Creating a biotech start-up comes with many tasks and challenges, along with the excitement of building out your company and business model. As you work in the early stages of hiring, fundraising, and meeting with investors, you also have a major operational focus on driving the science forward and building the laboratory infrastructure for your team.

Whether you are set up in a co-working/incubator space or opening a lab at a university, you’ll need to start managing inventory, equipment service schedules, ordering consumables and supplies, and, more importantly, handling new samples. Typically, many fall back on old habits from previous lab environments, including spreadsheets for tracking inventory, word processors for protocols, and physical notebooks for experimental recording and calculations. With the current AI/ML-enabled world of biotech, this is analogous to using a Polaroid to (unironically!) take photos or a landline to make phone calls.

Conversely, Digital Lab Platforms (DLPs) are the “smartphones” of your lab, an all-in-one solution that can manage samples, protocol, lab data, and files or automate data analysis, report generation, and submission to your manager. As you start your lab, consider the benefits of using a digital lab platform from the very beginning. Creating this standardization is key as you scale and require a more collaborative and organized environment.

Below, we look at some common start-up tasks and how DLPs can help them run more efficiently.

Onboarding and Processes Training

To avoid training new team members on multiple systems and showing the complex and unique ways your company manages data and other information, you can show them one system and train them on it. For the eLabNext Digital Lab Platform, our user guides are great references for new and experienced users of the system.* The guides have visuals and links to assist you in your eLabNext journey. 

*Pro tip: Create an eLabNext training protocol in your Protocols module to help manage the training process!

User Permissions and Auditing

As you grow your team and hire, you can use your Digital Lab Platform to manage who has control over certain functions in the lab. Whether it is signing off on an experiment or archiving samples from your inventory, you can organize these permissions in our permissions settings. You can control these functions anytime, make changes or updates when necessary, and standardize who is responsible for what is in your lab.

You can utilize eLabNext’s logs and audit trail features to keep track of all changes and updates in your lab. You can see what updates were made to a protocol or sample information. You can also view the equipment validation history. Even the most minor changes, like updating a user from “scientist” to “administrator” in your permission settings, are captured and logged in the permission change log.

Inventory Management

Digitizing lab assets from the start of your lab is important for maintaining a well-organized environment. With the eLabNext Digital Lab Platform (DLP), you can begin setting up all your new equipment, like freezers, centrifuges, tubes, pipettes, and more, as you build out your lab. To keep organized with service maintenance and calibrations, assign locations and log the service tech’s contact information for each piece of equipment to make scheduling service simpler. Your team can utilize the equipment booking calendar to reserve equipment while using it or to view when the equipment is in service.

As you start receiving and managing samples, you can easily track where the sample is being stored and create custom data fields to track things like quantity, collection date, sample type, and other metadata. Utilizing pre-barcoded tubes or containers from the start simplifies your sample management process. With our barcode scanning feature, your team can attach barcodes to sample tubes or utilize pre-barcoded tubes and easily scan them using our Mobile App (or any 2D scanner), enabling quick viewing/editing of all sample information.

Protocol Creation and Management

Digital Lab Platforms also enable you to manage all your protocols and SOPs with platforms. As you continue building your lab, standardize the team’s protocols by creating them directly in eLabNext, which enables you to generate formulas that make calculations easier and minimize any calculation mistakes. 

Starting a lab and building out all new protocols may seem overwhelming, but our AI Protocol Generator has made it much less daunting by facilitating the import of standard lab protocols (like Western Blotting, PCR, or buffer protocols).

ELN and Reporting

Managing and monitoring project progress is critical for ensuring your research is on track and under budget. Digital Lab Platforms, can help you standardize experiments and reporting and visually track which tasks have been completed. 

It also allows you to collaborate very effectively with your team, assign tasks, tag users in comments, and collaborate on different projects within your team. eLabNext is where your inventory and protocols come together; you can capture what samples were used in an experiment, the samples generated from the experiment, and what protocols were used. 

Other experiment section types in eLabNext include file and image sections, where you can add files and images from your experiments, and Microsoft Office sections, which allow you to upload and/or work directly with programs like Word, PowerPoint, and Excel for data reporting.

Get Started on Digitizing

As I’ve highlighted above, standardizing your operations to one multi-functional system (rather than many monofunctional systems) makes it much easier as you grow your team. The eLabNext platform allows users to customize the platform to their workflow and utilize the tool however it works for them. You get to define what information is captured within the system and how you want your team to use it. 

If you’d like to see how the eLabNext Digital Lab Platform can benefit your new lab, schedule a free demo here!

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In an ambitious leap forward for laboratory management software, eLabNext announces the launch of its new Sample and Inventory System, transforming how more than 60,0000 scientists manage their inventories. This comprehensive overhaul was driven by extensive customer feedback and an unwavering commitment to enhancing usability, functionality, and integration.  

A New Benchmark in User Interface and Usability  

At the heart of the eLabNext Inventory Management System lies a dramatically enhanced user interface meticulously redesigned to meet the latest standards for modern and intuitive software. This advancement improves the system's look and feel and greatly enhances usability for many of our customers. In our commitment to inclusivity, we also improved compliance with the Web Content Accessibility Guidelines (WCAG), making it a benchmark for modern software accessibility. 

