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Cambridge, MA — July 10, 2025 — SciSure, formerly SciShield and eLabNext, is proud to announce the formal sponsorship of The Engine, a nonprofit founded by MIT to  incubate and accelerate early-stage Tough Tech companies from idea to impact. This sponsorship marks the next step in a partnership rooted in a shared mission: helping startups accelerate the development of life-changing technologies, treatments, and healthcare solutions.

SciSure was recently formed from the merger of SciShield and eLabNext. Both companies were longtime partners of The Engine: For years, SciShield supported The Engine with environmental health and safety (EHS) and compliance infrastructure, while eLabNext powered the digital backbone of many resident companies. Now, by joining forces, SciShield and eLabNext are expanding their support—giving Tough Tech entrepreneurs connected access to the critical digital tools, compliance frameworks, and operational resources they need to scale and succeed.

"We’ve seen firsthand how important the right infrastructure is for Tough Tech startups to reach the market," said Jon Zibell, VP of Global Alliances and Marketing for SciSure "By deepening our partnership with The Engine, we’re making it easier for innovators to focus on what matters most—delivering breakthroughs that have a lasting impact on society."

“Given the fact that science is moving more and more towards Tough Tech and Tech Bio (as in technology-first, using advanced computational tools like AI, ML, cloud, and data engineering), it is so important to be able to create an API/SDK-Powered ecosystem for scientists that know that we can support their platforms, automation, and scalability.” said Zareh Zurabyan, VP of Commercial, Americas for SciSure. 

Through this enhanced partnership, The Engine’s resident companies will gain:

• Integrated Digital Operations: Access to eLabNext’s Digital Lab Platform (DLP), combining ELN, LIMS, lab automation, and compliance tracking into one connected system.
• Built-in EHS and Compliance Support: SciShield’s platform ensures startups meet regulatory standards from day one, reducing risk and saving time.
• Early Access Incentives: Special access to tailored onboarding, incentives, and ongoing support for adopting SciShield and eLabNext solutions. 
• Best-in-Class Software: A full suite of software solutions for Scientists, LabOps, and EHS to ensure the highest probability of achieving commercial success and making a lasting impact.
• Education and Collaboration: New opportunities for startups to participate in co-hosted workshops, hands-on training, and knowledge-sharing sessions on lab digitalization, safety, and compliance.

“For years, SciSchield and eLabNext have provided critical infrastructure and resources to fuel the growth of our residents,” said Alexa Monti, Vice President of Business Development and Strategic Partnerships at The Engine. “We are now thrilled to welcome their sponsorship as SciSure. Partnerships like this are essential to building a resilient innovation ecosystem that enables founders to transform breakthrough ideas into world-changing impact.”

About The Engine:

The Engine is a nonprofit incubator and accelerator dedicated to supporting early-stage Tough Tech companies tackling the world's greatest challenges. Founded by MIT in 2016, The Engine provides critical support for Tough Tech companies in the form of specialized lab and fabrication infrastructure, programming and mentorship, and an ecosystem of experts and investors. By bridging the gap between groundbreaking ideas and real-world impact, The Engine plays a crucial role in advancing solutions to complex global issues. For more information, visit engine.xyz

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Media Contact: 

press@engine.xyz

Media Contact:

Jon Zibell
Vice President of Global Alliances & Marketing
‍J.Zibell@scisure.com

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Over the past decade, the life sciences industry has undergone a foundational transformation, one that redefines how biology is explored, understood, and scaled. 

Traditional, wet-lab-driven biotechnology has shifted toward TechBio, a new model grounded in software engineering, data architecture, and AI-native platforms. This transition replaces linear, hypothesis-driven experimentation with computationally designed, automated, and feedback-driven discovery systems.

Whereas classical biotech workflows revolved around physical assays and post-hoc data interpretation, TechBio organizations prioritize upstream data infrastructure, machine learning (ML)-ready outputs, and modular technology stacks from the outset. 

Bioinformaticians have transitioned from support roles to strategic leadership, while wet lab execution has become programmable. Digital platforms, such as ELN/LIMS ecosystems, have become mission-critical infrastructure. Interdisciplinary teams spanning biology, data science, and software engineering collaborate in product-oriented models, similar to those found in SaaS companies. AI tools also augment decision-making at every stage, from target identification to manufacturing.

This guide outlines what TechBio is and ten defining pillars, providing detailed examples. It explores how TechBio organizations engineer not only therapies, but the digital systems that discover, refine, and validate them. 

As the boundary between biology and computation dissolves, TechBio positions itself as the operating system of 21st-century life sciences, offering a blueprint for faster, more scalable, and reproducible scientific innovation.

Welcome to TechBio, where biology meets software engineering, and the future is being coded before it's cultured.

What is TechBio?

TechBio is the convergence of biology, software engineering, and AI, replacing traditional wet-lab workflows with computationally driven, programmable discovery systems. It reimagines life sciences as a scalable, data-centric platform where interdisciplinary teams and digital infrastructure power faster, more reproducible innovation.

10 Pillars Defining the TechBio Transition

1. Data Architecture Before Wet Work

Traditional biotech began with the bench: Run experiments, generate data, and interpret the results later. 

In TechBio, the inverse is true. Teams now design the data schema, ontology, and analysis pipeline first, enabling smart experiment design, ML-ready outputs, and scalable platforms that can adapt over time.

If your data isn't structured for insight on Day 1, you're already behind. Here are some examples:

• Companies like Recursion Pharmaceuticals use imaging and structured data pipelines before any therapeutic hypothesis is even proposed.
• GLP-1 analog research is now modeled using pre-trained metabolic models that simulate downstream effects before synthesis.
• New CRISPR libraries are generated and screened in silico with predictive scoring models, optimizing guide RNAs based on large omics datasets.

This inversion – starting with data design before experimentation – has reoriented R&D pipelines around long-term scalability. As structured data becomes a strategic asset, TechBio companies are increasingly valued not just for their scientific breakthroughs but for the reusability of their data layers. This has profound implications for platform business models, partnerships, and cross-study insights.

2. AI-First vs. Hypothesis-First

Biotech works in a sequential logic: form a hypothesis, test it in an in vitro model, and iterate. 

TechBio builds AI-native systems that surface insights and correlations before human hypotheses even form, accelerating discovery.

The AI isn't replacing the scientist; it's augmenting their intuition at scale. Examples include:

• Insitro and Inceptive are generating drug candidates with ML from genetic/phenotypic data, especially in diseases like ALS and obesity.
• CRISPR screening now uses AI to predict essential gene targets before experiments, significantly reducing the time-to-lead.

The shift from hypothesis-driven to AI-augmented discovery marks a turning point in biological research. Rather than replacing scientists, AI now operates as a collaborative engine that surfaces new dimensions of correlation and causation. The competitive edge is shifting toward organizations that can orchestrate this human–machine loop efficiently, striking a balance between statistical signals and biological plausibility.

3. Platform Engineering as a Core Competency

In TechBio, companies aren’t just developing drugs; they're developing software platforms that standardize workflows, integrate third-party tools, and turn fragmented research into reproducible systems. 

Internal data platforms, LIMS/ELN integrations, and ML pipelines are essential baseline technologies for competitiveness. Some real-world examples include:

• Ginkgo Bioworks builds reusable cell engineering platforms akin to APIs for cells.
• Arda Therapeutics has created a modular inflammation discovery stack from single-cell data, standardizing the computational exploration of immune pathways and cells.

The rise of internal engineering teams and reusable software platforms in the life sciences mirrors the evolution of the tech industry. Platformization allows TechBio companies to rapidly launch programs across therapeutic areas, onboard partners, and generate real-time feedback loops. The result is a higher innovation velocity and better capital efficiency, traits that investors and pharmaceutical partners increasingly favor.

4. Bioinformaticians Are the New Bench Scientists

In a TechBio org, the bioinformatician is no longer “behind the scenes;” they're core to strategy, productization, and decision-making. Teams prioritize hires who can extract signal from noise, build predictive models, and interface with both biologists and back-end engineers. Bioinformaticians are now tasked with defining the experiment design, not just analyzing results after the fact.
As the bottleneck in modern biology moves from experimentation to interpretation, bioinformaticians have emerged as essential architects of discovery. Organizations that empower computational biology as a front-line discipline – not a downstream service – are demonstrating faster time-to-insight, better target validation, and smarter trial design. Talent acquisition in this field is now a core strategic priority.

5. Composable Lab Tech Stacks

Gone are the days of rigid, siloed lab systems. TechBio demands modular, API-connected ecosystems that allow seamless integration between ELN, LIMS, data lakes, assay instruments, and cloud analysis tools. 

Composability – the ability to select, assemble, and reconfigure components, such as services, modules, or APIs – is the new competitive advantage.

Top TechBio orgs are building integrated ecosystems where ELN, LIMS, and assay data sync in real-time, reducing batch errors and improving reproducibility. Composable architecture transforms labs from siloed environments into interoperable, cloud-connected ecosystems. 

This flexibility enables rapid tool swapping, real-time data syncing, and scalable digital operations. As composability becomes a prerequisite, the market is shifting toward vendors and platforms that emphasize integration, standardization, and cross-domain orchestration.

6. Experimental Automation as Software

Wet lab automation has evolved beyond the use of robotic arms. Now it’s programmable. TechBio teams treat lab execution as code: Experiments are version-controlled and modularized, making them reproducible across different geographical locations. Strateos and Emerald Cloud Lab are commercial examples of how this can work, letting scientists run remote assays, QC, and sample processing with code.
By treating lab execution as programmable infrastructure, TechBio closes the loop between in silico design and in vitro execution. Automation not only accelerates throughput but also unlocks a new paradigm of version-controlled science, where reproducibility and traceability become codified. The winners in this space will be those who can abstract biology into code without sacrificing fidelity.

