Humanities and data science
special interest group

The challenges and prospectsof the intersection of humanities
and data science:
A white paper fromThe Alan Turing InstituteThe Alan Turing InstituteAuthors
Barbara McGillivray (The Alan Turing Institute, and University of Cambridge)
Beatrice Alex (University of Edinburgh)
Sarah Ames (National Library of Scotland)
Guyda Armstrong (University of Manchester)David Beavan (The Alan Turing Institute)Arianna Ciula (King’s College London)
Giovanni Colavizza (University of Amsterdam)
James Cummings (Newcastle University)
David De Roure (University of Oxford)
Adam Farquhar
Simon Hengchen (University of Gothenburg)
Anouk Lang (University of Edinburgh)
James Loxley (University of Edinburgh)
Eirini Goudarouli (The National Archives, UK)
Federico Nanni (The Alan Turing Institute)
Andrea Nini (University of Manchester)
Julianne Nyhan (UCL)
Nicola Osborne (University of Edinburgh)
Thierry Poibeau (CNRS)
Mia Ridge (British Library)
Sonia Ranade (The National Archives, UK)
James Smithies (King’s College London)
Melissa Terras (University of Edinburgh)
Andreas Vlachidis (UCL)
Pip Willcox (The National Archives, UK)

Citation information
McGillivray, Barbara et al. (2020). The challenges and prospects of the intersection of humanities and data science: A White Paper from The Alan Turing Institute.
Figshare. dx.doi.org/10.6084/m9.figshare.12732164

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The Alan Turing InstituteContents
Summary ................................................................................................................................. 4
Preamble ................................................................................................................................. 7
Why this paper, why now ................................................................................................... 8Context .................................................................................................................................... 9
Scope ....................................................................................................................................... 12
Challenges, opportunities, and recommendations ........................................................ 14
Enablers and support structures .......................................................................................... 17
Conclusion ............................................................................................................................ 22
Acknowledgements ........................................................................................................... 23
References ............................................................................................................................ 24

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SummaryThis paper was produced as part of the activities of the Humanities and Data Science Special
Interest Group based at The Alan Turing Institute1. The group has created the opportunity for
fruitful conversations in this area and has brought together voices from a range of different
disciplinary backgrounds. This document shows an example of how conversations of this typecan benefit and advance computational methods and understandings in and between thehumanities and data science, bringing together a diverse community. We believe the Turing
can act as a nexus of discussion on humanities and data science research at the national (andinternational) level, in areas such as education strategy, research best practices, and funding
policy, and can promote and encourage research activities in this interdisciplinary area. Specificrecommendations aimed at the Institute include:– Allowing and encouraging PhD candidates from non-STEM backgrounds to be eligible
to apply for the Turing enrichment scheme, thus enabling more collaborations at theintersection between humanities and data science;– Identifying humanities as a priority area for the data science for Science programme2
and include the phrase ‘and Humanities’ into the name of the programme;– Joining existing training programmes aimed at digital humanities researchers andpractitioners to provide data science skills, building on previous experience such as with theDigital Humanities at Oxford summer school3.
– Ensuring representation and advocacy for the humanities in strategic and decisionmaking structures. This will stimulate diversity of engagements and impact across andpromote further interdisciplinary work.

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1 www.turing.ac.uk/research/interest-groups/humanities-and-data-science, (The Alan Turing Institute, 2020)
2 www.turing.ac.uk/research/research-programmes/data-science-science, (The Alan Turing Institute, 2020).
3 Researchers from the Turing convened two highly successful workshops in the 2019 edition of the Digital Humanities at Oxford Summer School(www.dhoxss.net), “From Text to Tech” and “Applied Data Analysis” and plans to continue such engagements are in place for future editions of thesummer school.

The Alan Turing InstituteMoreover, we outline the following more general recommendations to funders, academic
institutions, and researchers to further support research at the intersection between humanities
and data science:

1. Methodological frameworks and epistemic cultures.
We call for the use of a common methodological terminology in research at the intersection
between humanities and data science, and for a wider use of shared research protocols acrossthese domains. We recommend that authors make the methodological framework that they areusing explicit in their publications, and we call for inclusive research practices to be fosteredacross research projects.

2. Best practices in the use and evaluation of computational tools.
We encourage practices that ensure transparency and openness in research, and training
programmes that help to choose the most suitable computational tools and processes inhumanities research. We also call for computational tools to be evaluated in a dialogue between
data scientists and digital humanists.

3. Reproducible and open research.
We promote transparent and reproducible research in the humanities, covering data, code,
workflows, computational environments, methods, and documentation. Research funders
and academic institutions should put in place further incentives for humanities researchers topublish the digital resources, code, workflows and pipelines they create as legitimate research
outputs, e.g. in the form of publications in data journals.

4. Technical infrastructure.
As data and computing requirements grow, a horizontal infrastructure should be developed inorder to democratise access to digital resources and to guarantee their continued maintenanceand improvement. We also recommend that institutions teaching and supporting digital
humanities direct users to these shared infrastructures to promote their uptake.

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5. Funding policy and research assessment.
We encourage the creation of cross-council schemes which fund collaborative data science
projects, for example with humanities colleagues embedded in the teams from conception.
In evaluation commissions, funding bodies should recognise interdisciplinary research as
requiring to be evaluated by panels of experts themselves engaged in interdisciplinary research.
Where appropriate, research protocols in data science projects concerning humanities data
should allow for humanities perspectives (e.g. integration of data ethics issues and evaluationof machine learning results based on the needs of Humanities scholars). Institutions can invest
in resources that bridge the gap between data scientists and digital humanities scholars, forexample, by creating ‘safe’ spaces where practitioners across disciplines can create joint
agendas for collaboration. Funders and research bodies supporting data science should ensurethat their boards, and steering committees, comprise of those from a range of interdisciplinary
backgrounds, including from the humanities, to encourage this dialogue to flourish.

6. Training, education, and expertise.
We acknowledge the need to upskill humanities researchers in quantitative and computational
methods if they wish to, and to incorporate these methods in undergraduate and graduate
degrees. We also recognise that people educated in the scientific disciplines would benefit
from acquiring skills traditionally associated with the humanities. Consideration should be given
to the development of robust talent pipelines, as well as short skills-enhancement courses andworkshops, and university courses. Schemes for collaborative PhDs, internships and researchsecondments across disciplines, institutions, and businesses should be supported.

7. Career, development, and teams.
In a highly interdisciplinary research context, we encourage multiple career paths and working
models so that students and early career researchers gain a sense of which career options
might be open to them.

