Know Thy Sensor: Trust, Data Quality, and Data Integrity in Scientific Digital Libraries

Wallis, Jillian C.; Borgman, Christine L.; Mayernik, Matthew S.; Pepe, Alberto; Ramanathan, Nithya; Hansen, Mark

doi:10.1007/978-3-540-74851-9_32

Cited by 54 publications

(67 citation statements)

References 12 publications

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“…This echoes the work of Wallis et al [37] (p. 380), which has examined the criteria for "users to trust and interpret the data in scientific digital libraries". In a recent publication, Borgman et al [38] have described the outcomes from a project which explored the ability of the so-called "long tail" of researchers, as exemplified by small and medium sized laboratories (SMLs), to manage their data.…”

Section: Trust In Research Datasupporting

confidence: 52%

A Trust Framework for Online Research Data Services

2017

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Abstract:There is worldwide interest in the potential of open science to increase the quality, impact, and benefits of science and research. More recently, attention has been focused on aspects such as transparency, quality, and provenance, particularly in regard to data. For industry, citizens, and other researchers to participate in the open science agenda, further work needs to be undertaken to establish trust in research environments. Based on a critical review of the literature, this paper examines the issue of trust in an open science environment, using virtual laboratories as the focus for discussion. A trust framework, which has been developed from an end-user perspective, is proposed as a model for addressing relevant issues within online research data services and tools.

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Section: Trust In Research Datasupporting

confidence: 52%

A Trust Framework for Online Research Data Services

2017

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“…Furthermore, the ability of a scientist to assess the integrity and trustworthiness of a dataset tends to be enhanced when the scientist has greater knowledge about the factors-both social and technical-involved in the different stages of the dataset's production and curation [54]. Our CENS findings show that the production and use of multiple types of datasets significantly complicate these issues.…”

Section: Discussionmentioning

confidence: 91%

“…Our research on CENS found that scientists' ability to assess the integrity of data was essential for reuse. This ability depended on the knowledge the scientist possess of stages of the data life cycle-from research design to data storage and curation [54]. The life cycle of CENS data involved many steps, each dependent on preceding steps: the effect of decisions made at each step was cumulative throughout the life cycle [53].…”

Section: Trust In Datamentioning

confidence: 99%

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

Darch¹,

Borgman²,

Traweek³

et al. 2015

Int J Digit Libr

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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

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“…Spreadsheets often suffice for data management and analysis, especially if the number of observations and data elements is small. Spreadsheets may be the lowest common denominator among small research groups, and provide the means for data exchange within or between teams [35]. At the other end of this dimension are the large repositories necessary to manage the flood of data from telescopes, particle detectors, and other research instruments.…”

Section: Methods Of Data-driven Researchmentioning

confidence: 99%

Research Data: Who Will Share What, with Whom, When, and Why?

Borgman

2010

SSRN Journal

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Know Thy Sensor: Trust, Data Quality, and Data Integrity in Scientific Digital Libraries

Cited by 54 publications

References 12 publications

A Trust Framework for Online Research Data Services

A Trust Framework for Online Research Data Services

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

Research Data: Who Will Share What, with Whom, When, and Why?

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