7. Interdisciplinary Product Teams

TechBio orgs are structured like SaaS companies. Product managers, software engineers, data scientists, and bench biologists all contribute to the strategic path for products. Product-market fit isn’t just about efficacy; it’s about workflow usability, data interoperability, and analytical scalability. 

Dyno Therapeutics, a company using AI to discover and optimize better delivery of gene therapies, employs product managers and ML leads alongside virologists to design AAV capsid platforms with specific tropisms.
The productization of science, where multi-disciplinary teams own features, roadmaps, and outcomes, blurs the lines between R&D and product development. TechBio teams now operate like agile startups, iterating on therapeutic designs with the same velocity and feedback mechanisms as SaaS companies. This accelerates both discovery and market alignment, reducing the translational lag between R&D and impact.

8. Open Science Meets IP-Protected Infrastructure

Rather than hoarding findings in PDFs or publications, TechBio companies publish datasets, APIs, and tools while protecting their insights via proprietary ML models and data platforms. It's not just about the molecule or target; it's about the ecosystem that discovers it.

TechBio is redefining the balance between openness and defensibility. By releasing tools and datasets while protecting the infrastructure that operationalizes them, companies can build communities, accelerate adoption, and establish defensible moats around proprietary layers. This hybrid approach to IP strategy mirrors the open-core model in software and is fast becoming the norm in science-forward organizations.

9. AI-Augmented Decision-Making in R&D

From target identification to trial design, AI is infused across the R&D lifecycle. NLP models extract insights from literature, generative models design protein structures, and predictive models flag risks before they manifest. For example:

• GLP-1 and incretin research is being accelerated by multimodal AI models that predict cardiometabolic response based on genetic and dietary data.
• CRISPR off-target prediction tools, such as DeepCRISPR and CRISPR-Net, minimize risk before editing begins.

From discovery to development to manufacturing, TechBio companies are using predictive models to make faster, more informed decisions. This transition lowers risk, reduces cost, and improves outcomes, positioning AI-augmented pipelines as the gold standard for next-generation therapeutics.

10. Speed, Scale, and Signal

TechBio companies operate on startup timelines, not scientific timelines. They use cloud infrastructure, continuous data streaming, and rapid feedback loops to compress cycle times from months to days. Signal extraction and throughput are the key metrics. What used to take 18 months in a wet lab now happens in 6 weeks via computational modeling and robotic execution.

By adopting cloud infrastructure, continuous experimentation, and agile pipelines, companies can reduce the cycle time from question to answer and from idea to impact. As signal extraction becomes the metric that defines productivity, organizations are now judged by how efficiently they can learn, not just how much they can test.

The Venture Capital (VC) & Private Equity (PE) Outlook: Why TechBio Is the New Investor Mandate

The TechBio transition has fundamentally reshaped investor psychology in life sciences. Where traditional biotech relied on long timelines, binary risk, and molecule-centric valuations, today’s VC and PE firms are seeking software-first, platform-oriented, and AI-native biology companies that exhibit repeatable innovation, scalability, and enterprise value beyond a single therapeutic asset.

Key Investment Trends Driving Capital Deployment in TechBio

The biotech investment landscape is shifting, with VC deployment accelerating in late 2025 and favoring AI-native, TechBio firms modeled after high-growth SaaS companies. Private equity is moving away from traditional biotech roll-ups toward digital-first infrastructure plays, such as LIMS and automation platforms. Valuations are compressing for single-asset biotech firms but expanding for multi-modal platforms with in-house AI/ML capabilities. IPO and exit readiness now require both clinical and tech maturity, while firms lacking digital infrastructure face the greatest funding risk.

With TechBio firmly entrenched, here’s what the not-too-distant future looks like:

• Prioritizing Platform over Pipeline: Investors are favoring companies with data platforms or AI discovery engines that can generate multiple assets, rather than a single-drug pipeline. Look at Flagship and Andreessen Horowitz (a16z) continuing to back repeatable discovery systems, such as Generate Biomedicines and Inceptive, instead of molecule-first approaches.
• Computational Biology at a Premium: Companies with ML-native workflows, structured data ontologies, and in silico design capabilities are commanding higher valuations. Recursion Pharmaceuticals’ IPO and valuation, for example, were tied more to its image-based AI infrastructure than its lead program.
• Cross-Disciplinary Teams as a Signal of Quality: Interdisciplinary founding teams that blend machine learning, systems biology, and engineering are seen as higher-execution risk mitigators. PE firms are increasingly conducting technical due diligence not just on pipelines, but also on the infrastructure stack, data operations, and software engineering.
• Shift Toward B2B and SaaS Models in Life Sciences: A wave of investment is flowing into companies that serve the TechBio ecosystem, including cloud-native LIMS/ELN platforms, computational CROs, and automated lab systems. These provide recurring revenue, faster sales cycles, and infrastructure lock-in, metrics that closely align with the tech sector's investment benchmarks.
• AI as a Defensibility Layer: VCs are heavily weighing proprietary AI models as part of the IP moat. It's no longer enough to own a sequence; firms must own the system that designs or predicts the sequence. Investors now look at data exclusivity, model performance, API extensibility, partner integrations, and model improvement over time.

TechBio: Redefining the Future of the Life Sciences

The life sciences funding environment is undergoing the same disruption that has reshaped fintech, media, and cybersecurity: from asset-centric investing to platform- and systems-centric investing. As biology becomes programmable, investors no longer seek the best drug; they seek the best engine for discovering, designing, and optimizing drugs.

The overarching implications of this include a shift to:

• Hiring more software engineers and ML experts than lab techs
• VCs seeking platform-first models with recurring data assets
• Companies prioritizing cloud-native, ML-enabled workflows.
• Faster, reproducible, and AI-augmented discoveries.

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For companies, this means:

• Building infrastructure before pipelines
• Valuing reproducibility as a product
• Prioritizing software engineers and bioinformaticians as co-founders
• Designing business models around feedback loops, not just endpoints

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The leaders of this next chapter won’t just discover, they’ll design biology as an engineered system, built on platforms, powered by data, and scaled with AI.

While Biotech commercialized biology, TechBio will make biology computational.

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If you manage environmental health and/or safety (EHS) in a life science laboratory, you can set up safety protocols, and put up eye-spray stations that you test daily, but building a culture of compliance is a whole other can of worms. Furthermore, the link between EHS and sustainability efforts are not always top-of-mind for lab personnel.

So, how can you educate and compel researchers to be more sustainable?

In the modern lab, where digitalization continues to make research more efficient and streamlined, leveraging software platforms that centralize lab operations – including EHS and sustainability efforts – can help you avoid safety and sustainability mistakes that can cost millions of dollars and could lead to personnel injuries.

In this article, we’ll cover EHS in the life sciences, its link to sustainability,  how to enforce compliance digitally, leveraging tools such as ELN/LIMS, and the best practices for achieving this. Using a decentralized platform for EHS and other lab operations is a mistake you do not want to make, so we also introduce a new type of software tool for laboratories called a Scientific Management Platform (SMP), that combines EHS (especially Exposure Control), with your day-to-day ELN/LIMS.

Environment, Health, and Safety (EHS) in Life Sciences and Biotech

What is EHS?

EHS in life sciences and biotech involves policies and procedures designed to:

• Protect the environment from biohazards and chemical pollutants.
• Ensure employee health through safe practices and exposure controls.
• Maintain workplace safety in laboratories, manufacturing, and research facilities.

EHS Areas of Focus

Environmental Protection

• Waste Management: Disposal of biohazardous and chemical waste following strict guidelines.
• Energy Efficiency: Implementation of green lab initiatives to reduce energy use.
• Sustainable Practices: Incorporating biodegradable materials and reducing water use in lab operations.

Occupational Health

• Exposure Control: Limiting exposure to pathogens, hazardous chemicals, and radiation.
• Health Monitoring: Regular health screenings for employees working with toxic substances.
• Mental Well-being: Programs addressing stress in high-pressure research environments.

Workplace Safety

• Equipment Safety: Regular maintenance of autoclaves, centrifuges, and lab machinery.
• PPE Compliance: Ensuring the use of lab coats, gloves, goggles, and respirators where necessary.
• Emergency Readiness: Spill response training and protocols for accidents involving biological or chemical agents.

Top 10 EHS Examples in Life Sciences and Biotech

Academic and industry labs handle hazardous materials, dangerous equipment, and harmful biological agents. Effective EHS programs ensure compliance to mitigate the risks of these activities. Below are ten examples that highlight essential safety measures in these high-risk settings.

1. ‍Biowaste Management: Segregation and sterilization of biological waste.
2. ‍Fume Hood Usage: Safe handling of volatile chemicals and reagents.‍
3. Pathogen Containment: Use of biosafety cabinets for handling infectious agents.‍
4. Radiation Safety: Monitoring and control for radiological materials in research.‍
5. Chemical Inventory Systems: Tracking and proper storage of hazardous substances
6. ‍Ventilation Systems: Preventing air contamination in laboratories.‍
7. Incident Reporting: Documenting and analyzing lab accidents and near misses.‍
8. Ergonomic Lab Design: Preventing repetitive strain injuries among researchers.
9. ‍Hazard Communication: Clear labeling and documentation of chemical and biological hazards.
10. ‍Training Programs: Regular EHS training tailored to biotech-specific risks.

Real-World Applications of EHS

The examples above apply to various sectors within the industry, from R&D to large-scale manufacturing.

• Pharmaceutical R&D: Safely handling toxic reagents and ensuring GMP compliance.
• Biomanufacturing: Managing emissions and waste from large-scale bioreactors.
• Clinical Trials: Protecting researchers from potential pathogen exposure during specimen handling.
• Gene Therapy Labs: Strict protocols for handling genetically modified organisms (GMOs).

