Routledge International Handbook of Research Methods in Digital Humanities

Kristen Schuster, Stuart Dunn, Kristen Schuster, Stuart Dunn

• Editorial: Routledge
• Año: 2020
• ISBN:
  9780429670251
• Edición: 1
• Categoría: Education
• Categoría: Education Theory & Practice

This book draws on both traditional and emerging fields of study to consider consider what a grounded definition of quantitative and qualitative research in the Digital Humanities (DH) might mean; which areas DH can fruitfully draw on in order to foster and develop that understanding; where we can see those methods applied; and what the future directions of research methods in Digital Humanities might look like.

Schuster and Dunn map a wide-ranging DH research methodology by drawing on both 'traditional' fields of DH study such as text, historical sources, museums and manuscripts, and innovative areas in research production, such as knowledge and technology, digital culture and society and history of network technologies. Featuring global contributions from scholars in the United Kingdom, the United States, Europe and Australia, this book draws together a range of disciplinary perspectives to explore the exciting developments offered by this fast-evolving field.

Routledge International Handbook of Research Methods in Digital Humanities is essential reading for anyone who teaches, researches or studies Digital Humanities or related subjects.

Section III

Remediation and transmission

15
(Digital) Research Practices and Research Data

Case studies in communities of sociolinguistics and environmental humanities scholars

Vicky Garnett and Eliza Papaki

15.1 Introduction

From November 2017 to October 2018, the DARIAH Community Engagement Working Group¹ investigated matters of self-identity and whether this is reflected in the manner in which data is managed. In particular, we were interested in how researchers may identify themselves within the context of Digital Humanities, and whether or not their scholarly practices reflected their own academic identities. We studied the tools and methodologies employed by researchers at different career levels, and in different academic fields. This chapter showcases some of the results of this study.

15.2 Background

Attempting to give a definition to the term “Digital Humanities” can prove challenging. Historically, the term “Digital Humanities” derived from the term “Humanities Computing” in 2004 when the first ‘Companion to Digital Humanities' was published². However, while the field has been active for more than a decade, its definition keeps changing, because it is a constantly developing environment (Gavin & Smith, 2012).

With so many different definitions comes inspiration for more creative solutions. Discussions on what defines Digital Humanities have been captured in interview series, workshops or DH Days gathering different definitions from researchers in the field. Examples include training websites such as DariahTeach³, or randomly presented definitions on ‘What Is Digital Humanities?’ (Heppler, 2015). It seems therefore that while there is not one unifying definition, there is a large and growing literature invested in defining it.

15.2.1 Academic identities

This complexity in terms of definition relates to the equal complexities it creates for researchers attempting to identify themselves as digital humanists. Linda Evans, reflecting on Clegg's (2008) terms of self-identification, suggests that the interpretation of identity is formed through the application of labels to oneself, as she puts it “self-labelling and self-designation” (Evans, 2015, p. 259, original emphasis). She goes on to say that “Self-reports are […] the only reliable identity-indicators” (ibid, our editing).

Bauman (1996) points to the fluid nature of self-identification, noting that an individual would not want a fixed identity, preferring to ‘keep the options open’ (ibid: 18), and moreover may find themselves holding multiple and even contradictory identities. In an academic context, this is perhaps most relevant to those working within an interdisciplinary field of research.

Yet while the self-designation aspect to self-identification (including in the context of academia) is a strong and important element, Taylor (1989) notes the influence of ‘a defining community’ on a person's process of self-identification. This community can help the individual to contextualise their own place in the (academic) world through use of a shared ‘language’ and understanding. Jenkins (1996) adds that self-identification can be continuous and reflexive, combining internal notions of self with external definitions of oneself applied by others, once again adding in this product of community-driven identity.

This fluid and continuous nature of self-identity within academia can be influenced by changes at a policy level, as Henkel (2005) discusses. While framing her argument within the biosciences, the motivations of funding and reward are equally applicable to the Humanities and Social Sciences. Henkel acknowledges this draw (or indeed push) towards interdisciplinary and multi-modality within research, but concludes that while the dominance of discipline as a means of defining oneself has been challenged in recent years, it is still a ‘strong source of academic identity, in terms of what is important and what gives meaning and self-esteem’ (Henkel, 2005, p. 173).

While there has been a push towards interdisciplinarity since Henkel's study, challenges persist for those who wish to adopt a more interdisciplinary academic identity (Burgess, Garnett, O'Connor, & Ohlmeyer, 2018). This is reflected in the results of the SPARKLE project (Edmond et al., 2015), where participants did not self-identify primarily as digital humanists, despite having a sense of technology influencing their work. Lyall, speaking at a workshop in Dublin on Interdisciplinary Research in 2016 noted that dismissive attitudes towards interdisciplinary research endure where researchers have difficulties publishing interdisciplinary papers in well-regarded journals (Lyall, quoted in Burgess et al., as above). This difficulty is extended to challenges in obtaining funding for interdisciplinary research: better to stick to a single field where it is easier to both get published and win funding than to start swimming in uncharted waters.

