Research Data Management: Analyzing Research Data

A guide to help researchers manage, store, and share their research data. We also offer one-on-one or group sessions if you have additional questions.

Data Analysis Resources @ The U

The Marriott Library computers have over 300 software titles available for web and graphic design, statistical analysis, 3D modeling, and more. Contact the Knowledge Commons for more information about our software packages and the availability of remote applications. 

Available data analysis software include: 

  • Anaconda – Distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. The distribution includes data-science packages suitable for Windows, Linux, and macOS.
  • ArcGIS – A range of powerful and versatile mapping tools from ESRI allowing you to integrate data layers onto maps, globes, and models and share them through multiple platforms. See our Geospatial Data and Resources and Data Visualization, Computation, & Analysis guides. 
  • ATLAS.ti – Computer-assisted qualitative data analysis software that facilitates analysis of qualitative data for qualitative research, quantitative research, and mixed methods research. See our ATLAS.ti for Qualitative Research guide.
  • EViews – Statistical package for Windows, used mainly for time-series oriented econometric analysis.
  • – User Interface (UI) built on R to access various data science functionalities including data wrangling, visualization, statistics, machine learning, reporting, and dashboards.

  • Gephi – Open-source network analysis and visualization software package written in Java on the NetBeans platform.

  • Maple – Symbolic and numeric computing environment as well as a multi-paradigm programming language. It covers several areas of technical computing, such as symbolic mathematics, numerical analysis, data processing, visualization, and others. 
  • Mathematica – Software system with built-in libraries for several areas of technical computing that allow machine learning, statistics, symbolic computation, data manipulation, network analysis, time series analysis, Natural Language Processing (NLP), optimization, plotting functions and various types of data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other programming languages.
  • MATLAB – Multi-paradigm programming language and numeric computing environment developed by MathWorks. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages. 
  • MestReNova – Spectral data analyzing software, which can be run on Windows, Mac OS and whole range of Linux distributions. 
  • Minitab – Data analysis, statistical, and process improvement software tool.
  • Mplus – Statistical modeling program that offers researchers a wide choice of models, estimators, and algorithms in a program that has an easy-to-use interface and graphical displays of data and analysis results.
  • NVivo – Qualitative data analysis computer software package produced by Lumivero. NVivo is used across a diverse range of fields, including social sciences such as anthropology, psychology, communication, sociology, as well as fields such as forensics, tourism, criminology and marketing. See our Qualitative Research and NiVivo guide.
  • Praat – Free computer software package for speech analysis in phonetics. It can run on a wide range of operating systems, including various versions of Unix, Linux, Mac and Microsoft Windows.
  • GraphpadPrism – Create publication-quality graphs and analyze your scientific data with t-tests, ANOVA, linear and nonlinear regression, survival analysis, and more. GraphpadPrism can be integrated with LabArchives.
  • RStudio – Integrated development environment (IDE) for R and Python. It includes a console, syntax-highlighting editor that supports direct code execution, and tools for plotting, history, debugging, and workspace management. RStudio is available in open source and commercial editions and runs on the desktop (Windows, Mac, and Linux).
  • SPSS – Statistical software suite developed by IBM for data management, advanced analytics, multivariate analysis, business intelligence, and criminal investigation. See our Introduction to SPSS guide.
  • SAS – Statistical software suite developed by SAS Institute for data management, advanced analytics, multivariate analysis, business intelligence, criminal investigation, and predictive analytics. See our SAS Support guide.
  • STATA – General-purpose statistical software package developed by StataCorp for data manipulation, visualization, statistics, and automated reporting. It is used by researchers in many fields, including biomedicine, economics, epidemiology, and sociology. See our STATA Support guide.
  • StatPlus – Statistical software that allows you to perform various forms of analysis from data transformation and sampling to complex analysis, including as non-parametric and regression analysis, survival analysis, and a wide variety of other methods.
  • Integrated Data Viewer – Freely available 3D geoscience visualization and analysis tool that gives users the ability to view and analyze a rich set of geoscience data in an integrated fashion. 

Training and Resources @ The U

J. Willard Marriott Library

The Marriott Library offers free workshops to all students, faculty, and staff during the fall and spring semesters on a variety of topics including research skills, grant writing, and technology skills. Keep an eye on our events page for upcoming workshops. We also offer statistics tutoring during the academic year.

Our data librarians are also here to help you find the resources you need. If you would like to request a consultation, learn more about data analysis software, or suggest a training module, contact Kaylee Alexander ( or Madison Golden ( 

Digital Matters 

For those of you working on digital humanities (DH) research, Digital Matters houses various research projects whose aim is to create a different perspective on a particular question or topic using computation and technology, through partnerships and grants, with the ultimate goal of furthering critical discourse. In addition to funding opportunities for faculty and studies, Digital Matters provides consultations, workshops, and talks geared toward the digital humanities community. Check out their events page for upcoming workshops or contact Rebekah Cummings ( to learn more. 

Software Carpentries 

The DELPHI Initiative at the University of Utah is a member of the Carpentries Community and offers multiple training opportunities throughout the year. To date, they have offered workshops on Unix, Git, Python, and R. For the list of upcoming workshops, see DELPHI's full list of Education Workshops

One Utah Data Science Hub

The One Utah Data Science Hub a university-wide effort designed to enhance research and infrastructure in data science and data-enabled science. Led by Faculty Directors from across the university, the Hub facilitates interdisciplinary research focused on data science through the launch of two new initiatives: the Data Science and Ethics of Technology (DATASET) Initiative and the Data Exploration and Learning for Precision Health Intelligence (DELPHI) Initiative, and in alignment with the Utah Center for Data Science (UCDS). The Hub, in addition to the Software Carpentries workshops, offers a number of educational opportunities and events

DELPHI Initiative

Biomedical data science integrates large, complex data sets with innovative computational approaches to create actionable insights across biological and medical applications. The DELPHI Initiative aims to drive innovation in health and medicine by catalyzing biomedical data science research. In addition to the Software Carpentries workshops, the DELPHI Initiative offers a number of educational opportunitiesevents, and resources geared towards those working in the biomedical field. 

Biostatistics Core

The mission of the Study Design and Biostatistics Center (SDBC) is to advance high quality research at the University of Utah and affiliated institutions by:

  1. Providing expert collaborations for study design, health measurement, and statistical analysis
  2. Developing novel methods and software for advancing clinical/translational research
  3. Providing statistical and epidemiologic education to University researchers

The SDBC consists of over 40 statisticians, epidemiologists and quantitative health scientists, with the majority holding PhD and/or MD degrees.

Center for High Performance Computing (CHPC)

In addition to deploying and operating high performance computational resources and providing advanced user support and training, CHPC serves as an expert team to broadly support the increasingly diverse research computing and data needs on campus. These needs include support for big data, big data movement, data analytics, security, virtual machines, Windows science application servers, protected environments for data mining and analysis of protected health information, and advanced networking. If you are new to CHPC, the best place to start to get more information on CHPC resources and policies is their Getting Started page.

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