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The Alan Turing InstitutePreamble
The Humanities — academic disciplines that study aspects of human society and culture, suchas history, linguistics, politics, divinity, and literatures — are faced with opportunities presented
by digital tools and methods that could enable transformational innovative research. This
document reflects on what data-driven research within the humanities entails and presents,
priorities and recommendations for supporting and driving it forward. Our analysis applies
particularly, although not exclusively, to the UK academic landscape.
This paper was produced as part of the activities of the Humanities and Data Science Special
Interest Group based at the Turing4 , the British national-level research institute for Data Scienceand Artificial Intelligence. Founded in 2017, the special interest group has grown to include over
80 people. Over three years of intensive collaborations and discussions (2017-2020), including
community engagement events (such as the panel ‘Data Science & Digital Humanities: newcollaborations, new opportunities and new complexities’ at the Digital Humanities 2019conference5), the group has identified the need for a focused and coordinated reflection on ashared agenda for both the humanities and data science. This document is the result of those
discussions, sustained and strengthened by a fruitful and inclusive exchange of expertise andviewpoints. While not all authors are directly affiliated with the Institute, the Turing provided
the opportunity for these reflections to arise from an ongoing open conversation between
researchers and practitioners.
The primary audience of this document consists of researchers and managers from the Institute,as well as researchers from different backgrounds (humanities, computer science, statistics,
mathematics, social sciences), educators, policy makers, researchers and practitioners basedin cultural heritage organisations (Galleries, Libraries, Archives and Museums - GLAM) and thecreative sector. It will also be of interest to university leadership teams who may use it to inform
longer-term strategic and planning decisions based on the potential of the humanities to play apart within cross-institution data science actions.
We have chosen the term ‘Humanities and Data Science’ in recognition of the diversity ofactivities, approaches and interdisciplinary collaborations encompassed by current workand discussion in this space. The more established term for computational approaches tohumanities research, ‘Digital Humanities’, also appears throughout this document. While thereis broad consensus around the scope of digital humanities as an area of research and teachingwhere epistemology is entangled with digital technology, there are multiple definitions6. Ourdefinition of digital humanities is broad and inclusive, in alignment with Terras et al. (2013). For‘Data Science’ we refer to the definition offered by the Turing7 as the field that “brings together
researchers in computer science, mathematics, statistics, machine learning, engineering andthe social sciences” to study “the drive to turn [large amounts of] data into useful information,and to understand its powerful impact on science, society, the economy and our way of life”.
We recognise that there is a significant overlap between the fields of data science and artificial
intelligence (AI). However, we have decided to leave the latter out of the scope of this paper,
given its focus on the development of algorithms to perform actions in an autonomous way andthe emphasis we want to place in the ability of data science methods to reveal new insightsfrom humanities research data. We will also use the adjectives ‘data-driven’ and ‘computational’
to refer to certain aspects of the research process involving data science methods in thehumanities.

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4 www.turing.ac.uk/research/interest-groups/humanities-and-data-science, (The Alan Turing Institute, 2020).
5 https://doi.org/10.34894/B1UFVH
6 See whatisdigitalhumanities.com (Heppler, 2009-2014) for many differing viewpoints.
7 www.turing.ac.uk/about-us/frequently-asked-questions, (The Alan Turing Institute, 2020).

The Alan Turing InstituteWhy this paper, and why now
Since their beginnings, the digital humanities have engaged in an energetic debate about
their scope, defining features, and relationship to the wider humanities, and have establishedthemselves as a community of practice (Schreibman et al., 2004; Terras, 2010; Terras,
2013; Terras et al., 2013; Gold and Klein, 2016; The Digital Humanities Manifesto 2.0). The
computational focus has characterised the field from its initial explorations (Hockey, 2004;
Vanhoutte, 2013; Nyhan and Flinn, 2016) and the shift from the label ‘Humanities Computing’to ‘Digital Humanities’ was a catalyst for change. In the history of the field, recurring cycles
and productive tensions have arisen from the interfolding of computational methodologies andapproaches with hermeneutic and critical modes of analysis (see McCarty, 2005; Rockwell
and Sinclair, 2016; Jones, 2016). This document postulates that we are currently witnessing
another one of these junctures, one that is calling for a critical involvement with data science.
In many ways, we are seeing earlier methods blending into, or being extended by data science.
Digitisation workflows are being augmented with automatic information extraction, data
analysis, automated transcription of handwritten documents8, and visualisation of transcribed
content9. Techniques developed for history, literary studies, and linguistics are being scaled
towards larger datasets and more complex problems raising the bar of interpretability andquestioning the validity of data collection and analysis methods. On the other hand, the field of
data science has recently started to engage with non-STEM (Science, Technology, Engineering,and Mathematics) disciplines, by offering new data-driven modelling frameworks for addressing
long-standing research questions (Kitchin, 2014; Lazer et al., 2009) and proposing so-called‘human-centred approaches’ to data science, focussed on the interpretability of machinelearning models and a more active role for human input in algorithms (See Chen et al., 2016).
Moreover, in the current historical context we are witnessing an increased awarenessof the questions of diversity and inclusion in research and academia, and we are seeing
the creation of a strong movement aimed at addressing such issues globally. We
believe that this paper can play a role in reinforcing a positive message in this respect.1

88 See for example transkribus.eu/Transkribus (Transkribus, 2020).
9 See for example the report on ‘Post digitisation metadata enrichment’ commissioned by Jisc (Digirati, 2019).

The Alan Turing InstituteContext
Bridging the gap between humanities research and computational approaches has been oneof the core aims of the humanities computing and digital humanities from their inception (SeeNyhan and Passarotti, 2019, which explores one of a number of possible genealogies). Yet, overthe past decade there has been an increased interest in developing and supporting activities
that involve, as peer partners, communities of humanities scholars, the GLAM sector, creative
computing and data science researchers, prompted in some cases by the availability of a great
variety of large digital datasets for humanities research, coupled with emerging quantitative
research frameworks and relatively cheap computing resources10.
A number of initiatives, including targeted funding schemes (e.g. The Transatlantic Platform11,
Digging Into Data12, and Digital Transformations13), have promoted efforts to answer humanitiesrelated research questions, including methodological ones, through computational methods.
Communities, groups, and committees have emerged to support these efforts over the years14.
Examples include member organisations of the Alliance of Digital Humanities Organizations(ADHO)15, the Methods Network16, the Computational Humanities group in Leipzig17, theComputational Humanities committee18, Computational Humanities research19, the AHRCfunded Computational Archival Science (CAS) research network20, and the Advanced
Information Collaboratory21. Such groups have also organised a number of events andworkshops; a necessarily incomplete list includes ADHO-affiliated conferences from 1989
to the present day22, Computational Humanities 201423, CAS: Exploring Data - Investigating
Methodologies 201924, The Digital Experimentation Workshop series at The National Archives
UK (2017 - today)25, COMHUM 201826, and the Computational Humanities Research workshop27.
Moreover, a series of publications (Schreibman et al. (eds), 2004; Siemens and Scheibman (eds),
2008; Hughes, 2008; Biemann et al., 2014; Ortolja-Baird et al., 2019; Jenset and McGillivray, 2017;
among many others) have put forward epistemological reflections, methodological frameworks
and approaches for conducting research in this area. Initiatives such as the HathiTrust ResearchCentre (2020)28 and Stanford Literary Lab (2020)29 are leading similar work in the USA. While
there is no systematic analysis of the global landscape, initiatives such as Global Outlook: Digital
Humanities (2020)30 demonstrates the richness, heterogeneity and international breadth of theintersections between humanities research and computational approaches.
10 As already remarked for instance by Milligan (2012) when discussing a “third way of computational history”.
11 The Transatlantic Platform is a collaboration between Humanities and social science research funders across South America, North America
and Europe: www.transatlantic platform.com (2020).
12 The Digging into Data Challenge, a collaboration of international funders, aims to address how big data challenges change the landscape forsocial sciences and Humanities research: diggingintodata.org (The National Endowment for the Humanities, 2020).
13 The AHRC funded a number of projects under the Digital Transformations in Arts and Humanities theme intended explicitly to transformresearch methodologies in a number of areas.
14 Similar aims are shared by members of the TEI consortium (members.tei-c.org/Institutions) , or communities of domain-specialised practicesuch as Digital Medievalist (digitalmedievalist.wordpress.com) or Digital Classicist (www.digitalclassicist.org).
15 adho.org.
16 www.methodsnetwork.ac.uk/redist/pdf/finalreport.pdf.
17 ch.uni-leipzig.de/about.
18 www.ehumanities.nl/computational-humanities.
19 github.com/cohure/CoHuRe.
20 computationalarchives.net.
21 ai-collaboratory.net.
22 adho.org/conference.
23 www.dagstuhl.de/en/program/calendar/semhp/?semnr=14301.
24 blog.nationalarchives.gov.uk/computational-archival-science-cas-experimentation-knowledge-exchange-and-interdisciplinary-collaborations(Goudarouli, 2019).
25 www.nationalarchives.gov.uk/about/our-research-and-academic-collaboration/events-and-training/digital-experimentation-workshops (TheNational Archives, 2020).
26 wp.unil.ch/llist/en/event/comhum2018 (Laboratoire lausannois d’informatique et statistique textuelle, 2020)27 cohure.github.io/CoHuRe (Arnold et al., 2020).
28 www.hathitrust.org/htrc.
29 litlab.stanford.edu.
30 www.globaloutlookdh.org.

