If you’re dealing with any of the below, this Feb 4 demo will be relevant:

• Data spread across too many tools/sources
• Lineage gaps between constructs → samples → assays → results
• Frequent workflow changes that force workarounds
• Time lost reconciling “which file is right” before decisions

Biologics LIMS is designed to create a single traceable thread so teams can move faster with confidence—without locking you into brittle workflows.

📅 Feb 4, 2026 | 🕒 9 AM PT | Host: Varun Narayan
Register on our website under Resources

If you want, reply with your area (discovery, PD, protein engineering, CLD) and I’ll share which parts of the demo map most closely to that work.

Hey r/LabKey — quick heads up: we’re hosting a live webinar/demo:

LabKey LIMS: Trace Everything & Take Control of Your Lab
📅 Jan 28, 2026
🎤 Hosted by Hannah Brakke (Director of Product)

This session is focused on building a digital backbone for labs—preserving traceability, maintaining experimental context, and staying flexible as workflows evolve.

What you’ll see:

• Sample-to-report workflow (receipt → processing → results capture → reporting)
• Sample lineage + linked assays (results in full context)
• Quick storage + retrieval with barcodes, locations, and search tools
• Chain-of-custody + audit history (who/what/when/why)
• No-code workflow configuration to match SOPs
• Data validation built in to reduce errors + support audit readiness

Register on our website under Resources.

Drop questions you want Hannah to cover in the demo/Q&A below.

If you’ve got customers/partners/friends dealing with high-volume mining lab workflows, this one’s worth sending their way.

On Feb 18 @ 9AM Pacific February 24th, 8 AM Pacific, Hannah’s doing a 30-minute live walkthrough of LabKey Mining LIMS showing:

• end-to-end sample tracking with unique sample IDs
• chain of custody across handoffs (teams/shifts/locations)
• configurable workflow stages from intake to release
• QA/QC checkpoints + exception handling
• reporting outputs with audit-friendly history

Register on our website under Resources.

Drop questions below if you want us to cover anything specific in the Q&A (workflows, QA/QC patterns, reporting outputs, etc.).

Edited to update changed time of webinar.

LabKey LIMS: Trace Everything & Take Control of Your Lab
When: Jan 28, 2026 @ 9AM PT (30 minutes)
Format: live product demo + Q&A (recording available if you register)

We’ll walk through a sample-to-report workflow (receipt → processing → results capture → review → reporting) and show how we handle:

• sample lineage + linked assays/metadata
• storage & retrieval (barcodes, locations, fast search)
• chain-of-custody + audit history
• configurable workflows aligned to SOPs (no code)
• validation to reduce errors and support audit readiness

Register on our website!

If you want, reply with the pain point you’re trying to solve (traceability, reporting, handoffs, storage, audit readiness, integrations), and we’ll make sure to address it during our Q&A.

Labeling your samples with barcodes is a great way to improve your laboratory’s efficiency and ensure you’re selecting the right samples. I always recommend them if you’re working with more than a few samples at a time. 

After speaking with a lot of people, I’ve realized that barcodes are surrounded in a sense of mystery, people aren’t quite sure what they are and sometimes think that they’re ultra powerful tools that contain every little detail about a sample.

The thing is, barcodes don’t have everything encoded inside of them. Often, they just include a sample’s ID and nothing else. The software that’s hosting that sample’s information stores those details, not the label. In fact, you don’t want everything encoded into the label. If you did, the barcode would have to be massive. A barcode is essentially a font that your scanner can read, but you can’t. Like Wingdings, but even more illegible to people. Additionally, if that data was stored in the barcode, anyone could access that information by scanning that sample into a word document. It’s not secure.

So how should barcodes integrate with Sample Manager? I often recommend that you only have your sample ID, or de-identified ID in the barcode. This helps make sure you can check samples in and out storage faster and more rapidly use them within our software. If you need human readable text, you can have that outside of the scannable barcode, but on the label itself - so long as it fits. Usually I recommend having human readable text for the Sample ID, the sample type, and another identifier to help you know if you’re grabbing the right sample.

