Digitalisation is taking over our personal and professional lives. 

Now more than ever, life science organisations are digitising their lab tools and research operations to increase efficiency, enhance data management, foster collaboration, and ensure data security.

The application of artificial intelligence (AI) and machine learning (ML) has also become widespread, thereby generating deeper insights and answers to the grand (yet challenging) biological questions we face today.

This blog post will explore the increasing data management challenges academic, industry, government, and non-profit research organisations face in our rapidly evolving era of AI, automation, and multi-omics.

The Need for a Digital Solution for “Everything” in the Life Science Lab

The need for a comprehensive digital lab solution has become more evident as research data becomes more dispersed across various data analysis and information management systems. In today’s dynamic landscape, organisations seek a more centralised platform to oversee “everything” in a life science lab: Data, samples, protocols, notebook entries, reagents, inventories, instruments, and more. 

Moreover, the demand for interoperability and seamless integration with other systems is rapidly growing, along with the need to comply with ever-changing research governance, ethics, data security, and educational requirements.

To address these challenges effectively, the transition from traditional paper lab notebooks to electronic lab notebooks (ELNs) began over two decades ago and is now accelerating and growing globally. Adopting an ELN offers a range of benefits, including user-friendly interfaces, enhanced security measures, and compatibility with other systems.

By digitising laboratory processes, scientific progress and publications are expected to scale, regulatory compliance will improve, and job satisfaction and student learning experiences will be enhanced.

It is important to note that the success and impact of lab digitalisation depend on internal change management practices, process standardisation, and robust end-user training and support structures.

With these elements in place, life science organisations can fully leverage the potential of digital lab solutions and navigate the transformative journey toward a more efficient research environment.

How Do Digital Lab Platforms Help Research Operations and Management? 

There are many ways that digital lab platforms can benefit life science labs. Here, we review a few key publications that offer reliable data to support the advantages of using digital lab platforms.

Faster and FAIRer Data Quality Output

When utilised effectively, ELNs significantly increase the speed of data collection, analysis, and collaboration. 

Researchers who have successfully implemented ELNs have reported faster completion of research experiments compared to traditional paper notebooks. This is partly because modern research equipment generates digital data, allowing for seamless integration with ELNs. 

A 2022 Nature article highlighted that using ELNs frees up more time for actual research by reducing the time required for data collection, analysis, and manuscript preparation. Can you imagine how much time you could save if you didn’t have to print data on paper, trim the excess with scissors, and glue or tape it into a paper lab notebook? Moreover, the digitalisation of laboratory processes facilitates the standardisation of data collection and analysis, promoting transparency and reproducibility of experiments.

Another challenge scientists and researchers face is facilitating knowledge discovery of scientific data and its associated workflows and algorithms by machines and humans. FAIR data practices outline principles to make data Findable, Accessible, Interoperable, and Reusable, thus facilitating the uninhibited data flow to the broader scientific community. With ELNs, you can document all device setups, plan experiments, save digital experiment data, and add human or analogue observations, enabling researchers to comply with FAIR data practices seamlessly.

In addition to these benefits, certain ELN providers offer Application Programming Interfaces (APIs) and Software Development Kits (SDKs) that enable users to connect their ELN with other research software platforms and systems, such as Microsoft365, GraphPad Prism, and other third-party software.

These integrations streamline workflows, minimise errors and duplications, and enable easy data transfer or sharing between platforms.

Lab digitalisation enhances research output and future-proofs your processes by facilitating further integration and adapting to evolving inter-operational requirements. By embracing ELNs, researchers can experience accelerated research progress while establishing a robust foundation for their ongoing scientific endeavours.

Increased Regulatory Compliance

Beyond the obvious benefits like protecting sensitive data, intellectual property, and patents, an excellent digital lab platform ensures compliance with legal and cybersecurity standards; ELNs can also reinforce compliance with bio-risk and hazardous materials management regulations.

For example, ELNs can include features that facilitate proper handling, storage, and disposal of biological and hazardous materials. They provide audit trails and generate reports, simplifying compliance demonstrations during inspections or audits. In addition, ELNs enable project- and user-based organisation, rather than just the rigid and traditional user-based organisation seen in paper lab notebooks. Thus, the protocols, samples, and data from multiple individuals working on a specific project can be accessed from a single place within the ELN. This enables more accurate tracking of operations, as there may be personnel turnover throughout the course of a project or preparation of a manuscript.

In a review published in the Journal of Biosafety and Biosecurity, Sun et al. recommend using digital lab platforms to ensure safety, efficiency, and compliance with bio-risk management regulations in biosafety laboratories (BSLs).

These digital solutions streamline data collection, track the movement of biological and chemical samples, and maintain up-to-date Standard Operating Procedures. ELNs offer simple interfaces and customisable features for dealing with challenges, such as genetically-modified (GM) specimens, radioactive samples, or cytotoxic materials. 

By embracing ELNs and other digital lab solutions, researchers can enhance compliance with bio-risk management regulations, improve data traceability, and streamline processes related to handling hazardous materials. 

Enhanced Lab Personnel & Student Experience

ELNs offer a reliable and efficient way to maintain up-to-date records of experiments and research data. Equipped with digital features, these solutions enable scientists to collect, organise, templatise, and analyse data with improved efficiency. This saves time and ensures that information is readily accessible whenever needed.

Another notable advantage of ELNs is their positive impact on student learning experiences. Research from Riley et al. has shown that ELNs facilitate learning in laboratory settings. Students benefit more from quickly searching and retrieving information, streamlining their workflow, and feeling more engaged and motivated in their work. ELNs also support team-based learning, fostering collaboration and knowledge sharing among students.

Besides supporting student learning, ELNs enhance interdisciplinary collaboration and knowledge sharing among researchers. They enable scientists to collaborate more effectively with external partners, facilitating the transfer of knowledge and expertise and improving productivity and efficiency in the laboratory. 

By automating routine tasks such as data entry, calculations, and report generation, scientists can allocate more time to high-value research activities. Notably, ELNs that are interoperable with other systems are expected to add value to the everyday work of laboratory personnel, as they will further streamline workflows.

While adapting to change can be challenging for end-users, the benefits of a digital lab environment, backed with appropriate training and support, will undoubtedly have a positive and long-lasting impact on the experience of research staff and students working in laboratories.

Conclusion

In conclusion, electronic lab notebooks benefit organisations regarding research management and operations. While the use of ELNs and lab digitalisation is dependent on the internal rollout and support structure for these systemic changes, the evidence suggests that they can: 

1. Contribute to more efficient and collaborative research processes, which can ultimately lead to faster publication times.
2.  Facilitate compliance through improved tracking, documentation, and auditing.
3.  Improve the laboratory experience of students and lab personnel by reducing their administrative workload and freeing up time for their high-value work (i.e., performing research and data analysis and preparing manuscripts).

Our product, eLabJournal, is more than just an ELN. It is an all-in-one comprehensive Digital Lab Platform (DLP) for managing your research data, protocols, and inventory as well as having the capacity to integrate with existing research systems. 

Contact us for your free 30-day trial and/or a demonstration to see for yourself!

Lab workers are usually vigilant about taking appropriate precautions while performing high-risk activities or working with materials that they recognize as being highly hazardous.

Ironically, it’s during more routine tasks that lab workers become complacent and accidents occur. This is why it’s so important to review basic laboratory safety procedures with your staff — even if they seem experienced, and even if the procedures appear straightforward.

We spoke with SciShield safety expert Kimi Brown, NRCC-CHO, CSP, ARM, to compile14 general laboratory safety rules and guidelines that should be followed at all times.

Following safety procedures

1. Always wear eye protection when working with chemicals.

About 18,500 eye injuries occur in the workplace every year. That’s more than 50 injuries every day! As basic as it sounds, simply wearing proper eye protection can reduce the risk of these injuries significantly.

Eye protection has come a long way in recent years due to advancements in materials, design, and technology. Modern safety goggles and glasses are made to resist fogging and scratches so that lenses remain clear. They also come in a variety of ergonomic designs that provide a comfortable fit for different face shapes — so there’s no reason not to wear them.

One important caveat from our safety expert Kimi Brown: “Personal protective equipment (PPE) should never be selected without first doing a risk assessment to determine what the necessary kinds of equipment are and what level of protection is needed.”

2. Wear a face shield as well when working with large quantities of hazardous chemicals.

Face shields provide a layer of protection against chemical splashes — especially when used in conjunction with other lab safety equipment like goggles and respirators. Because face shields are often used when working with higher hazard materials and are not usually required and for every activity done in a standard lab, it can be more difficult to identify when they are needed and to enforce their use. This underscores the importance of doing a risk assessment to understand what safety equipment is required. Yet, only around 40% of lab researchers report wearing appropriate PPE at all times.

According to Kimi, it’s an issue of culture — not policy. “A laboratory with a strong safety culture that emphasizes not just compliance with policies, but also promotes personal and community safety responsibility will be best at recognizing risks and preventing accidents.”

3. Always wear gloves in the lab when handling chemicals.

Whether it be an annoying rash or a life-threatening burn, skin contact with harmful chemicals is a major cause of health issues among lab workers. Gloves are a simple solution to this problem, which might help to explain why they are the most common form of PPE used to protect workers from chemical exposure. In fact, more than $12 million is spent on disposable gloves every year, highlighting the crucial role that gloves play in chemistry lab safety.

Appropriate glove selection is key to providing adequate protection, so be sure to consult glove manufacturer's chemical compatibility specifications before deciding which gloves to use.

4. Wear chemical-resistant lab coats or aprons when working with large volumes or highly hazardous chemicals.

In 2020, there were approximately 3,540 cases of chemical burns that required time away from work to recover. To reduce the risk of chemical contact with the skin, lab personnel should wear chemical-resistant lab coats or aprons.

Keep in mind that, while regular lab coats are designed to provide a basic level of protection against minor splashes and spills, they don’t offer the same level of protection as chemical-resistant lab coats or aprons when it comes to handling corrosive substances and hazardous liquids. As with other PPE, a risk assessment can help you determine which type of lab coat is appropriate for the task.

5. Don’t wear clothing that leaves your arms or legs bare and unprotected, or that’s too loose and could get caught in lab equipment.

