Repairing Reproducibility: Fixing Digital Chaos with Better Infrastructure

There’s a quiet crisis going on in the life sciences.

Not one of discovery, but of trust.

For decades, researchers have pushed the boundaries of what we can cure, engineer, and sequence. But underneath the innovation lies a foundation that's worryingly fragile.

The uncomfortable truth?

Most published studies in biology can’t be reproduced, even by the original authors.

The cost?

Billions in lost R&D, failed drug programs, and an erosion of confidence in the scientific process itself.

This isn’t a story of bad actors or malicious data fabrication (though those certainly happen). It’s a story of fragmentation, friction, and foundational dysfunction.

Problems with Reproducibility: The Facts

In the last two decades, several hard truths about scientific reproducibility have come to light:

• A 2016 Nature survey revealed that 70% of researchers failed to reproduce someone else’s experiments, and more than 50% couldn’t reproduce their own work
• Amgen reported it could replicate only 6 out of 53 landmark oncology studies, a mere 11%
• Bayer HealthCare found that only ~20–25% of its internal target validation projects were completely reproducible. About 2/3 of the projects had prolonged timelines or were canceled due to inconsistencies between published data and internal data.
• An estimated $28 billion is wasted annually in the U.S. alone on irreproducible preclinical research.
• The Lancet has estimated that as much as 85% of biomedical research is ultimately wasted effort due to poor methodology, reporting, and design.
  

These numbers aren’t just headlines; they’re a mirror held up to the industry. These reports come from companies with billion-dollar drug pipelines, published in reputable journals. The data cannot be ignored and the implications ripple far beyond the lab bench.

From Pen & Paper Problems to Digital Chaos

Reproducibility issues aren’t new.

In the era of paper lab notebooks, the culprits were often simple: illegible notes, missing details, inconsistent materials, and poor documentation. But now, the scientific community faces a different kind of problem; one created by digital overload and systemic fragmentation.

Today’s labs are full of digital tools – electronic lab notebooks (ELNs), laboratory information management systems (LIMSs), etc. –  that streamline a number of day-to-day tasks.

However, there’s a lack of infrastructure to integrate these various tools and their users:

• Protocols may live in one of several ELNs, often not standardized across teams.
• Sample data and experimental results are siloed across spreadsheets, cloud folders, or aging LIMS platforms.
• APIs, if they exist at all, are brittle and rarely support real-time data harmonization.
• Automation platforms create structured data that cannot be easily integrated or analyzed alongside other tools.
• Contextual data, including decisions, anomalies, tribal knowledge, isn’t formally documented from digital communication tools, like Slack and email threads.  

We moved away from notebooks, in favor of “more organized” digital record-keeping, only to land in a more complex, distributed lab environment. The reproducibility crisis isn’t the result of carelessness; it comes from an inefficient digital lab ecosystem.

The Franken-stack: How We Got Here

Most labs didn’t build for scale, they patched.

A spreadsheet here.

An ELN over there.

A homegrown LIMS no one dares touch.

As science became more complex, so did the software stack, but without a plan for how all of these technologies would integrate.

What we’re left with is a "Frankenstack": Dozens of disconnected systems, none of which talk to each other cleanly.

• ELNs exist in silos, with no connection to sample registries.
• LIMS are often bespoke and inflexible, designed for QC workflows, not R&D experimentation.
• Data analysis tools require manual data cleaning before use.
• Inventory systems are either Excel-based or completely disconnected from experiment design.
• Communication about experimental context lives outside these systems — in email, Slack, or memory.

This patchwork stack fragments context, introduces human error, and makes knowledge non-portable.

The consequence?

Reproducibility is impossible to guarantee because the inputs, conditions, and decision points are scattered and ephemeral.

Why Infrastructure Matters More Than Ever

Reproducibility doesn’t fail at the point of analysis, it fails in the moment data is captured, recorded, and stored. If scientific outputs are generated without structure, traceability, or context, the ability to replicate them becomes hopeless.

The solution isn’t more tools.

It’s better infrastructure:

• Centralized platforms that unify sample tracking, data entry, protocol versioning, and results in one workflow.
• APIs that do more than connect systems; they standardize data across them.
• Audit trails that are automatic, comprehensive, and human-readable.
• Tools that don’t just collect information, but turn it into structured, mineable insight.

These aren’t "nice-to-haves." They’re the foundation for building modern, resilient scientific organizations.

