By Kevin Dyke
This post is the fourth in our Spring 2016 series on processing digital materials.
When it comes to working to process large sets of electronic records, it’s all too easy to get so wrapped up in the task at hand that when you finally come up for air you look at the clock and think to yourself, “Where did the time go? How long was I gone?” Okay, that may sound rather apocalyptic, but tracking time spent is an important yet easily elided step in electronic records processing.
At the University of Minnesota Libraries, the members of the Electronic Records Task Force are charged with developing workflows and making estimates for future capacity and personnel needs. In an era of very tight budgets, making a strong, well-documented case for additional personnel and resources is critical. To that end, we’ve made some efforts to more systematically track our time as we pilot our workflows.
Chief among those efforts has been a customization of the Data Accessioner tool. Originally written for internal use at the David M. Rubenstein Rare Book & Manuscript Library at Duke University, the project has since become open source, with support for recent releases coming from the POWRR Project. Written in Java and utilizing the common logging library log4j, Data Accessioner is structured in a way that made it possible for someone like me (familiar with programming, but not much experience with Java) to enhance the time logging functionality. As we know some accession tasks take a few minutes, others can run for many hours (if not days). Enhancing the logging functionality of Data Accessioner allows staff to accurately see how long any data transfer takes, without needing to be physically present. The additional functionality was in itself pretty minor: log the time and folder name before starting accessioning of a folder and upon completion. The most complex part of this process was not writing the additional code, but rather modifying the log4j configuration. Luckily, with an existing configuration file, solid documentation, and countless examples in the wild, I was able to produce a version of Data Accessioner that outputs a daily log as a plain text file, which makes time tracking accessioning jobs much easier. You can see more description of the changes I made and the log output formatting on GitHub. You can download a ZIP file with the application with this addition from that page as well, or use this download link.
Screenshots and a sample log file:
With this change, we are now able to better estimate the time it takes to use Data Accessioner. Do the tools you use keep track of the time it takes to run? If not, how are you doing this? Questions or comments can be sent to lib-ertf [at] umn [dot] edu.
Kevin Dyke is the spatial data analyst/curator at the University of Minnesota’s John R. Borchert Map Library. He’s a member of the University of Minnesota Libraries’ Electronic Records Task Force, works as a data curator for the Data Repository for the University of Minnesota (DRUM), and is also part of the Committee on Institutional Cooperation’s (CIC) Geospatial Data Discovery Project. He received a Masters degree in Geography from the University of Minnesota and can be reached at dykex005 [at] umn.edu.