Best Practices for Managing Turnover in Data Science Groups, Teams, and Labs

Sholler, Dan; Das, Diya; Guardia, Fernando Hoces de la; Hoffman, Chris; Lanusse, François; Varoquaux, Nelle; García, Rolando R.; Geiger, R. Stuart; McDevitt, Shana L.; Peterson, Scott N.; Stoudt, Sara

doi:10.31235/osf.io/wsxru

Cited by 3 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This case is in conversation with previous reflections on how to foster diversity and inclusion in data science (Geiger et al, 2019) and how to do data-intensive research in teams (Geiger et al, 2018;Sholler et al, 2019). DS Discovery has rapidly scaled since its inception in 2015 and constantly reflects on how to promote more inclusive data science both internally and externally.…”

mentioning

confidence: 86%

The data science discovery program: A model for data science consulting in higher education

Brown,

Mehta,

Ryali

et al. 2024

Stat

View full text Add to dashboard Cite

As one of the largest data science research incubator initiatives in the country, the University of California, Berkeley's Data Science Discovery Program serves as a case study for a scalable and sustainable model of data science consulting in higher education. This case contributes to the broader literature on data science consulting in higher education by analysing the programme's development, institutional influences; staffing and structural model; and defining features, which may prove instructive to similar programmes at other institutions. The programme is characterised by a unique structure of undergraduate consultations led by graduate student mentorship and governance; a streamlined, multidepartmental model that facilitates scalability and sustainability; and diverse modes for undergraduate consulting—including one‐on‐one ad‐hoc data science consultations, extended data science project development and management, peer mentorship and data science workshop instruction. This case demonstrates that universities may be able to initiate a low‐stakes, small‐scale data science consulting initiative and then progressively scale up the project in collaboration with multiple departments and organisations across campus.

show abstract

mentioning

confidence: 86%

The data science discovery program: A model for data science consulting in higher education

Brown,

Mehta,

Ryali

et al. 2024

Stat

View full text Add to dashboard Cite

show abstract

“…While DMPs are often living documents over the course of a research project, evolving dynamically with the needs or restrictions that are encountered along the way, there is great utility to codifying them either for our team's later use or for others conducting similar projects. DMPs can also potentially be leveraged into new research grants for our team, as these protocols are now a common mandate by many funders (30) . The group discussions that contribute to developing a DMP can be difficult, and encompass considerations relevant to everything from team building to research design.…”

Section: Polishing: Products Of the Refinement Phasementioning

confidence: 99%

“…The group discussions that contribute to developing a DMP can be difficult, and encompass considerations relevant to everything from team building to research design. The outcome of these discussions are often directly tied to the constructiveness of a research team and its robustness to potential turnover (30) . Sharing these standards and lessons learned in the form of polished research products can propel a proactive discussion of data management and sharing practices within our research domain.…”

Section: Polishing: Products Of the Refinement Phasementioning

confidence: 99%

Principles for data analysis workflows

2021

Self Cite

View full text Add to dashboard Cite

A systematic and reproducible “workflow”—the process that moves a scientific investigation from raw data to coherent research question to insightful contribution—should be a fundamental part of academic data-intensive research practice. In this paper, we elaborate basic principles of a reproducible data analysis workflow by defining 3 phases: the Explore, Refine, and Produce Phases. Each phase is roughly centered around the audience to whom research decisions, methodologies, and results are being immediately communicated. Importantly, each phase can also give rise to a number of research products beyond traditional academic publications. Where relevant, we draw analogies between design principles and established practice in software development. The guidance provided here is not intended to be a strict rulebook; rather, the suggestions for practices and tools to advance reproducible, sound data-intensive analysis may furnish support for both students new to research and current researchers who are new to data-intensive work.

show abstract

“…In prior Best Practices in Data Science meetings, we have discussed some of the challenges of doing data-intensive research in teams (Geiger et al, 2018b), including managing turnover (Sholler et al, 2019); fostering a diverse and inclusive data science team (Geiger et al, 2018a), and creating and sustaining robust workflows.…”

Section: Introductionmentioning

confidence: 99%

Resistance to Adoption of Best Practices

Sholler¹,

Stoudt²,

Kennedy³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

There are many recommendations of "best practices" for those doing data science, data-intensive research, and research in general. These documents usually present a particular vision of how people should work with data and computing, recommending specific tools, activities, mechanisms, and sensibilities. However, implementation of best (or better) practices in any setting is often met with resistance from individuals and groups, who perceive some drawbacks to the proposed changes to everyday practice. We offer some definitions of resistance, identify the sources of researchers' hesitancy to adopt new ways of working, and describe some of the ways resistance is manifested in data science teams. We then offer strategies for overcoming resistance based on our group members' experiences working alongside resistors or resisting change themselves. Our discussion concluded with many remaining questions left to tackle, some of which are listed at the end of this piece.

show abstract

Best Practices for Managing Turnover in Data Science Groups, Teams, and Labs

Cited by 3 publications

References 4 publications

The data science discovery program: A model for data science consulting in higher education

The data science discovery program: A model for data science consulting in higher education

Principles for data analysis workflows

Resistance to Adoption of Best Practices

Contact Info

Product

Resources

About