Lessons from Between the White Lines for Isolated Data Scientists

Many current and future data scientists will be “isolated”—working alone or in small teams within a larger organization. This isolation brings certain challenges as well as freedoms. Drawing on my considerable experience both working in the professional sports industry and teaching in academia, I discuss troubled waters likely to be encountered by newly minted data scientists and offer advice about how to navigate them. Neither the issues raised nor the advice given are particular to sports and should be applicable to a wide range of knowledge domains

A Case Report: Building communities with training and resources for Open Science trainers

To foster responsible research and innovation, research communities, institutions, and funders are shifting their practices and requirements towards Open Science. Open Science skills are becoming increasingly essential for researchers. Indeed general awareness of Open Science has grown among EU researchers, but the practical adoption can be further improved. Recognizing a gap between the needed and the provided training offer, the FOSTER project offers practical guidance and training to help researchers learn how to open up their research within a particular domain or research environment. Aiming for a sustainable approach, FOSTER focused on strengthening the Open Science training capacity by establishing and supporting a community of trainers. The creation of an Open Science training handbook was a first step towards bringing together trainers to share their experiences and to create an open and living knowledge resource. A subsequent series of train-the-trainer bootcamps helped trainers to find inspiration, improve their skills and to intensify exchange within a peer group. Four trainers, who attended one of the bootcamps, contributed a case study on their experiences and how they rolled out Open Science training within their own institutions. On its platform the project provides a range of online courses and resources to learn about key Open Science topics. FOSTER awards users gamification badges when completing courses in order to provide incentives and rewards, and to spur them on to even greater achievements in learning. The paper at hand describes FOSTER Plus’ training strategies, shares the lessons learnt and provides guidance on how to reuse the project’s materials and training approaches

To Tidy or not When Teaching R Skills in Biology Classes

An essential skill for STEM undergraduates is the ability to understand the world by manipulating, visualizing, and analyzing data to make or evaluate claims. Current online debate, without peer-reviewed literature, explores which of two common R syntax environments (base R or tidyverse) is best for teaching novice R users. In an in-person undergraduate course on evolutionary biology, we implemented two coding curricula: one using base R (n = 49 students) and the other using tidyverse (n = 58 students). We compared these two curricula using several dimensions of student success: interpretation of syntax, creation of appropriate data visualizations and analyses, &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;and an absence of sex bias in performance. A linear model revealed prior experience had the largest estimated effect, followed by syntax environment; sex had the smallest effect. Pedagogical approaches that ensure students have repeated opportunities for practice and that implement techniques to overcome student frustration and anxiety are likely more important than syntax environment when learning coding in biology classes. Furthermore, the small effect of sex combined with the high proportion of females in the biological sciences suggests introducing computer programming in biology may allow females to discover interest and ability that they may not have had if computer programming was the sole propriety of computer science departments. &nbsp;</p

Graphical scaffolding for the learning of data wrangling APIs

In order for students across the sciences to avail themselves of modern data streams, they must first know how to wrangle data: how to reshape ill-organised, tabular data into another format, and how to do this programmatically, in languages such as Python and R. Despite the cross-departmental demand and the ubiquity of data wrangling in analytical workflows, the research on how to optimise the instruction of it has been minimal. Although data wrangling as a programming domain presents distinctive challenges - characterised by on-the-fly syntax lookup and code example integration - it also presents opportunities. One such opportunity is how tabular data structures are easily visualised. To leverage the inherent visualisability of data wrangling, this dissertation evaluates three types of graphics that could be employed as scaffolding for novices: subgoal graphics, thumbnail graphics, and parameter graphics. Using a specially built e-learning platform, this dissertation documents a multi-institutional, randomised, and controlled experiment that investigates the pedagogical effects of these. Our results indicate that the graphics are well-received, that subgoal graphics boost the completion rate, and that thumbnail graphics improve navigability within a command menu. We also obtained several non-significant results, and indications that parameter graphics are counter-productive. We will discuss these findings in the context of general scaffolding dilemmas, and how they fit into a wider research programme on data wrangling instruction