Around 40% of all workplace injuries can be traced back to work attire and PPE. Of those, a little over 20% of injuries are due to the clothes worn on the job.

In the lab, loose clothing or exposed skin are contributing factors in numerous lab accidents. But we’re not just talking about tank tops and flip flops — poorly fitting PPE can be just as dangerous. Worth noting? Women are especially vulnerable to accidents arising from ill-fitting PPE like gloves and goggles because these items are often designed around men’s measurements. All lab workers should be given the opportunity to try on different sizes and select what will be safe, comfortable, and appropriate for them.

6. Do not work with hazardous chemicals or processes when alone in the lab.

One in five lone workers report struggling to get help after an accident. This alarming statistic underscores the importance of having someone with lab safety expertise present to intervene in an emergency or unsafe situation.

“A written procedure instructing lab workers how to identify and assess hazards is no substitute for keen attention and active supervision by a knowledgeable lab manager or Principal Scientist,” says Kimi, who is a Certified Safety Professional (CSP).

7. Make sure compressed gas cylinders are secured at all times.

The average compressed gas cylinder is 4 feet tall and weighs approximately 80 pounds, and is pressurized up to 2,200 pounds per square inch (psi). To put that in perspective, the average car tire pressure is only around 30-35 psi!

It’s no surprise, then, that compressed gas cylinders cause around 20 deaths and 6,000 injuries each year. Properly securing these cylinders can substantially reduce this risk by preventing them from tipping over or rupturing.

8. Handle any sharps carefully.

Approximately 385,000 sharps injuries occur every year among hospital workers. And that’s just the tip of the iceberg: many more sharps injuries occur in other settings, such as industrial labs and research facilities. This statistic highlights the need for careful handling of knives, syringes, and needles to prevent injuries and infections.

Transporting chemicals safely

9. Store and transport chemicals in containers made from appropriate materials.

Around 25% of chemical accidents stem from improper storage. Put another way, nearly 1 in 4 chemical accidents could be avoided by following proper chemical storage procedures. As the size of your chemical inventory increases, making sure chemicals are stored properly becomes a more complex task, highlighting the need for meticulous organization, oversight, and accurate chemical inventory to prevent unintended reactions.

10. Wear appropriate PPE when transporting dangerous chemicals.

Every year, workers suffer more than 190,000 illnesses and 50,000 deaths related to chemical exposures. These exposures can happen not only when working with chemicals, but also while moving them from one location to another within the lab. This is why it’s so important to adhere to chemical lab safety rules like wearing appropriate PPE at all times — not just when performing experiments.

11. Make sure containers are secured when in transport.

Although safe transport procedures should be part of your chemical hygiene plan, this tip isn’t just about following lab safety rules and regulations. Anyone involved in transporting chemicals needs to be aware of the dangers associated with the chemicals they’re transporting and know how hazardous they are.

“The basic recognition of hazards and assessing the level of risk presented by those hazards is not as easy to enforce by simply enforcing a policy or procedure,” says SciShield’s Kimi Brown. “Identifying potential hazards and understanding the likelihood and severity of the risks they present is a skill that requires attention, practice, and mentoring to develop.“

Knowing what emergency procedures are in place

12. Know where the eyewash stations and chemical showers are located in the lab.

In the event of a chemical exposure, time is of the essence. Quick access to emergency equipment like eyewash stations and chemical showers can significantly reduce the severity of chemical exposure incidents, possibly preventing long-term health consequences. Equally important is keeping them accessible and activating eye washes regularly to confirm they are functioning and to flush the pipes. However, only 12% of workplaces inspected by OSHA are in compliance with regulations for emergency eyewash stations and showers. Is yours one of them?

13. Know where the fire extinguishers are located, which type to use, and how to use them properly

Every year, hundreds of workers are killed and thousands more injured by workplace fires and explosions. These tragedies are largely avoidable, as 80% of fires can be put out by fire extinguishers — provided that people know their locations and when and how it is appropriate to use them. These topics should all be covered in your regular safety training.

14. In case of an emergency like a chemical spill or worker injury, notify your direct superior immediately.

Reporting incidents immediately is crucial to prevent the situation from escalating and ensure a rapid response. Yet, nearly 40% of lab researchers have been involved in an accident or sustained an injury that was not reported to an immediate supervisor or principal investigator. Once again, this underscores the importance of building a strong and supportive safety culture.

Final thoughts

Even though these 14 basic lab techniques and lab safety tips might seem overly simple, data and experts agree they are fundamental for safeguarding your lab environment. To explore how SciShield can complement your safety efforts, request a consultation with our team.

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In research, as in life, there are setbacks, tragedies, and mishaps.

Unforeseen electrical problems, accidental or purposeful human intervention, or extreme weather can all have lasting consequences for your lab’s samples, inventory, data, records, and, ultimately, the pace at which you recover and progress in your research.

Take, for instance, a recent story from Rensselaer Polytechnic Institute (RPI), where a custodial worker, annoyed by an alarm from an ultra low-temperature (ULT) freezer, allegedly flipped a circuit breaker, causing the freezer to heat up to -32℃ from its normal temperature at -80℃. 

The consequences were devastating: The destruction of samples collected over 25 years of research and at least $1 million in damages.

Over the past two decades, extreme weather events have also caused massive destruction to research laboratories. During Hurricane Katrina, many ULT freezers lost power, warming to room temperature. At Louisiana State University (LSU), 100% of animals housed in animal facilities were lost. Similar animal deaths were seen at NYU Langone Medical Center, an unfortunate consequence of Hurricane Sandy hitting New York City.

Lab Safety Procedures: Building Resilience Through Digitalization

Nothing can reverse the impact of these painful and sad situations. 

And while we may never be able to control the weather, there are ways to minimise the impact of the unforeseen events mentioned above. 

Future-proofing your lab against disaster relies on digitalisation of lab operations. Here are three considerations for moving your lab towards an “all digital” strategy.

Implement a Digital Lab Platform in Your Workflow

Rebuilding after losing samples, animal models, or data will likely require you and your team to regenerate samples or models, repeat experiments, and replicate and re-analyze data. Doing this requires rapid and unfettered access to protocol, sample, and experimental data.

Digital platforms and databases enable efficient organisation and storage of experimental data, making it easier to locate and retrieve archived information when needed. Furthermore, digitalisation promotes collaboration and knowledge sharing among researchers, fostering the exchange of ideas and accelerating the recovery and replication of lost samples, models, and data.

Many digital platforms utilise cloud computing and storage technologies, allowing for easy access to lab information anywhere in the world. So, if you need to evacuate your lab due to a natural disaster, accessing your data is as easy as logging into the platform once you get to safety.

Manage and Track Samples

If a freezer fails, as it might in the real-world situations described above, you’ll need to relocate samples to functional freezers rapidly and prioritise your most important samples. If you lose samples, you’ll need to access any related metadata about those samples so that you can repeat experiments and re-generate them.

Digital platforms provide centralised databases with sample information, including location, storage conditions, and related data, which can be recorded and easily accessed. Barcode or RFID-based tracking systems enable efficient sample identification, reducing the risk of errors and misplacements. Researchers can track samples throughout their lifecycle, from collection to storage, analysis, and disposal, ensuring proper handling. So, in the event of a freezer mishap, you can rapidly locate your most essential samples and get them back to optimal storage conditions.

Train Lab Personnel for Digitalisation

To safeguard your laboratory against unforeseen threats, everyone from lab technicians to lab directors must be trained and feel comfortable on your digital lab platform. By doing this, your team can tap into the true benefits of digitalisation, such as improved communication and collaboration, enhanced data integrity and security, and increased productivity. 

This type of shift in strategy doesn't happen overnight, though. It requires training, leadership, and a steady transition toward digitalisation. We’ve overseen so many labs going through the process of making this transition that we know the common pitfalls and have developed a process for mitigating them. When everyone is armed with a digital lab platform and the knowledge of how to use it, everyday efficiency increases, and you provide your lab with comprehensive preparation for dealing with unforeseen samples or data loss.

Embrace Lab Safety & Secure Your Digital Journey

Unforeseen events and disasters can devastate your lab work, causing samples, data, and research progress loss. While we cannot see the future, there are steps we can take to protect our labs and minimise the impact of such unpredictable incidents. 

Future-proofing your lab against loss requires a full embrace of digitalisation. By implementing a digital lab notebook, you can efficiently store and retrieve experimental data, facilitate collaboration, and accelerate the recovery and replication of lost samples and data.

If you want to learn more about how eLabNext or Sample360 can help streamline and protect your lab operations from unforeseen circumstances, schedule a personal demo today!

In the realm of life sciences, plasmids, self-sufficient double-stranded DNA molecules, are invaluable tools used extensively in laboratories for genetic engineering, recombinant protein synthesis, vaccine and therapy development, and gene function analysis. Owing to their ability to carry specific genes and regulate their expression, plasmids serve as crucial elements for developing gene therapies and vaccines, offering unparalleled control and selectivity.

However, managing an expanding plasmid library can be challenging, given that minute changes in their sequence can transpire during cloning, passaging, or optimizing for increased expression and efficiency. Additionally, their quality may degrade over time due to improper storage or contamination. The key to navigating these complexities is rigorous record-keeping and storage protocols involving unique identifiers, frequent quality checks, and the use of digital databases such as Microsoft Excel trackers, dedicated Laboratory Information Management System (LIMS) or Electronic Lab Notebooks (ELN). It’s crucial to exercise extreme caution when using these systems, as any inaccuracies in the plasmid backbone, antibiotic resistance, selection marker, or optimal bacterial cells to transform into can create confusion, errors, and an unnecessary drain on time and resources.

In this blog, we’ll introduce some of the common plasmids used in the life science space and provide some best practices for building, maintaining, managing, and storing a plasmid library.

The Most Widely Used Plasmids in R&D

Akin to choosing the right tool for a job, constructing a suitable plasmid library tailored to your research needs is vital. Researchers commonly have a variety of base plasmids and their derivatives in their repertoire, ready for use based on the type of experiment planned. For instance, to understand a gene's role in a disease model, you might construct a plasmid library consisting of various functional domains of the gene or variants missing specific domains and carrying targeted mutations. Maintaining organized information about each plasmid, including the backbone, cloning strategy, and purification strategy, is crucial for achieving reliable and reproducible results.