15.2.2 Research communities

According to Wenger (1998), there are four components identifying learning, namely: meaning, practice, community and identity.

These components of learning are defined as follows:

1. Meaning: talking about our ability to experience the world as meaningful;
2. Practice: talking about shared historical and social resources, frameworks and perspectives that sustain mutual engagement in action;
3. Community: talking about the social configurations in which our enterprise is defined and our participation is recognisable as competence;
4. Identity: talking about how learning changes who we are.

Here we focus on ‘Practice’ and ‘Community’, approaching and analysing the term “communities of practice” as an overlay concept of research communities. According to Kuhn's “The Structure of Scientific Evolutions”, there are different research paradigms rooted in research communities and practice (Kuhn, 1970). These paradigms can be characterised by different elements, namely:

• they can be centred around a specific problem, or set of problems, regarded as particularly significant in relation to the advancement of knowledge;
• they can be about shared practice and shared understanding about which research techniques are appropriate for investigating that issue;
• they can involve a sense of shared identity, which can be reinforced both through the processes of information exchange of the particular community (specialist publications and conferences) and through the interpersonal networks that practitioners establish in relation to their area of research.
• And fourth, these paradigms operate through groups of practitioners operating in research communities.

Therefore, research communities can exist at a number of levels. At the highest level, such a community is formed by all those engaged in scientific research. At a lower level there are communities operating at the level of subject disciplines, and within these there are sub-communities linked to particular areas and sub-disciplines. Communities, in other words, can exist at different levels and will vary in size. They can be quite small, particularly in the case of ‘cutting edge’ research, and membership of one community does not automatically exclude membership of another. It is a common practice that researchers can be in multiple communities according to their interests, affiliation, or status.

Rethinking for a moment the term ‘research communities' to the concept of ‘communities of practice’ (Lave & Wenger, 1991;Wenger, 1998; Wenger & Snyder, 2000), this last term derived from social learning theory and developed largely in connection with the management of knowledge in formal organisations, capturing also practices of researchers within academic institutions and research organisations. Compared with formal groups created within organisations, who follow a specific structure, tasks and identity, communities of practice can, and do, transcend boundaries of departments, organisations, locations and seniority. The idea behind these communities of practice is that they come into existence through the need to collaborate and learn. Following this, it is possible to have virtual communities based entirely on communication technologies that eliminate the need for face-to-face contact.

What brings them together as a community, though, is that they share a common purpose (Johnson-Lenz & Johnson-Lenz, 1999, cited in Denscombe, 2008) and that common purpose reflects a need to know what each other knows (Brown, cited in Denscombe, 2008). “Communities of practice are groups of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly”, according to Wenger-Trayner & Wenger-Trayner (2015). Members of such communities can interact in different ways and have different goals, however they are bound together due to the common (research) interests and knowledge that they want to exchange. Some examples of activities that such communities develop (as listed by wenger-trayner.com, as above) are:

• Problem solving
• Requests for information
• Seeking experience
• Reusing assets
• Coordination and strategy
• Building an argument
• Growing confidence
• Discussing developments
• Documenting projects
• Visits
• Mapping knowledge and identifying gaps

Depending on where the concept of communities of practice is applied (research, education, web, etc), the scientific domain, the goals of a community and its members, different kinds of activities are developed that best represent the common needs, interests and gaps.

The term ‘communities of practice’ has acquired some negative connotations as it raises concerns that it might elevate practice-based knowledge above more theoretical and abstract forms of knowledge. However, according to the Mixed Methods approach (Denscombe, 2008; Johnson, Onwuegbuzie, & Turner, 2007; Maxwell & Loomis, 2003), communities of practice have been treated primarily as a description of how research communities operate, rather than prescribing a path that ought to be followed. In this respect, there is no clear distinction between practitioners and researchers. Therefore, in this chapter, when referring to ‘communities of practice’ we refer to communities of researchers who work within academic and research institutions.

15.2.2.1 Research infrastructures for research communities

Research infrastructures (RIs) offer a means in which research communities can come together. According to the European Commission, RIs are facilities, resources and services used by the science community to conduct research and foster innovation⁴. They may include major scientific equipment (or sets of instruments); skilled personnel engaged in services, competence development and outreach; knowledge-based resources such as collections, archives or scientific data; and e-infrastructures, such as data and computing systems and communication networks⁵. Research infrastructures can be single-sited (a single resource at a single location), distributed (a network of distributed resources), or virtual (the service is provided electronicall...