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From the OpenGlam movement encouraging open licensing of digitised content31, to the open
cultural data movement for museum APIs32 to the collections as data movement encouraging
libraries and archives to provide collections in machine-readable form33, cultural heritageorganisations have increasingly devoted attention to establishing infrastructures and services34
that enable access and use of digital resources for humanities research, as well as employing
computational and quantitative techniques to inform their own approaches. They are also
increasingly embracing machine learning for processing mass digitised content35 and analysis
of collections as well as for process improvement36 and innovative forms of discovery andinterpretation37.
A number of ‘continuing education’ programmes aim to fill the educational gap between
the traditional offerings of museum studies, library and information studies and Humanities
departments and the demands for computational and quantitative skills38. Examples of suchprogrammes include:
1. General training events with multiple disparate workshops, such as the annual Digital
Humanities training events at Oxford39, Digital Humanities Summer Institute (DHSI) at
Victoria40, DHSI Atlantic at Cork41, Humanities Intensive Learning and Teaching (HILT)42,
European Summer School in Digital Humanities in Leipzig43;
2. Training events focussed on a specific topic or subject area, such as courses on
Natural Language Processing methods and techniques44, the Cambridge Cultural Heritage
data school45, the Qstep programme in Manchester, the new post-graduate certificate in
Computing for Cultural Heritage46, the Carpentries skills training series47, the Helsinki Digital
Humanities hackathon series48;
3. Resources for online self-tuition such as Programming Historian49, Digital Research
Infrastructure for the Arts and Humanities (DARIAH) Teach and DARIAH Campus50,
Parthenos51 Training Suite.

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31 openglam.org
32 www.freshandnew.org/2010/10/launch-of-the-powerhouse-museum-collection-api-v1-at-amped and museum-id.com/unlocking-potential-next-open-cultural-data-museums-mia-ridge
33 collectionsasdata.github.io.
34 Theoretical and practical work for information integration in the field of Cultural Heritage has been undertaken for the past twenty years byinitiatives such as the CIDOC Conceptual Reference Model (CRM, www.cidoc-crm.org) and Europeana (www.europeana.eu) with substantial
impact in digital heritage.
35 For example: transkribus.eu/Transkribus.
36 For example: livingwithmachines.ac.uk, www.kb.nl/en/news/2019/kb-explores-artificial-intelligence-to-generate-metadata, themuseumsai.
network and library.stanford.edu/projects/fantastic-futures.
37 For example British Library Labs (www.bl.uk/projects/british-library-labs), Library of Congress Labs (labs.loc.gov) and similar initiatives at theintersection with creative practitioners and industries.
38 In the UK The British Academy has devoted strategic focus to this; see www.thebritishacademy.ac.uk/tag/quantitative-skills-publications.
39 www.dhoxss.net.
40 dhsi.org.
41 www.ucc.ie/en/dhsiatlantic.
42 dhtraining.org/hilt/courses.
43 esu.culintec.de.
44 Teaching NLP for Digital Humanities, Teach4DH Workshop (ceur-ws.org/Vol-1918), the European Summer School in Logic, Language andInformation (ESSLLI).
45 www.cdh.cam.ac.uk/dataschool/cultural-heritage-data-school.
46 www.bl.uk/projects/computingculturalheritage.
47 carpentries.org.
48 www.helsinki.fi/en/helsinki-centre-for-digital-Humanities/helsinki-digital-Humanities-hackathon, which is sometimes advertised via the DARIAH-CLARIN DH course registry (registries.clarin-dariah.eu/courses).
49 programminghistorian.org
50 teach.dariah.eu and campus.dariah.eu.
51 www.parthenos-project.eu.

The Alan Turing InstituteOn the other hand, there has been growing interest from computer scientists, physicists, andapplied mathematicians to work on large data sets of Humanities data, such as text corpora,
images, and others (Kitchin, 2014; Clifford et al., 2016). These datasets are interesting for
researchers in these fields because of the complexity they often manifest. Humanities datasets
are often unstructured, fragmentary, ambiguous, contradictory, multilingual, heterogeneousand bounded by the subjectivities of their data collection (e.g. Alex et al., 2016; Guiliano andRidge, 2016) or limited by the data available or accessible to researchers at the time (e.g. Clifford
et al., 2016; Hauswedell et al., 2020). Moreover, humanities datasets offer rich case studies for
those interested in the statistical modelling of this messy and complex data (e.g. Underwood,2018). Although this research has immense promise to yield new insights into the historical andcultural record, it is imperative that humanists and computer scientists engage in meaningful
collaborations with each other in order to pursue it.
Both computational and humanistic domain knowledge are needed to engage with thesedatasets and their layers of complexity. Building on the strong tradition of interdisciplinary andintermural collaboration which has characterised the field of digital humanities, we need strongmodels of collaboration. Without such collaborations, there is a substantial risk that datadriven research does not say anything new or meaningful, repeats well-known distortions, orintroduces new forms of bias at an even larger scale. In such a rich and vibrant landscape, thisdocument aims to highlight a series of recommendations to help the communities involved
interact in such a way as to reach the full potential of interdisciplinary research.

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Scope
Research at the intersection between humanities and data science can take different shapes.
Our conversation has been framed by the four general areas outlined below. In this document
we focus more specifically on the first two, although some of our recommendations apply to all
four and others not listed here:– Computational humanities research. This area aims at applying existing methods
and developing new methods to create and/or analyse digitised and born-digital datasets
to answer both novel and established humanities research questions. Computational
humanities research typically relies on quantifiable evidence and adopts computational andautomatic procedures for processing and analysing data. Extensive work has been done
to describe this area and we refer to external resources52, as well as the lists of initiatives
presented in the previous section for a fuller overview. Examples of this research have been
undertaken on topics such as computational historical linguistics for the classical languages
for example to find how the meaning of words changes over time and which factors can be
identified for it (McGillivray et al., 2019), in the context of economic history cliometrics basedon the Seshat database (Turchin et al., 2015), art analytics and modelling (Whitehouse et al.,2019; Fraiberger et al., 2018), the history of the Humanities (Colavizza, 2018), and web archival
research (Nanni, 2018).
– Infrastructure for Cultural Heritage. This area is concerned with creating, storing andproviding access to repositories of complex and nuanced digital (structured and unstructured)data from GLAM organisations for their use in research, as well as investigating the questionof availability of digital resources as data, and accounting for biases and uncertainty inthem. This infrastructure, whose solutions include closed commercial and openly available
public systems, also enables the enrichment of cultural heritage data and metadata usingstate-of-the-art Data Science methods. Examples of research into existing and potential
future infrastructure offerings include, the Living with Machines project53, the University ofGlasgow’s GDD Network project, which explores the feasibility of creating a global registerof digitised material54, and the Collections as Data recommendations55 (Padilla et al., 2019).
Additional examples include the Big Data for Law project56 and the new programme Towards
a National Collection: Opening UK Heritage to the World57, which brings together the Artsand Humanities Research Council (AHRC) with The Department for Digital, Culture, Media &Sport (DCMS) and AHRC Independent Research Organisations (IROs)58 through the Strategic
Priorities Fund led by UK Research and Innovation59. In addition, many national libraries also
make collections data available on institutional websites, and/or offer API access to data60.
There are efforts to develop robust, open-source tools enabling text mining and search of themined output across large-scale text collections, e.g. the Defoe toolbox (Filgueira et al., 2019),
to avoid humanities researchers having to develop ad hoc technological solutions.1

