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The Long View: Infrastructure, T echnology and the Humanities Prof Sheila Anderson Centre for e-Research Department of Digital Humanities Kings College London T echne ology The word technology, which joined the Greek


  1. The Long View: Infrastructure, T echnology and the Humanities Prof Sheila Anderson Centre for e-Research Department of Digital Humanities King’s College London

  2. ‘T echne’ ‘ology’ • “The word technology, which joined the Greek root, techne (an art or craft), with the suffix ology (a branch of learning), first entered the English language in the seventeenth century. At that time, in keeping with its etymology, a technology was a branch of learning, or discourse, or treatise concerned with the mechanic arts” (Marx 2010)

  3. History of T echnology • Systems not technology (Hughes) • Patterns emerge – Small, localised, homogenous, centralised – T echnology transfer, change, innovation, disruption – Consolidation, dominant model vs interoperating networks • Interactivity, networks (Callon, Edwards)

  4. A Universe of Information • Mass of scientific data • Mass of data on the Internet, explosion in the use of social media • In the humanities library and archive digitisation programmes • Digital humanities • Publishers and commercial organisations • T ension between commoditisation and openness (to which I will return)

  5. We need research infrastructures…. • 2001 UK e-Science programme • 2003 Atkins US Cyberinfrastructure Report • 2006 Unsworth ‘Our Cultural Commonwealth’ ACLS Report • 2006 European Commission ESFRI Roadmap

  6. E-Science Programme • The e-Science programme was largely data driven building big structures for data and people: • “the enormous and growing capacity of computing, storage, communication and software systems – offered the opportunity not only to automate science but also to apply new methods that could revolutionise how science was performed” In this context research is done “through distributed global collaborations enabled by the internet, using very large data collections, terascale computing resources and high performance visualisation” (Atkins et al, 2009).

  7. But for the Humanities?? • Hermeneutic rather than experimental • Narrative, textual, rhetorical • Not seeking formal laws and explanations • Recursive, a constantly questioning process • Deep reading / reasoning of sources • “Scientists want to map the universe; humanities scholars want to investigate the universe in a single poem” (Prescott Inaugural Lecture, Jan 2012)

  8. • Franco Moretti challenged literary scholars to look beyond the small (around two hundred he suggested) number of literary works that most scholars work on throughout their lifetimes to consider the vast number of published works within which this literary canon sits. “... a field this large cannot be understood by stitching together separate bits of knowledge about individual cases, because it isn’t a sum of individual cases” (Moretti, 2005) • “ Role for arts and humanities: Encourage and support even more participation of the arts and humanities research communities in the e-Science Programme (we saw some excellent beginnings in our review). Arts and humanities are poised to achieve large benefit from e-science methods and infrastructure as the human record becomes increasingly digitised and multimedia.” (Recommendations, e-Science Review, 2009)

  9. A&H E-Science Programme • Seven projects and four PhD studentships • Series of related projects funded by JISC • New methods of enquiry but rooted in humanities questions • Medieval warfare on the grid • LaQuAt (linking and querying ancient texts) • HiTHeR (high throughput computing for humanities e-research) • Grid difficult; Integration tricky; HPC successful

  10. Mind the Gap Workshop • “There is a gap between research in the Humanities and Canadian high-performance computing (HPC) facilities, but it is not what we thought it was. We used to think humanists didn't need supercomputing - they were happy with a wordprocessor, email and the Web. Now it is clear that humanists have large multimedia datasets and big questions to ask of the history of human culture. Then we used to think the gap was primarily between facilities set up for queued batch programs and practices in the Humanities of asking questions repeatedly of "always-on" web services. Though there is still some truth to that gap, many HPC facilities have begun to support "portal" or "cloud" facilities that are always-on and can thus support Humanities practices. The gap now is really one of research culture and support. On the one hand we have to find ways of training and preparing humanities research teams to be able to imagine using existing HPC facilities, and on the other we have to develop the ability of HPC consortia to be able to reach out and support humanists.”

  11. Digging into Data “.... address how "big data" changes the research landscape for the • humanities and social sciences. Now that we have massive databases of materials used by scholars in the humanities and social sciences -- ranging from digitized books, newspapers, and music to transactional data like web searches, sensor data or cell phone records -- what new, computationally based research methods might we apply?” Dan Cohen, Tim Hitchcock and Geoffrey Rockwell received funding under • the 2009 call for proposals to undertake ‘data mining with criminal intent’: the application of data mining and statistical analysis to a large corpus of complex texts and information (127 milllion words of trial accounts from the Old Bailey Online) using analytical tools from TAPoR like Voyeur, information management tools like Zotero, and HPC facilities But, Hitchcock questions, are we losing the individual voice to positivist • statistics and graphs?

  12. The essence of the humanities…. • James Grossman, Exec Director, AHA: “And because we look for stories—for ways of synthesizing diverse strands into narrative themes—we usually look for interactions among variables that to other eyes might not seem related. By casting our insights into the form of narratives, we also make them more accessible than multivariate regression analyses could ever be—and arguably more amenable to uncertainty and ambiguity. I have little doubt that people asking big questions of Big Data would benefit from collaboration with the qualitative and interpretive perspectives historians bring to this kind of enterprise.” • Johanna Drucker: “self-reflexive critical models from the humanities are useful for looking at these graphic objects as interpretive forms no matter what field of knowledge is being represented” (Drucker, 2010)

  13. Visualising our Universe • “The infrastructure of scholarship was built over centuries. It includes diverse collections of primary sources in libraries, archives, and museums…” (Unsworth, 2006) • Understanding what is digital and what is not • We might imagine a virtual reading room similar to the physical splendour of the old British Library reading room when it was hosted in the British Museum which at one glance provided a thrilling sense of the richness and extent of the collections

  14. A Rallying Cry to the Humanities • Big data is here and will not go away • The humanities are surely uniquely placed to humanise big data • If the humanities do not engage with big data and the evolution of big structures then the commercial sector will (already is)

  15. Digital Eco-Systems • Neither big structures nor lightweight webs but digital eco-systems

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