Benefits of EHS in Life Sciences and Biotech

By tailoring EHS practices to the unique challenges of life sciences and biotech, organizations can achieve safer and more compliant operations while driving innovation. The benefits of EHS compliance include:

• Ensures regulatory compliance (e.g., OSHA, WHO, IFC).
• Reduces environmental impact of high-tech lab activities.
• Enhances employee safety and operational efficiency.
• Builds public trust by prioritizing ethical and sustainable practices.

What is the Link Between EHS and Sustainability?

EHS provides a foundation for a laboratory or organizations sustainability practices, by mitigating the environmental impacts of research, ensuring worker safety, and promoting efficient use of resources. In this context, sustainability refers to the adoption of business practices that meet present needs without compromising the ability of future generations to meet theirs. It involves integrating environmental stewardship, social responsibility, and economic viability to achieve long-term success.

EHS Integration with Sustainability:

• Energy Use Reduction: Transitioning to energy-efficient lab equipment.
• Carbon Neutral Labs: Offsetting carbon emissions from lab processes.
• Water Recycling: Using closed-loop systems in biomanufacturing.

Sustainability Through Digitization: The Role of a Scientific Management Platform (SMP) in Labs and Institutions

Introduction to Lab Sustainability through Digitization

Sustainability in labs and research institutions extends beyond environmental conservation; it encompasses efficient resource use, minimizing waste, and adopting practices that ensure long-term viability.

Digitization, particularly through the adoption of Electronic Lab Notebooks (ELNs), Laboratory Information Management Systems (LIMS), or all-in-one SMPs plays a pivotal role in driving sustainability by reducing physical resource dependence and enhancing operational efficiency.

What is an SMP?

An SMP is a digital ecosystem that unifies lab operations, digital research, and EHS. It’s distinguished from other ELN and LIMS platforms in that it is a single platform that serves the needs of an entire life science organization, including scientists, lab operations professionals, EHS and compliance personnel, and leadership.

Impact of an SMP on Lab Sustainability

1. Reduction of Paper and Physical Resource Use

• Traditional Practices: Labs have historically relied on paper-based lab notebooks, forms, and records, leading to significant paper waste.
• Digitized Solution: SMPs, as well as ELNs and LIMS, eliminate the need for physical notebooks and documentation, significantly reducing paper usage. A large institution can save thousands of sheets of paper annually by transitioning to digital systems.

2. Streamlining Data Management

• Resource Optimization: SMPs, ELNs, and LIMS centralize data storage, eliminating redundancies and enhancing accessibility, thereby reducing the energy costs associated with manual data retrieval and storage.
• Cloud Integration: Many systems operate on energy-efficient cloud platforms, further reducing on-site energy consumption.

3. Decreasing Redundancy and Waste

• Inventory Control: SMPs and LIMS improve inventory management, ensuring efficient use of reagents and consumables, reducing overstocking, and minimizing expired materials.
• Experimentation Efficiency: SMPs and ELNs support accurate experiment tracking, preventing redundant trials and saving time and resources.

4. Energy Efficiency in Lab Operations

• Digitally Monitored Systems: SMPs and LIMS can integrate with lab equipment to optimize energy usage, such as monitoring freezers, incubators, and other devices.
• Scheduling and Maintenance: These platforms help schedule equipment usage and maintenance, reducing unnecessary energy consumption.

5. Waste Management and Compliance

• Traceability: LIMS and SMPs enhance the traceability of samples and reagents, ensuring proper disposal of hazardous materials and compliance with environmental regulations.
• Analytics: The analytics modules in these systems can identify wasteful practices and recommend more sustainable workflows.

6. Remote Collaboration and Access

• Virtual Collaboration: SMPs and ELNs enable remote data sharing and collaboration, reducing the need for physical presence and associated travel, contributing to lower carbon footprints.
• Global Integration: Institutions can collaborate globally without duplicating experiments or resources.

Impact of an SMP on Institutional Sustainability

1. Institution-Wide Standardization

Digital systems promote uniformity across labs, ensuring sustainable practices are maintained at every level. Standardization also enables new personnel to quickly and easily adopt standardized practices during onboarding.

2. Educational and Training Benefits

Digital platforms make sustainability training more effective by integrating real-time data tracking and eco-conscious decision-making.

3. Long-Term Cost Efficiency

Though initial implementation costs are high, digitization reduces operational costs in the long term by minimizing resource wastage and improving process efficiency.

4. Scalability and Growth

Digitized labs are better equipped to scale sustainably as they require fewer physical expansions and utilize resources more efficiently.

Impact of an SMP on Global Sustainability

1. Alignment with SDGs

Digitized labs contribute to the United Nations’ Sustainable Development Goals (SDGs) by promoting responsible consumption and production (Goal 12) and climate action (Goal 13).

2. Carbon Footprint Reduction

Digitally driven labs contribute to global carbon reduction efforts through energy efficiency, reduced paper use, and waste minimization.

3. Circular Economy Participation

By optimizing resources and ensuring traceability, labs using ELNs and LIMS align with the principles of a circular economy.

Driving Safety and Sustainability Through Lab Digitalization

Digital tools, like SMPs, are transforming life science labs and organizations by enhancing safety, sustainability, and efficiency. As EHS initiatives align with sustainability goals, platforms like ELNs, LIMS, and SMPs streamline compliance, reduce waste, and optimize operations.

SMPs, in particular, centralize lab management, ensuring resource efficiency, data traceability, and proactive safety measures. By embracing digital transformation, labs can foster a culture of sustainability, minimize environmental impact, and enhance operational excellence, positioning themselves for long-term success in an evolving scientific landscape.

To learn more about SMPs and their role in lab safety and sustainability, contact us.

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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.

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Boston, MA--SciSure, the Scientific Management Platform (SMP) designed to unify scientific research, safety, operations, and compliance, today announced a strategic partnership with US Lab Partners, a leader in lab and facility operations and EHS (Environmental Health &Safety) consulting. Together, the organizations are launching a transformative “Virtual Incubator Model” that gives emerging and scaling life science organizations affordable access to world-class digital lab infrastructure and operational support.

Unlocking Modern Lab Management for Scientific Entrepreneurs

Emerging life science organizations have long faced a costly challenge: accessing high-quality EHS, lab operations, and compliance infrastructure before they have the resources or scale to support large software investments. The new SciSure and US Lab Partners collaboration eliminates this barrier. By combining SciSure’s comprehensive, scalable software suite with US Lab Partners’ expert consulting and implementation services, these organizations can now operate efficient, safe, and compliant labs from day one.

“Our customers have often told us they needed digital infrastructure long before they had the budget or internal resources to manage it,” said Philip Meer, CEO of SciSure. “This partnership ensures they no longer have to choose between premium software or on-the-ground expert services—they get both, seamlessly integrated.”

Better Together: A Complete Solution for Emerging Labs

US Lab Partners provides deep, hands-on expertise in lab setup, operations, and EHS compliance. They become an extension of the customer's internal team, guiding labs through complex requirements and day-to-day operations. SciSure complements this with an industry-leading platform encompassing Electronic Lab Notebooks (ELN), Laboratory Information Management Systems (LIMS), inventory tracking, and EHS workflows—all in one secure and scalable environment.

“Too often, emerging scientific companies are forced to rely on underpowered tools—systems that create data silos and are little more than glorified spreadsheets,” said Jon Zibell, Vice President of Global Alliances and Marketing at SciSure. “This partnership is designed to change that. We are delivering a seamless digital experience from day one, without sacrificing safety, compliance, or data integrity.”

“Digitizing lab operations is no longer optional—it’s critical for continuity, safety, and scientific integrity,” said Demet Aybar, CEO and Founder of US Lab Partners. “Together with SciSure, we’re delivering world-class software and hands-on expertise that have traditionally been reserved for Big Pharma, now accessible to startups and academic innovators.”

Impacting the Future of Scientific Innovation

This partnership marks a pivotal shift in how scientific organizations can launch and operate. By eliminating the traditional burden of high costs, fragmented systems, and lack of technical resources, the Virtual Incubator Model accelerates innovation while reducing overhead and risk.

Customers now gain access to:

• A fully digital and seamlessly integrated record-keeping system from day one
• End-to-end EHS and inventory management software and services
• A robust LMS with training content library
• Trusted partners who bring both software and service to manage lab setup, safety, and compliance
• ELN, LIMS, SOP’s, and Sample Management built-in

“This model reflects our shared mission: to help brilliant science thrive without operational bottlenecks,” Aybar added. “We’re here to make world-class lab infrastructure available without compromise.”

About SciSure

SciSure is the world’s first Scientific Management Platform (SMP), combining eLabNext’s Digital Lab Platform and SciShield’s trusted LabOps and EHS software into a unified solution designed by scientists, for scientists. SciSure supports over 550,000 Scientists, over 40,000 labs, and over 800 scientific organizations worldwide.

About US Lab Partners

US Lab Partners is a trusted leader in lab and facility operations and EHS consulting, helping life sciences companies and academic institutions design, launch, and operate scalable, compliant lab environments. Their experts help customers navigate regulatory landscapes, ensure compliance, and build lab environments that scale with scientific ambition.

Media Contact:‍

Jon Zibell
Vice President of Global Alliances & Marketing
j.zibell@scisure.com

Kyrie Stevens
Chief Business Officer, US Lab Partners, LLC
‍kyrie@uslabpartners.com

Let’s be honest—lab data is everywhere… just not where you need it, how you need it or when you need it.

You finish an experiment, type up your notes, log your samples, maybe even update a spreadsheet. Then weeks later, someone needs that same dataset—and suddenly you’re trawling through five different systems to piece it all back together. Sound familiar?

