8 Types of Critical Information Currently Underused in Life Sciences

A major portion of unused scientific data in Life Sciences negative results, omics datasets, clinical data, lab data and tacit knowledge remains fragmented and inaccessible. This data fragmentation slows R&D, drives duplicate experiments, weakens scientific reproducibility and wastes billions in value. 

To accelerate innovation, organisations must make data findable, accessible, interoperable and reusable (FAIR). Platforms like Sinequa for Life Sciences connect siloed data and transform dark data into actionable scientific insight.

In Life Sciences, organisations generate more data than they can realistically analyse or reuse. A significant portion of scientific information remains hidden in disconnected systems: incomplete experiment logs, unpublished negative results, under-explored omics datasets, and clinical data that never flows back into R&D. This unused scientific data creates a biased and partial view of evidence, leading teams to repeat failures, overlook weak signals and slow down therapeutic innovation.

The root causes are structural. Data lives in silos with different formats, standards and access rules, while regulatory and ethical constraints, although essential, are often implemented in ways that limit secure secondary use. To reverse this trend, unused data must be recognised as a strategic scientific asset. Aligning with FAIR principles and deploying interoperable platforms capable of reconnecting fragmented insights across the entire lifecycle is essential to fully unlock the value of biomedical research.

Why so much scientific data remains unused in Life Sciences

Life Sciences organisations appear data-rich, yet struggle to access and reuse critical scientific information. The root causes fall into three categories: technical fragmentation, operational silos, and access constraints around health data.

Technical barriers: fragmented systems, incompatible formats, limited FAIR alignment 

Scientific data is created in isolated systems LIMS, ELNs, imaging, omics pipelines, clinical databases and document repositories each with different formats, identifiers and metadata standards. Without interoperability or FAIR principles, even related datasets cannot be linked or queried together. 

Centralizing only a subset of data in lakes or warehouses leaves most information invisible and practically unusable. 

Operational barriers: siloed teams and weak governance 

R&D, clinical, regulatory and safety teams each maintain their own documentation practices. When projects end or experts move on, knowledge is lost and data becomes context-poor. 
The result: teams unknowingly repeat experiments, pay the cost again, and slow down scientific progress. 

Regulatory and ethical constraints: restricted access to clinical data 

Protecting patient privacy is essential, but fragmented consent and access rules make secondary use of clinical data slow and complex. Many high-value datasets remain underexploited simply because navigating approvals is too difficult. 

Platforms like Sinequa for Life Sciences enable secure, governed access by design, unlocking insights from clinical and real-world evidence without compromising compliance.

The 8 categories of underused scientific information in Life Sciences 

Unused scientific data in life sciences does not refer to a few marginal datasets sitting on a forgotten server. It encompasses entire categories of information that are systematically underexploited, across organisations and geographies. Eight categories stand out in particular. 

1• Unpublished and negative results left out of the scientific record 

Negative data failed experiments, inconclusive trials and rejected hypotheses rarely leave internal ELNs, emails or slide decks. 
This creates publication bias, duplication of effort and a distorted scientific understanding of targets and mechanisms. 

Making negative results searchable and discoverable prevents wasted investment and accelerates informed decision-making in drug discovery. 

2• Omics datasets generated but never fully analyzed 

High-throughput genomics, proteomics, metabolomics and transcriptomics generate costly, high-value assets but only a small portion is used in downstream analysis. 
Poor metadata, siloed storage and lack of interoperability mean most omics data becomes dark data. 

Connecting omics to clinical phenotypes can reveal: 

• biomarkers
• new drug targets
• precision medicine insights 

Yet these opportunities are often unrealised due to data fragmentation. 

3• Clinical and real-world data not reused for secondary insights 

Clinical trial data, EHRs, registries and claims datasets hold the most relevant evidence of safety, adherence, and treatment response  in real patient populations. 

But secondary use is slowed by: 

• complex access approvals
• privacy compliance (GDPR / HIPAA)
• steam separation between clinical and R&D teams 

As a result, some of the most impactful scientific data in Life Sciences remains underexploited for innovation. 

4• Experimental data trapped in laboratory systems 

Instrument outputs, assay readouts and protocol details are locked in: 

• LIMS
• ELNs
• spreadsheets
• proprietary imaging formats 

Without traceability across these silos, teams cannot reconstruct experiment context  which blocks validation, regulatory submissions and tech transfer. 

Unified scientific insight platforms reduce this friction and restore data lineage across the lab ecosystem. 

5• Unused biological samples and historical analog data 

Freezers and archives store billions in unused asset value: 

• tissue samples
• blood / DNA libraries
• historical experiment notebooks
• legacy storage media 

When samples aren’t digitized or indexed, they are effectively invisible  and new sample collection continues unnecessarily. 

Improved cataloging and digitization unlocks higher ROI on existing research investments. 

6• Poorly documented or low-quality datasets 

Data without: 

• metadata
• units 
• identifiers 
• version history 

…quickly becomes unreliable. 

Scientists avoid using such datasets in critical decisions, which turns them into unused scientific data. 
Automated metadata enrichment and quality checks help rescue part of this “lost science”. 

7• Synthetic data underused despite high potential 

Synthetic datasets offer: 

• privacy-preserving access to rare or sensitive data 
• better model training for AI/ML 
• simulated cohorts for exploratory research 

Yet hesitations around regulatory validation slow adoption in pharma and biotech. 
Still, synthetic data can dramatically reduce barriers to data reuse where real clinical data is restricted. 

8• Tacit scientific knowledge never captured or structured 

The most invisible category of unused scientific data: 

• expert reasoning
• weak signals observed 
• strategic learnings from past projects 

When stored only in minds, emails or hallway conversations, this knowledge disappears with staff turnover. 
By indexing unstructured content using biomedical NLP, organizations preserve and reuse their collective intelligence. 

