File naming and organization
As research projects progress, the number of files involved tends to grow rapidly. Keeping a consistent naming structure and organization for your project can save you and your colleagues time tracking down files, and can make them easier to analyze further in the research process.
- Data Management Planning Tool’s best practices for file naming
- Georgia Tech Library’s data organization
Data cleaning and tidying
Research datasets come in a multitude of shapes and structures, and very often need to be cleaned, corrected, and otherwise coaxed into shape before serious analysis can begin. Fortunately, “messy” data is a common problem for researchers, and there are a variety of tools available for you to format your data in a way that will make your workflow easier further down the line.
Among others, these tools are available at Dartmouth to help you clean your data:
- OpenRefine
- R and RStudio
- Stata
- SAS
- SPSS
Data visualization
Data visualization helps us better understand our data, whether we have a few points or a few million. In addition to helping us exploring our data during research, visualization also helps communicate potentially complex relationships and very subtle nuances to others in an impactful way.
There are numerous data visualization tools available for any level of experience, and the Library offers support and instruction in the use of many of them. Some of the tools that we suggest include:
- R and RStudio
- OpenRefine
- R and RStudio
- Stata
- SAS
- SPSS/ Tableau Public
- Stata
- SAS
- SPSS
- D3.js
- Leaflet.js
Individual consultations
We help faculty, student, and staff researchers tidy and visualize research data.
For help, email us at researchdatahelp@groups.dartmouth.edu.