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The challenges and prospects of the intersection of humanities and data science: A White Paper from The Alan Turing Institute
Get PDFThis 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 the
Data Study Group Final Report: ScotRail - Rethinking Mobility: Reducing Car Miles Through Rail and Road Insights in Scotland
No full textThe Scottish Government aims for Net-Zero emissions by 2045, with transport accounting for 36% of Scotland’s GHG emissions. Private vehicle use is a major contributor, particularly for commuting and long-distance travel. This project, a collaboration between ScotRail and The Alan Turing Institute, investigates how rail and road data can support sustainable mobility, reduce car miles, and enhance rail network resilience. The study explores the impact of rail disruptions on travel behaviour, models network-wide effects, and estimates carbon emissions
Introduction to Machine Learning with Time Series Tutorial for DSSGx-2020 at The Alan Turing Institute
No full text<p>Python tutorial on machine learning with time series for DSSGx 2020 at The Alan Turing Institute</p>
Data Study Group Final Report: National Biodiversity Network Trust - Spatiotemporal analysis of priority species records across England
No full text<p>Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country's top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges.</p><p>The National Biodiversity Network (NBN) is a collaborative partnership created to exchange biodiversity information. The NBN Trust, the charity which oversees and facilitates the development of the Network, has a membership including many UK wildlife conservation organisations, government, country agencies, environmental agencies, local environmental records centres and many voluntary groups.</p><p>The NBN Trust promotes the sharing and use of biodiversity data, which is achieved through their digital data sharing infrastructure, the NBN Atlas. The Atlas (<a href="https://nbnatlas.org/)">https://nbnatlas.org/)</a> is the UK's largest publicly accessible source of biodiversity data. Biodiversity data (also known as 'biological records' or 'species occurrence data') is information about what species are found where.</p><p>We worked with a dataset extracted from the NBN Atlas comprising all records of the 943 species of principal importance in England from 1970 to 2020. These priority species were identified as being the most threatened and requiring conservation action under the UK Biodiversity Action Plan, and are used in nature conservation to support policy, decision making and nature recovery. However, the dataset included 911 species only, as not all the 943 species of principal importance have been recorded on the NBN Atlas in the time period. This dataset included 10,202,929 records.</p><p><a href="https://www.turing.ac.uk/events/data-study-group-may-2023">Data Study Group - May 2023 | The Alan Turing Institute</a></p>
Cloud-first data science at the Alan Turing Institute: Talk from Campus Connections Summit at Imperial College London on 11 April 2018
No full textAn overview of how The Alan Turing Institute is using Azure to support its data science research programme.• What it's like using the cloud as our primary research compute resource.• How does $1,000,000 of Azure credit compare to a dedicated compute cluster?• Examples of Turing research supported by cloud compute.• Supporting researchers using the cloud.</div
Data Study Group Final Report: Sustrans - Towards Equitable Walking and Cycling Infrastructure for All
No full text<p>Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country's top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges.</p><p>This investigation was undertaken with data and advice provided by Sustrans — a UK-based charity which aims to make it easier for people to undertake active travel modes such as walking and cycling. Sustrans undertakes various initiatives and projects to achieve its objectives, including:</p><p>1. <strong>National Cycle Network: </strong>Sustrans developed the National Cycle Network — a network of walking and cycling routes that spans the UK; they now act as custodians for the network. The network aims to provide safe and convenient routes for active travel, connecting communities, urban areas, and rural regions.</p><p>2. <strong>Promotion of Active Travel: </strong>Sustrans promotes walking and cycling as viable transportation options through campaigns, educational programs, and awareness-raising initiatives. The organisation encourages individuals to choose active modes of travel for their daily commutes, leisure activities, and local journeys.</p><p>3. <strong>Infrastructure Development: </strong>Sustrans advocates for the development of cycling and walking infrastructure across the UK. This involves working with local authorities, communities, and stakeholders to improve and expand networks of dedicated paths, lanes, and facilities for pedestrians and cyclists.</p><p>4. <strong>Policy and Advocacy: </strong>Sustrans engages in policy development and advocacy efforts at national and local levels to shape transportation policies and infrastructure investments that prioritise sustainable travel options.</p><p>5. <strong>Research and Evaluation: </strong>Sustrans conducts research and evaluation studies to understand the impact of active travel and sustainable transportation on health, the environment, and communities. The findings contribute to evidence-based approaches and help inform future initiatives and projects.</p><p>This investigation focuses on the first of these initiatives — the National Cycle Network. This investigation aims to provide an improved understanding of how accessible the National Cycle Network (NCN) is to its users, and the types of people who use it to make access more equitable.</p>
The challenges and prospects of the intersection of humanities and data science: A White Paper from The Alan Turing Institute
Get PDFThis 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
The challenges and prospects of the intersection of humanities and data science: A white paper from The Alan Turing Institute
Get PDFThis 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
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The challenges and prospects of the intersection of humanities and data science: A white paper from The Alan Turing Institute
Get PDFThis 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
A graphical framework for interpretable correlation matrix models for multivariate regression
No full textIn this work, we present a new approach for constructing models for covariance matrices by considering the decomposition into marginal variances and a correlation matrix. The correlation structure is deduced from a user-defined graphical structure. The graphical structure makes correlation matrices interpretable and avoids the quadratic increase of parameters as a function of the dimension. We propose an automatic approach to define a prior using a natural sequence of simpler models within the Penalized Complexity framework for the unknown parameters in these models. We illustrate this approach with simulation studies of multivariate longitudinal joint modelling, where we demonstrate some properties of the method and two real data applications: a multivariate linear regression of four biomarkers and a multivariate disease mapping. Each application underscores our method’s intuitive appeal, signifying a substantial advancement toward a more cohesive and enlightening model that facilitates a meaningful interpretation of correlation matrices.AFS has been supported by the Ecosystem Leadership Award under the EPSRC Grant EP/X03870X/1 & The Alan Turing Institute, particularly the Turing Research Fellowship scheme under that grant
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