DWCHelper is now unmaintained. Forks are obviously fine :) The build
scripts for the Windows installer (
may be useful if you are trying to make a cross-platform, command-line
Go program. Good luck!
DWCHelper is a command-line utility to help format and clean up CSV files (for instance, exported from Microsoft Access). It:
Windows: An installer for the latest release can be found on the Releases page.
Linux: You can use the binary provided on the releases page, or easily build from source with the following steps:
go get -u github.com/fatih/camelcase
Navigate to the location of your CSV dataset in the console and run:
DWCHelper <input-filename.csv> <output-filename.csv>
For Windows users, this means you need to navigate to the folder
containing your CSV file in Windows Explorer, then click in the
navigation bar and type
cmd (and press Enter). The black command
prompt window that opens up is where you type
On the first run for each dataset, DWCHelper will prompt you for
various corrections to the data. It will save your choices in the
.settings file (in Windows Explorer, it appears as
with the type SETTINGS, but is still a normal text file that you can
open with Notepad) for subsequent runs; if you want to redo the
prompts, simply delete this file.
.settings file can be edited with a text editor to avoid redoing
the prompts for small changes. DWCHelper is fairly tolerant of errors
in this file and will simply ignore typos and terms that aren't
in your dataset.
The first line is a CSV list of terms to remove completely from the dataset during the conversion.
Any lines after that are term aliases. The first value on each line is the term to be renamed and the second value is the new name.
DWCHelper is one component of my 2019 Undergraduate Research and Creativity Award project, which is a collaborative effort with the Anthropology department at UNCG.
The eventual goal of the project is to provide a tool for researchers at different sites in Olduvai Gorge, Tanzania to easily share, compare, and combine datasets and create useful, publishable data visualizations.
In June of 2019, I will be traveling to Tanzania to excavate and analyze animal bones, and I hope to gain a broader understanding of the context surrounding these 1.4 to 2 million-year-old specimens. My objective is to understand what types of questions researchers may need answered in their quest to understand this period of human evolution.