Page XVIII

Borgman notes that no social framework exist for data that is comparable to this framework that exist for analysis. CF. Kitchen 2014 who argues that pre-big data, we privileged analysis over data to the point that we threw away the data after words . This is what creates the holes in our archives.

He wonders capabilities [of the data management] must be compared to the remarkably stable scholarly communication system in which they exist. The reward system continues to be based on publishing journal articles, books, and conference papers. Peer-reviewed legitimizes scholarly work. Competition and cooperation are carefully balanced. The means by which scholarly publishing occurs is an unstable state, but the basic functions remained relatively unchanged. while capturing and managing the "data deluge" is a major driver of the scholarly infrastructure developments, no Showshow same framework for data exist that is comparable to that for publishing.

p. 141

Initially, the digital humanities consisted of the curation and analysis of data that were born digital, and the digitisation and archiving projects that sought to render analogue texts and material objects into digital forms that could be organised and searched and be subjects to basic forms of overarching, automated or guided analysis, such as summary visualisations of content or connections between documents, people or places. Subsequently, its advocates have argued that the field has evolved to provide more sophisticated tools for handling, searching, linking, sharing and analysing data that seek to complement and augment existing humanities methods, and facilitate traditional forms of interpretation and theory building, rather than replacing traditional methods or providing an empiricist or positivistic approach to humanities scholarship.

summary of history of digital humanities

p. 100

Data are not useful in and of themselves. They only have utility if meaning and value can be extracted from them. In other words, it is what is done with data that is important, not simply that they are generated. The whole of science is based on realising meaning and value from data. Making sense of scaled small data and big data poses new challenges. In the case of scaled small data, the challenge is linking together varied datasets to gain new insights and opening up the data to new analytical approaches being used in big data. With respect to big data, the challenge is coping with its abundance and exhaustivity (including sizeable amounts of data with low utility and value), timeliness and dynamism, messiness and uncertainty, high relationality, semi-structured or unstructured nature, and the fact that much of big data is generated with no specific question in mind or is a by-product of another activity. Indeed, until recently, data analysis techniques have primarily been designed to extract insights from scarce, static, clean and poorly relational datasets, scientifically sampled and adhering to strict assumptions (such as independence, stationarity, and normality), and generated and alanysed with a specific question in mind.

Good discussion of the different approaches allowed/required by small v. big data.

p. 86

25% of data stored in digital form in 2000 (the rest analogue; 94% by 2007

Kitchin, Rob. 2014. The Data Revolution. Thousand Oaks, CA: SAGE Publications Ltd.

Kitchin, Rob. 2014. The Data Revolution. Thousand Oaks, CA: SAGE Publications Ltd.