Visualization methods are analytical techniques that transform numerical or categorical data into graphical representations to facilitate pattern recognition, hypothesis generation, and communication of complex structures. Methodologically, they encompass approaches such as scatter plots, heatmaps, dimensionality reduction projections (e.g., PCA, t-SNE, UMAP), networks, and spatial maps, each optimized for particular data types and relational structures. Key considerations include preserving relevant data distributions, scales, and topologies while minimizing distortions and cognitive overload. In research workflows, visualization methods are integral for exploratory data analysis, quality control, feature inspection, model diagnostics, and the interactive interrogation of high-dimensional datasets across disciplines such as bioinformatics, neuroscience, and systems science.