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How to analyze data settings rather than data sets, acknowledging the meaning-making power of the local. In our data-driven society, it is too easy to assume the transparency of data. Instead, Yanni Loukissas argues in All Data Are Local , we should approach data sets with an awareness that data are created by humans and their dutiful machines, at a time, in a place, with the instruments at hand, for audiences that are conditioned to receive them. All data are local. The term data set implies something discrete, complete, and portable, but it is none of those things. Examining a series of data sources important for understanding the state of public life in the United States--Harvard's Arnold Arboretum, the Digital Public Library of America, UCLA's Television News Archive, and the real estate marketplace Zillow--Loukissas shows us how to analyze data settings rather than data sets. Loukissas sets out six principles: all data are local; data have complex attachments to place; data are collected from heterogeneous sources; data and algorithms are inextricably entangled; interfaces recontextualize data; and data are indexes to local knowledge. He then provides a set of practical guidelines to follow. To make his argument, Loukissas employs a combination of qualitative research on data cultures and exploratory data visualizations. Rebutting the "myth of digital universalism," Loukissas reminds us of the meaning-making power of the local.

This book marks an important contribution to the fascinating debate on the role that information infrastructures and boundary objects play in contemporary life, bringing to the fore the concern of how cooperation across different groups is enabled, but also constrained, by the material and immaterial objects connecting them. As such, the book itself is situated at the crossroads of various paths and genealogies, all focusing on the problem of the intersection between different levels of scale

Sensing In/Security is a book project that investigates how sensors and sensing practices enact regimes of security and insecurity. It extends long standing concerns with infrastructuring and emergent modes of surveillance and securitization by investigating how digitally networked sensors shape practices of securitization. Contributions in this volume engage with the ways in which sensing devices gain political and epistemic relevance in various forms of security, from border security and migration control to drone regulation, epidemiological tracking, aerial surveillance and hacking practices. Using infrastructure and infrastructuring as a conceptual lens, these studies explore the conditions of possibility of sensing threats and in/security, rendering multiple worlds tangible and (sometimes even more importantly) intangible. Instead of solely focusing on the specific sensory devices and their consequences, this collection engages with the emergence of sensor infrastructures and networks and the shaping of such 'macro entities' as international organizations, states and the European Union. ; Full book (open access) will be published by Mattering Press (2021)

The article focuses on stories and storytelling practices as explanatory resources in standardization processes. It draws upon an ethnographic study of the development of a technical standard for data sharing in an ecological research community, where participants struggle to articulate the difficulties encountered in implementing the standard. Building from C. Wright Mills' classic distinction between private troubles and public issues, the authors follow the development of a story as it comes to assist in transforming individual troubles in standard implementation into an institutional issue for the ecological scientific community. The authors present the "hands-on" social science collaboration in this study as an example of a mechanism for supporting institutionalization of issues. Finally, the authors argue that narratives can serve as effective organizing principles within institutional settings, thereby providing an approach to understand the practical, substantive difficulties that occur in work with data in the sciences.