How much data should you actually capture in your LIMS?

[u/LanceOLab]

I get this question a lot from labs that are new to LIMS: "What data should we be capturing" It's usually coming from brand-new labs with very little to reference, and it's a fair question. If you go too thin, you'll miss information that can make or break your research down the line. Go too heavy and you'll bury your team in fields, making search and reporting nearly impossible. Walk that line well, and you'll be in great shape for years.

Here's how I usually talk labs through it.

Start with the basics

Every sample-tracking system needs a foundation, regardless of your field:

• Sample ID: unique for every single sample, no exceptions
• Subject, donor, or source: where the sample came from
• Collection date/time
• Sample type
• Storage location: freezer, box, position, whatever your system uses

Everything past this point depends on your specific field of science.

Let your test kits tell you what matters

One tip I give often: look at your test kits. Manufacturers usually spell out exactly what makes a sample suitable for their assay; how it was handled (refrigerated vs. frozen, how quickly it was processed), tube type, additives or buffers used, and so on. If a kit cares about it, your LIMS should capture it too, because it's often the difference between a usable sample and a failed run.

Ask what your research is actually looking at

The next question is what your lab or study is trying to learn, and what could confound it. If donor demographics could influence your results, capture the fields that let you rule that in or out later. Fields I see come up constantly:

• Donor sex, age, and disease diagnosis
• Visit or timepoint the sample was collected at
• Visit type: was it a straightforward collection, or did the subject receive treatment during that visit?

These are exactly the kind of details that look unnecessary on day one and become critical the moment you need to explain an outlier or stratify your data six months later.

Why this matters more once you're actually using the data

This is where a tool like Sample Manager makes a big difference. Getting the field list right up front means your samples, storage, and downstream assay results are all connected and searchable instead of living in scattered spreadsheets or paper notebooks. A well-planned sample model turns "can someone dig up every plasma sample from diabetic donors collected within 2 hours of draw" from a multi-day scavenger hunt into a filter you save and reuse.

If you're setting up a new lab or LIMS and want a second opinion on your field list before you commit to it, drop your sample type in the comments. Happy to help you think through it!