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52 For example: www.scottbot.net/HIAL/index.html@p=41533.html and cohure.github.io/CoHuRe.
53 www.livingwithmachines.ac.uk.
54 gddnetwork.arts.gla.ac.uk.
55 collectionsasdata.github.io.
56 Also see gtr.ukri.org/projects?ref=AH%2FL010232%2F1. This was previously known as the Legal Data
Research Infrastructure and was funded by the UK AHRC under the ‘digital transformations’ call in 2012.
57 www.gov.uk/government/news/government-investment-backs-museums-of-the-future and ahrc.ukri.org/newsevents/news/
first-awards-and-leadership-announced-towards-a-truly-national-collection/?utm_source=Twitter&utm_medium=social&utm_campaign=SocialSignIn&utm_content=.Social+Team%3A+funding+calls+and+events.This large funding effort has led to a number of projects. The National
Archives, UK, received funding, through the Towards a National Collection programme, for two foundational projects: Deep Discoveries, acollaboration with the University of Surrey’s Centre for Vision Speech and Signal Processing, V&A and Royal Botanic Garden Edinburgh, that aims
to create a computer vision search platform that can identify and match images across digitised collections on a national scale; and, Engaging
crowds: citizen research and heritage data at scale, a collaboration with the University of Oxford’s Zooniverse, Royal Botanic Garden Edinburgh,
and Royal Museums Greenwich, that aims at harnessing the capabilities of people-powered research to enrich understanding of cultural heritage
collections through digitally enabled participation.
58 An IRO is an organisation which is deemed by AHRC to have a large enough research ‘critical mass’ to be considered for AHRC funding in thesame way as a university. For more information, you can visit the IRO Consortium for the Arts and Humanities.
59 www.ukri.org/research/themes-and-programmes/strategic-priorities-fund.
60 For example, bl.iro.bl.uk, data.nls.uk, labs.kb.dk, lab.kb.nl, data.bnl.lu.

The Alan Turing Institute– History and critique of data science. This area analyses the characteristics of data
science work, sometimes with a focus on the historicity of datasets, and tackles ethical andmethodological questions aimed at improving current practices, for example, on issues suchas diversity (D’Ignazio and Klein, 2020) and privacy. It also problematizes the very definitionof data, considering their complexities, their inherent biases, their contextual and historicalnatures, in a critical and nuanced way (e.g. Drucker, 2011). Exemplary research in this area
includes, among many others, MacKenzie (2017)’s study of the interface between machinelearning and critical thought, Kaltenbrunner (2014; 2015)’s studies of infrastructure as arelational and emergent phenomenon that shapes data-driven humanities research andresearchers, and the questions they can ask, in complex ways, and Noble (2018)’s study of
how white patriarchy and algorithmic bias has resulted in the misrepresentation of women ofcolour and minorities in search engine results.
– Algorithmic creativity and cultural innovation in the arts and humanities. This area
focuses on computational creativity, aiming to perform creative tasks with the aid of machines
and to explore the plasticity of digital forms for delivering new radical ways of representationand mediation of the arts and humanities. This is the focus of various initiatives such as theTuring AI & Arts group61 and the Creative Informatics programme in Edinburgh62. Individual
artists are also increasingly including computational methods in their practice63. Examples ofprojects undertaken in collaboration with academic researchers, creative industries and the
GLAM sector include the King’s Digital Lab Digital Ghost Hunt64 experience and the AI andStorytelling project65.

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61 www.turing.ac.uk/research/interest-groups/ai-arts.
62 creativeinformatics.org.
63 See, for example, www.forbes.com/sites/tabithagoldstaub/2018/09/24/machine-dreams-art-and-artificial-intelligence, thegradient.pub/thepast-present-and-future-of-ai-art, www.bl.uk/events/imaginary-cities, data.nls.uk/projects/artist-in-residence and www.mutualart.com/Article/
The-Real-Future-of-Art-and-Artificial-In/D741A0C0C602F7E5.
64 digitalghosthunt.com.
65 www.kdl.kcl.ac.uk/our-work/ai-and-story-telling.

The Alan Turing Institute

Challenges, opportunities, andrecommendations
We envisage a future where humanities and data science researchers work together insynergy to realise the full potential of interdisciplinary work. But we recognise that there arecultural, practical, methodological, technical, financial, and infrastructural obstacles (Kemman,2019) which currently slow down and in some cases stop this bidirectional exchange66. We
therefore make the following series of recommendations, aimed at researchers in data science,
humanities, GLAM practitioners and researchers, creative industries, funders, and policy
makers. In some cases, when there are no definite answers, we recognise the challenges
and aim to keep the conversation open while recommending that opportunities are created toreflect upon and address those challenges.
The recommendations below are presented into two groups: one related to the research
process and one related to the support structures which are needed to enable it.1

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66 See also www.ukri.org/files/infrastructure/the-uks-research-and-innovation-infrastructure-opportunities-to-grow-our-capacity-final-low-res.

The Alan Turing InstituteResearch Process
1. Methodological frameworks and epistemic cultures
What does this mean? Incorporating new computational, quantitative, and data-driven
approaches into the way humanities research is conducted requires us to articulate andrethink the whole research process in new ways (McCarty, 2005). Following a long tradition of
methodological and theoretical work, we believe that dedicated efforts, tailored to the specificneeds of the individual disciplines, would help keep this work current and relevant in today’s
fast-changing technological landscape. We welcome a plurality of voices into this discussion.
What is the issue/context? Some disciplines have started to pave the way in this direction:
Jenset & McGillivray (2017) for historical linguistics, Graham et al. (2015) and McGillivray et al.
(2018) for a preliminary account in history, Clifford et al. (2016) for environmental history, Bode(2018), Eve (2019) and Kuhn (2019) for literary studies and Smithies (2017) for the humanities
more generally. However, more work is needed to develop general methodological reflections
and research agendas, and to ensure these discussions and frameworks are fully inclusive (Ali,2014; Earhart et al., 2016).
What do we propose and for whom? We call for a generous understanding of what counts
as ‘method’, ‘methodology’, and ‘methodological frameworks’, alongside existing more explicit
definitions. One practical way to achieve this is via a common terminology and a wider useof research protocols addressed jointly to both humanists and computational researchers. To
facilitate this common understanding, we recommend that authors make the methodologicalframework used explicit in their publications, so that methodological decisions can be fully
explained, motivated, and connected with the original research questions and interpretationof the research results. Aligned to this are issues of equality, diversity, and inclusion: we mustensure that a diverse range of voices (across the protected characteristics of race, religion or
belief, age, gender reassignment, sex, sexual orientation, marriage and civil partnership andpregnancy and maternity, disability, and nation) are involved fully in humanities data science
research, going forward, to avoid issues of digital colonialism, and avoid the perpetration ofexisting inequalities within the evolving research agenda (Risam, 2018).

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2. Best practices in use and evaluation of computational tools
What does this mean? This concerns the practical workflows and the decisions that are madein the research process at the intersection between digital humanities and data science.
What is the issue/context? Using pre-existing tools to deploy computational techniques inhumanities can result in work that is insufficiently critical of the quantitative methods on whichsuch tools depend, see e.g. discussion in Nanni et al. (2016).
What do we propose and for whom? To address this challenge, we encourage the adoption ofpractices that ensure transparency (as well as reproducibility and openness, see next point) in
the process and outputs of research, as well as the creation of training programmes that tackle
statistical and computational literacy, and the question of suitability of computational tools andprocesses (including data quality, iterations and prototyping) in humanities research (see point
below).
A related point concerns the development and evaluation of computational tools. In the same
way as other computational fields have accepted that assumptions and generalisations need tobe made in order to be able to evaluate methods and therefore foster new research, data-driven
research in the humanities must be ready to critically examine assumptions and uncertainties
in their evaluation benchmarks too (McGillivray et al., 2020). Moreover, it is important toalways clarify when such tools are adopted for data exploration or for extracting quantitative
evidence supporting an argument (Owens, 2012). In both settings, benchmarks should be built
in a dialogue between data scientists and digital humanists in order to assess the methods’
reliability and examine the types of errors to which they are prone. Eventually, when applicable,
computational results should be made falsifiable to allow the community to assess theirrobustness and replicability (Nguyen et al., 2019; Tahmasebi and Hengchen, 2019). Engaging
both humanists and data scientists with the same results is possible through methodologies
such as “explicitly decoupling, for reviewing purposes, the assessment of computational
results from those of interpretive work, to allow for broader engagement” (Colavizza, 2019).
disability, and nation) are involved fully in humanities data science research, going forward, toavoid issues of digital colonialism, and avoid the perpetration of existing inequalities within the
evolving research agenda (Risam, 2018).