Numerous plasmid variants are extensively utilized in research and development, with some of the most popular ones being pUC19 vectors, pET vectors, pGEX vectors, pBABE vectors, and lentiviral vectors. pUC19 vectors have been pivotal in DNA sequencing, recombinant protein production, genetic engineering of crops, and bacterial genetics study. pET vectors, known for high-level protein expression in E. coli, are renowned for their T7 promoter, selection markers, multiple cloning sites, fusion tags, and inducible expression. pGEX vectors, on the other hand, are used to express and purify recombinant proteins fused with glutathione S-transferase (GST) in E. coli. pBABE vectors enable retroviral gene transfer and stable gene expression in mammalian cells. Lastly, lentiviral vectors are preferred for gene transfer and gene therapy in mammalian cells, providing efficient gene delivery, gene editing, and potential uses in cancer therapy and vaccine development.

Molecular Biology Techniques for Working with Plasmids

A plethora of molecular biology techniques are employed in wet labs for the creation and upkeep of plasmid libraries, each tailored to the project's specific requirements. Some commonly utilized techniques include PCR amplification, restriction enzyme digestion, and ligation, which aid in gene or gene fragment amplification, isolation, and insertion into plasmids. Transformation is a fundamental procedure involving the introduction of plasmids into bacterial cells for replication and maintenance.

Post-transformation, antibiotic or fluorescence-based selection plays a crucial role in maintaining cells with plasmids. Sequencing aids in determining the DNA sequence of plasmids or libraries, thus facilitating the identification of specific genes or DNA fragments. DNA extraction and purification, encompassing processes like alkaline lysis, precipitation, and column-based or bead-based purification, are necessary for isolating DNA from bacterial cells. Innovative cloning techniques like Gibson assembly or Golden Gate assembly can also be employed for plasmid synthesis. Choosing the most suitable techniques for plasmid library construction and maintenance hinges on several project-specific factors, such as the library's size, the type of plasmids utilized, and the intended downstream applications.

Time to Take Your Plasmid Library to the Next Level

Building, managing, and analyzing a plasmid library can be complex, but with the right tools and strategies, you can create a sustainable resource that drives your research forward. Knowing how to maintain, store, and manage your plasmid library effectively is crucial to ensure consistent, reliable results in your work.

Luckily, we have curated an in-depth guide titled "The Ultimate Guide to Building, Managing, and Analyzing Your Plasmid Library". This guide provides comprehensive insights into the following:

• Creating a sustainable plasmid library
• Best practices for maintaining a plasmid library
• Best Practices for storing your plasmid library
• Utilizing software tools for In Silico Plasmid Library and Sequence Management

By utilizing this guide, you can optimize your strategies, streamline your processes, and keep your research at the cutting edge of scientific discovery.

If you’re reading this, you’re likely on a desktop computer, tablet, or phone. 

We often take the complex inner workings of these devices for granted, but what they do is incredible, managing input and output from a wide range of software and hardware. 

And at the center of it all is the operating system (OS), an essential piece of software that communicates with the central processing unit (CPU), hard drive, memory, and other software, integrating them so your device can operate correctly. It also enables you as a user to communicate with your computer, tablet, or phone and perform tasks through a simple visual interface without knowing how to speak your device’s language. 

While the basic function is the same, not all OSs are created equally: Apple’s OS provides a visually stunning interface with an emphasis on simplicity and integration. In the case of Microsoft’s OS, high performance, security, and usability are the priorities. 

For the past few years, my team and I have envisioned a world where an OS could exist in a life science lab. Instead of using a different program for each instrument, all instruments and equipment could be accessed and controlled using one software interface without prior knowledge about the specifics of their inner workings, bringing lab automation to a new level. This possibility would make experimentation accessible to personnel of all experience levels and save massive amounts of time on a lab-, department-, and organization-wide scale.

In the following blog, we’ll dive deeper into lab automation, the current limitations of automated instrumentations, and how our mission – building a “Lab OS” – can bring about the next generation of life science research. 

The Basics and Benefits of Lab Automation

Over the past few decades, the number of sophisticated automated liquid handling and analytical instruments has increased, arming scientists with powerful tools for advancing our understanding of the world around us.

There are 3 core components of lab automation that make it possible:

• Robotic systems: Robotic systems can perform a wide range of routine laboratory tasks, including liquid handling, sample preparation, plate handling, and assay processing. These automated systems are equipped with precise mechanisms and sensors that enable them to manipulate small volumes of liquid, accurately dispense reagents, and carry out repetitive pipetting steps with high precision. They can work around the clock, with minimal hands-on time, accelerating the pace of experimentation and increasing productivity.
• Instrument software: Robotics hardware is essential but is useless without software to tell it what to do and provide a user with a portal for controlling it. Automation software allows for the control and coordination of various instruments and devices in the laboratory. It enables the design and execution of complex experimental protocols, the scheduling of tasks, and the monitoring of instrument performance. 
• Data management and analysis systems: Data management and analysis systems facilitate the storage, retrieval, and analysis of experimental data generated from some instruments, making it easier for scientists to manage and interpret large volumes of information. Depending on the platform, a data management system may be a simple “one trick pony” or an end-to-end solution for the entire data lifecycle. 

Ultimately, combining these three components into an automated instrument setting that can perform everything from sample preparation to analysis, leads to significant benefits for many laboratories, including:

• Enhanced reproducibility: The reproducibility crisis in the sciences and the contributing factors have long been a boon to the advancement of research. Robotic systems combat several of these issues by performing tasks with high accuracy, reducing the risk of human error (though not eliminating it), and improving data quality. Automated processes also facilitate the replication of experiments, enabling researchers to obtain reliable and reproducible results, essential for scientific advancements and regulatory compliance.
• Long-term cost efficiency: While laboratory automation requires a relatively large initial investment, it can lead to significant long-term cost savings. By increasing throughput and productivity, automation optimizes resource utilization, reducing labor costs and minimizing the need for reagents and consumables. Additionally, automation reduces the risk of costly errors and rework, enhancing operational efficiency and cost-effectiveness.
• Safety and risk mitigation: By minimizing exposure to hazardous materials and repetitive strain injuries associated with manual handling, laboratory automation helps mitigate safety risks to personnel. Automated systems can handle potentially dangerous substances and perform tasks in controlled environments, reducing the risk of accidents and ensuring a safer working environment.
• Accelerated discovery: Automation expedites the R&D process, enabling scientists to conduct experiments faster. With the ability to process large numbers of samples and perform high-throughput experimentation, automation facilitates rapid data generation and analysis. This accelerated workflow promotes faster scientific discoveries, enhances innovation, and expedites the translation of research findings into practical applications.
• Standardization and compliance: Automation helps establish standardized protocols and procedures, ensuring consistency across experiments and laboratories. This standardization is crucial in regulated environments, where compliance with strict quality standards and regulatory requirements is necessary. Automation enables precise control over experimental parameters, data collection, and documentation, simplifying regulatory compliance and audit processes.
• Improved data management: Automation integrates with sophisticated software systems to seamlessly capture, analyze, and store data. This eliminates manual data entry, reduces transcription errors, and enhances data integrity. Automated data management enables real-time monitoring and tracking of experimental progress, ensuring efficient data organization and retrieval and facilitating data-driven decision-making.

Limitations to the Current Lab Automation Ecosystem

While the benefits of automation are clear, there are still limitations that remain.

Limitation #1: Scientific Experience and Instrument-Specific Training Requirements

Working with current automated laboratory instruments and equipment requires a thorough understanding of how manual life science protocols are designed and implemented. In addition, experience with the instruments' operation, functionality, and associated software is necessary, and training by or consultation with a technical expert is usually required before operating an instrument. This knowledge and training enable laboratory personnel to make informed decisions, troubleshoot issues, and optimize the performance of automated systems.

Each automated laboratory instrument has unique features, protocols, and software interfaces. Users must receive specific training on the instrument they will be working with to understand its capabilities, constraints, and maintenance requirements. Training programs provided by instrument manufacturers or third-party organizations familiar with the technology can help users gain expertise in operating the specific instrument effectively. However, this is not a long-term solution: Trainees will forget their training over time and make mistakes.

Limitation #2: Workflow Integration

Many workflows and protocols require multiple automated instruments with unique features, protocols, and software platforms. To create a fully-automated, cohesive workflow, lab personnel must understand each instrument’s role, requiring additional training. In addition, because there are multiple platforms at play and no unifying system that interfaces with them, manual communication and processing are needed to ensure a smooth integration, data transfer, and analysis. 

Limitation #3: Human Error

Automated instruments eliminate many aspects of human mistakes in the research process, yet there are several steps that are error-prone. Most systems require specific input parameters or configurations to perform tasks accurately. If errors are made during protocol setup, an instrument may inadvertently execute the wrong steps at a much larger scale than would be done if executed manually. This can result in erroneous data, unsuccessful experiments, and a massive waste of resources, reagents, and consumables. 

Automated instruments also require regular calibration and maintenance to ensure accurate performance. Failure to properly calibrate or maintain the equipment can lead to downstream complications, and (as above) if an error goes unnoticed, it may result in inaccurate results, necessitating retesting and wasting resources.

Lab OS: Launching the Next-Generation in Automation 

At the beginning of this blog, I asked you to imagine a fully connected lab controlled by a Lab OS. 

As you can see by the limitations outlined above, there is a need for the modernization of current laboratory automation. The current automated systems, with their robotics, software, and data management systems, are unnecessarily complex.

Furthermore, the “automation” of these instruments is a misnomer. Current instrumentation has reduced hands-on time significantly compared to manual protocols. Yet, trained personnel are still needed to tend to them to handle errors and ensure protocols are executed as intended. 

To bring about the next phase in laboratory automation, my team and I at Genie Life Sciences have created a unifying Lab OS called Genie LabOS, enabling the full realization of your current automation stack without purchasing a whole new fleet of instruments.

