This site is intended to be a one-stop resource for information broadly related to managing your digital life. You can access this page via the shortened URL vanderbi.lt/ds.
In addition to the resources on this site, you can browse the Digital Scholarship code repository to find bits of code that might help you.
Application hosting and management - Services like Amazon Web Services (AWS), DigitalOcean, and Code Ocean make it possible for you to deploy applications in the cloud. Docker allows you to containerize your applications so that they can easily be deployed. Using Wikibase, you can create a version of Wikidata populated with your own data.
Data cleaning and code organization - Open Refine is a powerful tool for manipulating and cleaning data. If you want to script and document data cleaning or other kinds of data pipelines you can use Jupyter notebooks (for Python or R) or R Markdown (for R).
Data management - There are many tools available to help you manage your data throughout the research life cycle, including data management planning, version control, and deposit of data and publications in a repository, such as Vanderbilt’s Institutional Repository.
Data visualization - Tableau Public is a commonly used tool for visualizing data on the fly. There are also powerful visualization libraries that can be used with R and Python.
Digital privacy and security - Being in control of the privacy and security of your work and data until you are ready to make it public is an important aspect of the scholarly process. Explore some of the useful tools that can help you in this area.
Digital publishing and curation - There are a number of powerful tools that you can use to make your scholarship available to the public. These include Omeka, Scalar, WordPress, Drupal, and GitHub Pages (using Jekyll).
Document markup - Transmitting data and text in a form where it can be understood programmatically is an important aspect of modern scholarship. Important markup systems include: LaTeX, XML, Markdown, and JSON.
Geospatial data - Tools for collecting and analyzing spatial data are an important part of the toolkit in many fields. We can help you learn how to use these tools for remote sensing and GIS in your research.
Linked Open Data (LOD) - Linked Data is a graph-based data representation that enables sharing and linking data among providers. Linked Open Data makes those data freely available. Querying LOD using SPARQL provides access to important datasets such as Wikidata.
Scripting/programming languages - Our office provides support for three of the programming languages most commonly used in research: Python, R, and XQuery. We provide links to do-it-yourself resources and support working groups where you can learn in person.
Understanding my computer - This part of the site unveils the mysteries of your computer on subjects including files and directories, installing and managing software, backups, and the command line.