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Ethnographic Research Overview

Ethnography is the study of the practices through which a community makes meaning, and the habits, values, social norms, assumptions, and languages that members use explicitly and implicitly to make sense of their activities and to guide behavior and relationships.1 These aspects constitute the culture of the community and are taught to its members both formally (i.e., through education and work) and informally (i.e., through word of mouth, on-the-job, via role models, “it’s not in the books”).

In conducting ethnography among specialized work communities, ethnographers seek to identify values, habits, formal and informal knowledge transfer processes, artifacts (e.g., information communication technology, interfaces, computational elements), language (e.g., disciplinary, colloquial), and habitual workarounds that inform members on how to do their work.2Ethnographic research can aid in developing new social and technical processes that will benefit the community being studied. Analysis of research data includes developing themes and identifying patterns in collaboration with community members and producing an interpretation of findings that can be wielded by collaborators.

Ethnographic studies of science and engineering workplaces are conducted to create robust accounts of social practices and technologies used in producing scientific discoveries and technology innovations.3 These accounts, known as “thick description,” provide insight into day-to-day work practices for science at the micro-level and in relation to political and institutional relationships, and historical backgrounds.4 Ethnographic research can contribute findings that reflect, in part, cultural norms, language, and perspectives—an important consideration for the development and adoption of new technologies and organizational support of scientific discovery.

The TREET project’s ethnographic research will offer insight into new ways of doing work and new computational tool requirements. Ethnographic research is being conducted to study and share analysis on social interaction, information flow and access to and analysis of scientific data brought about through the use of remote telepresence.5

TREET Ethnographic research goals include:

  • Ascertain and represent work practices, communication, and social relationships among scientists using remote telepresence
  • Provide insights into the cultural processes shaping domain-specific human-machine relationships
  • Participate in supporting six early career scientists and their undergraduate students to elucidate effective approaches for using remote telepresence to conduct (deep ocean) research
  • Contribute to education research goals for early career ocean scientists and their undergraduates as they use remote telepresence to the conduct of their (deep ocean) research

Robotic assets (mobile and immobile) are being used increasingly for oceanographic investigations. Examples include remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), autonomous surface vehicles (ASVs), and unmanned aerial vehicles (UAVs), as well as cabled observatories such as those for NSF’s Ocean Observatories Initiative (OOI). Consequently, there is substantial interest in exploring new modalities for oceanographic investigation—at and beneath the seafloor for marine geoscientists, from benthic to upper water-column communities for marine biologists, and throughout the ocean for biological, chemical and physical oceanographers. OOI, in particular, will engender the use of real-time data acquisition and analysis—especially in the context of cabled observatories. NOAA's Okeanos Explorer and E/V Nautilus were already designed to provide broadband Internet connectivity from deep within the ocean interior and MBARI's NSF-funded MARS (Monterey Accelerated Research System) observatory offers the scientific community a capability to observe the ocean at 900 meters on a round-the-clock basis. With multiple AUVs, gliders and floats in the ocean and downloading data whenever they appear at the ocean surface, large volumes of data are now returned, routinely, from mobile assets. All these developments represent separate facets of the evolving field of oceanography that, in turn, have encouraged the paradigm of using near real-time data to make decisions during the course of experimentation as opposed to the post-facto analysis of gathered data which has been traditional throughout the history of ocean-based research.

These developments have changed the scientific process in the following ways:

  • Increasingly, decision-making now occurs within and during the experimental process rather than subsequent to it.
  • Data gathering and analysis occurs at a substantially faster rate than before, leading to increased reliance on sophisticated computational methods and with younger oceanographers increasingly knowledgeable and capable of using regression techniques for automated sampling.6
  • Robotic assets are and have been re-tasked to deal with rapidly changing experimental goals, which has, in turn, resulted in opportunistic science.7
  • The increased use of robotic methods has resulted in increased volumes of data gathering that, in turn, have increased the pace of experimentation and data return.

As a consequence, scientists whose working community did not previously use remote telepresence methods are now interested in developing the use of such tools, both for their own research and, increasingly, to achieve the longer term goal of sustaining their fields by attracting and retaining the next generation of researchers through K-12 STEM (science, technology, engineering, math) programs. The ethnographic community of our study in the TREET project includes ocean scientists, with and without prior experience studying the oceans via telepresence, and at varying stages of their career and education, including principal investigators Chris German & Katy Croff Bell, six early-career scientists and their undergraduate students, and two expert mentors Steve Carey and Cindy Van Dover.


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