The OS is instrument-agnostic, enabling scientists and automation engineers to design protocols across all connected instruments and accessories without needing training on instrument-specific software or hardware. Genie makes lab automation approachable by filling in the tiresome details for your deck layout, tips, and liquid class settings for clean and efficient liquid handling.

In doing so, laboratory personnel at all skill levels have access to the capabilities of their automated instruments. Building protocols can be done with simple, drag-and-drop ease. In addition, virtual dry runs capture the majority of a researcher’s intent, eliminate errors without having to do trial-and-error wet runs and enable users to publish protocols for better sharing and oversight. 

Schedule a demo today to see how you can unleash the next generation of your laboratory’s automation capabilities.

The modern laboratory environment is pretty sophisticated: Specialized instruments can perform automated workflows, and software platforms make data collection and analysis more streamlined. Various platforms save researchers time and money, improve data accuracy and reproducibility, and make collaboration a breeze.

Yet, the number of instruments and software platforms in a lab can sometimes create challenges with data decentralization. Critical information may be stored in many different places rather than a centralized access point. Traditionally, software developers focused on creating one-dimensional software that did a single task well. In today’s lab, having everything in one place creates an advantage over some of the misperceived benefits of decentralization, such as increased security, privacy, and resilience.

With eLabNext, we can provide a cohesive Digital Lab Platform (DLP) that allows seamless integration and connectivity between your instruments, workflows, and data. This solves many issues with decentralized information that we’ve seen in many of our labs. 

In the blog below, we discuss 7 of the top issues we see with a decentralized data model. 

1) Data Integrity

With decentralized data, there is a risk of inconsistencies, duplicates, or errors. There may be a conflicting version of data stored across multiple instruments or software platforms and a breakdown in the integrity of the data. Ultimately, this can lead to inaccurate results and negatively impact the reliability or reproducibility of the laboratory's work.

2) Data Security

Decentralized data can be vulnerable to hacking or theft, especially if the data is not adequately secured or encrypted. Multiple access points for data provide multiple vulnerabilities.

3) Data Accessibility

Accessing and sharing data between different laboratory locations or with external partners can be challenging when data is decentralized. In science, collaboration is a pillar of progress, necessary for pushing the boundaries of what’s possible. Barriers to collaboration, such as decentralized data, can slow down partnerships and limit data analysis and interpretation. It can be difficult to access and share data between different laboratory locations or with external partners when data is decentralized.

4) Data Standardization

Data standardization refers to establishing common formats, structures, and protocols for data to ensure consistency and interoperability. With decentralized data, there is a risk of using different data formats or standards, making it challenging to integrate data from different sources for analysis and interpretation.

5) Data Management

Decentralized data poses a major problem for data organization. Managing consistency and integrity across multiple data locations is difficult, leading to challenges in finding, tracking, and using the data effectively.

6) Regulatory Compliance

Because of some of the risks discussed above, decentralized data may need to meet the regulatory requirements for data storage, access, and use. Regulatory agencies are mainly concerned with protecting the personal information of clinical trial participants and patients. If it’s not fully covered due to decentralization, regulatory agencies may require a centralized approach.

7) Data Backup and Recovery

Decentralized data can be vulnerable to data loss or corruption, and it can be challenging to implement a robust backup and recovery strategy to ensure the availability of the data in case of system failures or other issues.

Get Centralized with eLabNext

When going on a digital transformation journey, it is vital to limit data decentralization and consider how your software platforms and instruments can communicate.

As you review your past purchasing decisions and those of the future, look at API and SDK tools available that can help you create a flexible, cohesive system that centralizes and secures your data.

Contact us today if you are interested in our API and SDK capabilities as part of the eLabNext platform.

Antibodies are critical components of past, current, and future biomedical research. They have truly revolutionized our understanding of biology and the development of modern medicine. Both monoclonal and polyclonal antibodies aid in the detection, isolation, and quantification of proteins and different cell types as they are vital reagents for laboratory techniques such as enzyme-linked immunosorbent assay (ELISA), western blot, immunohistochemistry (IHC), flow cytometry.

As essential reagents in most laboratories, their management, quality, and organization are paramount. In the following blog, we’ll provide you with a primer on the top providers of antibodies in the biological R&D space, their primary applications in research, and best practices for managing a collection of antibodies.

Here’s what we’ll cover:

• The Top 10 Global Antibody Providers
• The Most Popular Antibodies
• 3 Research Fields where Antibodies are Indispensable
• Best Practices for Antibody Library Tracking
• Best Practices for Antibody Library Storage
• Conclusion

Top 10 Antibody Companies

Many companies provide antibodies, but the "top" antibody companies depend on a few personal factors, such as your specific research needs and your labs’ budget. 

Here are ten companies that are among the largest and most well-known providers of antibodies in the United States:

• Thermo Fisher Scientific (formerly Life Technologies)
• Abcam
• Santa Cruz Biotechnology
• Bio-Rad Laboratories
• Cell Signaling Technology
• BD Biosciences
• R&D Systems
• Sigma-Aldrich (now part of Merck KGaA)
• EMD Millipore (now part of Merck KGaA)
• Agilent Technologies

A cautionary note: This is by no means an exhaustive list. Many other reputable companies provide antibodies. It is important to carefully evaluate the quality and specificity of any antibodies before purchasing them for use in experiments.

The Most Popular Antibody Products

The most used antibodies can vary over time and across different research fields or trends, as the popularity of different targets and applications can shift over time. 

Here are a few examples of some of the most commonly used and sold antibodies in research:

• Anti-GAPDH (Glyceraldehyde-3-phosphate dehydrogenase) antibody: GAPDH is a ubiquitous enzyme that plays a key role in glycolysis and is often used as a loading control in western blotting experiments.
• Anti-beta-actin antibody: Beta-actin is a widely expressed cytoskeletal protein that is also often used as a loading control in Western blotting experiments.
• Anti-FLAG tag antibody: The FLAG tag is a small peptide tag often used to label and purify recombinant proteins in molecular biology experiments.
• Anti-GFP (Green Fluorescent Protein) antibody: GFP is a widely used fluorescent protein that is often used as a reporter in live-cell imaging experiments.
• Anti-CD3 antibody: CD3 is a cell surface protein found on T cells, and antibodies against CD3 are widely used to study T-cell function in immunology research.
• Anti-CD4 antibody: CD4 is another cell surface protein found on T cells, and antibodies against CD4 are widely used in immunology research to label and study various T-cell subsets.

These antibodies are popular because they are widely used across many large research fields, are relatively easy to work with, and have been validated by many research studies. Additionally, many of these antibodies have been on the market for a long time, so they have had time to become well-established and trusted by researchers.

3 Research Fields Where Antibodies Applications are Indispensable

Antibody libraries can be useful in various research fields, as they provide a ready source of diverse antibodies that can be used for various antibodies applications. 

Here are some of the best practices for antibodies tracking and naming in a library:

1. Immunology: The study of the immune system and its function often involves the use of antibodies to label and isolate different immune cell types, as well as to detect various cytokines, chemokines, and other immune molecules. Antibody libraries are used to generate and screen large numbers of antibodies against different targets, which can help identify new therapeutic targets or biomarkers.
2. Cancer research: Antibodies are widely used in cancer research to detect and target specific tumor cell biomarkers. In particular, monoclonal antibodies that target specific proteins on the surface of cancer cells are used as therapeutics in several contexts. Antibody libraries can help identify new protein targets or to generate and screen new monoclonal antibodies for cancer treatment.
3. Neuroscience: Antibodies are used in neuroscience research to label and detect specific proteins and cellular structures in the brain, such as neurotransmitter receptors, ion channels, and synapses. Antibody collections can be used to generate and screen antibodies against different neural targets, which can help identify new therapeutic targets for neurological disorders or improve our understanding of the brain and its function.

Many additional research fields, such as infectious disease research, plant biology, and others, use antibody collections. The specific research needs of a laboratory will determine the usefulness of an antibody library in a field or laboratory.

Best Practices for Antibody Library Tracking

Antibody tracking and establishing consistent naming conventions for antibody collections is critical to ensure the quality and accuracy, and reliability of these key reagents. If one antibody is mislabeled or misplaced, experimental results could be misconstrued, and the pace of research could be impeded. 

Here are some of the best practices for tracking and naming antibodies in a library:

1. Assign a unique identifier: Each antibody in the library should be assigned a unique identifier, such as a number or a combination of letters and numbers. This identifier should be used consistently across all documentation and tracking systems.
2. Document antibody information: In addition to the identifier, information about the antibody should be documented, such as the antigen it targets, the host species it was raised in, and the specific epitope it recognizes.
3. Use a tracking system: A tracking system, such as an electronic database or a laboratory information management system (LIMS), can help track the location and usage of each antibody in the library.
4. Standardize naming conventions: Consistent naming conventions can help avoid confusion and ensure accuracy. For example, naming conventions could include the antibody identifier, followed by the target antigen, and then the host species, such as "Ab1234-CD3-mouse".
5. Use barcoding or RFID technology: Barcoding or RFID (Radio Frequency Identification) technology can be used to track and locate individual antibodies within the library. Each antibody can be labeled with a unique barcode or RFID tag, which can be scanned or read to quickly identify and find the antibody.
6. Regularly update and review your library: It is important to regularly update and review the tracking and naming conventions to ensure they remain accurate and effective, especially as new antibodies are added to the library or experiments are conducted. 

Best Practices for Antibody Library Storage

Proper storage of antibodies in freezers is another crucial aspect for maintaining the stability and activity of a collection over time. 

Best practices for storing antibodies in freezers include:

1. Monitor freezer temperature: Use a thermometer to regularly monitor the temperature inside the freezer. It is recommended to use a thermometer with a calibrated probe that can be placed near the antibody storage area. The temperature should be maintained at -80°C for long-term storage.
2. Use freezer alarms: Set up an alarm system that alerts lab personnel in case of a freezer malfunction or temperature deviation. Many freezers come with built-in alarms, or you can use external alarms that are connected to the freezer.
3. Minimize freezer opening and closing: Minimize the frequency and duration of door openings to reduce the risk of temperature fluctuations. Encourage lab personnel to take out all the needed materials in one visit and avoid leaving the freezer door open for prolonged periods of time.
4. Maintain freezer organization: Ensure the freezer is organized and the antibody storage area is easily accessible. Use freezer racks or boxes that are clearly labeled and organized by antibody type or experiment to facilitate quick and easy retrieval.
5. Employ backup storage: Consider using a backup storage freezer or off-site storage for critical antibody samples to protect against potential freezer malfunctions or power outages.
6. Regular maintenance: Perform routine maintenance and cleaning of the freezer to ensure it functions properly. Clean and defrost the freezer as needed, and check for signs of wear and tear, such as damaged seals, that could affect its performance.

