Forum:Small‑Lab Data Management & Analytics Tool – What are your biggest pain points?

Question

[Novoo]

Hi everyone,

I’m a BSc Biotechnology student working on a lightweight lab data management & analytics tool aimed at small academic and startup labs. Before I build too much, I’d love to learn from your real‑world experiences.

If you have a minute, could you share:

How do you currently track samples and experiments? (Excel, paper notebook, commercial LIMS, etc.) What are your biggest headaches? (data entry errors, file version chaos, manual plotting, missing QC alerts…) Which features would save you the most time? (automated graphs, protocol templates, instrument integration, notifications…) Any “wish‑list” items? (e.g., cloud backup, multi‑user collaboration, easy exports for publications)

I’m building an MVP in Streamlit that will let you:

Log samples & experiments via web forms

Upload CSV results and instantly generate trend plots & summary stats

Search, filter, and export clean datasets

Your feedback will directly shape the tool’s design and feature set. Please drop your thoughts or rant about your current workflow below—every comment helps!

Thank you in advance — Novoo

[Mensur Dlakic]

My recommendation is to take a couple of days and research what is already available. Is it more productive to spend a week and test several solutions out there, or whatever time it takes to build and debug a new solution? I wrote many programs over the years that I didn't need to, so this comes from experience.

[karl.stamm]

I've seen a few of these posts, and you seem to be highlighting that it is 'lightweight'. I think that's a bad move - what do you think that means, what do you think the audience thinks it means? Nobody cares how many lines of code you write, in fact less features is less capability. I know for programmers it is a virtue to make things bare-bones, it will be faster, but you're not trying to support tens of thousands of users, you're going to have less than four people signed in to the app at once! Lightweight just means incomplete to them.

Answer 1

[GenoMax]

student working on a lightweight lab data management & analytics tool aimed at small academic and startup labs

Commendable aim. Wishing you success with it.

That said, this is an extremely challenging thing for a couple of reasons.

One is there is no "one size fits all" solution that can tackle workflows in diverse labs. Note: There are "enterprise" solutions that claim/aim to do this and they cost hundreds of thousands of dollars plus a lot of human time for tailoring the package to "fit". They are likely to work (to some extent) only in large enterprises with deep pockets and ability to do policy enforcement.

Second thing is, even for small labs, people loath to change existing workflows. What seems a straight forward action (to them) sometimes becomes tedious to capture in code. Then come the edge cases/what-if scenarios that become a minefield of its own. If you find people people willing to change their workflows in a way that suits your software then you may have an easier time.

Not sure if this is supposed to be a small project for your gradudate work or if you have visions of releasing this as (free?) or commercial software at some point. In any case you may want to start focused on a particular type of lab. Here is one example. https://www.bioinformatics.babraham.ac.uk/projects/sierra/ meant for sequencing labs. I am not sure if other people besides the owners use it.

Are you considering access restrictions (roles, who gets to see/do what), audit trails (who did what), SSO integration with institutional resources (which facilitates in not having to remember one additional account/pwd). These are all essential for a serious LIMS system.

instrument integration

Writing parsers for fixed outputs is doable but not sure if you have looked into "integrating" a real instrument (both way integrations would be the ultimate aim). On paper possible, because of API's that may exist but can be difficult in practice.

[Novoo]

Thanks so much for the thoughtful feedback—I really appreciate you laying out the practical challenges. Here’s how I’m addressing each of your concerns:

1. No “one‑size‑fits‑all” Totally agree—enterprise LIMS vendors spend massive budgets just to get close. I’m starting with a single lab vertical (ELISA immunology workflows) because those protocols are extremely consistent across small labs.

2. Resistance to workflow changes I won’t force anyone into a new process. The app will let you upload raw CSV/Excel exports straight from your plate reader—no special drivers or proprietary APIs needed. Protocol templates are fully configurable, so each lab can mirror exactly how they already work on paper or in spreadsheets.

3. Access controls & audit trails Even in the first version, I’ll include:

-Admin vs. Researcher roles so you can limit who edits protocols vs. who just runs assays.

-A simple audit log that timestamps every protocol change or data upload, so you can always trace “who did what, when.”

1. SSO & deep instrument integration I hear you that true two‑way instrument APIs and campus SSO are essential for enterprise LIMS—but they’re a heavy lift for an MVP. I’ll start with email/password accounts and focus on parsing the most common CSV/XML outputs. If there’s demand down the road, I can look at institutional SSO or more direct instrument links later.

Thanks again for helping me zero in on what really matters and pls feel free to critique anything that feels wrong with this plan too.

Answer 2

[Darked89]

I think the best is to strip functionalities to bare essentials: secure storage of the data plus metadata and the backups. For that you may investigate iRODS . Before that, try to come up with some file naming schemes, acceptable file formats, compression tools etc. IMHO whenever possible stay away from binary formats. It is trivial to have versioning, branches with Markdown documents easy to visualize using i.e. free version of GitLab. Doing the same with some mix of PDFs, Word docs etc. at least in my experience is problematic.

All the above works with users able to make a transition from the desktop oriented storage/data processing. So designing the system needs to start with getting the requirements from users and checking how flexible/open to adapt new solutions they are.