# Epilogue: Good Enough Practices¶

If you have made it this far, we assume you are persuaded that quantitative methods are potentially useful in humanities research. What comes next is up to you. We wrote this book with the hope that it would give students and researchers tools and ideas useful in solving problems and answering questions. We hope that the preceding chapters provided you with some useful resources for your own research.

As you embark on projects which involve data analysis, we invite you to consider the following practices and perspectives. The most important one comes first. It concerns a minimal set of “good enough practices”—a label we borrow from Wilson et al. [2017]—that anyone can and should adopt.

Adopt “Good enough practices in scientific computing”. Wilson et al. [2017] identify a minimal set of practices which “every researcher can adopt, regardless of their current level of computational skill.” They group these practices into six categories (data management, software, collaboration, project organization, tracking changes, and manuscripts). Examples of recommended practices include submission of code and data to a repository (such as arXiv or Zenodo) so others can access and verify results, the use of a version control system (such as git or mercurial) to track all changes over the life of a research project, and the careful preservation of the original (“raw”) data (which should never be edited “in-place”). Adopting these practices does take a modest investment of time and energy. But the benefits are considerable. The practices reduce the incidence of data loss, reduce the time it takes to perform analyses, and facilitate reproduction of results by others.