This is where the FAIR data principles come in. An acrynym for Findable, Accessible, Interoperable and Reusable, FAIR has become the gold standard for scientific data management and stewardship—but in most labs, they’re still more of an aspiration than reality. And the biggest hurdle? Fragmentation.

When your core digital lab systems—from Electronic Lab Notebooks (ELNs) to Lab Information Management Systems (LIMS)—don’t talk to each other, even the best science ends up buried in silos. It’s harder to share, harder to track, and almost impossible to reuse meaningfully without substantial manual effort or starting from scratch.

But things are changing. As more labs move toward integrated digital systems, interoperability and reusability are no longer pipe dreams—they’re becoming the new baseline. With the right platform in place, FAIR data can stop being a headache and start being a superpower.

The cost of fragmented systems and teams

Most labs don’t suffer from a lack of data—they suffer from a lack of framework.

One team logs experimental details in an ELN. Another updates sample records in a LIMS. Safety teams track hazardous materials and proceedures in four different standalone HSE systems. Procurement runs two different punch-out systems. IT tries to hold it all together with a few brittle integrations and a lot of crossed fingers.

This fragmentation isn’t just inconvenient—it’s corrosive. When systems don’t talk to each other, data can get duplicated, mislabeled or lost entirely. Scientists waste time chasing missing context, trying to validate results they didn’t generate, or re-running experiments that should’ve been reusable. Compliance becomes a nightmare. Audit trails break down. And collaboration slows to a crawl.

Worse still, disconnected systems foster disconnected teams. Everyone works hard—but in isolation. When every department has its own tools, its own workflows, its own naming conventions, even rudimentary questions can be complex. Which version is the latest? Who owns this dataset? Is it safe to reuse? Can we trust it?

It’s a heavy cost, paid in wasted hours, delayed results, missed insights and sometimes, preventable errors. And in a research landscape where funding is tightening, timelines are short, and patients are waiting, labs simply can’t afford to be this inefficient.

Rethinking the digital lab

So, what happens when systems start working together?

When your ELN, LIMS, HSE and other key tools don’t just coexist, but connect—you unlock something far greater than the sum of their parts. You get a lab where data flows freely, where processes are standardized, and where every team pulls in the same direction.

That’s what a Scientific Management Platform (SMP), like the one we’re building at SciSure, delivers. It unifies the once disconnected digital point solutions into a true home base for the lab. By embedding interoperability across your lab’s ecosystem, it turns isolated databases into a shared, transparent operational backbone.

The result? A single source of truth. Experiments logged in the ELN are automatically linked to sample records in the LIMS. Safety data and records update in real time across both EHS HSE and inventory logs. Procurement knows what’s in stock without needing to ask. Everyone sees the same data, in the right context, without manual data transfer, syncing or version control nightmares.

But what about your lab’s specific needs? A truly comprehensive platform should let you integrate the tools you rely on, whatever your research focus. That’s why we've put Custom Software Integrations and Software Development Kits (SDKs) at the heart of the SMP. Whether you're connecting a barcode reader, a microscopy suite or want to onboard your own in-silico modelling engine, the SDK lets you customize workflows and build extensions that work your way.

SciSure is built for that kind of flexibility. With 40+ (and growing) ready-to-use integrations and a developer toolkit that empowers labs to connect just about any system, it ensures your digital infrastructure is future-proof – ready to scale and adapt alongside your science.

And the impact goes far beyond convenience. Unified systems mean fewer errors, faster decisions, richer audit trails and reproducibility by default. Instead of bending your processes to fit the software, you can finally make the software fit your science.

How the Scientific Management Platform supports FAIR data

FAIR data is more than a framework—it’s the foundation for faster research, easier collaboration, and insights that don’t get lost in the shuffle. But achieving it takes more than good intentions. It takes systems that are connected, consistent, and built for scale.

Here’s how SciSure’s SMP helps labs turn the FAIR principles into everyday practice:

Findable

Disconnected systems bury information. SciSure makes it easy to find what you need—instantly and in context.

• All data entries (samples, protocols, experiment records) are fully searchable.

• Standardized metadata and tagging improves indexing and retrieval.

• Centralized dashboards help users locate resources across ELN, LIMS and inventory in seconds.

• Audit trails track who created what and when—so nothing is lost in handover.

Accessible

Data locked away in silos slows everything down. SciSure ensures authorized users can access the data they need without risking compliance.

• Role-based permissions control who sees what across departments.

• Cloud-based access from any device ensures data is never location-locked.

• Real-time collaboration allows multiple users to contribute without duplication.

• Historical data remains readable and structured—no more out-of-date formats or software dependencies.

Interoperable

When systems don’t speak the same language, you’re stuck with manual workarounds. SciSure connects the dots—automatically.

• Integrates ELN, LIMS, instruments and data sources into one ecosystem.

• Open API (Application Programming Interface) enables seamless communication between systems.

• Software Developer Kit empowers custom integrations tailored to your lab’s tools and workflows.

• Harmonized data formats make downstream sharing, analytics and AI integration easy.

Reusable

Data reusability means more than just accessing old data—it means being able to rely on it, repurpose it, and build on it without starting from scratch. SciSure makes reusability the default, not the exception.

• Embedded protocol versions, annotations and user history ensure traceability.

• Structured data formats make it easy to rerun analyses or replicate experiments.

• Data from past projects can be re-applied, modelled or scaled—without starting from scratch.

• AI tools can ingest structured data directly, unlocking new insights from old experiments.

FAIR data for successful AI integration

AI is no longer a future possibility; it’s already being embedded into lab workflows to accelerate discoveries and optimize workflows. But for AI to work effectively, it needs fuel. And not just more data—structured data.  

Machine learning models depend on clean, consistent, structured datasets to do their job. That means the way data is collected, labelled, and stored has a direct impact on what AI can achieve. This is where the SMP’s FAIR data becomes essential. Standardized formats. Clear metadata. Full experimental context. All indexed and accessible in real time.

Instead of spending months cleaning up legacy datasets, researchers can build AI-ready data by default. Whether you’re training a toxicity model, scaling high-throughput screening, or feeding historic assay data into a predictive engine, the infrastructure is already in place.

Better still, the system evolves with you. As models generate insights, those results can be looped back into the platform—tagged, timestamped and ready for re-analysis or verification.

AI can’t thrive in a data swamp. But with a FAIR data foundation and the right digital architecture, it becomes a powerful partner in discovery.

Make FAIR data work for You

FAIR data is much more than a buzzword. It’s becoming the baseline for credible, collaborative, future-ready science. But getting there means more than aligning with principles on paper. It means rethinking how your lab works—how data is created, shared, and used.

Connected platforms like SciSure’s Scientific Management Platform give labs the tools to make that shift. By unifying ELN, LIMS, HSE and more—while enabling deep custom integration—they turn FAIR into something practical, powerful and future-proof.

The result? Research that moves faster, teams that work better together and data that keeps on delivering long after the experiment is completed. If you’re ready to unlock the full value of your lab’s data—findable, accessible, interoperable and reusable by design—it’s time to get connected.

Contact us today to unlock the power of FAIR data in your lab!

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Streamlining Chemical Inventory Reporting for Fire Safety

Managing chemical inventory can be a daunting task for laboratories of all sizes.

From tracking quantities toward building fire codes and MAQ limits to staying on top of regulatory reporting requirements, the complexities can quickly overwhelm even the most organized teams.

But with the advent of chemical inventory reporting solutions like SciSure for Health & Safety (formerly SciShield), labs are experiencing a revolution in efficiency, organization, and safety.

Now we will explore the benefits of accurate, real-time reporting tied to your chemical inventory and dive into how these systems are transforming chemical inventory reporting, one click at a time:

Stay organized and eliminate storage chaos by digitizing your chemical inventory with container level tracking in real-time.

Efficiency

Chemical inventory reporting solutions make your team more efficient by reducing the time spent on common tasks by up to 80% — meaning that a task that normally takes you an afternoon to complete will take just a few minutes.

Instead of manually calculating totals toward MAQ limits, for instance, chemical inventory reporting software can do this for you. Not only does this save you time, it also reduces the chance of human errors.

What’s more, chemical inventory reporting software will make managing your chemical inventory easier. You can track containers, view storage recommendations and expiration dates, and complete audits on the go from any mobile device. This frees up scientists' time to focus their core work like applying for grants and conducting research while keeping your labs safe and compliant.

Organization

Chemical inventory reporting software provides a centralized platform for managing your chemical inventory data. Users can easily input, store, and update information about the chemicals they have on-site, including details like quantities, locations, and expiration dates.

The best chemical inventory solutions don’t stop there, though. They also offer real-time monitoring capabilities, allowing you to track changes in your chemical inventory as they occur. This helps you stay up-to-date with your reporting obligations and proactively manage chemical hazards.

Hazard Identification

With potentially hundreds of chemicals on hand, identifying the hazard profile of each one is a time-consuming task. But with a chemical inventory reporting software, it doesn't have to be.

Your software should allow you to instantly see the hazards associated with any chemical in your inventory. You don’t have to go searching for an SDS online or in a binder because that information is readily available at your fingertips.

Additionally, chemical inventory reporting software should give you a “big picture” view of chemicals and high-hazard materials in your labs. Which chemicals count toward your flammable solvents limit? How many peroxide formers are set to expire by the end of this month? With chemical inventory reporting software, these questions are easy to answer.

Accurate Reporting

With effective chemical inventory software, there’s no need for Excel spreadsheets full of complicated formulas and calculations. By pulling relevant data from the centralized database, chemical inventory reporting software automates the process of generating regulatory reports, such as Tier II/Right-to-Know (RTK) reports, streamlining reporting processes, meaning you won’t be scrambling to get it done before the deadline.

