Data, Knowledge Practices, and Naturecultural Worlds: Vehicle Emissions in the Anthropocene

This chapter details the various techno-cultural assemblages giving rise to data collected to model and measure anthropogenic worlds, arguing that data-based technologies both represent and co-produce the Anthropocene. It begins with a review of scholarship emerging at the intersection of science and technology studies and information studies that advances understanding of data infrastructure and knowledge practices, and their role within the anthropogenic assemblages that shape history. Drawing on a case study describing how vehicle emissions are measured and regulated in the US, I examine the materialities and mutability of technologies designed to produce data about air quality, along with the cultures and politics that shape them. I detail how US environmental health researchers and regulators grapple with the meaning of evidence and the basis for regulatory decisions as they confront the limits of automated data-collecting and modelling technologies. Finally, I meditate on the role of data-based technologies in mediating the environments we inhabit and the knowledge through which we perceive them

Attending to the Cultures of Data Science Work

This essay reflects on the shifting attention to the “social” and the “cultural” in data science communities. While recently the “social” and the “cultural” have been prioritized in data science discourse, social and cultural concerns that get raised in data science are almost always outwardly focused – applying to the communities that data scientists seek to support more so than more computationally-focused data science communities. I argue that data science communities have a responsibility to attend not only to the cultures that orient the work of domain communities, but also to the cultures that orient their own work. I describe how ethnographic frameworks such as thick description can be enlisted to encourage more reflexive data science work, and I conclude with recommendations for documenting the cultural provenance of data policy and infrastructure

Classification as Catachresis: Double Binds of Representing Difference with Semiotic Infrastructure

Background; This article explores the results of a three-year ethnographic study of how semiotic infrastructures-or digital standards and frameworks such as taxonomies, schemas, and ontologies that encode the meaning of data-are designed. Analysis: It examines debates over best practices in semiotic infrastructure design, such as how much complexity adopted languages should characterize versus how restrictive they should be. It also discusses political and pragmatic considerations that impact what and how information is represented in an information system. Conclusion and implications: This article suggests that all databased representations are forms of data power, and that examining semiotic infrastructure design provides insight into how culturally informed conceptions of difference structure how we access knowledge about our social and material worlds

Reading Datasets: Strategies for Interpreting the Politics of Data Signification

All datasets emerge from and are enmeshed in power-laden semiotic systems. While emerging data ethics curriculum is supporting data science students in identifying data biases and their consequences, critical attention to the cultural histories and vested interests animating data semantics is needed to elucidate the assumptions and political commitments on which data rest, along with the externalities they produce. In this article, I introduce three modes of reading that can be engaged when studying datasets—a denotative reading (extrapolating the literal meaning of values in a dataset), a connotative reading (tracing the socio-political provenance of data semantics), and a deconstructive reading (seeking what gets Othered through data semantics and structure). I then outline how I have taught students to engage these methods when analyzing three datasets in Data and Society—a course designed to cultivate student competency in politically aware data analysis and interpretation. I show how combined, the reading strategies prompt students to grapple with the double binds of perceiving contemporary problems through systems of representation that are always situated, incomplete, and inflected with diverse politics. While I introduce these methods in the context of teaching, I argue that the methods are integral to any data practice in the conclusion

Accountable Data: The Politics and Pragmatics of Disclosure Datasets

This paper attends specifically to what I call disclosure datasets - tabular datasets produced in accordance with laws requiring various kinds of disclosure. For the purposes of this paper, the most significant defining feature of disclosure datasets is that they aggregate information produced and reported by the same institutions they are meant to hold accountable. Through a series of case studies of disclosure datasets in the United States, I specifically draw attention to two concerns with disclosure datasets: First, for disclosure datasets, there is often political and social mobilization around the definitions that determine reporting thresholds, which in turn implicates what observations end up in the dataset. Changes in reporting thresholds can be traced along changes in political party power as the aims to promote accountability through mandated disclosure often get pitted against the aims to reduce regulatory burden. Second, for disclosure datasets, the observational unit - what is ultimately being counted in the data - is often not a person, institution, or action but instead a form that the reporting institution is required by law to fill out. Forms infrastructure the information that ends up in the dataset in notable ways. This work contributes to recent calls to promote the transparency and accountability of data science work through improved inquiry into and documentation of the social lineages of source datasets. The analysis of disclosure datasets presented in this paper poses important questions regarding what ultimately gets documented in the data, along with the representativeness and usefulness of these accountability mechanisms

