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

dc.contributor.authorMayernik, Matthew S.
dc.contributor.authorWallis, Jillian C.
dc.contributor.authorBorgman, Christine L.
dc.date.accessioned2020-06-06T13:06:55Z
dc.date.available2020-06-06T13:06:55Z
dc.date.issued2013
dc.date.issued2013
dc.description.abstractDistributed 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.de
dc.identifier.doi10.1007/s10606-012-9178-y
dc.identifier.pissn1573-7551
dc.identifier.urihttp://dx.doi.org/10.1007/s10606-012-9178-y
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/3904
dc.publisherSpringer
dc.relation.ispartofComputer Supported Cooperative Work (CSCW): Vol. 22, No. 1
dc.relation.ispartofseriesComputer Supported Cooperative Work (CSCW)
dc.subjectboundary objects
dc.subjectcollaboration
dc.subjectecology
dc.subjectenvironmental science
dc.subjectinfrastructure
dc.subjectscientific data
dc.subjectseismology
dc.subjectsensors
dc.subjecttechnology driven research
dc.subjecttrading zones
dc.titleUnearthing the Infrastructure: Humans and Sensors in Field-Based Scientific Researchde
dc.typeText/Journal Article
gi.citation.endPage101
gi.citation.startPage65
gi.citations.count30
gi.citations.elementKaren S. Baker, Helena Karasti (2018): Data care and its politics, In: Proceedings of the 15th Participatory Design Conference: Full Papers - Volume 1, doi:10.1145/3210586.3210587
gi.citations.elementØystein Godøy, Bard Saadatnejad (2017): ACCESS climate data management, In: Ambio S3(46), doi:10.1007/s13280-017-0963-1
gi.citations.elementBetsy Van der Veer Martens, Bradley G. Illston, Christopher A. Fiebrich (2017): The Oklahoma Mesonet: A Pilot Study of Environmental Sensor Data Citations, In: Data Science Journal, doi:10.5334/dsj-2017-047
gi.citations.elementElena Parmiggiani, Eric Monteiro, Vidar Hepsø (2015): The Digital Coral: Infrastructuring Environmental Monitoring, In: Computer Supported Cooperative Work (CSCW) 5(24), doi:10.1007/s10606-015-9233-6
gi.citations.elementChristine L. Borgman, Morgan F. Wofford, Milena S. Golshan, Peter T. Darch (2021): Collaborative qualitative research at scale: Reflections on 20 years of acquiring global data and making data global, In: Journal of the Association for Information Science and Technology 6(72), doi:10.1002/asi.24439
gi.citations.elementWolff-Michael Roth, Alfredo Jornet (2017): From Object-Oriented to Fluid Ontology: a Case Study of the Materiality of Design Work in Agile Software Development, In: Computer Supported Cooperative Work (CSCW) 1(27), doi:10.1007/s10606-017-9297-6
gi.citations.elementChung-Yi Hou, Matthew S. Mayernik, Steven Worley (2017): Building Community Informed and Driven Data Services at the National Center for Atmospheric Research, In: Practice and Experience in Advanced Research Computing 2017: Sustainability, Success and Impact, doi:10.1145/3093338.3093343
gi.citations.elementJillian C. Wallis, Elizabeth Rolando, Christine L. Borgman (2013): If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology, In: PLoS ONE 7(8), doi:10.1371/journal.pone.0067332
gi.citations.elementWolfgang Kaltenbrunner (2014): Infrastructural Inversion as a Generative Resource in Digital Scholarship, In: Science as Culture 1(24), doi:10.1080/09505431.2014.917621
gi.citations.elementEmily Maemura (2023): Sorting URLs out: seeing the web through infrastructural inversion of archival crawling, In: Internet Histories 4(7), doi:10.1080/24701475.2023.2258697
gi.citations.elementWolfgang Kaltenbrunner, Sarah de Rijcke (2019): Filling in the gaps: The interpretation of <i>curricula vitae</i> in peer review, In: Social Studies of Science 6(49), doi:10.1177/0306312719864164
gi.citations.elementMatthew S Mayernik (2019): Metadata accounts: Achieving data and evidence in scientific research, In: Social Studies of Science 5(49), doi:10.1177/0306312719863494
gi.citations.elementPeter Thomas Darch (2022): The core of the matter: How do scientists judge trustworthiness of physical samples?, In: Frontiers in Research Metrics and Analytics, doi:10.3389/frma.2022.1034595
gi.citations.elementAlyson L. Young, Wayne G. Lutters (2017): Infrastructuring for Cross-Disciplinary Synthetic Science: Meta-Study Research in Land System Science, In: Computer Supported Cooperative Work (CSCW) 1-2(26), doi:10.1007/s10606-017-9267-z
