The challenges and prospects of the intersection of humanities and data science: A White Paper from The Alan Turing Institute

McGillivray, Barbara; Alex, Beatrice; Ames, Sarah; Armstrong, Graham; Beavan, David; Ciula, Arianna; Colavizza, Giovanni; Cummings, James; Roure, David De; Farquhar, Andrew Hamish; Hengchen, Simon; Lang, Anouk; Loxley, James; Goudarouli, Eirini; Nanni, Federico; Nini, A; Nyhan, Julianne; Osborne, Noah; Poibeau, Thierry; Ridge, Mia; Ranade, S; Smithies, James; Terras, Melissa; Vlachidis, Andreas; Willcox, Pip

doi:10.6084/m9.figshare.12732164

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…Collaborative work with data scientists is one of the recommendations of a recent white paper from the Alan Turing Institute. 66 Research at the intersection of humanities and data science can take different forms, including computational humanities research, which aims at creating and/or analyzing digitized and born-digital data sets to answer humanities research questions. But to apply computational methods to data sets, an infrastructure needs to be in place.…”

Section: Mplp With Artifical Intelligence/machine Learningmentioning

confidence: 99%

More Data, Less Process: A User-Centered Approach to Email and Born-Digital Archives

Jaillant

2022

The American Archivist

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The overall aim of this article is to push for access to born-digital archives, including email archives. It argues that the digital revolution has led to huge changes, but it also brought us back to an earlier situation. The world of big (digital) data is not so different from the world of big (paper) data. There is a danger of repeating the mistakes that were made in the twentieth century with large paper archives, which have often remained uncataloged, hidden, and inaccessible to users. The first section looks at the impact of the More Product, Less Process (MPLP) movement on archival repositories over the past fifteen years. Originally conceived as a response to the huge increase in paper records and uncataloged collections, MPLP has been increasingly applied to digitized collections to increase access. However, few institutions have applied MPLP to born-digital collections, and accessibility remains a huge problem. In the next section, this article presents the kind of research that can be done once access to these born-digital collections is achieved. The final section examines the MPLP approach in relation to artificial intelligence/machine learning.

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Section: Mplp With Artifical Intelligence/machine Learningmentioning

confidence: 99%

More Data, Less Process: A User-Centered Approach to Email and Born-Digital Archives

Jaillant

2022

The American Archivist

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“…We aim to explain how text analysis works step-bystep using clear and simple examples. We thereby aspire to develop and broaden humanities and social science students' data-driven training and give them an understanding of how things work inside the box, something for which there is still a significant need in their core disciplines (McGillivray et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

The Online Pivot: Lessons Learned from Teaching a Text and Data Mining Course in Lockdown, Enhancing online Teaching with Pair Programming and Digital Badges

Alex¹,

Llewellyn²,

Orzechowski³

et al. 2021

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In this paper we provide an account of how we ported a text and data mining course online in summer 2020 as a result of the COVID-19 pandemic and how we improved it in a second pilot run. We describe the course, how we adapted it over the two pilot runs and what teaching techniques we used to improve students' learning and community building online. We also provide information on the relentless feedback collected during the course which helped us to adapt our teaching from one session to the next and one pilot to the next. We discuss the lessons learned and promote the use of innovative teaching techniques applied to the digital such as digital badges and pair programming in break-out rooms for teaching Natural Language Processing courses to beginners and students with different backgrounds.1 EFI is a new institute at the University of Edinburgh which will support interdisciplinary research and teaching for the whole institution. https://efi.ed.ac.uk

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“…The notion of 'openness' in research is defined by the Open Knowledge Foundation as 'A piece of content or data is open if anyone is free to use, reuse, and redistribute it-subject only, at most, to the requirement to attribute and share-alike' 1 . Open research not only covers open access academic articles, but more broadly the open publication of artefacts such as data, protocols, or other research products 2 .…”

Section: Introductionmentioning

confidence: 99%

“…First, the increasing availability of digital collections and the application of data-intensive methods in digital humanities (DH) has made it possible to answer research questions at a scale which was unimaginable before. Born-digital data such as dynamic data from social networks and human-generated or machine-generated web content are widely used, and present infrastructural challenges that are being noticed and addressed (see, e.g., [1]). In the field of HSS, projects and networks such as the Social Sciences & Humanities Open Cloud project (SSHOC) 5 , the Association of European Research Libraries (LIBER) 6 , and the Common Language Resources and Technology Infrastructure [2], among others, contribute to promoting open research values and offer researchers a support network to pursue them.…”

Section: Introductionmentioning

confidence: 99%

Deep Impact: A Study on the Impact of Data Papers and Datasets in the Humanities and Social Sciences

et al. 2022

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The humanities and social sciences (HSS) have recently witnessed an exponential growth in data-driven research. In response, attention has been afforded to datasets and accompanying data papers as outputs of the research and dissemination ecosystem. In 2015, two data journals dedicated to HSS disciplines appeared in this landscape: Journal of Open Humanities Data (JOHD) and Research Data Journal for the Humanities and Social Sciences (RDJ). In this paper, we analyse the state of the art in the landscape of data journals in HSS using JOHD and RDJ as exemplars by measuring performance and the deep impact of data-driven projects, including metrics (citation count; Altmetrics, views, downloads, tweets) of data papers in relation to associated research papers and the reuse of associated datasets. Our findings indicate: that data papers are published following the deposit of datasets in a repository and usually following research articles; that data papers have a positive impact on both the metrics of research papers associated with them and on data reuse; and that Twitter hashtags targeted at specific research campaigns can lead to increases in data papers’ views and downloads. HSS data papers improve the visibility of datasets they describe, support accompanying research articles, and add to transparency and the open research agenda.

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The challenges and prospects of the intersection of humanities and data science: A White Paper from The Alan Turing Institute

Cited by 6 publications

References 14 publications

More Data, Less Process: A User-Centered Approach to Email and Born-Digital Archives

More Data, Less Process: A User-Centered Approach to Email and Born-Digital Archives

The Online Pivot: Lessons Learned from Teaching a Text and Data Mining Course in Lockdown, Enhancing online Teaching with Pair Programming and Digital Badges

Deep Impact: A Study on the Impact of Data Papers and Datasets in the Humanities and Social Sciences

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