Abstract
The chapter argues that accessibility and flexibility are the two principles and practices that can bring big data projects the closest to a data factory ideal. The chapter elaborates on the necessity of these two principles, offering a reasoned explanation for their value in context. Using two big data social scientific research projects as a springboard for conversation the chapter highlights both the advantages and the practical limits within which accessibility and flexibility operate. The authors avoid both utopian and dystopian tropes about big data approaches. In addition, they offer a critical feminist discussion of big data collaboration. Of particular interest are also the manner in which specific characteristics of big data projects, especially volume and velocity, may affect multidisciplinary collaborations.
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Notes
- 1.
The designs of social media and their affordances for data scientists also impact scholars’ ability to work with big social data, for instance, APIs and terms of service change, affecting what data is available and under what conditions. Tools modify the core functions and impact the behaviors users are able to engage in – e.g., Twitter is removing usernames and media attachments from the 140-character limit; Facebook does not treat all crisis and find your friend functionality the same way. The legal milieu about rights to be forgotten differs between the USA and European Union. This obviously is not an exhaustive list but rather an illustrative one that makes clear that the changing technological landscape impacts the research we can do.
- 2.
This statement is not meant to conflate the epistemological differences or conflicts within the social sciences but rather to point to the broad stroke differences between the logics in computational or machine-based sciences and those found in the sciences that are people centered.
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Hemphill, L., Jackson, S.T. (2017). Accessibility and Flexibility: Two Organizing Principles for Big Data Collaboration. In: Matei, S., Jullien, N., Goggins, S. (eds) Big Data Factories. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-59186-5_2
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