As we mentioned above, one of the key features of chemical inventory software is its ability to track containers in real-time. This means that as changes are made to the chemical inventory, such as additions, deletions, or updates, the software automatically reflects these changes in your regulatory reports. This real-time synchronization ensures that reports are always up-to-date and accurate.

Compliance

Chemical reporting software empowers labs to efficiently manage their chemical inventory, enhancing accuracy and streamlining reporting – achieving compliance with fire codes and MAQs is simplified.

As fire code regulations evolve chemical inventory software will help you to maintain compliance. By storing all your chemical data in a single place, you'll have instant access to the information you need to adapt to changes. You'll never have to worry about the unknown of fire code compliance and MAQs because you can easily compare your current inventory with the new regulations.

Scalability

With more time to focus on research and funding opportunities thanks to your chemical inventory software, you might find your lab operations growing. The good news is that a robust software system like SciSure will scale with you as you grow.

As your operations expand, SciSure can accommodate increasing amounts of chemical inventory data and support additional users without sacrificing accuracy or efficiency. You can even manage permissions for different users or groups based on their job description (e.g., lab member, PI, etc.). This ensures that individuals throughout your organization are only able to view or change information that is relevant to their role.

Safety

At the end of the day, chemical inventory software makes your labs safer by helping you manage hazardous materials more effectively. It enables precise tracking of chemicals throughout their lifecycle, from purchase to disposal, so you can proactively address fire hazards such as expired chemicals or incompatible storage.

And, in the event of a fire, chemical inventory software is — quite literally — a lifesaver. Having access to an accurate chemical inventory, complete with the quantities and locations of chemicals, enables emergency responders to quickly assess the situation, identify potential hazards, and take appropriate measures to protect personnel and property.

Take control of your chemical inventory

Chemical inventory software solves the headache of fire code compliance and regulatory reporting by giving you control over your chemical inventory once and for all, organized and in compliance — with regulatory reporting in real-time, in just a few clicks. Next, learn about the key features to look for in chemical inventory software.

The power of accurate, real-time chemical reporting is the key to mitigating risks and enhancing fire safety.

Your takeaway

With the right software solution, you can save hours and days of complex report pulling for your regulatory reporting, MAQ's, TierII/RTK, and Fire Code compliance.

‍BOSTON, MA (April 2, 2025) – GA International, a global leader in laboratory identification solutions, under its LabTAG brand, announced today that its DYMO® LabelWriter™ 5-Series Printing Kits are now compatible with SciSure (formerly eLabNext)—an intuitive and flexible system for collecting, managing, and analyzing laboratory information. The integration is available in the eLabMarketplace through an Add-on, and provides researchers with a reliable, precise, and user-friendly solution for labeling in critical laboratory workflows.

“We’re excited to expand our partnership with SciSure by introducing this Dymo® LabelWriter™ 550 Add-On,” comments George Ambartsoumian, Founder and CEO of GA International. “This tool allows researchers to print labels directly from the eLabNext interface, making sample identification faster, easier, and more reliable. It’s another step forward in our shared mission to streamline lab workflows and improve sample and data management in laboratories.” 

The Add-on allows users access to pre-designed label templates tailored for LabTAG’s specialized DYMO-Branded CryoSTUCK® Labels directly within SciSure. These templates are optimized for the Dymo® LabelWriter™ 5-series printers, ensuring perfect formatting, dimensions, and layout alignment. This eliminates the need for manual adjustments and enables effortless label printing in just a few clicks. By combining the affordability of the Dymo® LabelWriter™5-Series (550, 550 Turbo, and 5XL) with LabTAG's durable cryogenic labels, the Add-on also offers a cost-effective labeling solution, particularly beneficial for labs working on a tight budget.

“We jump on every opportunity to enhance our platform by integrating tools that simplify workflows, collaborating with companies that share our passion for sample and data management, and streamlining lab operations for our users,” says Zareh Zurabyan, VP of Commercial, Americas. “This recent collaboration with LabTAG is a win for everyone and ensures that labs can focus on their research with confidence, knowing their labeling needs are met with innovative and reliable technology.” 

To learn more about SciSure and how to connect with LabTAG’s Dymo® LabelWriter™ 550 Printer Add-on, visit the Marketplace.

About LabTAG

GA International has over 25 years of experience as a leading manufacturer of specialty labels, supplying laboratory identification solutions to biomedical research labs, biobanks, hospitals, and other healthcare institutions. Since its inception, GA International has become a worldwide leader in cryogenic and chemical-resistant labels, strongly dedicated to R&D and customer service. 

For more information about GA International, please visit www.labtag.com

Press contacts

For Media & Communication Inquiries‍

Ishan Wadi, Marketing Leader
ishan.wadi@ga-international.com

For Technical Inquiries Related to the Add-On

Alexandre Beaudoin Gagne, IT Director
alexandre.beaudoin@ga-international.com

‍

On May 6, 2025, a significant shift will take place in the regulatory environment with the implementation of the new United States Government Policy for Oversight of Dual Use Research of Concern (DURC) and Pathogens with Enhanced Pandemic Potential (PEPP). Dual use research refers to studies that, although intended for beneficial purposes, may also pose risks if misused or if the knowledge gained could be exploited for harmful purposes. Enhanced pandemic potential refers to research involving pathogens that could potentially lead to widespread outbreaks.

This enhanced DURC/PEPP Policy expands the scope of research previously overseen by the 2012 Federal DURC, the 2014 Institutional DURC, and the 2017 P3CO Framework policies and organizes research into Category 1: involving dual use research of concern (DURC), and Category 2: involving pathogens with enhanced pandemic potential (PEPP).

This policy comes in the wake of heightened global concerns about biosecurity and the potential misuse of research, particularly in light of recent events that have underscored the need for stringent safety and oversight measures. It aims to ensure that sensitive research does not pose risks to public health and national security while fostering responsible scientific innovation.

All research institutions that receive funding from federal agencies are obligated to fully follow this Policy as a condition of their funding. According to section 5.6 of the Policy, “failure to follow the research oversight framework under this Policy may result in suspension, limitation, or termination of federal funding and loss of future federal funding opportunities for the research proposal and for other life sciences research at the research institution, as imposed by the federal funding agency.”

Breaking Down the New Policy Requirements

Under the new policy, research entities involved in DURC or PEPP must adhere to several key requirements:

1. Risk Assessment: Institutions must conduct thorough risk assessments for proposed research to identify any potential dual-use implications. This will involve evaluating how results can be used or misused and detailing necessary mitigation strategies.
2. Review Processes: The research institution, through an IRE (sometimes as a component of an IBC), reviews the PI’s initial assessment and confirms whether proposed or ongoing research is within the scope of Category 1 or Category 2 research
3. Training and Compliance: Institutions must ensure that all personnel involved in relevant research are adequately trained on the ethical, legal, and safety concerns associated with DURC and PEPP. A risk mitigation plan must be drafted if research falls under Category 1 or Category 2.
4. Transparency and Reporting: Research entities will be required to maintain transparent records and report any incidents or concerns related to misuse or safety violations promptly to relevant oversight bodies.

Concerns for Research Entities

This new oversight policy is understandably causing concerns among research entities due to fear of increased administrative burdens for researchers and reviewers, as well as potential for delays as institutions implement the new policy. The NIH has released a Notice for their implementation of the Policy for all NIH-funded research, including grants and cooperative agreements; with other funding agency implementation plans yet to be published.

How SciSure Can Help

The responsibility of assessing whether or not research falls under the scope of Category 1 and Category 2 research, as defined in the new USG Policy, falls on Principal Investigators and Researchers named in the application for which federal funding is received or proposed, at the proposal stage and continuously throughout the research lifecycle.

While the onus is on the PI to make these assessments, the DURC/PEPP Policy posits that the research institution is responsible for “ensuring that PIs are aware of and executing this responsibility appropriately.” One key approach to achieving this is by arming researchers with the tools to make compliance faster and easier.

With SciSure’s new DURC/PEPP Assessment in the Biosafety Management Module, researchers may easily self-assess whether research falls under Category 1 and/or Category 2 research. This speeds up the assessment cycle for Institutional Review Entity (IRE) and Institutional Contact for Dual Use Research (ICDUR).

Learn More

Interested in learning more about SciSure’s Biosafety Management Module or getting a DURC/PEPP Assessment? Schedule a personal consultation with one of our experts!

When you’re reviewing different chemical inventory systems, you need to be able to spot the bad ones quickly.

Not knowing which red flags signal potential problems can lead to a lot of wasted time, money, and energy. Much like buying a car, if you're not careful, you could end up with a lemon.

To help you weed out the wrong system (and to make sure you end up with a good one), here are four must-check warning signs to look for when you're purchasing chemical inventory software.

Chemical Inventory System Red Flags

Chemical inventory systems come in a wide range of sizes, specialties, and services. From free systems that only ask for an email, to home-brewed solutions using spreadsheets, to high-powered systems capable of handling multinational inventories and performing reports.

So what is the most important thing about a chemical inventory system? It meets your needs. An hour or two of thought about what your organization needs to accomplish with a chemical inventory system can save you ten times as much time (or more) in the future.

1. It’s a Glorified Excel Spreadsheet

Every chemical inventory system allows you to store chemical names and quantities and do a simple lookup. But if that's all it can do, then it's just a fancy (and expensive!) version of Excel.

The point of chemical inventory software isn't solely to store information, it's to help you use that information to increase lab safety, reduce risk, and lower costs. That includes monitoring purchases, overseeing chemical use and access, tracking chemicals by hazard class, ensuring appropriate disposal, and creating accurate regulatory reports (just to name a few).

To perform those tasks, your software must include a robust chemical database. An effective chemical database provides all the chemical information you rely on — chemical identity, properties, known regulatory implications, fire code information — right within the system.

