The NULab Resource Page is an ongoing curation of various datasets, tools, articles, codes, and advice for doing digital research. The three clusters below offer materials that will facilitate personal research and coding practice as well as class-work, assignments, and teaching. The process of digital work can be quite recursive and, though these clusters are meant as a guide, there is necessarily some overlap within each of them.
Often the most challenging aspect of digital work is finding an appropriate data set. The materials in this section provide a variety of data and links to other collections that will help get projects off the ground. The data that can be found here are in many different formats, both structured and unstructured, numerical and textual.
The text analysis resources here range from installing various languages (such as R and Python) onto your computer, running exploratory scripts of word tokenizations and counts, to more advanced approaches like topic modeling and word2vec.
This page is specifically focused on frameworks and guidelines for designing and creating spatial visualizations and working with geographic data for analysis.
Network science resources include tutorials, datasets, and other useful links for creating network visualizations and performing network analysis.
Resources for the production and maintenance of digital projects and other miscellaneous resources.