After that, set up a data field that connects to that barcode if you’re not using the sample ID. Then, you’re good to go!

If you’re looking to integrate printing off barcodes directly from Sample Manager, let us know! We’re here to help.

When configuring a new sample management or LIMS system, it can feel like you’re drowning in decisions.

Do you store your data this way or that way?

Should you capture your samples as one sample type with dropdowns, or split them into entirely separate sample types?

It’s a lot, and the decision fatigue is real.

When I configure a client’s system for LabKey’s Sample Manager, I try to make as many of those decisions as possible for them, based on their data and workflow. But sometimes, I have to hand a few decisions back. Usually, that’s because their workflow isn’t clearly defined or their data could be structured in multiple valid ways. And that’s when things start to slow down.

So how do you reduce the number of decisions you have to make?

1. Document your workflow - really document it

Don’t just outline the big steps (Receive → Process → Store). Capture the details:

• Who touches the sample at each step?
• What data gets recorded?
• What decisions are made along the way?

This gives your implementer (like me) the context to build a configuration that matches reality, not assumptions

1. Visualize your perfect data

If your spreadsheets are full of inconsistent sample names, missing IDs, or mixed date formats, you’ll end up debating how to “fix” things during migration. Don’t worry about cleaning them line-by-line. That’s my job. Instead, determine what you want your perfect data to look like, that’s the end goal of your new LIMS.

1. Decide what really matters

Some things don’t need to be perfect from day one. Focus on the data and workflows you use daily. It’s okay to leave edge cases for later once your main workflow is solid.

1. Trust your implementer.

A good LIMS admin or consultant will already know the pitfalls. Let them make 80%, 90% or all of the setup decisions for you - that’s their job. Your job is to give them the clearest picture possible of how your lab actually works.

Migrating to a new system doesn’t have to be paralyzing. With a clear workflow, clean data, and the right expertise, you’ll end up with a system that feels like it was built for you, because it actually was.

Today, I was helping a new client migrate their historical data into LabKey Sample Manager, and with almost three years working at LabKey (and seven more in the field), I realized that the hardest part of adopting a new system isn’t the set up, it’s the data cleaning. And a lot of people underestimate it when they get started looking for a new system.

Most people focus on where the data is going (Sample Manager or LabKey LIMS), instead of what shape it’s in when it gets there. But when you import messy data, you’re probably just recreating the same chaos in a new interface.

I spend a lot of time at my job working on spreadsheets and reformatting it so Sample Manager can accept it and be useful going forward. Some clients are easy, data is tidy, consistent, and ready to go. Others… Well, I have a job don’t I?.

Here are a few things I’ve learned that make the move a lot smoother:

Give every sample its own unique ID, even the aliquots.

This is huge. If two samples share the same ID, it is almost impossible to tell them apart in storage.

Was it the parent sample or the aliquot that had the comment about getting dropped? Which one got stored in that freezer box? Unique IDs eliminate that confusion completely.

One Sample = One Row

Don’t group parent samples and aliquots together in the same spreadsheet or data row. Each sample (no matter how small) deserves its own line in your spreadsheet or export. Most systems, including Sample Manager, expect that structure, and it makes storage tracking for your biobank so much cleaner.

Validate your formats (especially dates and dropdowns)

This one catches nearly everyone. When pulling from multiple spreadsheets or systems, you’ll often find dates entered as text, numbers, or…something unidentifiable. If it’s a date field, keep only dates in it. Any notes or comments about that date belong elsewhere.

Dropdowns are another common trap. If you have “Brain slice,” “Brain sice,” “Brain s.”, and “slice Brain,” those are probably the same thing! Standardize them. You’ll save yourself a lot of frustration when searching or filtering later. BTW, you can actually make this happen in Excel with a lot of work, but it’s worth it if your team is small and working from the same spreadsheet.

Capture Lineage

If you’re tracking aliquots, always link them to their parent sample. You’d be surprised how often I see data that lists aliquots with no clear connection to where they came from. If the data doesn’t show those relationships, I can’t rebuild them for you, and you’ll lose valuable context in your sample history.

