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NIH Library National Institutes of Health Library

Analysis Tools & Databases

The NIH Library licenses a number of bioinformatics tools. The licenses are either floating (access is provided from any NIH computer) and/or static (access is provided from one of the NIH Library Bioinformatics workstations). If you would like to recommend additional resources, please submit a request.

NIH Library Bioinformatics Workstations

The NIH Library provides two workstations to NIH staff that offer access to licensed bioinformatics software programs. These workstations are located in the main reading room, and are dedicated to high-throughput data analysis. To use a workstation, reserve online, call the NIH Library at 301-496-1080, or visit the Information Desk.

Workstation Hardware
1 Windows, 64 bit, 64GB RAM, and 1TB Storage
2 Windows, 64 bit, 64GB RAM, and 1TB Storage

Licensed Resources

NOTE: Resources are for NIH staff only.

Tool Description
JMP (Statistics software)

JMP is the data analysis tool of choice for hundreds of thousands of scientists, engineers and other data explorers worldwide. Users leverage powerful statistical and analytic capabilities in JMP to discover the unexpected. With JMP you can perform data analysis, data acquisition and cleanup, data visualization, reliability analysis, text exploration, statistical modeling, and it integrates with SAS, MATLAB, and R.
MATLAB

MATLAB is a programming and numeric computing platform used to analyze data, develop algorithms, and create models. 

We license the following MATLAB tools:

  • MATLAB (MLSMS)
  • Deep Learning Toolbox (NNSMS)
  • Statistics and Machine Learning Toolbox (STSMS)
R and RStudio (Open Source, Freely Available)

R is a programming language and environment for statistical computing and graphics. The R environment provides an integrated suite of software facilities for data manipulation, calculation and graphical display. R can import and export data in a variety of formats, both open source and proprietary, including plain text and comma-separated values (CSV), Excel spreadsheets (.XLS and .XLSX), files from a variety of statistical packages (including SAS, SPSS, and Stata), and more. For additional information about working with different data formats in R, see R Data Import/Export. To learn more about R, visit the R Project for Statistical Computing.

RStudio is an open source user interface for R, and it also provides access to some packages not available in the basic R environment.  To learn more about RStudio, visit the RStudio homepage.

R and RStudio are useful for a wide variety of data manipulation, analysis, and visualization tasks.  Because it is open source and uses literate programming (combining content and code), R facilitates research reproducibility. Learn more about using R to conduct research that can be easily recreated, understood, and verified.

Learning about R and RStudio

NIH Library Introduction to R Classes

The NIH Library Data Services team has created an Introduction to R series that is offered periodically, as well as additional R classes on data visualization, project management, version control, scholarly publishing with Quarto, and more. The series is modeled after Data Carpentry, and is designed to teach non-programmers to write modular code and to introduce best practices for using R for data analysis. Please consult the NIH Library Training Calendar for course offerings. 

Online Resources

  • Data Science Specialization through Coursera
    Coursera's Data Science Specialization is a series of 10 free Massive Online Open Courses (MOOCs). Taught by biostatisticians from Johns Hopkins University, the series of courses focuses on using R and RStudio for data analysis and visualization.
  • RStudio Knowledgebase
    Documentation, troubleshooting, and FAQs for RStudio.
  • DataCamp
    DataCamp provides a series of free, interactive learning modules on R, R Studio, and data science, from beginner to advanced level training.