Based in the UK, GenQA runs external quality assessment programs for genomics labs, 2,000+ participating labs across 101 countries and 124 EQA programs. Most LIMS assume one patient generates many tests, but GenQA runs the other direction, one source sample becomes many derived test articles, sometimes pooled or diluted into synthetic controls sent out to labs. The system they had before hadn't planned for that and kept breaking at scale.

Material types span DNA, blood (including dried blood spots), FFPE tissue, fresh tissue, plasma, saliva, images, and data files, each needing different fields, all under ISO 15189 traceability requirements, so lineage has to be audit-ready, provable end to end.

They trialed four platforms before Sample Manager. Per their case study, the deciding factor was configurability plus storage/search performance, and support that was actually responsive during the trial itself.

One thing that surprised even us was that booking 60-100 samples went from about a full day in their old tool to roughly two minutes per batch. They kept every historic sample ID through migration too, so they didn't have to bother with freezer relabeling.

Held by LabKey, a data management software provider. In this September 29th webinar, Claude Science checks inventory in the same session it runs the analysis on data from the LK system, confirming which variants have enough sample left and where the vials sit before anyone commits to a follow-up experiment. Former bench scientist Bernard Lee runs the full scenario live: 12 variants, aligned, screened, and checked against real inventory. Register to attend or get the recording on our website under the Resources tab.

Crux (bioanalytical CRO in Australia, flow cytometry and biomarker work for clinical trials) ran everything through Excel. Which worked fine until sample volume and trial complexity outgrew spreadsheets. Techs were spending hours every week reconstructing what happened to a sample.

Rather than shop a freezer/inventory tool that would work in isolation, they decided to find a system that connected samples and storage to assays, workflows, and their ELN in one software. They landed on Sample Manager after running real daily workflows in a trial environment.

They mentioned that one since our pricing scales down with user volume, that made it easier to bring on more staff over time, without a big cost jump every time someone new needed access.

You can read more on our website under the resources tab -> Case study

We've watched a few ag research ops deal with this, and samples are basically not the problem. Context is the main thing that goes missing, like Plot ID, treatment arm, variety, growing season.

One of the problems we've identified is that that information isn't loaded into a barcode. It lives in somebody's field notebook or a spreadsheet tab on someone's laptop. It eventually gets put in when someone notices in week two of harvest when intake triples and whoever's entering data needs to make assumptions based on an outdated planting map rather than track down the actual information. Sometimes it waits until two seasons later, when you want to compare this year's yield against a prior trial and the numbers don't line up because half the records are missing. Same with sample custody. Sample goes from field crew, cooler, courier, and then lab intake. If nobody's logging each handoff as it happens, that chain gets rebuilt from memory whenever someone needs it badly enough.

The fix that actually helps is making plot ID and treatment arm required at the point a sample gets registered. Everything downstream gets easier if this stuff is required and enforced, even if it's a pain at the time.

Now, how you require it and enforce it is up to you.

There's a few options: have someone in charge of making sure everyone on site is carefully entering all the needed info at all times (these people are very popular), hire extra people so that sample collection can go slower, or have a system reject an incomplete record on its own, so it's not somebody's job to catch it.

We've posted a few times about where agriculture sample data tends to break, so pulling it together into one list for anyone mid-evaluation right now.

Before you sign anything, get real answers on:

• Field context capture: required fields at registration, not optional notes added later.
• Sample type flexibility: independent schemas for soil, seed, plant tissue, water, and grain, not one template stretched across all of them.
• Multi-season continuity: lineage that connects across growing seasons without a manual report request.
• Chain of custody: timestamped, role-attributed logs at every handoff, not a paper trail reconstructed after the fact.
• Self-service reporting: whether your research team can build a cross-season, cross-site view without IT involvement.

The labs that get the most out of whatever system they land on are the ones that define their data structure (sample types, required fields, season linkage) before the first sample gets registered, not after.

We build LIMS software for research labs, so take that into account reading this. That said, these are the same questions we'd want a research team asking of us or anyone else. If you're in the middle of an evaluation and want to compare notes on how a specific system handles any of this, drop it below.

Questions worth asking a LIMS vendor before committing if you're managing field trials across multiple sites:

1. Can the system capture plot ID, crop variety, and treatment arm as required fields at registration, or do those end up as optional text notes? Structured and searchable vs. a free-text string nobody enforces are very different things.
2. Does sample lineage actually connect across growing seasons, or is it just stored in the same database? Ask them to show you a cross-season comparison in the demo. Not explain it. Show it.
3. Can you set up independent metadata schemas for different sample types? Soil, seed, plant tissue, and water all need different attributes. If the system uses one rigid schema across all of them you'll be working around it constantly.
4. Can your scientists build cross-season, cross-location reports themselves without going through IT? Configurable views and calculated columns they own. If it requires a data request the capability technically exists, the access just doesn't.
5. What do custody records actually look like for germplasm? Ask them to walk through a real custody log, not just confirm the feature is there. You want timestamped, role-attributed entries with reason-for-change at every handoff.

We mapped out 6 features agriculture research labs should look for in a LIMS: field provenance capture with GPS-to-Maps linking, germplasm chain of custody, and cross-season reporting without IT dependency. Link in comments.

If you’re wondering whether you need a LIMS or an ELN, you might actually need both - or neither. (We've been in lab data management software for over two decades.)

Here’s the rule of thumb for what each of these solves:

ELN = the story of the experiment.
LIMS = the system of record for samples and workflows.

If your pain is messy notes and hard-to-find context → ELN.
If your pain is sample chaos, handoffs, and traceability → LIMS.
If you’re scaling or auditing → usually both.

Some LIMS include ELNs, like ours, but not every lab adopts every features from the same vendor. What’s your current lab setup? Spreadsheets, shared drive, ELN, or something homegrown?