Broadening Participation in Computing
CIL is committed to broadening students' participation in computing research. As part of the efforts, CIL has been hosting or participating in George Mason's Aspiring Scientists Summer Internship Program (ASSIP) for providing high school students with research experiences, Summer Team Impact Program (STIP) for broadening undergraduate students' participation in computing research in the form of summer internship, and OSCAR Undergraduate Research Assistantship Program for eligible students with Federal Work-Study Award who are interested in computing research. We encourage students to participate in any of these programs to work with CIL.Training Resources
Github Tutorial: Basic Git operations and instructions.
Geo data carpentry: Examples of how to process geospatial data (e.g., geospatial join, intersection, aggregation, etc.)
Reproducibility: Brief introduction to reproducibility in scientific work and practical tools/methods to increase reproducibility of data analytic work and software products.
AIT722 Syllabus: Theories and Models in Geo-Social Data Analytics: CIL's course syllabus that contains a reading list about geo-social data analysis methods and theories behind them.
AIT602 Syllabus: Introduction to Research in Applied IT: Introductory materials for students who are interested in learning about scientific research in Information Science.
Data Analysis Tools
For quantitative data analysis (e.g., statistical analysis or machine learning), we use Python and R in the Jupyter Notebook environment.
For geospatial analysis, we use QGIS, GeoPandas, and R's GDAL/RGEOS/GeoSphere.
For data visualizations, we use R's ggplot2, ggmap, ShinyApp, D3, Plot.ly, and Leaflet.
For qualitative data analysis, we use Dedoose and Miro.