It should also include some form of data validation. Unlike spreadsheets, data validation helps prevent errors or duplicate entries from being entered into the system and ensures your system is operating from an accurate, single source of truth.

Finally, your chemical inventory software should provide a clean user interface and intuitive workflows that allow researchers and inventory specialists to get in, get out, and get back to their work in the shortest time possible. Otherwise, your team will likely avoid using the system. If the interface feels clunky, more often than not, that clunkiness will also show up in the way your data is organized.

2. Regulatory Report Constraints

No brainer, right? A chemical inventory system that can't generate regulatory reports (not just raw data) isn't worth the price. Without the value-add of one-click report generation, your team will still be stuck with the burden of compiling data by hand, reducing bandwidth and increasing the likelihood of human error.

If your system isn’t giving you a big leg up in terms of efficiency, then you’re missing out on a huge portion of the benefits you should be receiving.

To avoid investing in the wrong software, spend time developing a list of which reports you’ll need to create before you approach a potential vendor. These may include:

• EPCRA Tier II/Right-to-Know
• State and local reports (CERS, NYC RTK)
• Fire and building code reports
• And many more...

Each of these reports requires specific information based on context. Sometimes a system cannot support the management of non-chemical data, such as control areas or buildings. Other times, the system can provide the data you need, but in the wrong units. The last thing you want to deal with right before a deadline are manual measurement conversions from kilograms and liters to pounds and gallons (don’t get us started on gases…).

If the software does not meet your reporting needs, don’t waste your resources.

3. No Implementation Support

Even top-notch chemical inventory software can end up costing you time and money if it's not used to its full potential. Effective software comes from an effective business that provides support and a tested, trusted approach to implementation. It might seem obvious, but many buyers focus too heavily on features and overlook the headaches that come with a poor implementation process.

Instead, be sure to review the vendor's implementation services and come prepared to ask for details, like:

• How will data initially be imported into the system, and who will be responsible for importing it?
• Is there a clear timeline for implementation with major milestones so that your project stays on track?
• Does the vendor's team have a lot of experience with successful software implementations?

And perhaps most importantly:

• Do they make you feel comfortable reaching out for help?

Any vendor who doesn't have a formal implementation plan or avoids giving a clear answer should go straight to the "NO" pile — a failed implementation presents too much risk for monetary loss and time spent without sufficient coverage.

4. The System isn’t Built for Labs

It's hard to overstate the importance of finding a system that is specifically designed for lab settings. You need a chemical inventory system built by scientists, for scientists.

Think about it: You wouldn’t buy a Ferrari to pull a boat trailer, would you? It might be a great car for a corporate executive, but it would be a terrible choice for your needs — one use later, and you might be looking at a broken car, a huge bill for repairs, and a boat trailer that’s still right there in the same place it started.

Similarly, it doesn’t make sense for organizations to purchase a chemical inventory system that’s designed for an industrial setting. What works well for a plastics manufacturer could end up being near-useless in a research setting.

While both organizations need chemical inventory systems, their workflows, regulatory requirements, reporting needs, and even the types of users are much different, and their systems should reflect and support that.

One quick litmus test to determine whether a system was built specifically for labs is to look at the wording on their website. Steer clear of solutions that are marketed as “suitable for all industries” or employ wording like “sublocation 1” that feels foreign to your lab environment.

Another tip-off: If you notice a client list or testimonial page filled with industrial companies, that system may not be able to meet the specific needs of your research laboratory environment.

If you still aren’t sure, try floating a few example use cases and see how the vendor responds. You’ll know quickly whether the vendor understands your challenges and can help solve your problems.

Fool-Proof Your Software Shopping

Just like screening general EHS software, screening chemical inventory systems can be a difficult and time-consuming process — unless you know what to look for.

Here are a few things to remember so you don’t get stuck with a lemon:

• Chemical inventory software should do more than just store information like a spreadsheet — it should help you use that information to improve safety and lower costs.
• The best-fit chemical inventory software is designed specifically for lab settings and the types of reports and tasks they need to do.
• Don’t forget to look beyond the software itself to the vendor’s team that will be your partner and guide before, during, and after setup.

SciSure’s Chemical Inventory Software was built by scientists, for scientists. Built with a proprietary chemical database software originally developed by Stanford University, ChemTracker empowers EHS professionals by simplifying your chemical inventory management to reduce operating costs, streamline internal processes, and enhance site safety. Leverage our scalable SaaS solution to meet your unique laboratory needs.

If you'd like to learn more about how to build a better chemical inventory system, schedule a demo today!

Like an unannounced visit from your in-laws, chemical regulators usually show up when you’re least prepared and often throw your whole operation into a panic.

One of the best ways to be prepared for a chemical regulatory inspection is to perform regular internal audits of your chemical inventory and safety management. Internal audits can help your team identify gaps in your processes, procedures, and inventory before they get flagged (and possibly fined) by an outside regulator. Internal audits can also uncover hazards that could lead to accidents or injuries. What's more, they can help your organization operate with greater efficiency on a day-to-day basis.

So which issues should you look for during an internal chemical audit? Each organization will look a little different depending on the types of research performed and the chemicals on site. However, there are a few common problems that pop up frequently. To help you on your next internal audit, we’ve compiled a list of five common issues to watch for.

5 Common Issues to Look for During an Internal Audit

1. Incompatible chemicals stored together
2. Expired peroxide formers
3. Flammables stored in a regular fridge or freezer
4. Chemical inventory is out of date
5. Chemicals from other groups or spaces found

1. Incompatible Chemicals Stored Together

Chances are you’ve been on an inspection and seen this before: acids stored with bases. Or oxidizers stored with flammable solvents. Storing incompatible chemicals together can lead to an unintended reaction such as a fire, explosion, or the production of hazardous gasses.

Fortunately, internal audits can help identify storage issues before problems occur. For example, flags for the most common issues — such as acids and bases stored together — may be easy enough to spot that you can have multiple different inspectors keep an eye out for them. This will greatly reduce the potential for an accident without creating more work for your team.

There are also some controls you can implement to prevent these situations from happening in the first place. It starts with educating your user base on proper storage procedures. Make sure researchers know how to identify problematic chemical pairs, how to properly store chemicals, as well as what to do if they find chemicals stored incorrectly. Appointing a point person in each group can help ensure that these procedures are being followed and provide some much-needed accountability. Add to that a good chemical inventory system that gives you visibility into your chemical inventory, and you’ll be well-positioned to pass your next compliance inspection.

2. Expired Peroxide Formers

Peroxide formers are a common class of chemicals in many research spaces. They encompass a broad range of substances including ethers, acetals, and aldehydes. While peroxide formers are stable under normal conditions, expired peroxide formers can degrade and become unstable explosive materials. Like any hazardous chemical, expired peroxide formers need to be identified and disposed of properly. This includes peroxide formers that are opened without “date opened” marked on the bottle.

As we said before, the hallmark of an effective chemical safety management program is education. Ensure that everyone who uses peroxide formers (or works in a lab where they are used) has been trained on the correct procedures for adding dates, checking dates, and disposing of expired chemicals.

A robust chemical inventory system helps you not only locate peroxide formers, but also stores information about containers and users, manages training, and sends reminders about proper handling procedures. Taking a holistic approach to peroxide former safety will lead to much more effective chemical safety management initiatives.

3. Flammables Stored in a Regular Fridge or Freezer

Another common — and dangerous — issue frequently seen in research spaces is flammable substances, such as ether, stored in a conventional fridge or freezer instead of an explosion-proof one. Sometimes, this happens because a particular lab doesn’t have an explosion-proof fridge or freezer. Other times, it’s an oversight, a lack of knowledge, or simple complacency (“This chemical doesn’t really need to be stored in that fridge down the hall, does it?”).

It’s worth adding this item to your regular inspection checklist so it doesn’t get overlooked. Additionally, you can assign self-inspections to members of a group to specifically check for this issue. Making this the lab personnel’s responsibility improves awareness and accountability.

Your equipment tracking system should help you quickly locate labs that don’t have an explosion-proof fridge or freezer, and you can cross-reference that with the information from your chemical inventory system to ID the labs that also appear to have explosive substances that should be refrigerated.

When you inspect these labs, you can quickly double-check the regular fridge or freezer to make sure there aren’t any flammables stored improperly and talk to the lab members to see if there is anything you can do to help them store their chemicals safely.

4. Out-of-Date Chemical Inventory

An out-of-date chemical inventory is unsurprisingly a particularly common problem. Whatever the reason, if your internal audits don’t catch these instances, it can lead to a serious violation or fine from a regulator depending on the infraction.

As with most things, an ounce of prevention is worth a pound of cure. In the same way that it’s much easier to keep your house clean if you put your dishes in the dishwasher after every meal instead of letting them pile up, it’ll be much easier to keep your chemical inventory tidy if you have routine workflows for entering and updating information.

To do that, you’ll need to invest in a chemical inventory system that can not only accurately quantify your inventory but also enable scientists and inventory specialists to access, manage, and share information. When it comes to chemical inventory, the volume of work is simply too large to rely on hacked-together systems that aren’t carrying a great deal of the administrative burden for you.

5. Chemicals from Other Groups Found

In fast-paced research environments, it’s not unusual for containers from one group to end up in another. Researchers may borrow chemicals and forget to return them to their original shelf or storage location. At best, containers get misplaced and people waste time looking for what they need. At worst, hazardous materials end up unaccounted for or “temporarily” stored in a dangerous location.

Each chemical should have a designated storage area and be returned there after use. Of course, that’s easier said than done. Regular reminders can not only help researchers remember to put things back, but they can also help build better habits. By automating these reminders with chemical inventory software, you can dramatically improve compliance.

