Simon Tanner at King’s College London has released his new impact study at http://simon-tanner.blogspot.co.uk/2012/10/the-balanced-value-impact-model.html. This work provides a model for thinking about, measuring and documenting the impact of digital resources. I’m especially pleased to see our own TIDSR toolkit (http://microsites.oii.ox.ac.uk/tidsr/) referenced strongly in the document as an example of an “essential point of reference” to those wishing to measure impact.
The following call for papers comes in from my friend Lois Scheidt. They will be taking a critical look at big data, and what it means for the practice of research.
CFP “Small Data” in a “Big Data” World, Panel at International Congress of Qualitative Inquiry (ICQI) 2013 to be held May 15-18, 2013 on the campus of the University of Illinois, Urbana-Champaign IL.
Recently the academic research world has been flooded with discussion of the uses and implications of “Big Data.” For those of us whose research focuses on digital environments this discussion includes conferences, grants, special publications, and job announcements that focus on Big Data and the computational turn in social science and humanities research.
‘Big Data’ is not necessarily defined by the size of the data set, for humanities scholars have long been interested in huge textual and image-based corpora. Instead, ‘Big Data’ refers to the increasing complexity of relationships between data objects in a given set, often requiring large-scale computational and algorithmic resources for analysis. ‘Small Data’ research, on the other hand, often begins with a theoretical (e.g., critical race theory) or methodological (e.g., case study or ethnography) approach, which is then applied to digital data drawn from less-popular websites, YouTube videos, or even individual blog posts and comments.
Unfortunately, the tools used to analyze Big Data seem to be influencing modes of thought about new media and digital research away from the theoretical and towards the scientistic. For example, in a recent article Bruns and Burgess (2012) argue that humanist, interpretive studies of social media are ‘ideosyncratic, non-repeatable, and non-verifiable’. Although Bruns and Burgess concede that there is space for ‘traditional qualitative methods’, their suggestion is that these methods need to be ‘integrated and innovated’ upon in a ‘big data’ context.
Given the increasing amounts of attention (e.g., external funding, public policy, or student interest) ‘big data’ is accruing, where does this leave Small Data research and researchers? This panel seeks to explore the position of Small Data in relation to the discussion and/or use of Big Data. As the definition of Big Data is still in flux we are using Bruns & Burgess (2012) to ground our individual presentation. We are seeking presentations that will explore a variety of views on this turn toward Big Data and the impact on the researched, the researcher, and academia.
Bruns, A., & Burgess, J. (2012). Notes towards the Scientific Study of Public Communication on Twitter. Conference on Science and the Internet. Düsseldorf. Retrieved Oct. 8, 2012 from http://snurb.info/node/1678.
Individual presenters should submit a 150 word abstract to each of the organizers by Nov. 15, 2012.
Andre Brock, Assistant Professor, School of Library and Information Science University of Iowa email@example.com
Lois Ann Scheidt, Doctoral Candidate, School of Library and Information Science Indiana University firstname.lastname@example.org
Please forward this CFP to other potentially interested parties and groups.
The slides here were ones we presented about the end(s) of eresearch and the beginings of big data at the Association of Internet Researchers (AoIR) Internet Research 13 (http://ir13.aoir.org/) meeting in Salford.
The presentation focuses on various possible ends to the e-Research programme, including the possibility that it was also just about providing computation support for other disciplines, or that everyone will become ‘accidental e-researchers’ as computation becomes the norm and thus, like other infrastructures, disappears from notice. The third possibility, which is supported by our data, is that various foci over the years (the Grid, Clouds, big data) gain attention cumuluatively (in other words, don’t appear to replace each other, but to add to the mix of computation approaches across the disciplines.
We also discuss styles of science, and suggest that it is possible than an additional ‘algorithmic’ style has emerged that is not a separate style, but an overlay to many of the other styles as algorithmic and computation approaches become part of the regular toolkit of science, social science, and the humanities. Several examples of this are presented in the slides.