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3. Reproducible and open research
What does this mean? Work with computational methods occurs across a wide spectrum in thehumanities, from rigorously empirical to experimental and exploratory or creative approaches(Smithies, 2017). Accordingly, different degrees of reproducibility — intended as the ability toreproduce comparable results with the same data and same analysis methods — apply to thediffering research processes.
What is the issue/context? Reproducible and open research depends upon access to data
and appropriate computational infrastructure. New computational methods and approaches
are being undertaken in different computational environments, some of which are in closed
and/or proprietary infrastructure and others in open and/or public infrastructure. Very often
new and exciting computational methods are time-consuming and hard to implement, or havedependencies that are difficult to establish, raising additional barriers to making research
reproducible. The desire for reproducibility follows a broader trend in scientific disciplines anddata science in particular67, and more investment is needed in regard to sustainability of codeand open source frameworks. Journals such as the Journal of Open Humanities Data68 andResearch Data in the Humanities69, which focus on the publication of digital research objects
and their critical description, do exist, but they are niche venues and their awareness among
humanities researchers is still low.
What do we propose and for whom? We promote transparent and reproducible researchin the humanities, covering data, code, workflows (Liu, 2017), computational environments,
methods and documentation. We encourage partnerships and initiatives involving humanities
research groups and institutions like the Software Sustainability Institute70 and the UK Research
Software Engineer Association71. We encourage the uptake of existing open science principles,for example the FAIR Guiding Principles for scientific data management and stewardship, which
ask that research data is Findable, Accessible, Interoperable, and Reusable (FAIR) (Wilkinson
et al., 2016). Further incentives should be put in place for humanities researchers to publish thedigital resources, datasets, data models, software modules, workflows and data pipelines theycreate as legitimate research outputs, for example in the form of publications in data journals.1

67 www.turing.ac.uk/research/research-projects/turing-way-handbook-reproducible-data-science.
68 openhumanitiesdata.metajnl.com.
69 brill.com/view/journals/rdj/rdj-overview.xml.
70 www.software.ac.uk.
71 rse.ac.uk.

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The Alan Turing InstituteEnablers / support structures
4. Technical infrastructure
What is this about? This point concerns the large-scale digital infrastructure to host datasetsand computational algorithms, to document and publish data models and workflows, and morein general to support research at the intersection between humanities and data science space.
What is the context? Extensive research policy efforts at the European level have been devoted
to promoting investment in digital research infrastructures for the arts and humanities, and anumber of large projects are active in this space. There has been a long-standing developmentof shared technological infrastructure in the digital humanities for over a decade (see DARIAH
and CLARIN, and NEDIMAH for a methods taxonomy). However, there is a need to design
and implement non-project dependent general, large-scale research infrastructures for thehumanities (see Smithies, 2017) . This would give efficient access to developing technologies forhumanities researchers, and encourage their uptake across humanities disciplines. This would
also avoid the risk of fragmentation whereby individual projects build separate infrastructuresthat do not meet the requirements of FAIR, and are not maintainable and extensible overthe long term. This need is shared by humanities researchers and by most cultural heritageorganisations alike, since traditionally most humanities projects have been of a small-scale andlow-resource nature.
What do we propose and for whom? As data and computing requirements grow, a horizontal
infrastructure should be developed in order to democratise access to digital resources, and toguarantee their continued maintenance and improvement. We also recommend that institutions
teaching and supporting digital humanities direct users to these shared infrastructures topromote their uptake. Some questions remain open: what are the requirements? which are
general? and what are instead specific to the humanities? Which funding, governance, andimplementation models should be pursued?

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5. Funding policy and research assessment
What is this about? This point concerns the funding and research assessment landscape andhow it can support research at the intersection between humanities and data science.
What is the context/issue? Humanities research provides a crucial lens for data science andhas an essential role to play in addressing bias in data, inequalities, power structures, and social
impact of data-driven work. With respect to research funding, Digging into Data and TransAtlantic Platform are examples of successful funding programmes. However, current budgetary
constraints within single research councils still limit the support for collaborative research grants
and hinder ambitious large-scale interdisciplinary projects which can bridge data science andthe arts and humanities. In relation to research assessment, we welcome that digital research
outputs can now be returned in the context of the Research Excellence Framework (REF) in the
UK (Ciula, 2019). However, institutional recognition (by individual universities and departments,
especially in relation to the REF) is also needed to legitimise forms of research outputs suited
to collaborative computational work but do not have the same academic cachet as expected
forms of publication in the Humanities, which is often the single-authored monograph .
What do we propose and for whom? Impactful interdisciplinary work needs specific support,
and more recognition by funders, publishers, institutions and colleagues. This includes
recognition that the time required for interdisciplinary collaboration can reduce the time
available for publications and other traditional reward structures.
From the perspective of funding bodies, adequate cross-council schemes should be conceived
which fund data science projects in genuinely collaborative teams, for example with humanities
colleagues or hybrid roles embedded in the teams from conception. In evaluation commissions,funding bodies should recognise that interdisciplinary research requires its own evaluation byexperts, and it should not be penalised by receiving an average of the scores given by reviewers
from different fields (See Ranjbaran and Marras, 2011).
From the point of view of researchers, protocols should be developed for data science projects
which allow for the intersection with humanities perspectives such as integration of data ethics
issues and evaluation of machine learning results relying on criteria established in collaboration
with humanities scholars. We recognise the importance of institutional investment into
resources that bridge the gap between data scientists and digital humanities scholars. We canachieve this through creating ‘safe’ spaces where practitioners across disciplines can establish
a common language, develop mutual interests, and create joint agendas for collaboration.
This is fundamental to establish a coherent humanities and data science vision, and to
influence funding bodies’ policies to provide opportunities for interdisciplinary exploration andexperimentation. Finally, we encourage funding councils to embrace interdisciplinarity in theirown business: encouraging cross-fertilisation from the arts, humanities, social sciences, andsciences, at board and steering group level, allowing meaningful discussion that can embedinterdisciplinary drivers into institutional practices.