Regular internal audits can also help to uncover chemicals that are out of place. However, taking inventory with a pen and clipboard can be a time-consuming exercise — especially if you have hundreds or thousands of containers. Using chemical inventory software along with barcoding or RFID speeds up the process, making the auditing process a much more sustainable one.

Additionally, this is another great place to leverage self-inspections, as the personnel in research spaces might have an easier time recognizing when that tub of salts they borrowed or lent out last week wasn’t returned.

Final thoughts

Internal audits are a valuable tool to identify common issues such as incompatible chemical storage, expired or missing chemicals, and out-of-date inventory. Today, chemical inventory management technology has streamlined and improved the internal audit process. What once took weeks now can be completed in a single day, and you can reap the rewards of a safer, more organized research environment.

SciSure’s Chemical Inventory System was built by scientists, for scientists. Built with a proprietary chemical database originally developed by Stanford University, ChemTracker empowers EHS professionals by simplifying your chemical safety management to increase accuracy, streamline internal processes, and ultimately enhance site safety.

Leverage our scalable SaaS solution to meet your unique laboratory needs. Talk with a SciSure advisor today!

Today, protocols.io, a platform that enables academic and industry researchers to record and share up-to-date methods for research, announced that, under SciSure (formerly eLabNext) leadership, its platform is now available for integration with SciSure, an intuitive and flexible system for collecting, managing, and analyzing laboratory information. The seamless connections between protocols.io and the DLP enable users to better understand what protocol was used, by whom, on which samples, and when. This comprehensive digital documentation and trackability help ensure experimental reproducibility in the scientific research fields.

“Scientific research faces challenges with experimental reproducibility, and having tools to combat them is a top priority,” says Zareh Zurabyan, (formerly) Head of eLabNext, Americas.

“Integrating the protocols.io platform with SciSure puts reproducible protocols at each user's fingertips, allowing researchers to conduct more reproducible and trustworthy science. We are excited to further this goal with the protocols.io team.”

With the integration of the protocols.io add-on, SciSure users can access comprehensive, step-by-step protocols directly from their protocols.io account. In addition, the integration allows users to search a library of private and public protocols, which can be easily imported into their DLP.

“From our first meeting with SciSure to discuss integration options, it was clear that we share the same mission and approach to serving the research community,” explains Lenny Teytelman, Founder and President at protocols.io. “As we strive to encourage and support collaboration among researchers, it is essential that the tool makers collaborate as well. Working with the eLabNext team is a pleasure!” 

To learn more about SciSure and how to connect with the protocols.io add-on, visit the Marketplace.

About protocols.io

Protocols.io is a secure platform for developing and sharing reproducible methods. The platform enables scientists to make, exchange, improve, and discuss protocols. It was conceived in 2012 by geneticist Lenny Teytelman, and computer scientists Alexei Stoliartchouk and Irinia Makkaveeva to facilitate science communication and rapid sharing of knowledge. The protocols.io platform enables academic and industry researchers to record and share detailed up-to-date methods for research. It is part of Springer Nature and is headquartered in Berkeley, CA. 

Press contact 

Name: Emma Ganley, Director of Strategic Initiatives
Email: emma@protocols.io
Website: https://www.protocols.io/

Data integrity is a critical pillar of scientific research. It forms the foundation for credible, trustworthy results, ensuring that findings are valid and reproducible. Without it, the scientific process falters, and breakthroughs lose their impact. Yet, maintaining data integrity is far from easy. Labs today face a host of challenges, from human errors during manual data entry and fragmented systems to the constant evolution of complex regulatory requirements.

So, how can labs rise to these challenges and ensure their data remains accurate and reliable? 

In this blog, we will highlight:

• What is data integrity?
• Common threats to lab data integrity
• The role of digital tools in safeguarding data integrity
• How SciSure safeguards data integrity
• Practical tips for maximizing data integrity in your lab 

Read on as we dive into the critical importance of data integrity, uncover the common risks that jeopardize it, and highlight how innovative digital tools, like SciSure, can revolutionize data management for researchers and lab managers. With the right tools and strategies, labs can not only protect their data but also streamline operations and unlock new potential for groundbreaking discoveries.

What is data integrity?

Data integrity refers to the accuracy, consistency, and reliability of data throughout its lifecycle. It ensures that information remains unaltered and trustworthy from creation to storage and beyond. The core components of data integrity include:

• Accuracy: Data must reflect the true values or results it represents without errors or discrepancies.
• Consistency: Information should remain coherent and uniform across different systems, workflows, and datasets.
• Completeness: No critical information should be missing, ensuring a full and accurate picture of lab activities.

Why does data integrity matter?

• Research integrity: Data integrity underpins the credibility and reproducibility of research findings, a cornerstone of scientific progress.
• Regulatory compliance: Many lab operations require strict adherence to standards like EU Good Laboratory Practice, FDA 21 CFR Part 11, and ISO/IEC 27001, all of which hinge on robust data integrity practices.
• Operational efficiency: Consistent and accurate data streamline workflows, improve collaboration, and reduce errors, saving time and resources.

Common threats to lab data integrity 

Despite its importance, data integrity is often at risk due to a variety of factors. Understanding these challenges is the first step toward mitigating them:

• Manual data entry errors
  Handwritten notes and manual transcription are prone to mistakes, leading to inaccuracies in records and experimental outcomes. This can result in wasted resources and compromised results.
• Data fragmentation
  Disconnected systems or siloed datasets make it difficult to maintain consistent, accessible, and reliable information. Researchers may struggle to retrieve critical data quickly, delaying projects.
• Unauthorized access
  Without proper access controls, sensitive data is vulnerable to unauthorized modifications, compromising its integrity and leading to potential data misuse.
• Outdated systems
  Legacy software and paper-based methods lack the precision, security, and scalability needed for modern lab operations. These systems also make it harder to adapt to new regulatory or technological requirements.
• Compliance risks
  Manual processes and disparate systems make it challenging to meet regulatory standards, increasing the risk of non-conformities and penalties. Inconsistent documentation can exacerbate these risks.
• Data migration challenges
  Transferring historical data from legacy systems into modern platforms must be handled carefully. This process requires meticulous planning and execution.
• Time-consuming audits
  Preparing for audits without centralized data systems is labor-intensive and increases the likelihood of oversight or errors. This can lead to delays in regulatory approval processes.

These challenges underline the necessity for a modern solution that can streamline operations while reinforcing robust data management practices.

The role of digital tools in safeguarding data integrity

Digital solutions, from Electronic Lab Notebooks (ELNs), to Laboratory Information Management Systems (LIMS) and beyond, are transforming how laboratories manage and interact with data, providing significant advantages that address common data integrity risks. 

These benefits include:

Centralization of data 

By consolidating all lab data within a single, unified platform, digital solutions eliminate data silos and ensure coherence across the board. This reduces the risk of fragmentation, enabling lab teams to access, analyze, and share consistent, reliable information from one central source.

Automation and precision 

Automated workflows, data logging, and error-checking mechanisms reduce the likelihood of human error, a common issue in manual processes. These systems ensure accurate and consistent data capture, improving the reliability of results and freeing up time for lab personnel to focus on higher-value tasks.

Real-time data logging and tracking 

Digital tools enable labs to monitor data in real time, ensuring that the most current and accurate datasets are always available. This is particularly useful for labs that need to make time-sensitive decisions, as it provides immediate visibility into ongoing processes and results.

Access control and security 

Robust features such as role-based access, advanced encryption, and detailed audit trails provide an extra layer of security to protect sensitive information. By restricting access to authorized personnel and tracking every action, these tools minimize the risk of data breaches and unauthorized modifications.

Audit readiness 

Preparing for audits and inspections becomes much easier with digital platforms. Built-in audit trails automatically log activities, creating a transparent and easily accessible record of all data interactions. This ensures compliance with regulatory standards, saving labs time and effort during inspections or validations.

By embracing digital solutions, labs can not only reduce risks but also improve efficiency, accuracy, and overall data management, ultimately leading to better outcomes and streamlined operations.

How SciSure safeguards data integrity

SciSure is designed to address the unique challenges labs face in maintaining data integrity. Its comprehensive features ensure accurate, secure, and compliant data management. By combining the functionality of ELNs, LIMS, inventory management software and more into a single platform, it eliminates the fragmentation and inefficiencies often seen with separate systems.

By choosing SciSure, labs can achieve unparalleled efficiency and security, safeguarding their most valuable resource—their data.

Talk to one of our Lab Digitization Specialists—scientists who understand your needs—to guide you through your lab's digital transformation.

Practical tips for maximizing data integrity in your lab

Implementing data integrity practices in your lab doesn't have to be overwhelming. By focusing on clear, actionable steps, you can create a more efficient and reliable data management system that benefits both your team and your research outcomes. Here are 5 practical tips to get you started: 

1. Evaluate current practices
   Start by assessing your existing data management systems and workflows. Look for inefficiencies, risks of data loss, or areas where human error is more likely to occur. This evaluation will help you identify the most critical areas that need improvement and provide a foundation for implementing better practices. 

2. Digitize your operations
   Transitioning from paper-based systems to digital tools is one of the best ways to ensure consistency, accuracy, and ease of access. Digital platforms reduce the risk of misplaced data, ensure standardization, and often come with built-in features to enhance security and compliance. Consider adopting solutions that integrate seamlessly into your workflows. 

3. Train your team
   Proper training is essential to maintain data integrity. Make sure all team members understand the importance of accurate data handling and are familiar with the tools and systems in place. Regular training sessions can also address updates to protocols or software, ensuring everyone stays aligned with best practices.

4. Leverage vendor support
   Utilize the resources offered by your digital platform vendors. Platforms like SciSure often provide onboarding, training, and technical support to help labs implement their systems effectively. Take advantage of these services to ensure a smooth transition to new tools and to help your team maximize their potential. 

