Resources for getting help analyzing your data
Help working with Social Sciences and Humanities data
DSS consultants provide statistical and software assistance in quantitative analysis of electronic data, advising students and faculty on the choice and application of quantitative methods, the conversion of data from one format to another, and the interpretation of statistical analyses. The statistical packages supported are Stata, SPSS, and R.
The Visualization Hub in the Stokes library is both a space and a service to address the needs of public and international affairs students and faculty related to evolving digital research, data visualization, and qualitative analysis. Workshops are offered regularly, and one-on-one consultations are available on a rolling basis.
Quantitative and Analytical Political Science
QAPS offers a consulting service serving faculty members, postdoctoral researchers, and students in the Department of Politics and the Princeton School of Public and International Affairs, but also open to members of other departments across the social sciences and humanities. The service is designed to help with research design, statistics, formal theory, and computational questions arising from your research. We can offer you or your research assistants help and advice on a host of topics from data analysis and complex derivations to scraping data from the web and research database construction.
VizE Lab for Ethnographic Data Visualization
The VizE Lab for Ethnographic Data Visualization is a place for scholars who want to discover ways to collect and generate diverse forms of ethnographic data using the widening array of visual tools that can enhance intelligibility and insight into the complexities of the present. VizE Lab staff are available to meet individually with researchers and research groups to advise on a wide-range of topics related to ethnographic data and visualization
Help working with Computational, Engineering, and Natural Sciences data
The Research Computing Help Desk offers one-on-one meetings with research computing staff to get help on a range of topics, including:
- Getting started on the cluster(s)
- Navigating the file systems
- Understanding and troubleshooting error messages
- Installing and compiling software
- Writing SLURM submission scripts
- Debugging
- Programming strategies
Research Computing Visualization
These help sessions are an opportunity to meet with research computing staff for one-on-one help with data visualization and programming. They can help with visualization programs, techniques, and data formats as well as programming and cluster usage.
More Resources
The Princeton University Library has an ever-growing set of Research Guides, generally organized by discipline, but also including special topics, like Text Mining, Population Statistics, and Tools for Neuroscience Workflows.
There are also online resources from other sources. Data Carpentry is a great place to start. It offers online lessons on data wrangling, with courses that cover specific fields using tools such as R and python. They have a growing set of areas that they cover, including:
Ecology data
Data organization in spreadsheets, data cleaning with OpenRefine, Data analysis and visualization in R or python, using SQL for data management
Genomics data
Working with genomics data, and data management and analysis for genomics research, including best practices for organization of bioinformatics projects and data, use of command line utilities, use of command line tools to analyze sequence quality and perform variant calling, and connecting to and using cloud computing
Social Sciences data
How to organize tabular data, handle date formatting, carry out quality control and quality assurance and export data to use with downstream applications, use OpenRefine to explore, summarize, and clean tabular data reproducibly, and import data into R, calculate summary statistics, and create publication-quality graphics
Geospatial data
Managing and understanding spatial data formats, understanding coordinate reference systems, and working with raster and vector data in R for analysis and visualization.