Teaching Undergraduate Researchers to Manage Their Data

One of the easiest parts of undergraduate research to overlook is data management.

When a project is small, it may not seem like a major issue. A student runs an experiment, saves a spreadsheet, makes a graph, and moves on. But as projects grow—and as students graduate, new students join, and experiments accumulate—it becomes much harder to keep track of where everything lives.

That is why I developed a standardized data management system for my undergraduate research lab.

The goal is not simply to keep folders organized. It is to teach students that good research should be traceable, reproducible, and easy for someone else to continue.

Our system centers around three tools: OneDrive serves as the official research record, Canvas provides easy student access to training materials and commonly used protocols, and the lab notebook documents the day-to-day experimental work.

Give Every Project the Same Structure

Each research project follows the same basic folder organization, with separate locations for project overviews, experimental plans, raw data, processed data, figures, protocols, and manuscripts or presentations. Consistency makes a big difference. Students do not have to decide where something belongs every time they generate a new file, and mentors can quickly locate the information they need. It also makes transferring a project from one student to another much easier.

Protect the Raw Data

One of the most important habits students learn is that raw data should remain untouched. If data need to be cleaned, normalized, reformatted, or analyzed, students work from a copy and store that separately from the original. This may seem obvious to experienced researchers, but it is an important concept to teach explicitly. Students need to understand the difference between the original experimental record and the files they create during analysis.

Connect the Lab Notebook to the Digital Record

Modern experiments often generate far more data than can reasonably fit into a traditional lab notebook.  Instead, I use the notebook as the bridge between the physical experiment and the digital files. Students record what they did, which protocol they used, important observations or deviations, and where the corresponding electronic data are stored. Ideally, another student should be able to pick up that notebook months later and understand both what happened and where to find the supporting data.

Organize as You Go

The other major lesson is that data management cannot become an end-of-semester activity. Raw data should be uploaded promptly, experimental plans should be saved before experiments begin, and analysis files and figures should be organized as they are created. Trying to reconstruct several months of research at the end of a semester almost always leads to missing files, forgotten details, and confusion. A little organization every day is much easier.

Data Management Is Part of Research Training

Ultimately, I see data management as another research competency. We spend a lot of time teaching undergraduate students laboratory techniques, instrumentation, data analysis, and scientific writing. They should also learn how to maintain a research record that someone else can understand and continue. That is especially important in an undergraduate lab, where students regularly graduate and projects are passed to the next researcher. A good system allows the research to continue even after the student who started it has moved on.

I have put together a more detailed Undergraduate Research Data Management Policy that outlines our folder structure, file-naming expectations, raw data practices, protocol storage, lab notebook requirements, and other specifics for students who want the full framework.

This is free, always will be — but if you want to say thanks, a chai latte tip is always appreciated ☕

Venmo @Jessica-Pullan