No Scientist Left Behind: The Case for Data Education (Part I)

I always assumed my advisor had a great data and computer file management system.  But when I asked her for a particular piece of information, it took her a week to find it and get it into a useable form for me.

This paraphrased statement was made by a Geology PhD graduate student while I interviewed him about his Excel use and data practices.  It speaks to a larger problem: there is a lack of basic data management knowledge among scientists at all levels.  More often than some advisors would like to admit, they are just as confused, sloppy, or disorganized as their students when it comes to data management- they just hide it better.

Students and postdocs often assume data management and organization is something to be figured out for themselves: they should develop their own organizational system, experiment with spreadsheet layouts, and perhaps occasionally contribute to organizing the data and files for their lab group.  This notion of trial-by-fire is likely coming from the top down.  The advisors of these scientists-in-training had to figure out how to manage their data without assistance, and therefore their students should too.  The consequences is that data management skills are sub-par all the way up the academic chain.

There is a case to be made for standing on the shoulders of giants, or not reinventing the wheel, or some other version of that colloquialism.  There is so much to learn in graduate school (and so many opportunities for failure): software, hardware, field techniques, laboratory techniques, standards and protocols, and instrument operation, note to mention course work and comprehensive exams.  I argue that a young scientist’s time is better spent figuring these myriad components of being a researcher, rather than fumbling through how to manage and organize their data.

Just like parents give their children allowance to prepare them for handling money in the future, advisors should instruct their “children” in good data management by first learning a bit about good data practices themselves, and then training up their advisees by giving them data to organize, showing them the system used by the lab, and encouraging them to experiment with software or hardware that might mesh well with their own style of data organization.  Final words:

To advisors: Train up your students in good data management!

To scientists-in-training: Ask your advisor about their data management and organization schemes.  Determine whether their systems might work for you, and how you can improve on them to fit your needs.

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