The consequences of unused scientific data on research and innovation 

The cumulative effect of these eight categories of underused information is far from benign. It shapes the trajectory of both science and business. 

Biases and blind spots caused by incomplete evidence 

When only positive, well-structured and easily accessible data is visible, organisations develop a distorted understanding of reality. Risks appear lower than they are; success rates look higher; and blind spots remain unchallenged. This undermines reproducibility, feeds over-optimistic projections and can ultimately damage trust in science. 

Overspending and unnecessary duplication of experiments 

Unused scientific data in life sciences also translates directly into overspending. If it is faster to rerun an experiment than to find existing results, teams will rerun. If previous failures are not visible, they will repeat them. The result is a hidden tax on R&D productivity, paid in time, budget and opportunity cost. 

Delayed therapeutic and clinical innovation 

Every dataset that remains unused is a hypothesis that was never fully tested. When potential safety signals, early efficacy markers or promising repurposing ideas stay buried in disconnected systems, patients wait longer for better treatments. Improving the reuse of scientific data is not only a matter of efficiency; it is also a matter of patient impact.

How Sinequa For Life Sciences Enables FAIR Access to Underused Scientific Data in Life Sciences 

The good news is that unused scientific data in life sciences is not a fatality. Organisations can take concrete steps to reactivate this hidden value. 

Improve documentation and standardization at data creation 

The first step is to make sure that new data is born FAIR or as close to FAIR as possible. That means: 

• capturing essential metadata at the time of creation,
• using controlled vocabularies and standard identifiers,
• documenting protocols, transformations and quality metrics,
• aligning documentation practices across teams. 

These measures may seem ordinary, but they are the foundation for any future reuse. Without them, even the most advanced platforms will struggle to extract reliable meaning from the data. 

Build interoperable and linkable infrastructures 

The second step is to invest in infrastructures that can bridge heterogeneous systems without requiring everything to be moved into a single monolithic repository. Sinequa for Life Sciences follows precisely this logic: it connects to LIMS, ELNs, DAMs, imaging systems, clinical and regulatory databases, document repositories and email systems, and builds a unified, searchable index on top of them. 

By combining semantic search, vector search and Life Sciences-specific NLP, such platforms make it possible to navigate across molecules, mechanisms, experiments, patients, documents and discussions in a single experience. This is how disconnected fragments turn into an integrated knowledge graph. 

Identify, catalog and reactivate unused datasets 

Once the technical layer is in place, organisations can systematically map their unused datasets: which omics archives have never been revisited, which trial datasets have limited secondary analyses, which sample inventories lack visibility, which historical records remain analog. 

Building catalogs with clear ownership, status and potential value is essential to prioritise reactivation efforts. Not all unused data is worth rescuing, but some will have a disproportionate impact when brought back into circulation. 

Integrate automation, quality checks and AI 

Finally, automation and AI can dramatically reduce the friction associated with data curation and reuse: 

• automatic metadata extraction and enrichment,
• entity recognition for genes, proteins, diseases, compounds, 
• automated quality checks and anomaly detection, 
• suggestion of related datasets, documents or experts. 

Sinequa for Life Sciences integrates these capabilities to transform raw, fragmented information into analysis-ready data and insights. This prepares the ground not only for human exploration, but also for advanced analytics and machine learning. 

A practical framework to assess the value of underused data 

Not all underused data deserves the same level of effort. Organisations need a simple, pragmatic framework to decide where to invest.

Dimension	Key Questions	Goal
Scientific	Does it support mechanistic understanding or biomarker discovery?	Improve research outcomes
Operational	Does it reduce duplication or speed decisions?	Gain efficiency
Economic	What was the cost to generate it? What value can it unlock?	Improve ROI

Learn more:

• The 5 symptoms of fragmented scientific data in Life Sciences
• Unified Information in Life Sciences: Accelerating Innovation, Compliance, and Patient Outcomes
• Fragmented R&D and Clinical Data: Invisible Impacts and Hidden Risks

FAQ

01

Why do so many scientific datasets produced in Life Sciences end up unused or underused?

Because they are created in isolated systems, with inconsistent documentation and no common governance. Fragmentation, lack of FAIR practices and complex access rules mean that many datasets are effectively invisible to the teams who could benefit from them.

02

What is the scientific and economic impact of unused research data and unused samples in Life Sciences?

Unused scientific data in life sciences leads to biased evidence, reduced reproducibility, duplicated experiments and slower innovation. Unused samples and datasets represent billions in sunk costs globally, as well as missed opportunities for new discoveries and indications.

03

How can Life Sciences teams identify which unused scientific datasets or samples are worth reactivating?

By combining scientific, operational and economic criteria in a structured assessment framework. Teams should look for datasets that are both feasible to reuse (technically and legally) and highly relevant to strategic research questions. Unified discovery platforms help reveal where these assets sit today.

04

What practices help Life Sciences organisations reuse unused scientific data, whether clinical, omics or experimental?

Key practices include adopting FAIR principles, standardising metadata, digitising historical records, implementing clear governance for secondary data use, and deploying platforms like Sinequa for Life Sciences that connect structured, unstructured and tacit information into a single discovery layer.

05

When can synthetic data help compensate for scientific datasets that remain unused or inaccessible in Life Sciences?

Synthetic data is most useful when privacy, consent or governance constraints limit access to real patient data, or when existing datasets are too small or unbalanced for model training. It can accelerate exploration and development, but must always be complemented by validation on real clinical data.

If you suspect that your organisation is sitting on a large volume of unused scientific data in life sciences, you are probably right and you are not alone. The question is no longer whether this data exists, but how quickly you can turn it into a strategic advantage.

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