Conclusion

Managing an antibody library in the lab involves keeping track of many reagents, ensuring their quality, and organizing them to facilitate their use. By following the best practices above, you can help ensure that your antibody library is adequately stored and maintained, which will help ensure the quality and reliability of your research.

On top of these best practices, you can facilitate easy access to the antibody collection by implementing lab inventory management software, such as those offered by eLabNext.

To learn more about how our platform can enable efficient and effective management of your antibody collection, contact us for a personal demo. 

eLabNext has incorporated DMPTool, a free online platform for creating data management plans (DMPs), into its library of digital lab platform add-ons. With the addition of DMPTool, research labs and their affiliated institutions can generate DMPs for a wide range of funding organizations – including the National Institute of Health (NIH) – and review or download them directly from eLabNext’s software, enabling more effortless collaboration, grant drafting, proposal submission, and continued compliance.

What is DMP (Data Management Plan)?

A Data Management Plan (DMP) is a structured document that outlines how data will be handled both during a research project and after its completion. It details the types of data to be collected, methodologies for data collection and analysis, plans for sharing and preserving data, and strategies for ensuring data security and privacy. The DMP is essential for maintaining data integrity and ensuring that the data can be effectively used for future research, audits, or replication of the study. Funding agencies, research institutions, and published journals often require its usage to ensure good research practices and compliance with ethical guidelines.

Why are Data Management Plans Important?

Proper data management and sharing ensure that all scientific data (and associated metadata) is findable, accessible, interoperable, and reusable to the present and future scientific community. Following current guidelines from funding agencies guarantees that discoveries are attributed to the right scientists and empowers future researchers to reuse data for additional scientific advances.

The NIH, a major funding source for R&D life science labs, has prioritized data management and sharing. They expect “...researchers to maximize the appropriate sharing of scientific data, taking into account factors such as legal, ethical, or technical issues that may limit the extent of data sharing and preservation.” Accordingly, the NIH has published extensive resources and policy documents for all NIH grant awardees to implement in their operations, with a recent update to the policy in early-2023.

But writing and submitting a data management and sharing plan – now required by many other public and private funding organizations – is challenging, requiring in-depth descriptions of data types, analysis methods, standards that will be followed, timelines for data preservation and access, potential roadblocks, and how compliance will be checked and ensured. In addition, different funding agencies have unique requirements which are continuously being updated, putting pressure on individual researchers and their academic, non-profit, government, or industrial organisations to perform pre-submission quality control checks to ensure adherence with each funding agency’s current guidelines. Finally, after grants are awarded, it can be difficult for all laboratory personnel to access and understand DMPs, leading to non-compliant data management practices and, potentially, data loss.

What Is DMPTool and How Does It Work

DMPTool, an open-source, free, web-based platform, enables researchers to draft data management and share plans that comply with funding agencies by providing a simple agency-specific DMP template. The writing wizard streamlines writing by asking a user about each element of their DMP and providing sample responses in an easy-to-use interface. By breaking down the required elements, DMPTool brings ease and simplicity to grant submissions.

In addition, more than 380 institutions and organizations have implemented DMPTool as an integral part of their grant review process, enabling affiliated users to access organization-specific templates and resources, suggested text and answers, and additional support to further facilitate internal review and approval. DMPTool also directly links to funding organisations websites to ensure that the platform is up-to-date with the latest requirements and best practices.

These benefits have led to the widespread adoption of DMPTool, with over 96,000 researchers using the online application to submit more than 92,000 DMPs.

Efficient Proposal Review, Submission and Data Management Plan Implementation with eLabNext Integration

eLabNext provides a flexible, multi-dimensional software solution for the ever-evolving needs of the life science lab. One defining characteristic of the platform is its ability to expand functionality. The addition of the DMPTool to our eLabMarketplace library of add-ons is the most recent example of this and one that was requested by Harvard Medical School (HMS) users of both platforms.

The eLabNext integration of DMPTool will enable users at HMS and elsewhere to pull DMPs from DMPTool and present plan summaries within eLabNext, along with a link to download the complete plan. Therefore, any eLabNext user can access the DMP and reference as they perform research. This benefits researchers by helping maintain compliance and facilitating full DMP life cycle management from the grant drafting process through the post-award period.

Try DMPTool in a free trial

About DMPTool

DMPTool is a free, open, online platform designed to assist researchers in creating and managing data management and sharing plans. It provides a collection of templates and resources, step-by-step guidance, and comprehensive examples to guide researchers through the process of developing effective DMPs that align with funder requirements and best practices.

Please scroll down for the English translation

Do caderno de anotações ao software de gerenciamento, a migração do papel ao digital é global, e acontece em todas as áreas.  O investimento em digitização de laboratórios de pesquisa nas universidades, P&D, biotechs ou pequenas empresas possui grande potencial crescimento, no entanto, ainda se encontram em fase inicial. Por que a digitalização está demorando tanto para acontecer na América Latina?

Primeiro, precisamos voltar um pouco no tempo. Em 2019, a pandemia escancarou que muitos ramos da biotecnologia precisavam acelerar a transformação digital, para chegar perto da taxa de desenvolvimento, por exemplo, da indústria de diagnóstico ou farmacêutica.  Além disso, a perspectiva socioeconômica herdada pós-covid não era das melhores. Pequenas empresas foram as mais afetadas e vivenciamos um cenário acentuado e complexo devido as debilidades estruturais existentes na região, reforçando a necessidade de explorar cada vez mais a transformação digital para fortalecer as instituições¹.

Como observamos o mercado mais desenvolvido na digitalização é o mercado diagnóstico. Um exemplo é uma das maiores empresas na America Latina, o DASA - Diagnósticos da América S.A. – que investiu milhões para a transformação digital para melhor atendimento ao paciente e redução de custo de operação². Essa mesma lógica pode se aplicar aos laboratórios de pesquisa, biotechs e statups no Brasil, que também tem sido uma tendência crescente nos últimos anos, com a adoção de tecnologias digitais para aprimorar a coleta, análise, armazenamento e compartilhamento de dados. Hoje, revistas cientificas de relevância exigem o compartilhamento de dados brutos para publicação³ e imagine você conseguir compartilhar com apenas um clique? Ou acessar dados do lab em qualquer lugar do mundo.

Não podemos negar que com a realidade da região sempre teremos que contar com as instabilidades socioeconômica e política gerando inseguranças sobre os investimentos que serão injetados nas universidades e startups. Investir ou planejar o seu projeto considerando soluções de software é uma ação que torna esse ambiente mais sustentável e é essencial para a saúde e manutenção do lab. E que o investimento - conseguido com muito suor - seja aplicado de forma otimizada trazendo maior produtividade e inteligência na utilização de recursos e pessoas e garantindo que os dados e amostras sejam protegidas e armazenadas com segurança.

Você está preparado para abandonar o seu caderno e viver uma nova era?

Referências bibliográficas:

1. Perspectivas Económicas de América Latina 2020: transformación digital para una mejor reconstrucción
2. 2022: as contribuições da Dasa para entregar mais saúde aos brasileiros
3. Nature: Data sharing is the future

How Digitization Can Optimize Laboratories in Latin America

From notebooks to management software, the migration from paper to digital is global, and happening in all areas.  Investment in digitizing research labs in universities, R&D, biotechs or small companies has great growth potential, but is still in its early stages. Why is digitization taking so long to happen in Latin America?

First, we need to go back in time a bit. In 2019, the pandemic made it clear that many branches of biotechnology needed to accelerate their digital transformation, to get close to the development rate of, for example, the diagnostic or pharmaceutical industry.  In addition, the socioeconomic outlook inherited post-covid was not the best. Small businesses were the most affected and we experienced a sharp and complex scenario due to the existing structural weaknesses in the region, reinforcing the need to increasingly exploit digital transformation to strengthen institutions¹.

As we have observed the most developed market in digitalization is the diagnostic market. An example is one of the largest companies in Latin America, DASA - Diagnósticos da América S.A. - that has invested millions in digital transformation to improve patient care and reduce operating costs². This same logic can be applied to research laboratories, biotechs and statups in Brazil, which has also been a growing trend in recent years, with the adoption of digital technologies to improve data collection, analysis, storage and sharing. Today, relevant scientific journals require the sharing of raw data for publication³ and imagine being able to share with just one click? Or access lab data from anywhere in the world.

We cannot deny that with the reality of the region we will always have to reckon with socioeconomic and political instabilities generating insecurity about the investments that will be injected into universities and startups. Investing or planning your project considering software solutions is an action that makes this environment more sustainable and is essential for the health and maintenance of the lab. And that the investment - made with a lot of sweat - is applied in an optimized way, bringing more productivity and intelligence in the use of resources and people, and ensuring that data and samples are protected and stored safely.

Are you ready to abandon your notebook and live a new era?

Every scientist knows the frustration of digging through countless Excel spreadsheets and paper notebooks in desperate search of crucial data, forgotten experimental details, and critical reagent locations. As we’ve discussed before, digitizing your lab is how to get around these troubles. 

However, sifting through the available options and difficult-to-decode acronyms can be overwhelming.

You may have noticed that most digital platforms for the life sciences are classified as a Laboratory Information Management System (LIMS) or Electronic Lab Notebook (ELN). On paper, they sound the same, but there are some critical distinctions between them. In this blog post, we’ll explore the differences between an ELN and LIMS, discuss their advantages, and provide valuable tips to help you choose the right solution for your lab.

ELN vs LIMS: Everything You Need to Know

Let’s start by breaking down just what an ELN and LIMS are and their benefits.

What is an ELN?