5. Monitor and audit regularly
   Routine monitoring and audits are key to ensuring long-term compliance and high-quality data management. Establish a schedule for reviewing your systems and processes and address any gaps or issues that arise. Regular audits not only improve accountability but also create opportunities to refine and optimize your workflows over time. 

Research integrity is non-negotiable

Data integrity is more than a regulatory requirement—it’s a foundation for credible and impactful science. It ensures the accuracy of research, streamlines compliance, and facilitates collaboration. If you’re ready to take research integrity in your lab to the next level, consider partnering with SciSure. Our platform is engineered to help labs like yours achieve exceptional standards in data management and operational efficiency.

Take the next step in safeguarding your lab data. Schedule a free demo!

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When you begin the process of searching for chemical inventory management software, it can be difficult to distinguish between all the different options — many of which may seem similar on the surface.

You might see several systems that offer similar features, such as container tracking, safety data sheets, and reporting. One thing you’ll see no matter what is vendors claiming their system is “the best”.

But how do you know that the software you're considering will be the best fit for your organization specifically? At SciSure, we want you to find the right software for your needs — whether that means working with us or not.

In this article, we'll walk you through some key indicators that you've found the right software for your organization so you can confidently recommend the best solution to your team.

Signs You Found the Right Software for Chemical Inventory Management:

1. The software tracks key data in a report-forward database structure (Think MAQ's & CFATS Reports)
2. It’s easy to imagine using the software in your everyday tasks
3. The workflows available can seamlessly scale
4. Searching and filtering tools are available to easily answer inventory questions
5. Maintaining your inventory feels sustainable
6. The software can connect to systems that fulfill other safety needs

1. The software tracks key data in a report-forward database structure

Chemical reporting is one of the most time-consuming and expensive aspects of chemical inventory management. So, it makes sense that reporting should be one of the first things you look for when evaluating a prospective software system.

When you need to create a report, the last thing you want is scattered or incomplete data. Reporting should be integrated with your chemical inventory and not exist in a separate sphere. Any time your team needs to produce a report, they should have the information they need at their fingertips.

Reporting capabilities can differ widely between systems, so it’s important to weigh your options carefully. When you’re looking at a new system, consider all the different reports your team needs to generate. Do you need to create reports for DHS CFATS/COI? Do you spend a lot of time compiling data for Tier II reporting, or is that one you aren’t concerned about? Having a list will help you narrow down your search to software systems that will meet your reporting needs (or quickly ID systems that won’t work), saving you time down the road.

2. It’s easy to imagine using the software in your everyday tasks

When searching for new chemical inventory management software, what matters most is selecting a system you’ll actually use. You know you’ve found the right system if you can immediately envision yourself using it to locate chemicals across your organization. Or if you can picture using it to run one-click reports. A good system should not only save you time – it should result in less uncertainty, greater confidence, and fewer headaches.

We recommend creating a list of your most common tasks, such as data entry, reporting, ordering and disposal, and so on. Then think about how long it takes to complete these tasks. Some people may even find it helpful to keep a time log over a week or two to see how much time they’re spending on various activities.

When evaluating a potential system, consider whether the new system will save you substantial time and energy on the most critical, or tedious, tasks. Many vendors will help you calculate the potential time savings. Ask for “lookalike” customer success stories to gain insight into the potential impact of this software for your organization.

3. The workflows available will seamlessly scale

Purchasing chemical inventory software is a big investment. It's important to choose a system that not only meets your needs today but can scale with you in the future. Will this software meet your organization’s needs five years from now?

A lot can change over a year, and a system that seems like a dream today can turn into a nightmare if your organization grows. Look at the workflows you outlined above, and imagine you’ve got to perform those tasks with double the chemical volume, or with a new class of hazard you haven’t had to track before. Does it still seem manageable? Or is it hard to imagine how you could get it all done? Ideally, you want to license a system that fits your needs now and can accommodate your requirements in the future.

A big part of this comes down to the vendor’s team. They should be willing to listen and explain how the system can scale to accommodate your future needs. Ultimately, you want a customer support team you can rely on if the road gets bumpy.

4. Searching and filtering tools are available to quickly answer inventory questions

You’ll know in a matter of minutes if the software you’re considering checks this box. A good chemical inventory management system should allow you to answer questions like, “Where are all our peroxide formers?” and “How much flammable solvent do we have in this building?” quickly and accurately. After all, there’s a reason you’re moving away from spreadsheets and disorganized filing cabinets to an advanced organizational system.

A good search tool should return information fast. You shouldn’t have time to get up for a cup of tea while you’re waiting for your results to populate. It should also return all relevant results — not leave you scratching your head at why a particular chemical isn’t showing up when you know it should be.

When it comes to filtering, there are two big things to look for. First, you should be able to filter on several dimensions at once — for example, filtering for oxidizers within a specific building. Second, you should have a diverse range of filtering options, such as location, hazard class, and regulatory implications. There’s nothing more frustrating than when you go to filter results by certain criteria, only to realize that option doesn’t exist.

5. Maintaining your inventory feels sustainable

A chemical inventory system is only as good as the data within it. So even if a software system has all the features you’re looking for, you might want to reconsider if it seems like a lot of work to maintain.

Try to keep in mind that maintaining an accurate chemical inventory is an ongoing effort with many moving parts. Activities like bulk edits, controlling spaces and groups, and ensuring consistency in chemical identification are all important aspects of maintaining your inventory. You know you've found the right software if you can perform these tasks easily. Otherwise, if it’s a lot of work to keep up with your inventory, there’s a good chance you’ll end up with outdated and unreliable results.

6. The software can connect to systems that fulfill other safety needs

Although it’s great to find a solution that meets your chemical inventory requirements, it’s important to remember that you’re not buying a software solution in a vacuum. Before you even consider buying chemical inventory management software, you should make sure that it can work with your other safety systems.

For example, is the data in your chemical inventory management system accessible to inspectors or internal auditors? If you need to share data with regulators or first responders, how will that work?

It's a good idea to explore these possibilities thoroughly before you sign on the dotted line. No matter how much you like the look of a new chemical inventory system, if it isolates your data from other groups or systems that need to share information, it’s not the right software for you.

Final Thoughts

Chemical inventory management software offers many advantages. It can help your organization save time, answer questions with confidence, and maintain a high state of audit readiness — but that’s only if you select the right system.

Finding the right software for your organization is no small challenge, and there’s a lot to think about before you commit to a purchase decision. By paying attention to the six signs we’ve outlined in this article, you’ll be able to make a smart choice and reap the full rewards of a chemical inventory management system.  

SciSure’s Chemical Inventory Management Software was built by scientists, for scientists. Built with a proprietary chemical database originally developed by Stanford University, ChemTracker empowers EHS professionals by simplifying your chemical safety management to increase accuracy, streamline internal processes, and ultimately enhance site safety.

Leverage our scalable SaaS solution to meet your unique laboratory needs. Talk with a SciSure for Health & Safety specialist today!

‍BOSTON, MA – Elemental Machines and SciSure (formerly eLabNext) announced today that the Elemental Machines’ Connector add-on will now be available for free in the eLab Marketplace, eLabNext’s application library for extending and personalizing the functionality of their Digital Lab Platform. The Elemental Machines’ Connector supports wireless Element Machines' sensors, which can monitor and record real-time data on temperature, humidity, and other environmental factors, allowing labs to respond instantly to temperature deviations among storage devices with customizable cellular alerts. 

“We are proud to continue partnering with SciSure and serving customers worldwide,” says Ed Seguine, CEO of Elemental Machines. “With the launch of our new Elemental Machines’ Connector add-on, we’re showcasing our long-term vision and commitment to this partnership. This release not only supports our existing customers but offers new customers the chance to experience the value of the integration. By merging insights from Elemental Machines with eLabNext, users can gain a deeper, more sophisticated understanding of samples before and during scientific processes.”

The integration also gives users full visibility into equipment status and sample storage, driving informed decision-making, better resource management, greater reproducibility and compliance, and enhanced lab workflows. The Elemental Machines’ Connector automatically captures, documents, and provides access to data through cellular networks on a variety of environmental factors for audits and reviews to improve experimental accuracy.

“Centralizing lab data is one of the core missions of our platform,” explains Zareh Zurabyan, VP of Commericial, Americas. “The Elemental Machines’ Connector opens up SciSure software to collect environmental data generated by lab equipment, giving users easy access to all temperature readings, including the duration and magnitude of deviations. Whether you are doing clinical work, R&D, diagnostics, or long-term biobanking, it is important to have insight into sample integrity, which generates terabytes of data using a range of equipment. This integration allows users to collect historical data on temperature deviations across the entire sample journey through the lab, giving them more confidence in their sample data.”

The Element-T sensors are capable of continuously monitoring and transmitting temperature data from freezers, refrigerators, and lab spaces. Download the Elemental Machines' Connector for SciSure from the Marketplace and start integrating real-time data into your lab operations today.

About Elemental Machines

Elemental Machines’ Intelligent Operations Platform leverages the best of Internet of Things (IoT) technology and custom hardware/software solutions, delivering actionable insights to operators in life sciences, biopharma labs, biobanks, analytical labs, and manufacturing facilities. By connecting the physical and digital worlds, the platform simplifies, optimizes, and augments operations. Leaders in science, facilities, and technology trust Elemental Machines’ ecosystem for insights that accelerate innovation, predict outcomes, and shift from reactive to proactive management. With scalable solutions from incubator to enterprise, custom integrations to fit each operation’s unique needs, and a wide ecosystem of partnerships, Elemental Machines ensures operational excellence and the future-proofing of lab and manufacturing processes. 

Press contact: 
Name: Kevin Ghiasi, Vice President of Global Alliances
Email: kevin@elementalmachines.com
Website: elementalmachines.com