We examined 53 survey responses spanning 4 levels across each of the 19 competencies. We used R packages data.table v1.16.0 (Barrett et al 2024), dplyr v1.1.4 (Wickham et al 2023), ggplot2 v3.5.1 (Wickham 2016), tidyr v1.3.1 (Wickham et al 2024) and plotly v4.10.4 (Sievert 2020) in R v4.4.1 and RStudio v2024.04.1 (R Core Team 2024) to process and visualise the survey data. Survey responses "Not Applicable" were recoded as zero. We modelled the career stages as a linear series to explore the extent to which these were linearly associated with the competencies' mean scores using Spearman's rank correlation coefficient. We compared each competency's correlation pattern to the mean across all competencies to identify instances where the score exceeded the 95% confidence intervals of the mean trend. We quantified the correlations of each competency pair's scores across the levels. All correlation coefficient p values were Benjamini-Hochberg corrected. In addition, we performed principal components analysis (PCA) on the responses across the levels to explore the extent to which the competencies were correlated with one another.
Figure Legends
Fig 1. Geographical distribution of (A) participants in the working group that developed the new competencies for core facility scientists and (B) respondents to the survey. Survey responses provided the dataset on which figures 2–4 are based.
Fig 2A. The competencies' mean survey scores: the correlation of career stage (x-axes) with mean competency score. The r values denote Spearman's rank correlation coefficient for each competency. The thin coloured lines per plot show the 95% confidence intervals. The black dashed line is the average correlation pattern across competencies.
Fig 2B. The competencies' mean survey scores. Bar plots display the mean score for each competence across the four career stages. Error bars indicate the standard error.
Fig 2C. The competencies' mean survey scores: competencies’ mean scores for each career stage where scientist I is shown by grey circles, scientist II by blue triangles, scientist III by navy squares, and the (M) managerial stage by filled orange circles. The competencies are grouped into those related to: bioscience (A3-C3), data science (D3-F3), computer science (G3-I3), professional conduct (J3-M3) and the core-facility-focused competencies proposed by this study (N1-S1).
Fig 3. The pairwise correlations between competencies based on Spearman's rank correlation coefficient. The range is from 0 (blue) to 0.5 (white) to 1.0 (red). The correlation values are shown for each pair.
Fig 4. Principal component (PC) analysis of the correlations across the competency data shown across PC1 (accounting for 32% of all variation) and PC (accounting for 10% of all variation).
Figure authors: Tim Downing (Pirbright Institute, UK) & Lara Nonell (Vall d'Hebron Institute of Oncolog, Spain)
References:
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R Core Team. 2024. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org.
Sievert C. Interactive Web-Based Data Visualization with R, plotly, and shiny. 2020. Chapman and Hall/CRC. https://plotly-r.com
Wickham H, François R, Henry L, Müller K, Vaughan D. dplyr: A Grammar of Data Manipulation. 2023. https://CRAN.R-project.org/package=dplyr
Wickham H. ggplot2: Elegant Graphics for Data Analysis. 2016. Springer-Verlag New York. https://ggplot2.tidyverse.org
Wickham H, Vaughan D, Girlich M. tidyr: Tidy Messy Data. 2024. https://CRAN.R-project.org/package=tidyr