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Participatory Observation Methods Within Data-Intensive Science: Formal Evaluation and Sociotechnical Insight

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Wisdom, Well-Being, Win-Win (iConference 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14596))

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Abstract

This paper presents a framework enabling qualitative researchers to gain rich participatory access to study scientific practices within collaborative, funded research projects. Participatory observation methods provide unique access to scientific sites for social studies of science but require authentic and mutually beneficial motivations for qualitative researchers’ participation. We illustrate a successful approach to configuring such collaborations by presenting the case of our participatory observation of an intensive NSF-funded Data-Intensive Science (DIS) training, as members of the evaluation team. We detail how our dual-purpose data collection methods informed both funder-facing evaluation materials and our own subsequent research publications, completed in parallel to the training’s core objectives. We organize our site-specific findings on scientific practice around the themes of Technology, Practices, and Culture. Participatory evaluation of grant-funded science is a rich and under-utilized form of site access for sociotechnical researchers that can facilitate mutually beneficial scientific convergence.

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Notes

  1. 1.

    https://new.nsf.gov/funding/opportunities/training-based-workforce-development-advanced.

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Acknowledgements

This research was supported by NSF Award #1730390. The authors acknowledge the efforts of Melanie Feinberg, Arcot Rajasekar, Nirav Merchant, Hao Xu, and many others involved in the Cybercarpentry workshops program at UNC Chapel Hill.

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Authors and Affiliations

  1. University of Texas at Austin, Austin, TX, 78701, USA

    Elliott Hauser

  2. University of Washington, Seattle, WA, 98195, USA

    Will Sutherland

  3. University of North Carolina, Chapel Hill, NC, 27599, USA

    Mohammad Hossein Jarrahi

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  1. Elliott Hauser
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  2. Will Sutherland
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  3. Mohammad Hossein Jarrahi
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Correspondence to Elliott Hauser .

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Editors and Affiliations

  1. iSchool organization, Berlin, Germany

    Isaac Sserwanga

  2. University of Tsukuba, Tsukuba, Japan

    Hideo Joho

  3. Jilin University, Changchun, China

    Jie Ma

  4. Stockholm University, Stockholm, Sweden

    Preben Hansen

  5. Wuhan University, Wuhan, China

    Dan Wu

  6. University of Tsukuba, Tsukuba, Japan

    Masanori Koizumi

  7. University of California, Los Angeles, CA, USA

    Anne J. Gilliland

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Hauser, E., Sutherland, W., Jarrahi, M.H. (2024). Participatory Observation Methods Within Data-Intensive Science: Formal Evaluation and Sociotechnical Insight. In: Sserwanga, I., et al. Wisdom, Well-Being, Win-Win. iConference 2024. Lecture Notes in Computer Science, vol 14596. Springer, Cham. https://doi.org/10.1007/978-3-031-57850-2_19

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  • DOI: https://doi.org/10.1007/978-3-031-57850-2_19

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