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6. Training, education, and expertise
What is this about? The growing availability of digital and digitised data relevant to humanists
is increasingly allowing scholars to engage with both old and new questions using quantitativeand computational methods. Furthermore, societal challenges calling for humanistic inquiry
are often tied with the development of novel digital technologies. As technological approacheschange and develop, it is crucial that the digital humanities community keep up to date withnew skills, both to engage fully with their affordances, but also to experiment with how they canbe applied to the humanities. In parallel, skills traditionally associated with the humanities are
increasingly in demand among data scientists and are still lacking from most of the scientificeducation programmes.
What is the context? Traditional humanities education is often based on close, interpretative
reading of dense texts and other artefacts, and also based on a high degree of specialisation.
Consequently, while the humanities have developed a core set of methods and techniques forthe rigorous interpretation of their sources, traditionally they lack training in the core subjectsof modern data, computer and information science: mathematics, statistics and computer
programming. To avoid becoming users of black-box tools and techniques, humanities scholars,teachers and professionals need to broaden their training and expertise by integrating or
expanding it beyond the boundaries of the educational offering of their disciplines. This needis not fundamentally different from that of other academic disciplines, such as medicine,
life sciences and the social sciences. The demand for computational and quantitative skills
among digital humanities scholars, GLAM professionals and humanities students can be seenat different levels. Some humanists wish to learn about the basic terminology and workingsof quantitative and computational concepts but would prefer to collaborate with computerscientists rather than learn to program or do statistical analyses themselves (See Cummings,2019). Other humanists are actively interested in complementing their skillset with data,
computer, and information science. On the other hand, there is an increasing need for data
scientists with non-humanities backgrounds to question some of their assumptions about data.
This comes with the understanding that ‘humanities questions’ can help them do research whichgrapples with the complex reality of human society and culture in deeper and more useful ways.
Moreover, we recognise the need to train data scientists in understanding the added value ofhumanities research, so that those complex problems can be tackled in their complexity. One
example is the recent research on detecting word meaning change in language. This has beentackled by computational linguists effectively, but by making strong simplifying assumptions,which means that humanists do not engage with this research.

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What do we propose and for whom? The challenge here is to find a way to train and upskill
humanities researchers in quantitative and computational methods, while at the same time
incorporating the basic principles from these methods throughout undergraduate and graduate
degrees, so humanities graduates are well equipped to lead projects but also potentially
undertake careers in research software engineering and data science for arts and humanities.
Consideration should be given to the development of robust talent pipelines, as well as shortskills-enhancement courses and workshops, and university courses. A set of basic courses
in data science and software engineering, ideally shared across the community, while notturning into a full programme, would offer the foundational skills to support humanists in having
structured and informed conversations with computer scientists and data scientists needed ininterdisciplinary projects. In addition, schemes to sustain collaborative PhDs, internships andresearch secondments across disciplines, institutions and businesses should be supported.
At the same time, we need to open channels of communication between computational
research in the humanities and ‘mainstream’ humanities fields and promote cross-disciplinary
discussions. While a subsection of researchers develops new tools and methods at the leadingedge of technology, we need more established digital methods to be accepted, evaluated, andincorporated by humanities disciplines, given the benefits they can offer to established linesof humanities inquiry. Theorised approaches to these digital methods will also help inform
evaluation criteria for new tools and methods (see Best Practices section).

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7. Career, development, and teams
What is this about? When basic data science principles and quantitative methods are woven
into undergraduate and postgraduate humanities curriculums, we will start to see cohorts ofpeople with the ‘blended’ scholarly and technical profiles necessary for advanced methods,
which will bring new perspectives to industry positions as data scientists and data analysis.
They will also bring technical skills to the careers traditionally chosen by humanities graduates,
such as civil service, management, human resources, but also marketing, publishing, education,and so on. If we focus on careers in universities and cultural institutions, these people will becapable of undertaking advanced degrees in data science and the humanities, and would ideallybe able to choose two pathways: a scholarly route into a traditional academic career, or a more
technical route into a professional career in data science or Research Software Engineering(RSE) . Those who choose the latter route will be in high demand and will join teams whoare beginning to be offered quality career paths (aligned to both academia and industry) anddesigning team structures tailored to data science and the humanities. Both routes will offer
career opportunities outside academia, across all sectors (Health, IT, Government, Finance,
NGO etc) and organisational functions (Operations, Management, Policy Analysis etc.), ensuring
the humanities retain their position as a major contributor to all aspects of civil and commercial
society and culture.
What is the context/issue? RSE career paths and models for RSE teams, despite being central
to UK e-infrastructure strategy , are nascent. On the other hand, digital humanities curricula offer
very different models across Europe and globally. While this diversity is important for the field
to expand and thrive, to sustain career pipelines and profiles suitable to work at the crossroads
between humanities and data science the next generation needs training programmes geared
to practice and process-oriented methodologies . The current generation of RSEs working at theintersection of data science and the humanities need to help define their own career pathways
and ways of working, but also foster new generations. A combined, and collaborative, approachto both education and career development is needed at the project, institutional, national, andinternational level. At the same time, from the point of view of academic digital humanists, thetime and investment needed to acquire technical skills can often mean that digital humanities
CVs and careers are atypical from traditional humanities arcs, and the necessary continual
upskilling in the digital domain is often orthogonal to a traditional academic humanities careerof increasing specialism, which results in issues regarding employment, promotion, and tenure.
What do we propose and for whom? We need to encourage multiple career paths andworking models, so that students gain a sense of which career options are open to them,and the current generation of RSEs (and their managers) have resources they can adapt tothe local circumstances. Examples exist (See Smithies, 2019), but more needs to be done
to encourage teams and their institutions to think critically about the RSE role, problematise
the issues, and ensure they are aligned to workforce needs in Higher Education (HE) and thecreative, government, and commercial sectors: How do we implement financial models andincentive schemes that encourage the kind of flexible working RSEs need? How do we makean RSE career in data science and the humanities appealing enough to attract people who
might otherwise choose to apply their skills to STEM disciplines, or work outside the researchcommunity? How do we increase diversity in RSE, so creativity, research quality, and innovation
improve? How do we balance the need for rigorous and transparent engineering processes
with the need for flexibility, creativity, and acceptance of failure?

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Conclusion
In this document we have outlined a series of recommendations which will hopefully support
research at the intersection between data science and humanities and allow it to advance in
new ways. We also welcome the support we have received by the Institute in exploring therecommendations presented here. We see this document as a marker in the sand of what has
been achieved so far and we acknowledge the extraordinary potential for ground-breaking
new research at the intersection between data science and humanities. This research has
intangible value and outputs that can contribute to the development of the UK culture andindustry, strengthening the value of intellectual work and collaborations in the service of sharedcultural activities.
We would like to end this document with a note on the nature of this interdisciplinary exchange.
We believe that this interdisciplinary exchange is bidirectional. In fact, we observe that muchof the debate around engaging the humanities with possibilities in data science happens withwhat we could call a ‘techno-futurist’ approach: bringing the promise of data science to thehumanities. However, humanities researchers have their own skill sets, value systems, methods,
expertise and approaches which the data science community can learn from, and this should
be a two-way exchange of approaches and knowledge.
Humanists combine rich knowledge of their own field and expertise in analysing andinterpreting gaps in the data, including the significance of negative results. Training in thehumanities provides skills which allows researchers to excel in the detection and confrontationof bias, the analysis of tacit power structures, ethical, feminist, and non-capitalist approaches
to information flow and data analysis, and close-reading approaches to placing the human, andhuman society, at the centre of debates. These are the skills which data science is - latterly- beginning to acknowledge that the industry and research needs, in order to build ethical,
supportive technologies that can be classed as ‘data science for social good’ (although that isrife with its own value judgements).
Building on its position as the UK national institute for Data Science and Artificial Intelligence,
we propose that the Turing acts as a nexus of the discussion on humanities research at thenational (and international) level, engaging on topics such as education strategy, research bestpractices, and funding policy. The recommendations contained in this document are a firststep in this direction, and we aim to develop them further through specific activities and more
publications in the future.

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The Alan Turing InstituteAcknowledgements
We would like to thank Bethany Johnstone for her help in copyediting this text.
This work was supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1.

