Ten simple rules to cultivate transdisciplinary collaboration in data science

Sahneh, Faryad Darabi; Balk, Meghan A.; Marina, Kisley,; Chan, Chi Kwan; Fox, Mercury; Nord, B.; Lyons, Eric; Swetnam, Tyson L.; Huppenkothen, Daniela; Sutherland, Will; Walls, Ramona; Quinn, Daven P.; Tarín, Tonantzin; LeBauer, David; Ribes, David; Birnie, Dunbar P.; Lushbough, Carol; Nearing, Grey; Fischer, Jeremy; Tyle, Kevin R.; Carrasco, Luis; Lang, Meagan; Rose, Peter W.; Rushforth, Richard; Roy, Samapriya; Matheson, T.; Lee, Tina; Brown, C. Titus; Teal, Tracy K.; Papeş, Monica; Kobourov, Stephen G.; Merchant, Nirav

doi:10.1371/journal.pcbi.1008879

Cited by 12 publications

(11 citation statements)

References 20 publications

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Section: Introductionmentioning

confidence: 99%

“…"-Brené Brown, Braving the Wilderness.Data science is rapidly changing the landscape of scientific research. Research leveraging data science tools (hereafter, data science research) is increasingly widespread across disciplines, as software for large dataset analysis grows in power and reach [1][2][3][4][5]. With this expansion in data science research, coding and data analysis skills are becoming more valuable to early career researchers [6][7][8].…”

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confidence: 99%

“…To ensure equitable participation in research and access to high-mobility career opportunities as this field expands, it is critical that data science research is welcoming and inclusive [9,10]. Beyond the moral imperative, improving inclusion will also advance the field, given that data science research is highly collaborative and team members bring diverse backgrounds and technical skills [5].There are many cultural, economic, institutional, and social barriers to inclusion in academic research in general and data science in particular [11][12][13]. Science and data science have historically been dominated by members of identity groups with power and privilege, as marginalized identity groups were excluded from educational opportunities [12,14].…”

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confidence: 99%

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Ten simple rules to cultivate belonging in collaborative data science research teams

Gaynor

Azevedo²,

Boyajian³

et al. 2022

PLoS Comput Biol

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Ten simple rules to cultivate belonging in collaborative data science research teams

Gaynor

Azevedo²,

Boyajian³

et al. 2022

PLoS Comput Biol

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“…Applied data science, whether in industry or in academic research, does not happen in a vacuum; the work is highly collaborative and interdisciplinary, often involving a team of experts in analysis, modeling, coding, and domain expertise (Harris et al, 2013;Sahneh et al, 2021). Sometimes a data scientist also has relevant subject area expertise (psychology, medicine, business, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Sometimes a data scientist also has relevant subject area expertise (psychology, medicine, business, etc. ), but often data scientists contribute skills in computation, coding, or visualization to a project and rely on another individual for disciplinary expertise (Sahneh et al, 2021). Parti and Szigeti (2021) emphasize the importance of interdisciplinary projects between social scientists and data scientists working together to solve real world problems in a world where big data increasingly overlaps with major social science research questions.…”

Section: Introductionmentioning

confidence: 99%

Making Data Right

Marsolek

Barrick

Kubas

et al. 2022

Advances in Library and Information Science

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Learning how to wield data ethically and responsibly is a critical skill for data scientists, but one that is often lacking from traditional curriculum. Libraries have a long history of teaching data stewardship and sharing, and, in collaboration with collegiate research support entities, are good candidates to expose students engaging in data science to data ethics. This chapter presents four case studies on how the University of Minnesota Libraries and its partners have deeply integrated ethics into data management instruction. The chapter will highlight ethics for general data management instruction to undergraduate and graduate students from various disciplines, human subject data de-identification, qualitative methods and sharing, and biodiversity location data. Together, the case studies show how libraries and their partners are a natural fit to advance the work of data science curricula when it comes to managing data and the myriad ethical considerations that go along with this work.

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Inclusive collaboration across plant physiology and genomics: Now is the time!

2023

Plant Direct

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Within the broad field of plant sciences, what are the most pressing challenges and opportunities to advance? Answers to this question usually include food and nutritional security, climate change mitigation, adaptation of plants to changing climates, preservation of biodiversity and ecosystem services, production of plant‐based proteins and products, and growth of the bioeconomy. Genes and the processes their products carry out create differences in how plants grow, develop, and behave, and thus, the key solutions to these challenges lie squarely in the space where plant genomics and physiology intersect. Advancements in genomics, phenomics, and analysis tools have generated massive datasets, but these data are complex and have not always generated scientific insights at the anticipated pace. Further, new tools may need to be created or adapted, and field‐relevant applications tested, to advance scientific discovery derived from such datasets. Meaningful, relevant conclusions and connections from genomics and plant physiological and biochemical data require both subject matter expertise and the collaborative skills needed to work together outside of specific disciplines. Bringing the best expertise to bear on complex problems in plant sciences requires enhanced, inclusive, and sustained collaboration across disciplines. However, despite significant efforts to enable and sustain collaborative research, a variety of challenges persist. Here, we present the outcomes and conclusions of two workshops convened to address the need for collaboration between scientists engaged in plant physiology, genetics, and genomics and to discuss the approaches that will create the necessary environments to support successful collaboration. We conclude with approaches to share and reward collaboration and the need to train inclusive scientists that will have the skills to thrive in interdisciplinary contexts.

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Ten simple rules to cultivate transdisciplinary collaboration in data science

Cited by 12 publications

References 20 publications

Ten simple rules to cultivate belonging in collaborative data science research teams

Ten simple rules to cultivate belonging in collaborative data science research teams

Making Data Right

Inclusive collaboration across plant physiology and genomics: Now is the time!

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