grounding in the technical concepts necessary to understand and engage with the second part of the book. The second part of the book is a wide-ranging exploration of topics central for a deeper understanding of data modeling in digital humanities. Chapters cover data modeling standards and the role they play in shaping digital humanities practice, traditional forms of modeling in the humanities and how they have been transformed by digital approaches, ontologies which seek to anchor meaning in digital humanities resources, and how data models inhabit the other analytical tools used in digital humanities research. It concludes with a glossary chapter that explains specific terms and concepts for data modeling in the digital humanities context. This book is a unique and invaluable resource for teaching and practising data modeling in a digital humanities context”—

Notes

Data modeling in a digital humanities context

Go to annotation“We need to teach literacy in the basic modeling systems and tools early on, ideally even before students reach university. We need to emphasize the scholarly importance of modeling decisions even as we teach our students how to create and publish digital materials, whether those are games or research data, or digital archives or creative works. The “how” of digital humanities needs to be accompanied by an equally compelling “why” that expresses the motivations and ideologies that animate these digital materials. And in a complementary way, we need to teach students to attend to the modeling decisions our tools are making for us, or preventing us from making, and teach them to be resourceful about keeping their data from being too closely entrapped by specific tools.” (“The shape of data in the digital humanities”, 2019, p. 23)

Glossary

  • in/formal models

Notes

Go to annotation“Debates about method are ultimately debates about our models. In a more specific sense, our models represent the shaping choices we make in representing and analyzing the materials we study.” (“The shape of data in the digital humanities”, 2019, p. 3)

  • Data modeling is about making our assumptions explicit

  • Thinking and researching through data was done already before the digital turn

  • Data modeling can be a difficult topic, taking over problems from the past and being treated differently depending on the domain. On top of that, reflection on data modeling was stronger in the beginning of the digital humanities, and less so now

  • data models

    • can have constraints
    • enable collaboration
    • have pedagogical value
    • can be the subject of studies to examine how people were thinking about their data

Go to annotation“In effect, modeling processes write our knowledge about the content and semantics of our data into that data in formal terms, giving the data a kind of intelligence and self-awareness.” (“The shape of data in the digital humanities”, 2019, p. 7)

  • data models can be explizit through markup or hidden in an algorithm, which knows how to read and label data
  • modeling as well as bringing data into the models has obvious and hidden costs and those rise exponentially with complexity
  • data models structure how we can work with data; relational databases, XML, LMNL or COCOA all have their structures which enable and disable certain things

Go to annotation“When do these differences actually matter, and how? Are our data modeling systems simply tools to be understood and used pragmatically, or do they carry cultural significance that informs the data we create with them?” (“The shape of data in the digital humanities”, 2019, p. 10)

  • a good fit in modeling is when data can be entered without redundancy, intuitive usage, scopes and granularities what work with our data and easy analysis of the corpus in the end
  • nonetheless often practitioners chose a technology for it’s features (power) and their own knowledge, not because of the techs fitness
  • everybody is creating and using data, putting them into data models, but for dh people, it’s also very important to be critical in creating and use
  • it’s good to be tool-agnostic: Go to annotation“a tool-agnostic approach would be to identify the underlying functional and semantic distinctions motivating the different behaviors (authorial vs. editorial notes, biographical notes vs. word glossing, and so forth) and build these distinctions into the modeling of the data” (“The shape of data in the digital humanities”, 2019, p. 15)

Go to annotation“Every digital practitioner knows that the quickest way to discover flaws in one’s data is to load it into a tool—any tool—preferably as part of a public demonstration. This is humorous lore but also expresses an important fact: our data may exist apart from tools, but it reaches its fullest realization through enactment, through an active exploration of the patterns and ideas it enables.” (“The shape of data in the digital humanities”, 2019, p. 16)

  • approaches

    • model first, then refine with use and decide what has to be left out
    • derive model from the data and work on it until models stabilizes

  • digital scholars need to be critical and knowledgeable about their data models

Go to annotation“As humanists, we are trained to see symbolic or ideological significance in representational structure. So while the aesthetics or “elegance” of our data models—which as described above is rooted in functional properties—may lead us to seek a deeper meaning in our data structures, the problem of how to understand the cultural meaning of such structures is a methodological question that still awaits a rigorously framed response.” (“The shape of data in the digital humanities”, 2019, p. 19)

See also