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References
Alex, B., Grover, C., Oberlander, J., Thomson, T., Anderson, M., Loxley, J., Hinrichs, U. and Ke
Zhou, Ke. (2016). Palimpsest: Improving assisted curation of loco-specific literature. In: Digital
Scholarship in the Humanities, 32(1), pp.i4–i16.
Alex, B., Alexander, A., Beavan, D., Goudarouli, E., Impett, L., McGillivray, B., McGregor, N., &Ridge, M. (2019). Data Science & Digital Humanities: New collaborations, new opportunities
and new complexities. Digital Humanities 2019, Utrecht, The Netherlands. staticweb.hum.
uu.nl/dh2019/dh2019.adho.org/panels/index.html
Ali, M (2014). Towards a decolonial computing. In: Ambiguous Technologies: PhilosophicalIssues, Practical Solutions, Human Nature, International Society of Ethics and Information
Technology. pp. 28-35.
Biemann, C., Crane, G., Fellbaum, C. and Mehler, A. (eds) (2014). Computational Humanities- bridging the gap between Computer Science and Digital Humanities. Dagstuhl
Seminar
14301.
http://cpb-us-w2.wpmucdn.com/u.osu.edu/dist/4/27964/files/2016/01/
DagstuhlSeminarFinalReport-2a7n3h7.pdf
Bode, K. (2018). A World of Fiction: Digital Collections and the Future of Literary History. USA:The University of Michigan Press. https://doi.org/10.3998/mpub.8784777
Chen, N., Kocielnik, R., Drouhard, M., Peña-Araya, V., Suh, J., Cen, K., Zheng, X. and Aragon, C. R.
(2016). Challenges of Applying Machine Learning to Qualitative Coding. In: CHI 2016 workshop
on Human Centred Machine Learning (HCML 2016)
Ciula, A. (2019). KDL Checklist for Digital Outputs Assessment (Version 2.0). Zenodo. http://doi.
org/10.5281/zenodo.3361580
Clifford, J., Alex, B., Coates, C., Watson, A. and Klein, E. (2010). Geoparsing History: Locating
Commodities in Ten Million Pages of Nineteenth-Century Sources. In: Historical Methods,
49(3), pp.115–131. http://doi.org/10.1080/01615440.2015.1116419
Colavizza, G. (2019). Are we breaking the social contract? In: Journal of Cultural Analytics.
September, pp.1-10. http://doi.org/10.22148/001c.11828
Colavizza, G. (2018). Understanding the History of the Humanities from a Bibliometric Perspective:
Expansion, Conjunctures, and Traditions in the Last Decades of Venetian Historiography (1950–2013). In: History of Humanities, 3(2), pp.377–406. https://doi.org/10.1086/699300
Cummings, J. (2019) Opening the book: data models and distractions in digital scholarly editing.
In: International Journal of Digital Humanities, 1, pp.179–193. https://doi.org/10.1007/s42803019-00016-6
D’Ignazio, C., Klein, L. (2020). Data Feminism. USA: MIT Press

25

Digirati. (2019). Post digitisation metadata enrichment tools evaluation report. Jisc. https://digitisation.jiscinvolve.org/wp/files/2019/12/SM20C-Evaluation-Report-v-1.1-for-distribution.
pdf

The Alan Turing Institute

Drucker, J. (2011). Humanities Approaches to Graphical Display. In: Digital Humanities Quarterly,5(1), pp.1-52. www.digitalhumanities.org/dhq/vol/5/1/000091/000091.html
Earhart, A., Gil, A., Risam, R., Bordalejo, B., Galina, I., Hughes, L., Terras, M. (2016). Quality
Matters: Diversity and the Digital Humanities. In Digital Humanities 2016: Conference Abstracts.
Kraków: Jagiellonian University & Pedagogical University, pp. 61-63. http://dh2016.adho.org/
abstracts/14
Eve, M. P. (2019). Close Reading with Computers: Textual Scholarship, Computational Formalism,
and David Mitchell’s Cloud Atlas. Stanford: Stanford University Press.
Filgueira, R., Jackson, M., Terras, M., Beavan, D., Roubickov, A., Hobson, T., Ardanuy, M.C.,
Colavizza, G., Krause, A., Hetherington, J., Hauswedell, T. (2019). defoe: A Spark-based Toolbox
for Analysing Digital Historical Textual Data. In: 2019 IEEE 15th International Conference on
e-Science (e-Science). IEEE 15th International Conference on e-Science (e-Science). USA: San
Diego
Fraiberger, Samuel P., Roberta Sinatra, Magnus Resch, Christoph Riedl, and Albert-László
Barabási. (2018). Quantifying Reputation and Success in Art. In: Science, 362(6416), pp.825-829.
https://doi.org/10.1126/science.aau7224
Gold, M. K. and Klein, L. F. (2016). Debates in the Digital Humanities. USA: University of Minnesota
Press.
Goudarouli, E., (accepted, to be published 2020). From Hilary Jenkinson to Artificial Intelligence
Era: How digital innovation reshapes archival thinking and practice. A. Wiggins and A. Prescott(eds.) In: Archives: Power, Truth and Fiction, the 21st Century Approaches to Literature. Oxford:
Oxford University Press.
Goudarouli, E., Sexton, A., Sheridan, J. (2018). The Challenge of the Digital and the Future
Archive: Through the Lens of The National Archives UK. In: Philosophy and Technology, 32(1),
pp.173-183. https://doi.org/10.1007/s13347-018-0333-3
Graham, Shawn, Milligan, Ian and Weingart, Scott (2015). Exploring Big Historical Data: The
Historian’s Macroscope. Imperial College Press. https://doi.org/10.1142/p981
Grosz, B.J., Grant, D.G., Vredenburgh, K., Behrends, J., Hu, L., Simmons, A. and Waldo, J., (2019).
Embedded EthiCS: integrating ethics across CS education. In: Communications of the ACM,
62(8), pp.54-61.
Guiliano, J., & Ridge, M. (2016). The Future of Digital Methods for Complex Datasets: An
Introduction. In: International Journal of Humanities and Arts Computing, 10(1), pp.1–7. https://doi.org/10.3366/ijhac.2016.0155
Hockey, S. (2004). The History of Humanities Computing. In: Schreibman, S., Siemens, R.,
Unsworth, J. (eds), A Companion to Digital Humanities. Oxford: Blackwell. www.digitalhumanities.
org/companion/view?docId=blackwell/9781405103213/9781405103213.xml&chunk.id=ss1-2-1
Hughes, L. (ed.) (2008). The AHRC ICT Methods Network. London: Office for Humanities
Communication. www.methodsnetwork.ac.uk/redist/pdf/finalreport.pdf

26

The Alan Turing Institute

Jones, S. E., (2016). Busa, R. S. J. and the Emergence of Humanities Computing: The Priest and
the Punched Cards. London: Routledge. www.routledge.com/products/9781138186774
Kaltenbrunner, W. (2014). Infrastructural Inversion as a Generative Resource in Digital
Scholarship. In: Science as Culture, 0(0), pp.1–23. https://doi.org/10.1080/09505431.2014.917
621
Kaltenbrunner, W. (2015). Scholarly Labour and Digital Collaboration in Literary Studies. In:
Social Epistemology, 29(2), pp.207–33. https://doi.org/10.1080/02691728.2014.907834
Kemman, M. (2019) Boundary Practices of Digital Humanities Collaborations. In: DH Benelux
journal, 1, pp.1-24.
Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. In: Big data & society, 1(1),pp.1-12. https://doi.org/10.1177/2053951714528481
Kuhn, J. (2019). Computational text analysis within the Humanities: How to combine working
practices from the contributing fields? In: Language Resources & Evaluation, 53, pp.565–602.
https://doi.org/10.1007/s10579-019-09459-3
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A. L., Brewer, D., ... & Jebara, T. (2009).
Computational social science. In: Science, 323(5915), pp.721-723.
Liu, A. (2017). Assessing Data Workflows for Common Data ‘Moves’ Across Disciplines. May
2017. http://doi.org/10.21972/G21593
Mackenzie, A (2017). Machine Learners: Archaeology of a Data Practice. USA: The MIT Press.
McCarty, W. (2005). Humanities Computing. UK: Palgrave Macmillan.
McGillivray, B. (2014). Methods in Latin Computational Linguistics. Leiden: Brill.
McGillivray, B., Colavizza, G., and Blanke, T. (2018). Towards a Quantitative Research Frameworkfor Historical Disciplines. In: Piotrowski, M. (ed), COMHUM 2018: Book of Abstracts for theWorkshop on Computational Methods in the Humanities 2018, Université de Lausanne, June
4-5, pp.29-31. Zenado. https://zenodo.org/record/1312779#.W2B4I62ZNTY
McGillivray, B., Hengchen, S., Lähteenoja, Palma, M., Vatri, A. (2019). A computational approachto lexical polysemy in Ancient Greek, In: Digital Scholarship in the Humanities, 34(4), pp.893907. UK: Oxford University Press. https://doi.org/10.1093/llc/fqz036
McGillivray, B., Poibeau, T. and Ruiz Fabo, P. (2020). Digital Humanities and Natural Language
Processing: Je t’aime...moi non plus. In: Digital Humanities Quarterly, 14(2), pp.1-45. www.
digitalhumanities.org/dhq/vol/14/2/000454/000454.html
McGillivray, B. (accepted, to be published 2020). Computational methods for semantic analysis
of historical texts. In: Schuster, K. and Dunn, S. (eds). Routledge International Handbook of
Research Methods in Digital Humanities. London: Routledge.
Milligan, I. (2012). Mining the ‘Internet Graveyard’: rethinking the historians’ toolkit. In: Journal of
the Canadian Historical Association/Revue de la Société historique du Canada, 23(2), pp.21-64.
https://doi.org/10.7202/1015788ar

