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

Simple item page
dc.contributor.authorBorgman, Christine L.
dc.contributor.authorWallis, Jillian C.
dc.contributor.authorMayernik, Matthew S.
dc.date.accessioned2020-06-06T13:07:05Z
dc.date.available2020-06-06T13:07:05Z
dc.date.issued2012
dc.date.issued2012
dc.description.abstractScience and technology always have been interdependent, but never more so than with today’s highly instrumented data collection practices. We report on a long-term study of collaboration between environmental scientists (biology, ecology, marine sciences), computer scientists, and engineering research teams as part of a five-university distributed science and technology research center devoted to embedded networked sensing. The science and technology teams go into the field with mutual interests in gathering scientific data. “Data” are constituted very differently between the research teams. What are data to the science teams may be context to the technology teams, and vice versa. Interdependencies between the teams determine the ability to collect, use, and manage data in both the short and long terms. Four types of data were identified, which are managed separately, limiting both reusability of data and replication of research. Decisions on what data to curate, for whom, for what purposes, and for how long, should consider the interdependencies between scientific and technical processes, the complexities of data collection, and the disposition of the resulting data.de
dc.identifier.doi10.1007/s10606-012-9169-z
dc.identifier.pissn1573-7551
dc.identifier.urihttp://dx.doi.org/10.1007/s10606-012-9169-z
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/3925
dc.publisherSpringer
dc.relation.ispartofComputer Supported Cooperative Work (CSCW): Vol. 21, No. 6
dc.relation.ispartofseriesComputer Supported Cooperative Work (CSCW)
dc.subjectcyberinfrastructure
dc.subjectdata curation
dc.subjectdata practices
dc.subjectenvironmental sciences
dc.subjectescience
dc.subjectscientific collaboration
dc.subjectscientific software development
dc.subjectsensor networks
dc.subjecttechnology research
dc.titleWho’s Got the Data? Interdependencies in Science and Technology Collaborationsde
dc.typeText/Journal Article
gi.citation.endPage523
gi.citation.startPage485
gi.citations.count56
gi.citations.elementJilian C. Wallis (2012): The Distribution of Data Management Responsibility within Scientific Research Groups, In: SSRN Electronic Journal, doi:10.2139/ssrn.2269079
gi.citations.elementJustin Middleton, Emerson Murphy-Hill, Kathryn T. Stolee (2020): Data Analysts and Their Software Practices: A Profile of the Sabermetrics Community and Beyond, In: Proceedings of the ACM on Human-Computer Interaction CSCW1(4), doi:10.1145/3392859
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
gi.citations.elementRoberto Di Cosmo (2020): Archiving and Referencing Source Code with Software Heritage, In: Lecture Notes in Computer Science, doi:10.1007/978-3-030-52200-1_36
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.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.elementTeresa Gomez-Diaz, Tomas Recio (2022): Research Software vs. Research Data I: Towards a Research Data definition in the Open Science context, In: F1000Research, doi:10.12688/f1000research.78195.1
gi.citations.elementJu Yeon Jung, Tom Steinberger, John L. King, Mark S. Ackerman (2022): How Domain Experts Work with Data: Situating Data Science in the Practices and Settings of Craftwork, In: Proceedings of the ACM on Human-Computer Interaction CSCW1(6), doi:10.1145/3512905
gi.citations.elementYasmin Alnoamany, John A. Borghi (2018): Towards computational reproducibility: researcher perspectives on the use and sharing of software, doi:10.7287/peerj.preprints.26727v1
gi.citations.elementPierre Alliez, Roberto Di Cosmo, Benjamin Guedj, Alain Girault, Mohand-Said Hacid, Arnaud Legrand, Nicolas Rougier (2020): Attributing and Referencing (Research) Software: Best Practices and Outlook From Inria, In: Computing in Science & Engineering 1(22), doi:10.1109/mcse.2019.2949413
gi.citations.elementYuzhuo Wang, Kai Li (2024): How do official software citation formats evolve over time? A longitudinal analysis of R programming language packages, In: Scientometrics 7(129), doi:10.1007/s11192-024-05064-6
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.elementGeorgia Panagiotidou, Jeroen Poblome, Jan Aerts, Andrew Vande Moere (2022): Designing a Data Visualisation for Interdisciplinary Scientists. How to Transparently Convey Data Frictions?, In: Computer Supported Cooperative Work (CSCW) 4(31), doi:10.1007/s10606-022-09432-9
