Working beyond the confines of academic discipline to resolve a real-world problem: A community of scientists discussing long-tail data in the cloud

Abstract

This project interrogates a workshop leader and whole-meeting talk among a group of scientists gathered at a workshop to discuss cyberinfrastructure and the sharing of both ‘light’ and ‘dark’ data in the sciences. This project analyzes discourses working through the workshop talk to interrogate the social relations, interdisciplinary identities, concerns, and commonalities in the sciences and in relation to emerging opportunities for computing and data sharing in the cloud. The findings point to the efficacy of arranging scientists around data collection processes for collaborative work as opposed to groupings around data type, discipline, work sectors, or collection location. This research provides an opportunity to consider the democratization of data, academic boundaries in the sciences, as well as interdisciplinary and collaborative problem-solving processes that happen in groups across academic and applied contexts.

From nostalgia to knowledge: Considering the personal dimensions of data lifecycles

“…The paper builds upon the author's dissertation research surrounding data practices in astronomy (Stahlman, 2020), which emerged through years of immersion in studying and supporting long tail data management in astronomy and ecology (Brooks et al, 2016;Heidorn et al, 2015; F I G U R E 1 A data lifecycle model; adapted from the United States Geological Survey Science Data Lifecycle Model (Faundeen et al, 2013) Heidorn et al, 2018;Stahlman & Heidorn, 2020). The dissertation project itself sought to develop a more comprehensive understanding of the nature and accessibility of long tail data generated over several decades in astronomy, and the primary data collection activity was an online questionnaire completed by authors of astronomy journal articles.…”

Research data sharing in the Australian national science agency: Understanding the relative importance of organisational, disciplinary and domain-specific influences

“…The importance of intrinsic motivation for data sharing has been found in other studies besides this one. For example, Brooks, Heidorn, Stahlman and Chong [52] found that researchers emphasize common good and the potential for transformative science when explaining their efforts to support data sharing in the context of institutionalized pressures and economic pressures constraining data sharing.…”

Playing with data and its consequences

“…"Big data" are "changing life in many ways (...) from checking traffic jams via Google Maps to looking at the new health trends analyzing Internet searches and being able to predict a new flu outbreak" (18). But as data and data science become "democratized", data management and reuse demand a novel "sense of sharing, community and access" (Brooks, et al, 2016). How do tactics and tools evolve in response to data/infrastructure?…”

“…Following in the footsteps of "pre-big data" informational activism embodied by Anonymous and WikiLeaks, today individuals and groups crowd-source data for mapping purposes, use data for journalistic reporting, and gather data to support their campaigning efforts. Scholars have gradually devoted their attention to these emerging forms of engagement with data -from data journalism (e.g., Baack, 2018) to democratic participation (Couldry and Powell, 2014), from aid and development (e.g., Dalton, et al, 2016) to research (e.g., Brooks, et al, 2016). How individuals make sense of the data and their fabric is also becoming an object of academic inquiry (see, for example, Kennedy, et al, 2016;Lupton, 2018).…”