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

Lookup NU author(s): Dr James CummingsORCiD


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This paper was produced as part of the activities of the Humanities and Data Science Special Interest Group based at The Alan Turing Institute. 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 type can benefit and advance computational methods and understandings in and between the humanities 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 (and international) level, in areas such as education strategy, research best practices, and funding policy, and can promote and encourage research activities in this interdisciplinary area. Specific recommendations 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 the intersection between humanities and data science;– Identifying humanities as a priority area for the data science for Science programme and include the phrase ‘and Humanities’ into the name of the programme;– Joining existing training programmes aimed at digital humanities researchers and practitioners to provide data science skills, building on previous experience such as with the Digital Humanities at Oxford summer school.– Ensuring representation and advocacy for the humanities in strategic and decisionmaking structures. This will stimulate diversity of engagements and impact across and promote further interdisciplinary work.Moreover, we outline the following more general recommendations to funders, academic institutions, and researchers to further support research at the intersection between humanitiesand 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 across these domains. We recommend that authors make the methodological framework that they are using explicit in their publications, and we call for inclusive research practices to be fostered across 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 in humanities 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 to publish 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 in order to democratise access to digital resources and to guarantee their continued maintenance and improvement. We also recommend that institutions teaching and supporting digital humanities direct users to these shared infrastructures to promote their uptake.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 evaluation of 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, for example, by creating ‘safe’ spaces where practitioners across disciplines can create joint agendas for collaboration. Funders and research bodies supporting data science should ensure that 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 and workshops, and university courses. Schemes for collaborative PhDs, internships and research secondments 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

Publication metadata

Author(s): McGillivray B, Alex B, Ames S, Armstrong G, Beavan D, Ciula A, Colavizza G, Cummings J, De Roure D, Farquhar A, Hengchen S, Lang A, Loxley J, Goudarouli E, Nanni F, Nini A, Nyhan J, Osborne N, Poibeau T, Ridge M, Ranade S, Smithies J, Terras M, Vlachidis A, Willcox P

Publication type: Online Publication

Publication status: Published

Series Title:

Year: 2020

Acceptance date: 04/08/2020

Publisher: The Alan Turing Institute

Place Published: London, UK


DOI: 10.6084/m9.figshare.12732164