Why Infrastructure is Everything

It’s easy to think of infrastructure as “the pipes behind the walls.” But in life sciences, your infrastructure is your science.

Whether you’re managing molecular assays, CRISPR edits, sample transfers, or regulatory data, your tech stack shapes what’s possible, what’s traceable, and ultimately what’s reproducible.

Right now, too many organizations are building high-stakes science on low-integrity digital foundations.

What Does Infrastructure in Life Sciences Actually Mean?

It’s more than software. True digital infrastructure for scientific R&D means:

• Standardized Data Models: Consistent formatting, structure, and taxonomy across experiments, instruments, and departments.
• Workflow-Driven Systems: Tools that reflect how real scientific work happens, not just generic data entry forms.
• Interoperability by Design: APIs and integrations that are robust, real-time, and allow seamless data flow between systems.
• User Accountability & Audit Trails: Every action tracked and contextualized, automatically.
• Scalable Configuration: The ability to evolve as science evolves, without technical debt or vendor lock-in.
• Searchable, Structured Data Lakes: Not just storage, but queryable knowledge for retrospective analysis, meta-studies, and ML readiness.

This infrastructure is what separates scientific documentation from scientific intelligence.

What Good Infrastructure Looks Like

To get reproducibility right, we need to think like systems architects, not just scientists.

A reproducibility-ready infrastructure is:

1. Unified

All core experimental functions, including sample tracking, protocol execution, data capture, and result analysis,  live in a single connected platform or are interoperable by design.

2. Context-Rich

Every result is linked to its experimental conditions, sample lineage, protocol version, and user interaction history automatically.

3. API-first

The system is built to push and pull data in real time, enabling automation, dashboards, and analytics without data silos.

4. Flexible

You shouldn’t need a full migration every time your sample type, equipment, experimental workflow, or reagents change. Good infrastructure is modular, configurable, and adaptable to evolving workflows.

5. Designed for Discovery

Data doesn’t just sit in silos. It’s structured and indexed so teams can learn from it. AI and ML can only add value if the data is consistent and queryable.

This Isn’t Just IT’s Job; It’s a Strategic Priority

Behind every workflow is an infrastructure decision, made intentionally, or by default.

If the goal is to accelerate drug development, pass regulatory audits, or scale teams globally, infrastructure isn’t a backend function, it’s a core driver of scientific velocity.

The difference between a lab that consistently innovates and one that drowns in its own data often comes down to this:

Do you control your infrastructure, or does your infrastructure control you?

Elevating the User Experience (UX)

Ask any scientist how they spend their day, and you won’t hear “pushing the boundaries of molecular innovation.”

You’ll hear something more like: “I was digging through old ELN entries, chasing down a protocol version, cross-checking a spreadsheet, and trying to remember what that weird sample label meant.”

This is not a software problem.

This is a user experience (UX) problem.

The Solution: A Unified Lab Platform That Prioritizes SX

The future of scientific work will not be defined by feature lists or flashy dashboards; it will be defined by how easy it is to find, trust, and act on critical data.

This is where a unified platform becomes essential; particularly one that:

• Brings experiments, samples, protocols, and results into a single, connected workspace
• Lets scientists move seamlessly from planning to execution to analysis, without leaving the system
• Embeds communication, approvals, and auditability into the workflow itself
• Surfaces contextual insights, not just files, when they’re needed most
• Is designed with the actual scientist in mind, not just IT admins or regulatory reviewers

This is what it means to prioritize UX.

What UX-Driven Lab Platforms Enable: Reproducibility and More

When the platform is unified and intuitive, the benefits are immediate and exponential:

• More reproducible results because all actions and data are captured in context
• Faster onboarding for new scientists who no longer need to learn six tools to get started
• Better collaboration between bench scientists, computational teams, QA, and leadership
• Clearer handoffs between professional services, customer success, and commercial teams
• Stronger data integrity across the entire lifecycle of a project

UX is not a luxury; it’s the critical layer that enables science to scale, safely and intelligently.

You Can’t Fix Science Without Fixing the Scientific Experience

If we care about speed, reproducibility, and collaboration, we have to care about experience. Because scientists don’t just need better tools, they need better systems that align with how they think, work, and share knowledge.

And that system needs to be unified, intuitive, and built for the realities of modern R&D.

UX must become central to how we build the next generation of scientific platforms.

Because when scientists are empowered to focus, to find clarity, and to trust their systems, they don’t just work better — they discover faster.

To learn more about how to optimize your lab, contact us for a free 30-minute consultation.

‍