Data Analysis

Data analysis is an important stage in research, enabling researchers to process, extract and interpret insights from raw data. It involves exploring, cleaning, transforming, and modeling data to derive patterns, validate hypotheses and draw evidence-based conclusions.

Selecting appropriate software tools and computational infrastructure is critical for ensuring high performance, accuracy, and reproducibility in data-intensive scientific workflows.

Software for Data Analysis

Python and R are popular programming languages for data analysis. Python is a general-purpose programming language well-suited for processing large and complex datasets. It benefits from a rich ecosystem of libraries such Pandas, NumPy, and Seaborn and is ideal for handling large datasets. On the other hand, R is specifically designed for statistical computing and excels in data visualization. Popular R packages for data analysis and manipulation include dplyr, data.table, and tidyr, while the ggplot2 package and its extensions are widely used for data visualization.

Data Analysis Infrastructure

The Leibniz Supercomputing Centre (LRZ) provides various infrastructure services to efficiently adaptable and reproducible data science workflows. The Data Science Storage (DSS) service enables seamless access between storage and compute nodes. The specialized infrastructure for AI model training and inference, LRZ AI systems, offer access to NVIDIA GPUs, and supports interactive development tools (Jupyter Notebook, JupyterLab, RStudio Server, and TensorBoard).

For more information on LRZ’s services, refer to LRZ AI Systems. Additionally, you can find tips on storage solutions here.

Reproducible research

Reproducibility is crucial to achieve high-quality research. This enables easier communication of your research, allows others to validate findings and reuse the work in a time-efficient manner. We recommend the following for ensuring reproducibility:

Using version control systems like Git to track changes in data analysis scripts
Sharing code, data and results with tools such as Jupyter Notebooks and R Markdown
Ensuring reproducibility by using containers (e.g Docker) and workflow management tools (Snakemake, Nextflow)
Clearly documenting methodology and data sources
Adopting good coding practices to ensure clean and reliable code
Provide metadata to describe datasets, variables, formats, and provenance.

Get in touch!

Do you need support with performing effective data analysis in your research? The TUM Research Data Hub provides guidance on topics related to data visualization, software tools, LRZ solutions, and ethics in data analysis. For specialized assistance with statistical analysis and experimental design, researchers can consult TUM|Stat service.