An ELN is a software platform designed to record and manage data, observations, sample information, and experimental methods that one would conventionally scribble into a paper lab notebook. ELNs are an excellent solution for keeping up with growing regulatory pressures to maintain data integrity and security. Moreover, they allow you to easily collaborate with team members, record experimental observations, integrate with instruments, create detailed reports, and search using simple keyword queries.

Benefits of using an ELN

• Searchability - Given their digital nature, entries into ELNs are easily searchable, which makes them very time-efficient.
• Easy collaboration - ELNs allow labs to share data, notes, and images with colleagues, making it an excellent solution for working on projects and experiments with a team.
• Security - ELNs allow for digital signatures, so sign-off on projects and experiments can be done easily and securely.
• Traceability - ELNs provide a comprehensive audit trail of all actions taken within the system, making it easy to track who has done what and when. ELNs also include inventory and equipment management, making tracking and managing consumables and lab equipment easy.
• Standardization - ELNs can include a protocol module, enabling you to set up individual or group working templates, making it easy to standardize processes and workflows.

What is a LIMS?

In contrast to an ELN, LIMS is software designed to manage and automate laboratory workflows and operations. It is ideal for running repetitive testing or working in a quality assurance or biobanking lab since it minimizes the probability of human errors. Moreover, they allow you to track samples (and associated metadata), attach instrument records to samples, create basic analytical reports, and manage lab tasks and inventory.

Benefits of using a LIMS

• Consistency - LIMS can help labs maintain consistency by closely following predetermined workflows or templates and ensuring precise and reproducible results. 
• Standardization - LIMS help run repetitive testing or work in QC/QA or clinical labs since they are designed to streamline processes and provide easy access to essential data.
• Automation - LIMS can help automate certain procedures, such as report generation, sample management, or inventory tracking. 
• Traceability - LIMS can help you easily track samples, protocols, experiments, and results, saving time and effort. 

What are the Differences Between ELNs and LIMS?

While ELNs and LIMS are digital software platforms for laboratory data management, the two have some significant differences. ELNs are designed for many of the same functions as traditional paper notebooks, such as recording experimental protocols with the added benefits of searchability, data organization, and collaboration tools. LIMS functions focus on streamlining repetitive tasks and workflows from sample tracking to data analysis and reporting. They are typically used by large laboratories that manage lots of samples and data.

Choosing Between an ELN or LIMS: Which System is Right for You?

Now that you know the main features, benefits, and differences between ELNs and LIMS, it is time to decide which solution is right for you. 

In short, choosing a software solution that fits your and your labs’ needs is best. 

But what are those needs? The first thing is to meet with everyone who will use the ELN or LIMS software and better understand what they will be using it for. Are you looking to track samples from routine and well-defined tests? Or are you looking to organize notes, protocols, and data from experiments? If team collaboration is essential to your organization, an ELN may be the way to go. 

Next, consider the industry you work in. For instance, biotech and pharma companies doing drug discovery or early-stage development testing may find an ELN a more suitable solution. In other laboratory environments, like a QC or QA facility, a LIMS may be better suited for your tasks.

Moreover, consider the regulatory environment your lab is operating in. If you work in a standardized environment where workflow is predetermined and not very flexible, a LIMS is likely a better option.

Lastly, ELNs and LIMS come with very different price tags. If budget is a concern, research beforehand and get an accurate quote to get the most value for your money. 

ELN or LIMS: Webinars

The webinar will provide an outline of the differences between LIMS and ELNs, and how you to decide which one is more suitable for your lab.

You will learn:

• What is the difference between LIMS and ELNs?
• How to choose which one best suits your lab? 
• What are the advantages of ELNs?

Let's wrap up!

Ultimately, the choice between a LIMS and ELN will largely depend on what you're trying to accomplish, your primary lab needs, your work and regulatory environment, and your budget. Understanding what each system does can drastically help guide your decision. And as the next generation of holistic digital lab software and AI-driven solutions enter the life science market, the problems that can be solved using these platforms will evolve and change, further streamlining laboratory operations.

If you want to learn more about how eLabNext’s digital lab solutions accelerate progress in the life sciences industry, schedule a personal demo today.

Leaders can come from anywhere within an organisation in the life sciences, where innovation and adaptation are essential. The newest research technician hired last week can be as effective at enacting widespread change through high-quality leadership as the 25-year industry veteran in the C-suite. In fact, change is often most efficiently implemented from the ground up rather than the top down. After all, the end user who has to use a new product or implement a new process daily is ultimately the best advocate for change.

So, what qualities does it take for an excellent leader to enact lasting change? 

In my experience, bringing the eLabNext digital lab platform to life science organisations big and small, I can tell you it’s no one thing. Good leadership stems from several shared attributes. Effective communication, inspiration, and others are all important, but it’s more than that. 

Here are 7 leadership qualities I’ve seen have a hugely positive impact when labs, big and small, are shifting to eLabNext’s digital platform.

Set Timelines Or Else Time Will Run Out! 

For any organisation, short- and long-term goals are critical. They provide a direction and focus for the months and years ahead and can fill lab personnel with a sense of purpose. 

To implement a new software platform (or any other change), focus on the 1-month, 3-month, 6-month, and 1-year milestones. The more specific and actionable your goals are, the better. With them, you may find yourself, your team, and your organisation more robust, with an idea of when and where to start or what success should look like. 

Here are some examples of what these goals might look like if you were adopting eLabNext’s platform:

• Month 1: Get all physical items in the lab, including storage units, instruments, equipment, samples, and supplies, digitised.
• Month 3: Digitise all protocols and projects and ensure everyone in the lab is comfortable using the new system. If they’re not, create a training plan to resolve this.
• Month 6: Everyone in the company will utilise the new platform’s features to their full potential.
• End of Year 1: Management has implemented protocols for reviewing data and analytics. The company has standardised and grandfathered in all workflows. If applicable, several automation features have been used to save time.

Of course, if you’re leading the charge on a different type of change, your goals will differ, but just be sure to set actionable, specific goals and timing associated with each.

Take Baby Steps, Get a Big Leap

One month is four weeks. That’s an average of 30 days or 720 hours or 43,200 minutes. Sometimes it doesn’t feel like it, but when you plan it, you can easily designate a few hours a week for taking the “baby steps” of setting a basic foundation and infrastructure for your new change. 

If we take our first month’s goal from above, here’s what each baby step might look like for an eLabNext implementation plan:

• Week 1: Set up all freezers and other storage units.
• Week 2: Set up all equipment and supplies.
• Week 3: Set up all sample types.
• Week 4: Import all of your legacy samples into eLabNext.

Divide and Conquer!

You can’t do everything. No leader can. 

And you don’t have to. 

Together, as a team, you have a whole arsenal of strengths. And with those, you can divide and conquer the tasks ahead of you. 

Teamwork makes the dream work, and in the case of eLabNext, the dream is to digitise your lab. 

You can divide the tasks between the people in the team, and each person can take ownership of different portions of the project, depending on their strengths. 

Felicia can do the freezers, while Steve can set up the sample types. All while Emmanuel does the equipment. 

This way, you allow many perspectives, encourage discussion and brainstorming between folks, build team camaraderie, strengthen the digital foundation, and set yourself up to be a digitally healthy and sustainable lab for years to come. 

Lead by Example

As you’re dividing and conquering, lead by example. Pick one of the weekly “baby steps” and do it flawlessly within the timeline provided. 

And if you don’t, own up to your team and find a collective solution.

This will set the tone for everyone, inspire and encourage, and solidify your group’s learnings as tribal knowledge to be passed down to all new hires. Practising what you preach and vouching for what you know can benefit the whole lab. 

Don’t Be Afraid to Ask For Help

If you’re confused or overwhelmed, going to someone for support or guidance can help you solve a problem or accomplish a task without wasting time. Asking others for help can sometimes feel weak, but all good leaders “know what they don’t know.” To continue with the example of implementing eLabNext’s platform, you can always request help from our experienced technical support (which prides itself on its expertise and customer success) or search through our resource library. 

Incentivize Key Users

Who doesn’t love a free lunch? At the 1-month mark, once all goals have been completed, you might consider rewarding key personnel that have helped you drive change. You could order food for the entire team or use the vendor (if applicable to your change) to help. 

When we’ve transitioned labs to our eLabNext platform, sponsoring a lunch and learn helps us build relationships and enables more effective communication. It also incentivises key users, which trickles downhill to inspire and motivate the rest of the team.

Review, Report, and Reap the Benefits

Review your overall progress at each milestone and report to the team. It is essential to see the change you’ve envisioned come to fruition! When we get buried in our tasks, we have difficulty stopping and smelling the roses. 

With eLabNext, the roses are your digital representation of your physical lab. Celebrate the first 100 experiments recorded. Or the first 1,000 samples created. These rewards can make it fun for people in the lab to stay encouraged and excited to keep on with everything they’re doing. 

Ready to lead the journey to digital transformation? Schedule a personal demo of our digital lab platform today!

The essence of a successful and well-functioning quality control (QC) lab lies in the name itself. Achieving, maintaining, and continuously improving quality is the ultimate goal in ensuring patient safety. 

So, how can regulated labs maintain these high-quality standards and successful processes?

Many factors – such as those defined in ISO 15189:2012 or by the Clinical & Laboratory Standards Institute (CLSI) – play a role in QC lab operations. This blog focuses on managing sample and inventory processes, data, documents, and records and how digital software platforms play an essential role. 

QC Lab Requirements and Challenges

QC labs handle and process many samples, ranging from raw materials to in-process samples, drug products, and finished products. As these samples are analyzed, large amounts of data are generated, including QC test results, calibration reports, and more. 

Properly managing the sample chain of custody and associated specifications is critical for consistently high quality. And, not surprisingly, it comes with challenges.

As lab personnel processes samples and runs release testing of materials and samples, the data must be managed to ensure all information is accurate, accessible to qualified personnel, secure and traceable. 

Let’s go through some common difficulties with the samples, inventory, data, documentation, and records management process.

Sample and Inventory Management

Every step in the sample collection, handling, and testing process must be carefully controlled and tracked by QC personnel. In addition, QC lab testing methods and the overall process must be verified and validated. Inventory management is similar: the procedures for raw materials, reagents, equipment ordering, storage, and expiration must be controlled and tracked.

