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Syracuse University Libraries

Research Data Services: Analysis & Visualization

Supported Software

Online Research Tools

Researchers may find the following tools useful in their work. Emphasis is given to free (or at least having free components) and online tools or services.

Electronic Lab Notebooks:

 

  • ELN at Harvard Medical School - The Electronic Lab Notebook Matrix has been created to aid researchers in the process of identifying a usable Electronic Lab Notebook solutions to meet their specific research needs. Through this resource, researchers can compare and contrast the numerous solutions available today, and also explore individual options in-depth.
  • RSpace - An ELN for researchers to organize, manage and collaborate on their projects.
  • Hivebench - Biology-focused experiment, lab and project management.
  • Docollab - Project management system, collaboration.
  • Benchling - Life Sciences focused experiment, lab and project management.

Data Analysis/Visualization:

  • TableauPublic - Free version of their desktop and online data visualization platform. All data uploaded to TableauPublic is available to everyone on the Internet. The paid versions allow restricted access.
  • StatCrunch - Simple online data analysis and survey package.
  • Dataviz - Data visualization for time, geographic and comparative data.
  • OpenRefine - Data cleaning and exploration tool.

Directories of Research Tools:

Statistical Software

Research Data Services currently supports the three major statistical software packages, SAS, Stata, and SPSS.

Syracuse University has site licenses for each of these packages and offers them at a highly discounted price for students, faculty and staff. To order your own copy  please go to ITS' Software Licenses page and follow the instructions there.

The Library also has ArcGIS available for your use. These can be used with the data management and statistical analysis capabilities of SAS, Stata and SPSS for some truly interesting and cutting-edge projects.

 

 

Deciding Which Package to Use

All statistical software packages have their good points and their bad points. Which to use is a difficult but important decision. We describe each package below to help you decide which to use. Please be aware that if you have data in SAS format, for example, but prefer to use Stata (or SPSS), then you are not stuck using SAS. You can use StatTransfer to convert the SAS data into Stata.

Data Analysis:

  • Stata: Stata is a relatively (compared to SAS and SPSS) easy to learn package which give you a choice among a command-line interface, syntax or program file (called a "do-file" in Stata), and pull-down, fill-in-the-blank GUI interface. Stata is very good with time-series data and has many survival analysis routines. Stata also gives you the ability to program your own commands. One drawback to Stata is that it loads the entire dataset into memory, so if your dataset is very large, you may not be able to use Stata. This is a relatively rare occurrence, however. Generally, if you have little or no experience with any statistical package, Stata is probably your best choice.
  • SAS: SAS is the biggest of all statistical packages (as well as being the largest privately-owned software company). SAS can do just about anything you will ever need to do. SAS also has a pretty steep learning curve. There is a fill-in-the-blank interface (SAS/ASSIST) available, but it is not as well-developed as Stata's or SPSS's. To really make the best use of SAS, you must write a program.
  • SPSS: SPSS is another very popular statistical package. It has probably the best GUI interface of the three packages, as well as the ability to write programs. Like SAS, you can probably do everything you will ever need to in SPSS. You can do most of your work in the GUI, but not all, so you may need to learn how to program in SPSS. Like SAS, programming in SPSS has a pretty steep learning curve.
  • R: R is an open-source (free) data management, analysis and processing language. Although it is very versatile, it also has a rather steep learning curve. R is good if you need to conduct unusual or highly customized analyses and if you will be doing data analysis on a regular basis (at least weekly). If you will be analyzing data only a few times a year, then you are better of with another package, particularly SPSS.

Still not sure what package you should use or want to know what else is "out there"? Then check out the DiRT Directory.