You have a great research question that you want to answer with text data mining (TDM) methods, and you've got some Python under your belt or you've decided to see what you can learn from a browser-based tool like Voyant. You're ready to get started on a computational text analysis project. But wait!
In May and September of 2017, the Library wrote posts (read them here and here) about a number of publisher research data policies. Over the last year, publishers have engaged in conversations with institutions, funders, and not-for-profit organizations to examine how they can better shape and influence the sharing of research data.
The bDrive repository offers everyone at UC Berkeley unlimited storage, strong search capabilities, and mobile access. This storage is an important data management resource for research teams. The standard web client, however, does not always work well when dealing with very large files, many files, or deep folder structures. The web client’s connection is slow, and can disconnect in the midst of a lengthy, time-consuming transfer.
Last week, one of my teammates, at Old Dominion University, contacted me and asked if she could apply some of the techniques I adopted in the first paper I published during my Ph.D. She asked about the data and any scripts I had used to pre-process the data and implement the analysis. I directed her to where the data was saved along with a detailed explanation of the structure of the directories.