Integrating Data Science Tools into a Graduate Level Data Management Course

Pascuzzi, Pete E.; Nelson, Megan Sapp

doi:10.7191/jeslib.2018.1152

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…Finally, according to this study and many others (e.g., Chew et al, 2021;Pascuzzi & Nelson, 2018;Wiljes & Cimiano, 2019), there is never too much IPA (Interaction, Practice, and Application) in training. Still, participants achieving excellent competence in a basic training are improbable.…”

Section: The Value Of Brdm and Lessons Learnedsupporting

confidence: 55%

“…Besides the informational type of contents, some courses and workshops include more technically oriented RDM topics such as data analysing and visualising, wrangling, merging, cleaning, and publishing data sets, as well as building and using relational databases for data gathering, organising, and querying (Carpentries, n.d.;Pascuzzi & Sapp Nelson, 2018;Qin & D'ignazio, 2010;Read et al, 2019;Research data service, n.d.;Wright & Andrews, 2015).…”

Section: Common Contents In Rdm Educationmentioning

confidence: 99%

“…Participants have typically requested more practical exercises, disciplinespecific cases, hands-on learning, and interactivity to concretise generic RDM principles to develop the training and deepen their competencies (Adamick et al, 2013;Byatt et al, 2013;Chew et al, 2021;Pascuzzi & Sapp Nelson, 2018;Wiljes & Cimiano, 2019). Nevertheless, fictitious cases not closely connected to participants' own research have been stated as uninteresting in feedback (Peters & Vaughn, 2014).…”

Section: Lessons Learned In Rdm Educationmentioning

confidence: 99%

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Multi-Stakeholder Research Data Management Training as a Tool to Improve the Quality, Integrity, Reliability and Reproducibility of Research

Rantasaari

2022

LIBER

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To ensure the quality and integrity of data and the reliability of research, data must be well documented, organised, and described. This calls for research data management (RDM) education for researchers. In light of 3 ECTS Basics of Research Data Management (BRDM) courses held between 2019 and 2021, we aim to find how a generic level multi-stakeholder training can improve STEM and HSS disciplines’ doctoral students’ and postdoc researchers’ competencies in RDM. The study uses quantitative, descriptive and inferential statistics to analyse respondents’ self-ratings of their competencies, and a qualitative grounded theory-inspired approach to code and analyse course participants’ feedback. Results: On average, based on the post-course surveys, respondents’ (n = 123) competencies improved one point on a four-level scale, from “little competence” (2) to “somewhat competent” (3). Participants also reported that the training would change their current practices in planning research projects, data management and documentation, acknowledging legal and data privacy viewpoints, and data collecting and organising. Participants indicated that it would be helpful to see legal and data privacy principles and regulations presented as concrete instructions, cases, and examples. The most requested continuing education topics were metadata and description, discipline specific cultures, and backup, version management, and storage. Conclusions: Regarding to the widely used criteria for successful training containing 1) active participation during training; 2) demand for RDM training; 3) increased participants’ knowledge and understanding of RDM and confidence in enacting RDM practices; and 4) positive post-training feedback, BRDM meets the criteria. This study shows that although reaching excellent competence in a RDM basics training is improbable, participants become aware of RDM and its contents and gain the elementary tools and basic skills to begin applying sound RDM practices in their research. Furthermore, participants are introduced to the academic and research support professionals and vice versa: Stakeholders will get to know the challenges that young researchers and research students encounter when applying RDM. The study reveals valuable information on doctoral students’ and postdoc researchers’ competencies, the impact of education on competencies, and further learning needs in RDM.

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Section: The Value Of Brdm and Lessons Learnedsupporting

confidence: 55%

Section: Common Contents In Rdm Educationmentioning

confidence: 99%

Section: Lessons Learned In Rdm Educationmentioning

confidence: 99%

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Multi-Stakeholder Research Data Management Training as a Tool to Improve the Quality, Integrity, Reliability and Reproducibility of Research

Rantasaari

2022

LIBER

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“…The majority of these courses focus on the programming languages R and Python, two free, open source software environments that enable users to clean, analyze, and visualize data as well as perform more complicated research computing tasks [6]. Librarians at the National Institutes of Health, New York University Langone Health, Stanford University, the University of Arizona, and the University of California, Los Angeles, teach R programming to researchers [7–11], and librarians at Purdue University have integrated R programming into their semester-long research data management course for graduate students [12].…”

Section: Introductionmentioning

confidence: 99%

Why do biomedical researchers learn to program? An exploratory investigation

Deardorff

2020

jmla

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ObjectiveAs computer programming becomes increasingly important in the biomedical sciences and more libraries offer programming classes, it is crucial for librarians to understand how researchers use programming in their work. The goal of this study was to understand why biomedical researchers who enrolled in a library-sponsored workshop wanted to learn to program in R and Python.MethodsSemi-structured in-depth interviews were performed with fourteen researchers registered for beginning R and Python programming workshops at the University of California, San Francisco Library. A thematic analysis approach was used to extract the top reasons that researchers learned to program.ResultsFour major themes emerged from the interviews. Researchers wanted to learn R and Python programming in order to perform their data analysis independently, to be an informed collaborator, to engage with new forms of big data research, and to have more flexibility in the tools that they used for their research.ConclusionsLibrarians designing programming workshops should remember that most researchers are hoping to apply their new skills to a specific research task such as data cleaning, data analysis, and statistics and that language preferences can vary based on research community as well as personal preferences. Understanding the programming goals of researchers will make it easier for librarians to partner effectively and offer services that are critically needed in the biomedical community.

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Effect of online research data management instruction on social science graduate students’ RDM skills

Zhou

Kogut

et al. 2022

Library & Information Science Research

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Integrating Data Science Tools into a Graduate Level Data Management Course

Cited by 3 publications

References 16 publications

Multi-Stakeholder Research Data Management Training as a Tool to Improve the Quality, Integrity, Reliability and Reproducibility of Research

Multi-Stakeholder Research Data Management Training as a Tool to Improve the Quality, Integrity, Reliability and Reproducibility of Research

Why do biomedical researchers learn to program? An exploratory investigation

Effect of online research data management instruction on social science graduate students’ RDM skills

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