gi.citations.elementJose María Álvarez-Rodríguez, Giner Alor-Hernández, Jezreel Mejía-Miranda (2018): Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments, In: Scientific Programming, doi:10.1155/2018/8467413
gi.citations.elementChristine L. Borgman (2020): Bibliographie, In: Qu’est-ce que le travail scientifique des données ?, doi:10.4000/books.oep.14792
gi.citations.elementMarius Mikalsen (2014): A Case Study of an Information Infrastructure Supporting Knowledge Work in Oil and Gas Exploration, In: COOP 2014 - Proceedings of the 11th International Conference on the Design of Cooperative Systems, 27-30 May 2014, Nice (France), doi:10.1007/978-3-319-06498-7_8
gi.citations.elementGobinda Chowdhury, Joumana Boustany, Serap Kurbanoğlu, Yurdagül Ünal, Geoff Walton (2017): Preparedness for Research Data Sharing: A Study of University Researchers in Three European Countries, In: Lecture Notes in Computer Science, doi:10.1007/978-3-319-70232-2_9
gi.citations.elementJesper Simonsen, Helena Karasti, Morten Hertzum (2019): Infrastructuring and Participatory Design: Exploring Infrastructural Inversion as Analytic, Empirical and Generative, In: Computer Supported Cooperative Work (CSCW) 1-2(29), doi:10.1007/s10606-019-09365-w
gi.citations.elementMatthew S. Mayernik (2015): Research data and metadata curation as institutional issues, In: Journal of the Association for Information Science and Technology 4(67), doi:10.1002/asi.23425
gi.citations.elementJoel Cutcher-Gershenfeld, Karen S Baker, Nicholas Berente, Dorothy R Carter, Leslie A DeChurch, Courtney C Flint, Gabriel Gershenfeld, Michael Haberman, John Leslie King, Christine Kirkpatrick, Eric Knight, Barbara Lawrence, Spenser Lewis, W Christopher Lenhardt, Pablo Lopez, Matthew S Mayernik, Charles McElroy, Barbara Mittleman, Victor Nichol, Mark Nolan, Namchul Shin, Cheryl A Thompson, Susan Winter, Ilya Zaslavsky (2016): Build It, But Will They Come? A Geoscience Cyberinfrastructure Baseline Analysis, In: Data Science Journal 0(15), doi:10.5334/dsj-2016-008
gi.citations.elementSrishti Gupta, Julia Jablonski, Chun-Hua Tsai, John M. Carroll (2022): Instagram of Rivers: Facilitating Distributed Collaboration in Hyperlocal Citizen Science, In: Proceedings of the ACM on Human-Computer Interaction CSCW1(6), doi:10.1145/3512944
gi.citations.elementKaren S. Baker, Matthew S. Mayernik (2020): Disentangling knowledge production and data production, In: Ecosphere 7(11), doi:10.1002/ecs2.3191
gi.citations.elementChristopher Wood, Stefan Poslad, Antonios Kaniadakis, Jennifer Gabrys (2017): What Lies Above, In: Proceedings of the 2017 Conference on Designing Interactive Systems, doi:10.1145/3064663.3064757
gi.citations.elementMarius Mikalsen, Eric Monteiro (2018): Data Handling in Knowledge Infrastructures, In: Proceedings of the ACM on Human-Computer Interaction CSCW(2), doi:10.1145/3274392
gi.citations.elementChristine L. Borgman, Andrea Scharnhorst, Milena S. Golshan (2019): Digital data archives as knowledge infrastructures: Mediating data sharing and reuse, In: Journal of the Association for Information Science and Technology 8(70), doi:10.1002/asi.24172
gi.citations.elementChristine L. Borgman, Peter T. Darch, Ashley E. Sands, Jillian C. Wallis, Sharon Traweek (2014): The ups and downs of knowledge infrastructures in science: Implications for data management, In: IEEE/ACM Joint Conference on Digital Libraries, doi:10.1109/jcdl.2014.6970177
gi.citations.elementChristine L. Borgman, Peter T. Darch, Ashley E. Sands, Irene V. Pasquetto, Milena S. Golshan, Jillian C. Wallis, Sharon Traweek (2015): Knowledge infrastructures in science: data, diversity, and digital libraries, In: International Journal on Digital Libraries 3-4(16), doi:10.1007/s00799-015-0157-z
gi.citations.elementHelena Karasti, Jeanette Blomberg (2017): Studying Infrastructuring Ethnographically, In: Computer Supported Cooperative Work (CSCW) 2(27), doi:10.1007/s10606-017-9296-7
gi.citations.elementGötz Hoeppe (2018): Mediating Environments and Objects as Knowledge Infrastructure, In: Computer Supported Cooperative Work (CSCW) 1-2(28), doi:10.1007/s10606-018-9342-0

Files