Analyze & Collaborate slice from biomedical data lifecycle wheelConsiderations for Data Analysis

Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making.

The choices you make while analyzing your data can also contribute to effectively managing your research data:

  • Document your steps: Consider the software you use for analysis, and whether those applications automatically generate information about your data files and process steps. Keeping track of your steps can save you time when you want to recreate your work, or share your methodology with others! Use Electronic Lab Notebooks, Collaborative Tools & Software, and Image Management platforms.

  • Keep your data safe: Describe your data as you capture it, organize your files, and make smart choices about where you store your data. Since some software programs produce files that are proprietary and can only be opened in their applications, consider saving data in formats that can be opened by different software programs. Ensure you are working with Analysis Ready Datasets.

  • Boost your skills: If you’re new to using an application, or just want to learn more about software you use regularly, look for training opportunities. See the RDMWG Calendar for upcoming trainings on a variety of data management and data science topics.