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This working paper resulted from two (hybrid) workshops conducted in May and June 2022, chaired by Professor James Smithies (King’s College London) at the request of Tao-Tao Chang, AHRC Head of (Research) Infrastructure. The workshops were conceived and organised by Dr. Anna-MariaSichani (AHRC Policy and Engagement Fellow), and hosted at The Alan Turing Institute. Contributors are listed in Appendix 4.

Living with Machines rethinks the impact of technology on the lives of ordinary people during the Industrial Revolution. The project queries metadata for large-scale newspaper collections. This poster describes the workflow of building a database infrastructure that facilitates quicker and easier humanities research on heterogeneous and complex newspaper data.

This is a re-recording of Beyond Digital Humanities: Weaving Humanities Research Software Engineering and AI, invited talk 15 March 2023 to open the University of Georgia's in Athens inaugural Humanities Festival. A YouTube version is available for casual watching. Join David from The Alan Turing Institute's Research Engineering Group, the UK's national institute for data science and artificial intelligence, on an interdisciplinary journey showcasing cutting-edge digital humanities scholarship and the leading role of research software engineers in the Living with Machines project. The project is a flagship research initiative that aims to rethink the impact of technology on the lives of ordinary people during the Industrial Revolution (1780-1920), bringing together historians, geographers, linguists and curators, alongside research software engineers and research data scientists. Through wrangling 109 billion words of newspapers, 129 thousand sheets of maps, and 180 million census entries, David will demonstrate how the research team has brought new insights while maximizing collaboration. He will also discuss the responsible use of AI, the challenges of navigating intellectual property rights and processing sources securely, and how data science has a lot to learn about representativeness and bias from the humanities. Finally, David will look towards the future of digital research infrastructure in the UK. Can he resolve his identity: digital humanaut, software scribe or research wrangler?

The DigiSpec Project was funded by the UK Arts and Humanities Research Council in 2022 under its “Scoping Future Data Services for the Arts and Humanities” programme. As part of the evidence base for its report, the project collected twenty short Case Reports from experts in the field of born-digital data services. Each of these describes the current and future activities and requirements of a significant UK project or service. These Case Reports are published in this collection. The DigiSpec Project also commissioned three longer Case Studies, which are being published separately- TABLE of CONTENTS Author Project Pages David Beavan, Timothy Hobson (Alan Turing Institute) Living with Machines 1-4 Giles Bergel, Abhishek Dutta, Andrew Zisserman (University of Oxford) National Library of Scotland Chapbooks 5-8 Aruna Bhaugeerutty (Oxford) Oxford University Museums Digital Collections Service 9-12 Samantha Blickhan (Zooniverse & Adler Planetarium) ALICE: The Aggregate Line Inspector & Collaborative Editor 13-14 Toby Burrows (University of Oxford) Knowledge graphs – Mapping Manuscript Migrations 15-19 Alan Chamberlain (University of Nottingham) Rider Spoke – Riders Have Spoken (Archive) 20-21 Arianna Ciula (King’s College London) King’s Digital Lab Infrastructure at King’s College London 22-28 Ian Cooke (British Library) The UK Web Archive 29-32 Ian Cooke & Stella Wisdom (British Library), Graeme Hawley (National Library of Scotland) Emerging formats 33-37 Tim Crawford, Golnaz Badkobeh, & David Lewis (Goldsmiths University of London), Alastair Porter (Universitat Pompeu Fabra, Barcelona), Laurent Pugin & Rodolfo Zitellini (RISM Digital, Bern) F-Tempo (Full-Text search of Early Music Prints Online) 38-41 Nicholas Cronk, Birgit Mikus (University of Oxford) Challenges of Scholarly Editions: Digital Voltaire and Digital d’Holbach 42-44 Neil Jefferies (University of Oxford), Peter Cornwell (Data Futures) Annotation: anəstor 45-47 Neil Jefferies (University of Oxford), Peter Cornwell (Data Futures) Redelivery: freizo 48-50 Huw Jones (Cambridge University) Cambridge Digital Library – TEI Metadata 51-54 Christopher Melen (Royal National College of Music) PriSM Sample RNN 55-58 Kieron Niven (Archaeology Data Service) High Speed 2 (HS2) Historic Environment Digital Archive 59-63 Rachel Proudfoot, Nicola Barnet, Masud Khokar, John Salter (University of Leeds) White Rose Etheses Online: theses as complex digital objects 64-69 Gethin Rees (British Library) Open Geospatial Data Application and Services viewer (OGDAS) 70-72 Pedro Maximo Rocha & Amy Sampson (The National Archives) Environmental monitoring system and its integration with digital twins 73-75 Martin Wynne (University of Oxford) Literary and Linguistic Data Service [formerly Oxford Text Archive] 76-78, The "DigiSpec: Scoping future born-digital data services for the arts and humanities" project was funded by the UK Arts and Humanities Research Council (AHRC) under research grant number AH/W007592/1.