Many QC labs accommodate large volumes of samples daily. A significant challenge is processing, tracking, maintaining accurate records, and ensuring all samples are correctly handled.

Inventory management is another challenge in QC labs, as keeping track of supplies, equipment, and chemicals can be time-consuming and complex. Guaranteeing the required materials are in stock at the right time and stored in a way that protects integrity can be a constant difficulty. If they aren’t correctly managed, there is a risk of incorrect or expired materials being used, which can impact the quality of results. Furthermore, ineffective tracking of usage and ordering trends can lead to inefficient spending.

Data Management

Data accuracy, reliability, and timeliness are essential for QC. Accomplishing this takes rigorous attention to the evolving regulatory requirements for data management, such as electronic signatures, 21 CFR Part 11 compliance, and data backup and recovery processes.

With a combination of manual testing procedures and automated instruments, several challenges related to data management emerge. This includes assuring the security of sensitive information and avoiding data loss due to system failures or human error. Another challenge is integrating data from different sources and formats into a centralized database that supports downstream data analysis and reporting in a robust, flexible way.

Document and Record Management

On top of data management, lab standard operating procedures (SOPs), protocols, and test records must be securely managed. This requires proper storage and access controls to prevent unauthorized access, tampering, or data breaches. In addition, consistent adherence to established procedures and practical training and personnel monitoring is essential for maintaining the integrity of the testing process, demonstrating compliance with regulations, and supporting continuous improvement in QC labs.

Overcoming QC Barriers with Digital Laboratory Platforms

Digital lab platforms (DLPs) ameliorate the sample tracking and data management woes discussed above. They proved a standardized, comprehensive approach to most QC processes, reducing the risk of errors, providing a fully traceable account of lab operations, improving overall efficiency, and ensuring regulatory compliance.

Here’s how:

• Centralized and standardized QC operations: DLPs enable digital record keeping for tracking and managing all samples, inventory, data, documents, and records. It also implements a process for the consistent execution of workflows, reducing the risk of human error.
• Thorough regulatory compliance: Many DLPs offer automatable processes, full traceability, and audit-ready capabilities. Organization of the abovementioned information (e.g., samples, inventory, data, etc.) in a centralized place also helps drive compliance by maintaining accurate records, automating processes, and enabling a transparent ‘birds-eye view’ of laboratory operations.
• Streamlined reporting: A DLP can facilitate creating a transparent and reliable reporting process to communicate valuable quality information to all relevant stakeholders. Furthermore, reporting can be automated, enhancing the overall efficiency of the lab and supporting more confident decision-making.
• More secure data: DLPs provide a highly secure framework for implementing and maintaining safe processes for collecting, storing, and sharing information. Most DLPs have access control, encryption, backup, and disaster recovery capabilities.

Try eLabNext’s DLP for Your QC Needs

Digital platforms help solve typical sample tracking and data management challenges in a QC environment.

Book a personal demo today to see how eLabNext’s DLP fits into your QC lab!

Sustainability has become more important than ever as we become increasingly determined to reduce its impact on the planet and reverse climate change. If we want to maintain our current quality of life, ensure future biodiversity, and protect the health of our global ecosystem, leaders must implement more sustainable practices. 

If you read that sentence again, you’ll notice that sustainability is centred around protecting “life” – either the lives of humans or the millions of other species we share the planet with. Accordingly, sustainability has become more critical in an industry where life is part of the namesake: the life science sector. With more and more companies, universities, and government labs hiring sustainability officers and publishing Environmental, Social, and Governance (ESG) reports, it's clear that the industry has made sustainability a major priority. 

While the increase in participation is worth celebrating, there’s still a long way to go, especially regarding lab sustainability. For example, estimates suggest the world’s labs produce more than 5.5 million tons of plastic waste annually. The global pharmaceutical industry is 55% more carbon emission intensive than the automotive industry. Meanwhile, 4.4% of worldwide global greenhouse gas emissions are produced by the healthcare sector (e.g., hospitals and laboratories) alone. 

A cultural shift in the life science industry needs to occur. And what better time to discuss it than on Earth Day? 

With more sustainable lab practices and lab equipment, we can all do our part toward a healthier future. We’ll discuss how below.

Climate Change Is Affecting us All

Climate change is already impacting human health, not to mention damaging the environment and the habitats of animals around the globe. Hotter temperatures lead to more heat waves, higher cases of heat-related illnesses, increased risk of wildfires, and more drought. Storms become more frequent, including hurricanes and typhoons. Melting ice sheets cause the sea level to rise, putting millions of people at risk.

Weather changes also make it harder to herd, hunt, and fish. Heat stress can limit water sources, causing crop yield to drop. As we struggle to feed the world, we’re losing species 1,000 times faster than any other time in recorded human history.

All of these negative impacts are a direct result of human activity. We burn fossil fuels to generate power for manufacturing plants, homes, and transportation. We use fossil fuels to produce plastics, electronics, building materials, and more. We cut down forests to make space for farms and pastures. All of these elements play significant roles in producing the greenhouse gasses that warm our planet and threaten the way we live and the future of our planet.

And as activity and investment in the life sciences accelerate, our collective environmental footprint will scale accordingly.

Prioritize Sustainability in Labs: a Call-to-Action

Companies that take measures now can significantly reduce future costs and risks and simultaneously increase their value. Many businesses in the life science sector already partner with government organizations and global institutions that will ultimately set environmental regulations. 

It’s also better for the bottom line. In a review of 200 studies on sustainability in the corporate world, 88% showed that good ESG practices lead to better operational performance. 80% showed a positive correlation between stock performance and good sustainability practices.

A Digital Solution for Building More Sustainable Labs

Many companies invest in data-driven technology to improve production, R&D, and supply chain continuity. For example, AI, engineered automation innovations, and overall lab digitalisation are aiding in implementing more sustainable lab practices. Digitalisation can help minimize lost resources by decreasing the number of needlessly repeated experiments. 

Many research companies unnecessarily waste money purchasing excess or redundant reagents and materials. Digital inventory tracking trims much of this waste by giving lab personnel a continuously updated view of current stocks, making ordering more efficient. This highlights an important issue: There needs to be an adequate, efficient, and pre-existing digital infrastructure for many labs to move in a more sustainable direction. 

One of the most prodigious energy consumers in labs around the globe is the storage of samples in freezers. With sample management, we can minimise and manage the contents of freezers more efficiently, limit the number of freezers required, and cut down on energy use.

Digitalisation can also help companies organize messy data into easily accessible and searchable information. Likewise, companies can set regulations to measure and report on sustainability efforts and waste management, then provide direction for their existing personnel on how to meet these guidelines. Of course, proper funding is necessary to ensure that employees can invest in sustainable lab equipment and practices that will pay off in the long run.

Sharing is a  Sustainable Lab Practice 

Open inter- and intra-lab collaboration offer another excellent opportunity for reducing the environmental impact of R&D. Shared equipment results in lower utility loads and savings on energy by removing duplicate instrumentation that uses significant energy and takes up precious laboratory space. Additionally, sharing reduces the need to expand building ventilation and utilities to serve excess equipment.

Additionally, sharing data can reduce the number of experiments necessary, further limiting the need for resources and lowering the environmental footprint of the life science industry. Digitalisation enables the free flow of data between collaborators. For example, using electronic lab notebooks (ELNs) simplifies and automates the documentation of experiments, reducing the labour required, eliminating the need for paper lab notebooks, and making it easier to share information. 

This practice also allows us to reduce the amount of lab space used. Digitalisation allows us to access and analyze data from anywhere. In some cases, fewer staff members can keep an entire lab running safely and efficiently. The more efficient labs become, the less energy and resources we need, and the more sustainable this sector can be.

Digitalisation is Part of a Comprehensive Solution for Lab Sustainability

Despite all the benefits of the digital sustainable lab practices highlighted above, there is a downside to consider: storage. The big data revolution is in full swing, and data storage is essential to the data lifecycle. In a digitized world, we’ll depend on servers to store and access that information. Those servers require energy and maintenance, which drives CO₂ emissions. 

Thus, we must continually investigate and monitor the CO₂ emissions of such technology in the life sciences. A recent study estimated the CO₂ emissions from a genome-wide association analysis (GWAS) analysis to be 4.7 kg of CO₂ to 17.3 kg of CO₂, depending on which software version is used. 

For context, a passenger car emits about 14.3 kg of CO₂ per 100 kilometres.

We can make servers more sustainable by using the lessons above on sharing and collaboration. Using central servers, which are operated with more energy-efficient practices than smaller local servers, and using green energy as a power source can reduce the environmental impact of data storage significantly. 

Protecting the Planet with Sustainable Labs

Sustainability improves the quality of our lives, protects our ecosystem, and preserves natural resources for future generations. While digitalisation is a challenge, it has enormous potential to aid in reducing CO₂ emissions if we can wisely deploy it. 

As more labs turn to digital inventory and data management solutions, the life science industry can share data, instruments, and servers more efficiently, reduce energy consumption by cold storage, and ensure efficient operations.  As a result, we can create less waste and produce fewer greenhouse gas emissions. 

If you’re looking for a path to digitalisation this Earth Day, eLabNext’s digital lab platform can facilitate the process. Schedule your demo today, and we’ll show you how we can turn your lab into a lean, green research machine.

Softwaresystemen als LIMS (laboratorium-informatiemanagementsysteem) en ELN (elektronisch labjournaal) verrijken de mogelijkheden om labs te digitaliseren. Die noodzaak wordt breed gevoeld in de labwereld, signaleert Margriet Mestemaker van eLabNext, inhakend op de toegevoegde waarde en actualiteiten als data-integriteit, dataveiligheid en AI ofwel kunstmatige intelligentie.

De mogelijkheden om laboratoria te digitaliseren nemen gestaag toe, met steeds meer functionaliteit in software-systemen, aldus Margriet Mestemaker, accountmanager Benelux bij eLabNext. LIMS en ELN worden beiden voor deze digitalisering gebruikt, al ziet ze wel verschillen. “Een laboratorium-informatiemanagementsysteem is vaak ‘stug’, vastgelegd voor een bepaald labproces met veel functionaliteit en weinig flexibiliteit. Een elektronisch labjournaal biedt juist veel flexibiliteit en mogelijkheden voor koppelingen met andere systemen, om een passende oplossing te customizen. Dat is volgens mij de toekomst van labdigitalisering.”