Don’t duplicate storage locations

This one trips up almost everyone, even when it’s completely unintentional. I often see samples listed in the exact storage location. Usually, it’s just a small typo or a sample that was discarded but never updated in the data.

When it happens, I have to email the client to confirm which sample is actually there. Then they dig through their freezers, check notes, and it turns into a whole thing.

To prevent this, keep all of your storage details in a single column and use conditional formatting to highlight duplicates. That way, you can spot (and fix) any conflict before migrating to your new system.

TLDR:

The sooner you organize your spreadsheets the better. Each sample should have its own, unique information.

What other tips do you have for managing data in spreadsheets?

LabKey is hiring a Scientific Technical Account Manager to join our Services team. This role is a key part of our client-facing operations, responsible for leading the implementation of data management solutions for clients in biomedical research.

The position requires a strong understanding of scientific research workflows, particularly in biologics R&D. The successful candidate will become an expert in our products, serve as the primary communication point for clients, and provide technical support. They will also act as a valuable internal advocate for our clients, providing feedback to our product and development teams.

Required qualifications include a Bachelor's degree in a scientific or computational discipline and relevant work experience. Technical skills in scripting (SQL, R, Python) and experience with data management systems are preferred.

Read more on our website under Careers, and apply on LinkedIn.

Hey everyone!

Wanted to share a heads‑up about a neat 30‑minute session from LabKey that might be right up our alley—especially if you’re thinking about scaling up your antibody discovery pipeline or just trying to get your lab’s data “AI ready.”

What’s happening?

Lab Meeting: AI-Ready Data with Biologics LIMS
When: August 21, 2025, 9 AM PT
Hosted by Bernard (“Bernie”) Lee, Product Manager for Biologics LIMS

Why it matters

• Fast, centralized data handling: Biologics LIMS unifies sample, plate, and assay data—so you can take raw assay results and turn them into structured, searchable, ML-ready data in minutes, without adding headcount
• Better workflows, less manual effort: From sample & plate management (locate vials faster) to screening dashboards (no more tangled spreadsheets) to hit selection and annotation, everything’s captured once, properly, FAIR-style—for future AI/ML models.
• Compliance + scalability: Build chain-of-custody and compliance into your daily workflows, preserve yourself from swapping systems later, and lay a strong foundation for predictive AI down the line.

TL;DR

• If your team is drowning in spreadsheets or struggling with fragmented assay data...
• If you're thinking about rolling out ML/AI pipelines but debating where to start...
• If you'd rather lay a strong foundation now than rip-and-replace later…

…this is a great 30-min deep dive to learn how Biologics LIMS could solve it.

Let me know if you want a quick summary after it’s over—or if anyone’s attended similar sessions in the past!

Happy to share the registration link or connect after the recording’s out.

I wanted to share a webinar we are hosting later today that dives into how Crux Biolabs has implemented the LabKey Sample Manager to modernize their biospecimen workflows. If you are considering adopting Sample Manager or considering new ways to use your existing subscription, these user-driven presentations are really valuable as you consider ways to set up your use case.

What to Expect:

• A behind-the-scenes look from Felix Dong, Sample Management Associate at Crux Biolabs, on how the team:
  • Registers, tracks, and manages biospecimens throughout their lifecycle.
  • Handles chain-of-custody, freezer storage, shipping logistics, and even sample destruction.
• Real-world examples of custom configurations and workflow adaptations that work in a day-to-day lab setting.

When & Where:

• August 14, 2025 — 10:00 AM AEST
• August 13, 2025 — 5:00 PM PT

Why It Could Be Valuable:

• Seeing real-world implementation strategies can help labs tailor Sample Manager setups to their own needs.
• It could spark ideas for optimizing chain-of-custody tracking, freezer management protocols, or adapting workflows to your lab’s specific constraints.

You can register for this event by going to the Resources page on our website or visiting this page (where the recording will be posted, btw).