Who’s Got the Data? Interdependencies in Science and Technology Collaborations

Borgman, Christine L.; Wallis, Jillian C.; Mayernik, Matthew S.

doi:10.1007/s10606-012-9169-z

Cited by 81 publications

(123 citation statements)

References 55 publications

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“…As the research focus changed, so did the organization of the collaborations. Sensor data emerged early on as a boundary object that was understood differently by science and technology partners (Borgman et al 2007;Borgman et al 2012). Research methods for sensor deployments and statistical analysis emerged later as objects that lay on the boundary between collaborators.…”

Section: Discussionmentioning

confidence: 99%

Unearthing the Infrastructure: Humans and Sensors in Field-Based Scientific Research

Mayernik

Wallis²,

Borgman³

2012

Comput Supported Coop Work

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Abstract. Distributed sensing systems for studying scientific phenomena are critical applications of information technologies. By embedding computational intelligence in the environment of study, sensing systems allow researchers to study phenomena at spatial and temporal scales that were previously impossible to achieve. We present an ethnographic study of field research practices among researchers in the Center for Embedded Networked Sensing (CENS), a National Science Foundation Science & Technology Center devoted to developing wireless sensing systems for scientific and social applications. Using the concepts of boundary objects and trading zones, we trace the processes of collaborative research around sensor technology development and adoption within CENS. Over the 10-year lifespan of CENS, sensor technologies, sensor data, field research methods, and statistical expertise each emerged as boundary objects that were understood differently by the science and technology partners. We illustrate how sensing technologies were incompatible with field-based environmental research until researchers "unearthed" their infrastructures, explicitly reintroducing human skill and expertise into the data collection process and developing new collaborative languages that emphasized building dynamic sensing systems that addressed human needs. In collaborating around a dynamic sensing model, the sensing systems became embedded not in the environment of study, but in the practices of the scientists.

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Section: Discussionmentioning

confidence: 99%

Unearthing the Infrastructure: Humans and Sensors in Field-Based Scientific Research

Mayernik

Wallis²,

Borgman³

2012

Comput Supported Coop Work

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“…They were much less interested in the scientific data per se, regarding it as background context. The converse was true in the case of the domain scientists [14].…”

Section: Lack Of Shared Interests Across a Project Teammentioning

confidence: 96%

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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“…And of course, variations in the way that data are described with metadata and the problems associated with sharing. Christine Borgman, who has extensively dealt with this (Borgman, 2008;Borgman;Wallis;Mayernik, 2012) refers to the concept of data as: "Facts, numbers, letters and symbols that describe an object, idea, condition, situation or other factors" and also "digital manifestations of literature (including text, sound, still images, moving images, models, games or simulations)".…”

Section: Research Data: a Discipline Problem As Seen From The Social mentioning

confidence: 99%

Data and metadata research in the social sciences and humanities: An approach from data repositories in these disciplines

Gómez¹,

Méndez²,

Hernández-Pérez³

2016

EPI

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This paper studies research data repositories in the social sciences and humanities (SSH), from the Registry of Research Data Repositories (re3data), paying particular attention to metadata models used to describe the datasets included in them. 397 repositories are reviewed at the general level, including those of a multidisciplinary nature. We discuss and reflect on the special features of research data in these disciplines, and on coverage and information collected by re3data. The metadata schemas and standards most commonly used in SSH repositories are analyzed, with special emphasis on the six main repositories. KeywordsRepositories; Research data; Metadata; Social sciences; Humanities; Re3data. ResumenSe estudian los repositorios de datos de investigación en ciencias sociales y humanidades (CSH), recogidos en el Registro de repositorios de datos de investigación (re3data), prestando especial atención a los modelos de metadatos que utilizan para describir los datasets incluidos en ellos. Se revisan a nivel global los 397 repositorios que, según re3data, recogen datos de investigación sobre esas disciplinas, incluidos, los de carácter multidisciplinar. Se discute y reflexiona sobre las particularidades de los datos de investigación en estas disciplinas y sobre la cobertura e información que recoge re3data. Se analizan los esquemas y estándares de metadatos más utilizados en los repositorios de CSH, con un análisis más pormenorizado de los seis repositorios de datos especializados más importantes. Palabras claveRepositorios; Datos de investigación; Metadatos; Ciencias sociales; Humanidades; Re3data.Gómez, Nancy-Diana; Méndez, Eva; Hernández-Pérez, Tony (2016). "Social sciences and humanities research data and metadata: A perspective from thematic data repositories". El profesional de la información, v. 25, n. 4, pp. 545-555.

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Who’s Got the Data? Interdependencies in Science and Technology Collaborations

Cited by 81 publications

References 55 publications

Unearthing the Infrastructure: Humans and Sensors in Field-Based Scientific Research

Unearthing the Infrastructure: Humans and Sensors in Field-Based Scientific Research

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

Data and metadata research in the social sciences and humanities: An approach from data repositories in these disciplines

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