Paperless lab

Het ‘paperless lab’ is echter nog geen gemeengoed, volgens Mestemaker. “Iedereen gebruikt een smartphone en heeft geproefd van de digitale mogelijkheden. De labsector draait echter op structuur en routine en blijft daarom nog te vaak hangen bij papier. Labs die alles nog op papier doen zie ik niet veel meer, maar de huidige automatiseringsoplossingen zijn vooral hapsnap ingevoerd. Men zoekt nog naar één centrale plek waar de complete labworkflow digitaal is georganiseerd.” De labwereld wil dus één oplossing waar alles samenkomt, van inventarisbeheer, analyseplanning en dataverzameling tot kwaliteitscontrole en communicatie over de resultaten.

“De labsector blijft nog te vaak hangen bij papier”

Margriet Mestemaker van eLabNext

Overstap op LIMS of ELN

De uitdaging hierbij is dat de overstap naar een ELN- of LIMS-systeem meestal een verandering vergt. “Men zit dan nog vast aan een bepaalde werkwijze en eigen workflow: ‘We deden het altijd zo’. Daarom is het verstandig om met een open blik te kijken hoe men de workflow zo kan aanpassen dat het logisch past bij het nieuwe systeem. Ik zie vaak dat iemand in een trial met een nieuw systeem ervaart dat de bestaande manier van werken toch niet de meest praktische is.”

Witness signing

Natuurlijk is er behoefte om meer labhandelingen te automatiseren, maar zeker zo belangrijk is de compliance: zorgen dat die handelingen volgens de voorschriften worden verricht. Traceability is hier het sleutelbegrip, voor procesbeheersing, kwaliteitscontroles en toelatingsprocedures voor bijvoorbeeld een nieuw medicijn. Alles moet worden gelogd en navolgbaar zijn bij audits. Dat vraagt om vaste templates, gestructureerde workflows en borging van data-integriteit, aldus Mestemaker. Een digitaal hulpmiddel waarnaar steeds meer vraag komt is ‘witness signing’: het zetten van een digitale handtekening door een expert of toezichthouder, bijvoorbeeld voor akkoord op een protocol of afsluiting van een experiment, waarmee de data dan zijn vastgelegd. “Dit wordt tegenwoordig voor alle aspecten van het labproces gevraagd. Het beperkt bijvoorbeeld de vrijheid om af te wijken van de workflow en maakt datamassage een stuk lastiger.”

LIMS, ELN en dataveiligheid

Over data gesproken: zorgen over dataveiligheid leven breed in de labwereld, weet Mestemaker. Daarom zouden alle datacenters voor hosting van LIMS- en ELN-webapplicaties in de (publieke of private) cloud gecertificeerd moeten zijn voor informatiebeveiliging volgens ISO 27001. Gebruikers kunnen ook alles in eigen huis houden, op eigen servers, maar dat heeft niet haar voorkeur. “Die optie vind ik het minst veilig, omdat gebruikers dan zelf verantwoordelijk zijn voor cybersecurity, back-ups, enzovoort, terwijl dat niet hun core business is.”

“Een veelbelovende ontwikkeling, maar het heeft nog wel wat jaren nodig voordat het dagelijkse praktijk is”

Margriet Mestemaker van eLabNext

AI en big data

Uiteindelijk draait labdigitalisering om data, en dat worden er steeds meer. Kunstmatige intelligentie (AI) komt dan in beeld om uit big data zinvolle informatie te halen. Bijvoorbeeld uit meetresultaten correlaties tussen parameters afleiden of foto’s van celculturen snel analyseren. “Dit is een veelbelovende ontwikkeling, maar het heeft nog wel een aantal jaren nodig voordat het dagelijkse praktijk is op het lab.”

Voordelen labdigitalisering

Verandering kost tijd, weet Mestemaker, of het nu specifiek om de cloud of AI gaat of om automatisering en digitalisering in brede zin. “Dat is geen onwil, al is er wel sprake van enig conservatisme. Maar als voorlopers met fantastische resultaten komen, zal de rest snel volgen. Beschouw daarom positief-kritisch de workflows op je eigen lab, onderzoek de voordelen van een compleet digitaal labplatform en kijk vooral met een open blik naar labdigitalisering.”

________________________________________________

Flexibel platform voor ELN

Het gebrek aan automatisering en traceability in hun researchlab voor biotechnologie was in 2010 voor twee Groningse onderzoekers aanleiding om eLabNext te starten. Ze begonnen met inventarissoftware en dat groeide uit tot een platform voor labdigitalisering: elektronisch labjournaal, inventarisbeheer- en sample-trackingsysteem, labprotocolmanager en eLab Marketplace. De marktplaats bevat apps, ook van derden, voor koppeling aan de software van eLabNext om de functionaliteit verder uit te breiden. Dankzij de flexibele opzet is de software van eLabNext ook geschikt voor gebruik buiten de biotech R&D, bijvoorbeeld in een analytisch-chemisch lab. Het bedrijf is wereldwijd actief, telt bijna vijftig medewerkers en is nu onderdeel van laboratoriumleverancier Eppendorf.

Hans van Eerden

Lees op LabInsights

We are happy to share a recap of the panel discussion that took place during our new office opening in Glendale, CA, last month. The event was an excellent opportunity for attendees to network with the various booths, and we've provided highlights of each one so that you can reach out to them directly.

We'd like to take a moment to express our gratitude to SmartGate VC and Hero House for their warm hospitality and welcome. It's an honor to be part of such a vibrant AI ecosystem, and we're thrilled to be contributing our biotech expertise to it. We also extend a warm welcome to Mayor Ardy and Senator Portanito, who joined us to celebrate this exciting new chapter.

Key Takeaways

• The AI Revolution is happening as you read this, whether we like it or not, and those who prepare for it will benefit tremendously. Those that don’t will fall behind, especially in the biotech/pharma industry. This is also very closely related to the Academic and Healthcare industries.
  • Resource #1: Harvard Business Review – “The Ai Resolution is Just Beginning"
  • Resource #2: “10 Actionable Steps for Using AI in Your Research Lab”
• Erwin Seinen, Founder of eLabNext
  • The development of new technologies is opening up new possibilities,
    demonstrated by this use-case of conservation efforts that include the
    potential to bring back extinct species.
  • The use of big data analytics and machine learning is playing an ever
    an increasingly important role in advancing scientific research.
• Zareh Zurabyan, Head of eLabNext, Americas
  • Key to a successful R&D Lab is having a connected digital ecosystem and being AI-ready. Utilizing digital tools that have the capability to integrate with one another and a system that organizes all of your data and allows you to classify them and develop deep neural networks to leverage in the future.
  • Digital Lab Strategy is critical to having a successful laboratory.
  • Learn how Colossal Biosciences uses AI to bring back extinct animals like the Wooly Mamoth and the Dodo.
• Mehdi Saghafi, Bayer’s Principal Data Engineer
  • Implementing Digital Solutions is very simple; you need to have a very strategic approach to it right from the beginning, i.e. having timelines, and very specific goals of digitizing sample data, reporting data, and equipment data, and tackling them one by one, with agile project management. Learn about “Adoption Barriers and How to Overcome Them”.
  • Having an open ecosystem is necessary for a comprehensive and holistic solution for a large company like Bayer. There are many scientists, many operations, and many digital tools that are used. Having a connection between them is vital in ensuring efficiency and limiting any chance of data loss. Find out more.

• Lucy Abgaryan, Founder of GrittGene and ProoneLabs
  • There is a shift from previous generations to new ones. It is essential to train your staff accordingly in the benefits of digitising your lab and being innovative and early adopters of new technologies, like AI. If you are a PI, a Research Tech, that is about to go on a digital journey, ensuring a proper training regimen and defining digital strategy right from the beginning is vital for success. Learn more about how Moderna does this.
• Taylor Chartier, Founder of Modicus Prime
  • During a global recession, you can't afford to not invest in cost-saving technologies that will accelerate your research.  Empower your scientists with AI tools that will automate their workflows to achieve repeatable results faster.
  • Quality control over your research processes is just as important as the quality of your research product.  AI softwares make routine lab processes less burdensome and error-prone, giving scientists both structure and peace of mind as they conduct experiments that save time and resources formerly wasted on poor-quality studies.

LinkedIn Profiles

• Zareh Zurabyan, Head of eLabNext, and AI/Biotech Consultant
• Erwin Seinen, Founder of eLabNext, and Ai/Biotech Consultant
• Lucy Abgaryan, Founder of GrittGene and Proone Labs
• Mehdi Saghafi, Principal Data Engineer of Bayer Pharma
• Taylor Chartier, Modicus Prime, CEO and Founder of Modicus Prime
• Ashot Arzumanyan, Founder of SmartGateVC, Investor

Featured Booths and Contact Information

CompanyContact InformationNikon Instrument, Inc.Junya Yoshika, Senior Scientist, junya.yoshika@nikon.com
Fumiki Yanagawa, General Manager, fumiki.yanagawa@nikon.com
Henning Mann, Business Development and Partnerships, henning.mann@nikon.comEppendorfLoreline Lee, Sales Director, lee.l@eppendorf.comImplen Inc.Austin Brazzle, Product Specialist, abrazzle@implen.comOhan Cardiovascular InnovationsVahagn Ohanyan, President, vohanyan@ohcvi.comBrinter Inc.Tom Alapaattikoski, CEO, tom.a@brinter.comMicroscapeJohn Francis, CTO and Co-founder, john@microscape.xyzPurpose BioLital Gilad-Shaoulian, CEO and Founder, lital@purposebio.comModicus PrimeTaylor Chartier, Founder and CEO, taylor@modicusprime.comAmaros AIBen Toker, Co-Founder/CTO, ben@amaros.aiOkomeraSidarth Radjou, CEO, sidarth.radjou@okomera.comMetaba A.EyePhilip Sell, CEO, events@metaba.us