27

Nanni, F., Kümper, H., and Ponzetto, S. P. (2016). Semi-supervised textual analysis andhistorical research helping each other: Some thoughts and observations. In: International

The Alan Turing InstituteNanni, F., (2018). Collecting primary sources from web archives: A tale of scarcity and abundance.
In: Brugger, N. and Milligan, I. (eds). The SAGE Handbook of Web History. UK: SAGE publications
Limited.
Nguyen, D., Liakata, M., DeDeo, S., Eisenstein, J., Mimno, D., Tromble, R. and Winters, J.,(2019). How we do things with words: Analysing text as social and cultural data. arXiv preprint
arXiv:1907.01468. pp.1-24. https://arxiv.org/abs/1907.01468
Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New
York: NYU Press.
Nyhan, J. and Passarotti, M. (2019). One origin of Digital Humanities: Fr Roberto Busa in his own
words. Switzerland: Springer Nature.
Nyhan, J. and Flinn, A. (2016). Computation and the Humanities: Towards an Oral History of
Digital Humanities. Switzerland: Springer.
Ortolja-Baird, A., Pickering, V., Nyhan, J., Sloan, K. and Fleming, M. (2019). Digital Humanities in
the Memory Institution: The Challenges of Encoding Sir Hans Sloane’s Early Modern Cataloguesof His Collections. In: Open Library of Humanities, 5(1), pp.44. http://doi.org/10.16995/olh.409
Owens, T. (2012). Discovery and justification are different: Notes on science-ing the humanities.
Personal Website: www.trevorowens.org/2012/11/discovery-and-justificationare-differentnotes-on-sciencing-the-humanities
Padilla, T., Allen, L., Frost, H., Potvin, S., Russey R. E., and Varner, S. (2019). Final Report --Always Already Computational: Collections as Data (Version 1). Zenodo. http://doi.org/10.5281/
zenodo.3152935
Piotrowski, M. (2012). Natural Language Processing for Historical Texts. Morgan & Claypool
Publishers. In: Synthesis lectures on human language technologies, 5(2), pp.1-157. https://doi.
org/10.2200/S00436ED1V01Y201207HLT017
Piotrowski, M. (2018). Digital Humanities: An Explication. In: Burghardt, M. & Müller-Birn, C. (eds.),
INF-DH-2018. Bonn: Gesellschaft für Informatik e.V. https://dx.doi.org/10.18420/infdh2018-07
Prisoner, T. (n.d), The Digital Humanities Manifesto 2.0. In: Humanities Blast Publications. www.
humanitiesblast.com/manifesto/Manifesto_V2.pdf
Ranjbaran, Farzam and Marras, Cristina (2011) European Peer Review Guide: Integrating Policies
and Practices into Coherent Procedures. ESF Member Organisation Forum on Evaluation of
Publicly Funded Research. Strasbourg. http://repository.fteval.at/148
Risam, R. (2018). New Digital Worlds Postcolonial Digital Humanities in Theory, Praxis, andPedagogy. USA: Northwestern University Press.
Rockwell, G. and Sinclaire, S. (2016). Hermeneutica: Computer-Assisted Interpretation in the
Humanities. USA: The MIT Press,.
Schreibman, S., Siemens, R. and Unsworth, J. (2004). A Companion to Digital Humanities.
Oxford: Blackwell. www.digitalhumanities.org/companion
Smithies, J. (2017) Software Intensive Humanities. In The Digital Humanities and the Digital
Modern. Basingstoke: Palgrave Macmillan.
28

The Alan Turing Institute

Smithies, J. (2017) Towards a Systems Analysis of the Humanities. In: The Digital Humanities
and the Digital Modern. Basingstoke: Palgrave Macmillan.
Smithies, J. (2019). Research Software (RS) Careers: Generic Learnings from King’s Digital Lab,King’s College London. Zenodo. https://doi.org/10.5281/zenodo.2564790
Smithies, J. (2017). The Digital Humanities and the Digital Modern. Basingstoke: Palgrave
Macmillan.
Tahmasebi, N. and Hengchen, S. (2019). The Strengths and Pitfalls of Large-Scale Text Mining
for Literary Studies. In: Samlaren: tidskrift för svensk litteraturvetenskaplig forskning, 140,
pp.198-227. www.diva-portal.org/smash/get/diva2:1415010/FULLTEXT01.pdf
Terras, M., Nyhan, J. and Vanhoutte, E. (2013). Defining Digital Humanities: A Reader. London:
Routledge.
Terras, M. (2010). The Digital Classicist: Disciplinary Focus and Interdisciplinary Vision. In:
Bodard, G., Mahoney, S. (Eds.). Digital Research in the Study of the Classical Antiquity. pp. 171191. London: Routledge.
Terras, M. (2012). Being the Other: Interdisciplinary work in Computational Science and the
Humanities. In: McCarty, W., Deegan, W. (Eds.), Collaborative Research in the Digital Humanities.
pp. 213-230. London: Ashgate.
Turchin, P., Brennan, R., Currie, T., Feeney, K., Francois, P., Hoyer, D. and Manning J., Marciniak,
A., Mullins, D., Palmisano, A., Peregrine, P., Turner, E. and Whitehouse, H. (2015). Seshat: The
Global History Databank. In: Cliodynamics: The Journal of Quantitative History and Cultural
Evolution, 6(1). https://doi.org/10.21237/C7CLIO6127917
Underwood, T. (2018). Why an Age of Machine Learning Needs the Humanities. Public Books[blog]. December 5, 2018. www.publicbooks.org/why-an-age-of-machine-learning-needs-thehumanities
Vanhoutte, E. (2013). The Gates of Hell: History and Definition of Digital | Humanities | Computing.
In Terras, M., Nyhan J. & Vanhoutte, E. (eds.) Defining Digital Humanities: A Reader. Farnham:
Ashgate Publishing, pp.119-156.
Whitehouse, H., François, P., Savage, P., Currie, T., Feeney, K., Cioni, E., Purcell, P., Ross, R., Larson,
J., Baines, J., Haar B., Covey, A. and Turchin, P. (2019). Complex Societies Precede Moralizing
Gods throughout World History. In: Nature, 568, pp.226-229. https://doi.org/10.1038/s41586019-1043-4
Wilkinson, Mark, Dumontier, Michel, Aalbersberg, Ijsbrand et al. The FAIR Guiding Principles
for scientific data management and stewardship. Scientific Data 3, 160018 (2016). https://doi.
org/10.1038/sdata.2016.18

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