Training your algorithms: Challenges in feeding quality lab data to AI

Contributors : Shawani Shome

Date : May 2025

At a rate never seen before, machine learning (ML) and artificial intelligence (AI) are revolutionizing scientific research and innovation. From drug development to material science, these AI, machine learning, and data science applications optimize a number of tasks by identifying patterns in large data sets and providing predicted insights that would be hard or impossible for humans to find.

There’s a catch, though.

Advanced algorithms are only as good as the data they are trained on, just like people. The quality of data input into artificial intelligence systems can have a substantial impact on the outcomes of models in scientific labs. “Garbage in, garbage out” couldn’t be more accurate. Only until clean, organized, contextualized data is put into AI can it generate significant, long-lasting insights.

The significance of artificial intelligence ingesting high-quality laboratory data, the difficulties involved, and the steps that organizations must take to overcome those difficulties are all covered in this blog.

The Foundation: Why Data Quality Matters in AI

The fundamental idea behind machine learning is that it relies heavily on pattern identification. When training an algorithm, the system processes huge volumes of data to infer predictions such as forecasting a compound’s efficacy to ambient experimental conditions for a reaction.

However, for the prediction to be accurate, the data would need to be:

Clean: devoid of mistakes, repetitions or superfluous details.

Structured: regularly arranged and prepared for examination.

Contextualized: includes extensive metadata that explains the context and significance of the data.

The absence of them increases the likelihood that the AI model’s output will be pointless and misleading.

The Challenges in Lab Environments

Even with the advent of digitalization, lab-produced data is frequently lacking in terms of AI readiness. The following are the main challenges in the way of organizations:

1. Data Entry: Most laboratories still operate with unstructured data in spreadsheets, PDFs, or handwritten notebooks to document experimental outcomes. Even in digital environments, data often exists in silos – scattered across ELNs, LIMS, and proprietary databases. Absence of structure and centralization makes it near-impossible to aggregate and prepare the data efficiently for AI training.
2. Inconsistent Terminology and Formats: There are many different format and nomenclature for scientific data, depending on the team or organization. For instance, one lab might refer to it as “HCl,” another as “Hydrochloric acid,” and still another as “HCl (aq).” This misunderstanding will cause the algorithm to become confused and maybe misclassify.
3. Contextual and Metadata Gaps: The lab results often miss out on some of the most important contextual information, such as instrument settings, other environmental conditions, or even the investigator’s own comments, which serve to accurately interpret the data. These situations can become a cause for AI algorithms to misinterpret results or simply miss other important variables affecting the outcome.
4. Data Quality Issues and Human Errors: Data entry errors, incorrect measurements or missing records are usually found in research environments. These tainted data can bias an AI system, lowering its accuracy and trust in its results.
5. Volume vs. Veracity: In the realm of AI, the more data, the better – provided it is reliable data. Lab data tends to come in tons; yet, if a lot of this data is considered noisy or irrelevant, it will contribute less to the ability of a model to be improved. Here veracity is as important as quantity, if not more.

Why Contextualized Data Is Important

Context is what gives meaning to data. For AI to comprehend the relationship and to predict meaningfully, it needs context, not just values in the columns, but knowledge about reasons and means.

A prime example is a dataset that presents the results from a high-throughput screening assay. Without information on compound concentrations, cell line, incubation times or specific detection technology, the algorithm would not gain reliable learning on what compounds were genuinely active and under what conditions. In other words, context transforms raw values into actionable insights.

Strategies for Feeding Better Data to AI

Maximizing the benefits of using AI in scientific research has to do mainly with preparing for data-readiness in organizations. Here are a few strategies to consider:

1. Standards in data entry and ontology: Standardized vocabulary and taxonomy across the organization would ensure uniformity. Ontologies like ChEBI for chemical entities or Gene Ontology (GO) for biological processes may create such uniformity and interoperability.

2. Invest in data curation: Data curation is the process of cleaning, structuring and enriching data before spooling into AI turnkey models. Either dedicated or automated tools will identify errors, fill in missing data and annotate datasets further with relevant metadata.

3. Integrate Systems for Centralized Access: APIs or middleware that connect ELNs, LIMS, and other laboratory systems can also help construct a centralized repository of data. In this way, data is accessible, searchable, and structured appropriately for machine learning pipelines.

4. Encouragement in Data Culture in the Lab: Training scientists and lab staff about the significance of data quality and providing user-friendly digital tools can lead to better-quality practices in both data recording and stewardship.

5. Use Data Provenance and Lineage Tracking: Knowledge the point of origin and transformation of data is needed for trust and reproducibility. Blockchain-type data provenance tools or audit trails can be used to maintain data lineage.

Conclusion

AI is poised to make a remarkable change in scientific research; however, this is only possible if the training datasets are trusted, structured and meaningful. In laboratories, where variability is intrinsic and data complexity is high, preparing data for AI becomes an arduous yet indispensable endeavor.

Organizations that fund cleaning their lab data, contextualizing it and standardizing it are in a strong position to capitalize on AI. Quality data in the transition from automation to intelligence of the scientific workflow is no longer just an input; it is a competitive advantage.