Living with Machines is a five-year collaborative project. It aims to generate new perspectives on the effects of the mechanisation of labour on the lives of ordinary people in Britain during the 'long nineteenth century' (c.1780-1918), by developing computational and historical techniques and research questions for working with historical sources. This document contains the project's first 'Delivery Plan', a formal document that defines the project management processes used to execute and control the project. We have published it in response to interest from a range of sources about how we set up a project of this sort. This first version was drawn up at the start of the project, before the full team was in place. It was updated in March 2019, a few months after the project's official start date. This preamble was largely written in January 2020. On this project we regard the Delivery Plan as a living document that has been, and will continue to be, updated as the project progresses. Subsequent versions will also be deposited. This project, funded by the UK Research and Innovation (UKRI) Strategic Priority Fund (SPF), is a multidisciplinary collaboration delivered by the Arts and Humanities Research Council (AHRC), with The Alan Turing Institute, the British Library, the University of Cambridge, the University of East Anglia, Exeter University, and Queen Mary University of London. Established in 2018, the SPF builds on Sir Paul Nurse���s vision of a ���common fund���, to support high quality multidisciplinary and interdisciplinary research programmes, which could have otherwise been missed through traditional funding channels. Living with Machines was one of the projects selected in the first phase of this funding, and therefore had few UK-models to look to for models of how to plan and deliver a project of this size and disciplinary breadth.

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.

Although there has been a drive in the cultural heritage sector to provide large-scale, open data sets for researchers, we have not seen a commensurate rise in humanities researchers undertaking complex analysis of these data sets for their own research purposes. This article reports on a pilot project at University College London, working in collaboration with the British Library, to scope out how best high-performance computing facilities can be used to facilitate the needs of researchers in the humanities. Using institutional data-processing frameworks routinely used to support scientific research, we assisted four humanities researchers in analysing 60,000 digitized books, and we present two resulting case studies here. This research allowed us to identify infrastructural and procedural barriers and make recommendations on resource allocation to best support non-computational researchers in undertaking ‘big data’ research. We recommend that research software engineer capacity can be most efficiently deployed in maintaining and supporting data sets, while librarians can provide an essential service in running initial, routine queries for humanities scholars. At present there are too many technical hurdles for most individuals in the humanities to consider analysing at scale these increasingly available open data sets, and by building on existing frameworks of support from research computing and library services, we can best support humanities scholars in developing methods and approaches to take advantage of these research opportunities.

Although there has been a drive in the cultural heritage sector to provide large-scale, open data sets for researchers, we have not seen a commensurate rise in humanities researchers undertaking complex analysis of these data sets for their own research purposes. This article reports on a pilot project at University College London, working in collaboration with the British Library, to scope out how best high-performance computing facilities can be used to facilitate the needs of researchers in the humanities. Using institutional data-processing frameworks routinely used to support scientific research, we assisted four humanities researchers in analysing 60,000 digitized books, and we present two resulting case studies here. This research allowed us to identify infrastructural and procedural barriers and make recommendations on resource allocation to best support non-computational researchers in undertaking ‘big data’ research. We recommend that research software engineer capacity can be most efficiently deployed in maintaining and supporting data sets, while librarians can provide an essential service in running initial, routine queries for humanities scholars. At present there are too many technical hurdles for most individuals in the humanities to consider analysing at scale these increasingly available open data sets, and by building on existing frameworks of support from research computing and library services, we can best support humanities scholars in developing methods and approaches to take advantage of these research opportunities.

Lots of money has been spent digitising heritage collections. Digitised heritage collections are data. But non-computationally trained scholars don't know what to ask of large quantities of data. Often they do not have access to high performance computing facilities. We aim to address this fundamental problem by extending research data management processes in order to enable novel research and a deeper understanding of emerging research needs.