What lies beneath?: Knowledge infrastructures in the subseafloor biosphere and beyond

Abstract

We present preliminary findings from a threeyear research project comprised of longitudinal qualitative case studies of data practices in four large, distributed, highly multidisciplinary scientific collaborations. This project follows a 2 × 2 research design: two of the collaborations are big science while two are little science, two have completed data collection activities while two are ramping up data collection. This paper is centered on one of these collaborations, a project bringing together scientists to study subseafloor microbial life. This collaboration is little science, characterized by small teams, using small amounts of data, to address specific questions. Our case study employs participant observation in a laboratory, interviews (n = 49 to date) with scientists in the collaboration, and document analysis. We present a data workflow that is typical for many of the scientists working in the observed laboratory. In particular, we show that, although this workflow results in datasets apparently similar in form, nevertheless a large degree of heterogeneity exists across scientists in this laboratory in terms of the methods they employ to produce these datasets-even between scientists working on adjacent benches. To date, most studies of data in little science focus on heterogeneity in terms of the types of data produced: this paper adds another dimension of heterogeneity to existing knowledge about data in little science. This additional dimension makes more complex the task of management and curation of data for subsequent reuse. Furthermore, the nature of the factors that contribute to heterogeneity of methods suggest that this dimension of

Data Citation as a Bibliometric Oxymoron

“…How these roles should be credited in data citation, and how they should be weighted in contributions to scholarship are open questions. The labor associated with data management and software engineering tends to be lower in status than the scientific work that leads to peer-reviewed papers (Darch et al, 2015).…”

“…Funding agencies may require that data resulting from projects they support be shared and reused, but few such agencies have been willing to invest heavily in data stewardship. Overall, better knowledge infrastructures are needed to manage, discover, and exploit research data and information (Borgman, 2015;CrossRef, 2013;Edwards et al, 2013).…”

“…When the searcher is a computer, these discovery mechanisms fail (Van de Sompel, 2012). A goal for the next generation of search technologies, especially for data discovery, is to support machine actionable links for entire provenance chains (Bechhofer et al, 2010;CrossRef, 2009CrossRef, , 2014Klein et al, 2014;Pepe et al, 2010;Simons, 2012;Van de Sompel, 2015;Van de Sompel, Hochstenbach, & Beit-Arie, 2000;Van de Sompel & Lagoze, 2009).…”

Knowledge infrastructures in science: data, diversity, and digital libraries

“…Resulting scientific papers are usually single-authored, or involve at most a handful of co-authors [76]. Standardization of methods across the scientific domain is minimal, to the extent that each scientist may use different tools and techniques to generate datasets similar in form and intent [38]. Responsibility for data management falls to the scientists who produced the data.…”

Many Methods, Many Microbes: Methodological Diversity and Standardization in the Deep Subseafloor Biosphere

“…For more details about how and why methodological heterogeneity arises in the domain, see Darch et al (2015).…”