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.elementPeter T. Darch, Christine L. Borgman, Sharon Traweek, Rebekah L. Cummings, Jillian C. Wallis, Ashley E. Sands (2015): What lies beneath?: Knowledge infrastructures in the subseafloor biosphere and beyond, In: International Journal on Digital Libraries 1(16), doi:10.1007/s00799-015-0137-3
gi.citations.elementAmy X. Zhang, Michael Muller, Dakuo Wang (2020): How do Data Science Workers Collaborate? Roles, Workflows, and Tools, In: Proceedings of the ACM on Human-Computer Interaction CSCW1(4), doi:10.1145/3392826
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.elementPeter T. Darch, Ashley E. Sands, Christine L. Borgman, Milena S. Golshan (2020): Do the stars align?: Stakeholders and strategies in libraries' curation of an astronomy dataset, In: Journal of the Association for Information Science and Technology 2(72), doi:10.1002/asi.24392
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.elementMatthew S. Mayernik, Jillian C. Wallis, Christine L. Borgman (2012): Unearthing the Infrastructure: Humans and Sensors in Field-Based Scientific Research, In: Computer Supported Cooperative Work (CSCW) 1(22), doi:10.1007/s10606-012-9178-y
gi.citations.elementKaterena Kuksenok (2015): Adoption and Adaptation of Data Science in Oceanography, In: Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing, doi:10.1145/2685553.2699329
gi.citations.elementAlex H. Poole (2016): The conceptual landscape of digital curation, In: Journal of Documentation 5(72), doi:10.1108/jd-10-2015-0123
gi.citations.elementGötz Hoeppe (2014): Working data together: The accountability and reflexivity of digital astronomical practice, In: Social Studies of Science 2(44), doi:10.1177/0306312713509705
gi.citations.elementRobert Kosara (2023): Notebooks for Data Analysis and Visualization: Moving Beyond the Data, In: IEEE Computer Graphics and Applications 1(43), doi:10.1109/mcg.2022.3222024
gi.citations.elementJean Marc Nacife, Kennedy de Araújo Barbosa, Estela Najberg (2021): Bibliometric analysis of Organisational Behaviour, In: International Journal for Innovation Education and Research 10(9), doi:10.31686/ijier.vol9.iss10.3459
gi.citations.element(2014): References, In: Digital Asset Ecosystems, doi:10.1016/b978-1-84334-716-3.50012-9
gi.citations.elementDeborah A. Garwood, Alex H. Poole (2018): Project management as information management in interdisciplinary research: “Lots of different pieces working together”, In: International Journal of Information Management, doi:10.1016/j.ijinfomgt.2018.03.002
gi.citations.elementJu Yeon Jung, Tom Steinberger, Chaehan So (2023): Towards Actionable Data Science: Domain Experts as End-Users of Data Science Systems, In: Computer Supported Cooperative Work (CSCW) 3(33), doi:10.1007/s10606-023-09475-6
gi.citations.elementGodwyll Aikins, Catherine G. P. Berdanier, Kim-Doang Nguyen (2024): Data proficiency in MAE education: Insights from student perspectives and experiences, In: International Journal of Mechanical Engineering Education, doi:10.1177/03064190241290897
gi.citations.elementAyoung Yoon (2016): Red flags in data: Learning from failed data reuse experiences, In: Proceedings of the Association for Information Science and Technology 1(53), doi:10.1002/pra2.2016.14505301126
gi.citations.elementJoana Beja, Leen Vandepitte, Abigail Benson, Anton Van de Putte, Dan Lear, Daphnis De Pooter, Gwenaëlle Moncoiffé, John Nicholls, Nina Wambiji, Patricia Miloslavich, Vasilis Gerovasileiou (2022): Data services in ocean science with a focus on the biology, In: Ocean Science Data, doi:10.1016/b978-0-12-823427-3.00006-2
gi.citations.elementJeffrey C. Carver, Nic Weber, Karthik Ram, Sandra Gesing, Daniel S. Katz (2022): A survey of the state of the practice for research software in the United States, In: PeerJ Computer Science, doi:10.7717/peerj-cs.963
gi.citations.elementRock Yuren Pang, Ruotong Wang, Joely Nelson, Leilani Battle (2022): How Do Data Science Workers Communicate Intermediate Results?, In: 2022 IEEE Visualization in Data Science (VDS), doi:10.1109/vds57266.2022.00010
gi.citations.elementClaire Jean-Quartier, Fleur Jeanquartier, Sarah Stryeck, Jörg Simon, Birgit Söser, Ilire Hasani-Mavriqi (2024): Sharing practices of software artefacts and source code for reproducible research, In: International Journal of Data Science and Analytics, doi:10.1007/s41060-024-00617-7
