Lab informatics — the strategy and application of information technology solutions in the lab — spans a range of disciplines and domains. Fortunately, standards such as ASTM E1578 provide a shared vocabulary. However, in practice, different lab domains create their own way of communicating about processes, data, and results. These “dialects” aren’t just quirks of language, they shape how labs approach their work, set priorities, and innovate. They also affect a lab’s ability to share data and collaborate with other labs and organizations that may use different terminology.

Dialects in lab informatics

Within this context, dialects refer to the specialized terminology and conventions used to express concepts and tasks in various types of laboratories. Sometimes these differences in language result in multiple words for the same concept. Other times, a dialect points to something deeper, highlighting a distinct way of working, a different business emphasis, or a unique set of constraints. Note that dialects differ from taxonomies and ontologies, which define concepts and their connections.

The specialized dialects of different lab domains

Regardless of domain, labs rely on a universal core vocabulary that includes samples, assays, instruments, and workflows. This baseline language allows systems to communicate and teams to collaborate across disciplines. 

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However, once you step inside a specific type of lab, the dialect changes. As the following examples show, dialects can reveal what matters most in that environment.

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• Quality Control (QC) labs speak in the language of compliance and deviations. The focus is on ensuring products consistently meet specifications, with informatics systems acting as strict guardians of data integrity. This dialect drives testing that is highly repeatable, auditable, and aligned with regulatory expectations, where even small deviations trigger formal responses.
• Research labs use a more flexible dialect centered on exploration, hypotheses, and iteration. In this environment, informatics must support rapid change. This involves tracking evolving experimental designs, capturing context, and allowing re-interpretation of results. The language reflects curiosity and creativity, shaping workflows that prioritize discovery and innovation over standardization.
• Clinical labs operate in a dialect that emphasizes patients, results, and turnaround time. Informatics must prioritize speed, accuracy, and reliability because every data point has a direct impact on patient care. The language of clinical labs conveys urgency and trust, and shapes systems that can scale to high throughput while maintaining uncompromising quality.
• Forensic labs rely on a dialect based on evidence, chain of custody, and defensibility in court. Every action must be traceable, and every result must withstand legal scrutiny. Informatics systems in this environment are designed to create unbroken audit trails and detailed documentation, ensuring that findings remain credible in highly regulated and adversarial settings.
• Next-generation sequencing (NGS) labs use the technical dialect of pipelines, variants, and bioinformatics workflows. The scale of data and complexity of analysis make automation and computational integration central to the workflow. This dialect emphasizes the need for informatics systems to guide process execution to allow scalability and precision.  In turn this pushes informatics systems toward advanced data management and high-performance computing solutions.
• Environmental labs speak in a dialect shaped by contaminants, permits, and compliance limits. Informatics systems must handle large volumes of monitoring data, support regulatory reporting, and ensure defensibility over long time horizons. The dialect mirrors a culture of vigilance and accountability, where results are not just immediate but part of broader environmental trends.
• Pharmaceutical development labs use a dialect built on formulation, stability, and CMC (chemistry, manufacturing, and controls). Informatics must bridge the gap between R&D and production, supporting experiments while also capturing the structured data required for regulatory submissions. This dialect underscores the dual mission of advancing science and satisfying global health authorities, shaping workflows that are both innovative and compliant.

When dialects collide

The differences between lab dialects become most obvious when teams or systems want to communicate across domains. Sometimes the result is friction due to mismatched terminology, duplicated effort, or confusion over responsibilities. However, these collisions can also spark innovation by forcing labs to see familiar problems through a new lens.

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One of the challenges of working with different dialects is that the meaning of terms can be “lost in translation.” While a deviation in a QC lab is a formal, regulated event, in a research setting, it could be part of a normal iteration. Without clarity, the same term might generate alternate assumptions about urgency, impact, or required response. 

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Different dialects can also lead to issues with system interoperability. For instance, informatics systems configured for one dialect may not map cleanly to another, forcing additional customization or manual translation of terms. They can also result in a culture clash. A QC lab’s insistence on guardrails can feel restrictive to research scientists, while a research lab’s exploratory language may seem undisciplined to QC teams.

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Nevertheless, opportunities can also arise with the difference in dialects:

• Borrowing rigor: Research teams can adopt elements of QC’s controlled language to bring more structure to exploratory projects, improving reproducibility.
• Borrowing flexibility: QC labs can learn from a research lab’s adaptive dialect, building informatics guardrails that allow more agility without compromising compliance.
• Cross-pollinating ideas: Environmental labs’ long-term monitoring approaches could inspire clinical labs to think about patient outcomes not just at the point of care, but over extended timelines.
• System innovation: Informatics consultants who understand various dialects can design solutions that enable both rigidity and adaptability, supporting a broader range of users.

When dialects collide, the most successful outcomes come from acknowledging the differences, making the underlying priorities explicit, and designing workflows and systems that respect both perspectives. Rather than smoothing over the differences, organizations that lean into them often find creative solutions that a single dialect might not have produced on its own.

Embracing the differences in informatics’ dialects

Lab informatics is supported by a shared foundation, but enriched by domain-specific dialects. Each dialect highlights a priority, whether that’s rigor, flexibility, urgency, defensibility, scalability, monitoring, or compliance. Together, they form a richer whole. Organizations and informatics professionals that recognize these variations can design lab informatics systems which are more adaptable, resilient, and insightful — and provide interoperability as a basis for scientific collaboration and innovation.

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Contact us today to learn how your lab informatics solutions could support cross-domain data exchange, enhancing business and scientific value.

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