Skills

Before starting statistical analyses and graphical representations, it is essential to perform data treatment to ensure the quality and suitability of the data for analytical methods. This step is crucial for obtaining reliable and meaningful results. 

The process I follow begins with data validation, which involves verifying and correcting inaccuracies. This includes detecting and addressing missing values, outliers, and inconsistencies to ensure data accuracy. Data aggregation and summarization, involve combining data from various sources or levels and organizing it into tables. These tables facilitate efficient data visualization and enable the rapid application of descriptive statistics. Statistical measures such as mean, median, and standard deviation are used to summarize central tendencies and variability. Additionnally, I am skilled in curve fitting, which involves creating fits and extracting data from these fits to analyze trends and relationships with greater precision. Data exploration is conducted to visualize and understand the dataset structure, using statistical and graphical tools to identify the most important variables relevant to the research question. While I am capable to applying imputation methods (e.g., mean or median imputation) and transformations (e.g., logarithmic transformations) to refine data for analysis, I generally prefer to limit data manipulation whenever possible. 

I have experience working with large datasets and optimizing workflows for efficient processing, especially when dealing with complex experimental data from multiple sources. I emphasize automation and reproducibility in my data treatment by scripting and automating processes, ensuring consistency, reducing human error, and allowing for rapid updates when new data is available. 

I am proficient in conducting a wide range of statistical analyses to extract meaningful insights from experimental data. 

My approach involves rigorously selecting appropriate tests based on the data distribution and underlying assumptions. Before performing any analysis, I ensure the validity of the results by testing for normality (e.g., Shapiro-Wilk test) and verifying the homoscedasticity of variances (e.g., Levene's test, Bartlett's test). This ensures that the statistical methods chosen are well-suited to the characteristics of the dataset. I have extensive experience with both parametric (e.g., t-tests, ANOVA, linear regression) and non-parametric (e.g., Mann-Whitney U test, Kruskal-Wallis test) statistical tests, applying them as needed depending on data assumptions. To further enhance the robustness of my findings, I am skilled in applying multiple comparison corrections and post-hoc tests (e.g., Tukey’s HSD, Holm-Bonferroni Method). Additionally, I am adept at graphical statistical analyses, such as Principal Component Analysis (PCA) and Redundancy Analysis (RDA) to uncover patterns in complex datasets.

The software I primarily use for statistical analyses includes R and SigmaPlot, both of which offer robust tools for implementing advanced statistical models and generating publication-ready visualizations.

Effective graphical representation is key to visualizing and communicating data insights. This process involves creating clear, informative, and visually appealing graphics that help in understanding complex datasets and highlighting key trends or relationships. 

I use various types of graphs and charts to represent data visually, carefully selecting the appropriate formats based on the results to be highlighted and the statistical analyses performed. This includes univariate (e.g., histograms, boxplots), bivariate (e.g., scatter plots, line graphs), and multivariate analyses (e.g., scatterplot matrices, correlation matrices, heatmaps). I also design graphics to effectively represent data variability and display statistical results, such as p-values or statistically significant groups. 

The software I primarily use for creating visualizations includes R, Excel, PowerPoint and SigmaPlot. These tools offer extensive functionalities for creating wide range of charts and graphs, from basic plots to complex, publication-ready visuals. Each software provides robust and complementary features for customizing graphics to effectively communicate data findings. 

Ensuring transparency and maintaining data integrity throughout the research process is essential for producing rigorous and reproducible results. From data treatment to statistical analysis and graphical representation, I prioritize practices that uphold the highest standards of quality and clarity.

All data treatments, including cleaning, aggregation, and manipulations, are meticulously documented and justified, along with all assumptions and decisions throughout the statistical analyses, to provide full transparency regarding any adjustments. Visual representations are designed to be clear and precise, avoiding misinterpretation. Graphs and charts include necessary details such as confidence intervals and p-values, offering an accurate representation of the variability and significance of the data.

This commitment to transparency and data integrity ensures that the data, analyses, and conclusions presented in my publications are reliable, reproducible, and meet the standards for high-quality scientific publication.