Data Sharing at Scale: A Heuristic for Affirming Data Cultures

Addressing the most pressing contemporary social, environmental, and technological challenges will require integrating insights and sharing data across disciplines, geographies, and cultures. Strengthening international data sharing networks will not only demand advancing technical, legal, and logistical infrastructure for publishing data in open, accessible formats; it will also require recognizing, respecting, and learning to work across diverse data cultures. This essay introduces a heuristic for pursuing richer characterizations of the “data cultures” at play in international, interdisciplinary data sharing. The heuristic prompts cultural analysts to query the contexts of data sharing for a particular discipline, institution, geography, or project at seven scales – the meta, macro, meso, micro, techno, data, and nano. The essay articulates examples of the diverse cultural forces acting upon and interacting with researchers in different communities at each scale. The heuristic we introduce in this essay aims to elicit from researchers the beliefs, values, practices, incentives, and restrictions that impact how they think about and approach data sharing – not in an effort to iron out differences between disciplines, but instead to showcase and affirm the diversity of traditions and modes of analysis that have shaped how data gets collected, organized, and interpreted in diverse settings

Interview with Deborah Winslow of the National Science Foundation

Chapter Abstract: In this chapter the editors interview Dr. Deborah Winslow about her work at the National Science Foundation (NSF) and the evolution of data management plans (DMPs) in Anthropology and the Social, Behavioral and Economic Sciences (SBE). She outlines what the NSF expects to see in a DMP and what not to include. The conversation moves into how anthropologists collaborate with “adjacent disciplines” and how the ideas and terms for data, and the expectations of data change. She emphasizes thinking about the kind of data you will collect and what you plan to do with those data later, in terms of requirements for sharing and ultimately archiving them. The conversation ends with a discussion about student research and formulating appropriate research questions. Book Summary: Summary: For more than two decades, anthropologists have wrestled with new digital technologies and their impacts on how their data are collected, managed, and ultimately presented. Anthropological Data in the Digital Age compiles a range of academics in anthropology and the information sciences, archivists, and librarians to offer in-depth discussions of the issues raised by digital scholarship. The volume covers the technical aspects of data management-retrieval, metadata, dissemination, presentation, and preservation-while at once engaging with case studies written by cultural anthropologists and archaeologists returning from the field to grapple with the implications of producing data digitally. Concluding with thoughts on the new considerations and ethics of digital data, Anthropological Data in the Digital Age is a multi-faceted meditation on anthropological practice in a technologically mediated world

Moving Ethnography: Infrastructuring Doubletakes and Switchbacks in Experimental Collaborative Methods

In this article, we describe how our work at a particular nexus of STS, ethnography, and critical theory—informed by experimental sensibilities in both the arts and sciences—transformed as we built and learned to use collaborative workflows and supporting digital infrastructure. Responding to the call of this special issue to be “ethnographic about ethnography,” we describe what we have learned about our own methods and collaborative practices through building digital infrastructure to support them. Supporting and accounting for how experimental ethnographic projects move—through different points in a research workflow, with many switchbacks, with project designs constantly changing as the research develops—was a key challenge. Addressing it depended on understanding creative data practices and analytic workflows, redesigning and building technological infrastructure, and constant attention to collaboration ethics. We refer to this as the need for doubletakes on method. We focus on the development of The Asthma Files, a collaborative ethnography project to understand the cultural dimensions of environmental health, and on the Platform for Experimental Collaborative Ethnography, digital infrastructure first built to support The Asthma Files but now available as a community resource for archiving, analyzing, and publishing ethnographic data and writing. A key finding is that different traditions and practices of ethnography require different infrastructures. © 2021 Finnish Society for Science and Technology Studies. All rights reserved

The Comprehensive Assessment of Neurodegeneration and Dementia: Canadian Cohort Study.

BackgroundThe Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS-ND) cohort study of the Canadian Consortium on Neurodegeneration in Aging (CCNA) is a national initiative to catalyze research on dementia, set up to support the research agendas of CCNA teams. This cross-country longitudinal cohort of 2310 deeply phenotyped subjects with various forms of dementia and mild memory loss or concerns, along with cognitively intact elderly subjects, will test hypotheses generated by these teams.MethodsThe COMPASS-ND protocol, initial grant proposal for funding, fifth semi-annual CCNA Progress Report submitted to the Canadian Institutes of Health Research December 2017, and other documents supplemented by modifications made and lessons learned after implementation were used by the authors to create the description of the study provided here.ResultsThe CCNA COMPASS-ND cohort includes participants from across Canada with various cognitive conditions associated with or at risk of neurodegenerative diseases. They will undergo a wide range of experimental, clinical, imaging, and genetic investigation to specifically address the causes, diagnosis, treatment, and prevention of these conditions in the aging population. Data derived from clinical and cognitive assessments, biospecimens, brain imaging, genetics, and brain donations will be used to test hypotheses generated by CCNA research teams and other Canadian researchers. The study is the most comprehensive and ambitious Canadian study of dementia. Initial data posting occurred in 2018, with the full cohort to be accrued by 2020.ConclusionAvailability of data from the COMPASS-ND study will provide a major stimulus for dementia research in Canada in the coming years