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.elementJames Howison, Julia Bullard (2015): Software in the scientific literature: Problems with seeing, finding, and using software mentioned in the biology literature, In: Journal of the Association for Information Science and Technology 9(67), doi:10.1002/asi.23538
gi.citations.elementLu Jiang, Xinyu Kang, Shan Huang, Bo Yang (2022): A refinement strategy for identification of scientific software from bioinformatics publications, In: Scientometrics 6(127), doi:10.1007/s11192-022-04381-y
gi.citations.elementMaryam Bugaje, Gobinda Chowdhury (2018): Data Retrieval = Text Retrieval?, In: Lecture Notes in Computer Science, doi:10.1007/978-3-319-78105-1_29
gi.citations.elementManuel Domingo D'Angelo del Campo, Pamela García Laborde, Luciano O. Valenzuela, Josefina M. B. Motti, Marilina Martucci, Patricia I. Palacio, Ricardo Aníbal Guichón (2018): Información bioantropológica publicada de Patagonia Austral. Un abordaje de la situación actual desde el data-sharing, In: Revista del Museo de Antropología, doi:10.31048/1852.4826.v11.n1.18068
gi.citations.elementElliott Hauser, Will Sutherland, Mohammad Hossein Jarrahi (2024): Participatory Observation Methods Within Data-Intensive Science: Formal Evaluation and Sociotechnical Insight, In: Lecture Notes in Computer Science, doi:10.1007/978-3-031-57850-2_19
gi.citations.elementSusan Landau (2020): Categorizing Uses of Communications Metadata: Systematizing Knowledge and Presenting a Path for Privacy, In: New Security Paradigms Workshop 2020, doi:10.1145/3442167.3442171
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.elementDakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, Alexander Gray (2019): Human-AI Collaboration in Data Science, In: Proceedings of the ACM on Human-Computer Interaction CSCW(3), doi:10.1145/3359313
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.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.elementMatthew S. Mayernik, Amelia Acker (2017): Tracing the traces: The critical role of metadata within networked communications, In: Journal of the Association for Information Science and Technology 1(69), doi:10.1002/asi.23927
gi.citations.elementEdwin Andrés Sepúlveda Cardona (2021): Marketing científico: investigadores y prosumidores en la ciencia, In: Poiésis 41, doi:10.21501/16920945.4217
gi.citations.elementJose J. De Vega, Robert P. Davey, Jorge Duitama, Dairo Escobar, Marco A. Cristancho‐Ardila, Graham J. Etherington, Alice Minotto, Nelson E. Arenas‐Suarez, Juan D. Pineda‐Cardenas, Javier Correa‐Alvarez, Anyela V. Camargo Rodriguez, Wilfried Haerty, Juan P. Mallarino‐Robayo, Emiliano Barreto‐Hernandez, Monica Muñoz‐Torres, Narcis Fernandez‐Fuentes, Federica Di Palma, (2019): Colombia's cyberinfrastructure for biodiversity: Building data infrastructure in emerging countries to foster socioeconomic growth, In: PLANTS, PEOPLE, PLANET 3(2), doi:10.1002/ppp3.10086
gi.citations.elementAndrew Iliadis (2018): Algorithms, ontology, and social progress, In: Global Media and Communication 2(14), doi:10.1177/1742766518776688
gi.citations.elementTeresa Gomez-Diaz, Tomas Recio (2022): Research Software vs. Research Data I: Towards a Research Data definition in the Open Science context, In: F1000Research, doi:10.12688/f1000research.78195.2
gi.citations.elementTiffany C. Chao, Melissa H. Cragin, Carole L. Palmer (2014): <scp>D</scp>ata <scp>P</scp>ractices and <scp>C</scp>uration <scp>V</scp>ocabulary (<scp>DPCV</scp>ocab): An empirically derived framework of scientific data practices and curatorial processes, In: Journal of the Association for Information Science and Technology 3(66), doi:10.1002/asi.23184
gi.citations.elementXu Wang, Zhisheng Huang, Frank van Harmelen (2020): Evaluating Similarity Measures for Dataset Search, In: Lecture Notes in Computer Science, doi:10.1007/978-3-030-62008-0_3
gi.citations.elementRebecca D. Frank, Elizabeth Yakel, Ixchel M. Faniel (2015): Destruction/reconstruction: preservation of archaeological and zoological research data, In: Archival Science 2(15), doi:10.1007/s10502-014-9238-9
gi.citations.elementMaryam Bugaje, Gobinda Chowdhury (2017): Is Data Retrieval Different from Text Retrieval? An Exploratory Study, In: Lecture Notes in Computer Science, doi:10.1007/978-3-319-70232-2_8
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.elementDeborah A. Garwood, Alex H. Poole (2021): FAIRising Pedagogical Documentation for the Research Lifecycle, In: Communications in Computer and Information Science, doi:10.1007/978-3-030-71903-6_7

Files

Collections

JCSCW Vol. 21 (2012)