"We are thrilled to finally bring many of the most requested enhancements and features to all our customers with the official release of the new eLabNext Inventory System. Sample and inventory management and its comprehensive integration with our Electronic Lab Notebook have always been key deciding factors for customers choosing eLabNext to digitalize their laboratory.

With the release of our new sample and inventory management functions, we are ready to better serve the needs of our customers in the years to come." 

Wouter de Jong, Co-founder and Managing Director at eLabNext 

Innovative Features for Unmatched Flexibility 

eLabNext introduces several ground-breaking features with its latest inventory system, including: 

• Adjustable 3-panel View: A direct response to customer requests, offering seamless visualization of the compartment and sample information on a single page. Users can now customize panel width and easily access sample details while viewing a compartment.
• Enhanced Sample Management: The system introduces intuitive drag-and-drop for organizing sample boxes and collapsible sample series for efficient management of samples in batch. 
• Group-Shared and Customisable Views: Enhancing collaboration, users can now share customized data views across the inventory, ensuring consistency and streamlined operations. 
• Storage location and Compartment Overviews: Extended visualization of compartments and their available storage capacity as well as bulk management capabilities for compartments 
• Extended export options for Samples: Generate export in PDF, Excel, and CSV formats directly from samples listed in the inventory, catering to the needs of our customers to enhance inventory reporting. 

Full API and SDK Compatibility 

Our new Sample and Inventory Management System is built on eLabNext's API and is compatible with its Software Development Kit (SDK), ensuring seamless integration and extension through the eLabNext Developer platform. This compatibility enables users and partners to efficiently build, test, and release add-ons. 

Continued Development and Future Enhancements 

eLabNext is not stopping here; the release of its new Inventory System lays the groundwork for a series of planned enhancements, focusing on seamless integration, advanced search capabilities, and improved inventory management for biobanks. Upcoming features include equipment integration into the Electronic Lab Notebook (ELN), email notifications for sample and equipment management, and enhanced stock tracking and reordering.

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eLabNext Developer is the newest addition to our Digital Lab Platform (DLP). It's the 1st ever democratic end-to-end developer experience that allows you to build new or extend existing functionalities to automate, integrate, and optimize your lab operations in a sustainable, secure, and compliant digital ecosystem. 

With the launch of our Developer portal, we are democratizing access to your favorite digital molecular biology, chemistry, and AI tools, elevating your lab's day-to-day operations, and revolutionizing how your researchers do research!

The core feature of eLabNext Developer is that it enables companies and third-party vendors using the eLabNext Biotech software-as-a-service (SaaS) to develop their add-ons on top of our core platform. This capability has several high-value benefits for both developers (via eLabNext Developer) and eLabNext customers (via Marketplace), as discussed below.

Opportunity to Reach a New Audience

A developer portal can be a stage where developers can showcase their add-ons to users of the underlying platform. By listing their add-ons in our Marketplace, third-party vendors gain exposure to our highly targeted audience of potential customers already using the core eLabNext platform. These users will likely be interested in complementary solutions that enhance their software's functionality, making it an ideal place for vendors to showcase their offerings. This eliminates the need for vendors to invest time and resources into marketing independently, allowing them to focus exclusively on developing high-quality add-ons instead.

While an add-on may be offered for free through a platform, exposure to this new audience can indirectly lead to monetization, either through additional purchases or subscriptions from the add-on company outside of eLabNext.

Community Engagement

Community engagement provides add-on developers with a valuable feedback loop for their products. Exposure to a new audience can provide add-on developers with a new way of interacting with users and tracking usage statistics. Vendors can gather insights into how their services are being used within the eLabNext platform, what features are most valued, and where there may be room for improvement. 

This feedback helps vendors refine their existing offerings and validates the market demand for their solutions. Additionally, engaging with the existing eLabNext community allows vendors to build relationships with potential customers, establish credibility, and gain valuable product endorsements by associating with an established and trusted brand.

Flexible Functionalities

Allowing companies to develop their add-ons enables them to complement the software better to suit their specific needs. This can increase efficiency and productivity, as eLabNext customers can tailor the software to fit their workflows.

Tailored Solutions to Unique Needs

The flexibility offered by the eLabNext Developer portal allows customers to tailor the platform to their specific requirements. Customers in the biotech industry often have unique workflows, data management needs, and compliance requirements. With access to a wide range of add-ons developed by third-party vendors, customers can choose and integrate solutions that best fit their needs, ensuring that the platform aligns closely with their business processes and objectives.

Adaptability to Changing Requirements

Biotech research and development is a dynamic field characterized by rapid advancements and evolving regulatory landscapes. Customers need software solutions that can adapt to these changes quickly and seamlessly. The flexibility eLabDeveloper provides enables developers to create add-ons to address this new requirement and allows customers to quickly and easily adapt to these changing requirements in the eLab Marketplace. For instance, the NIH recently required all grant applications to submit a data management and sharing plan (DMP). DMPTool, a recent addition to the eLab Marketplace, enables eLabNext users to pull DMPs from DMPTool and present plan summaries within eLabNext, along with a link to download the complete plan.

Flexible User Experience

Every organization within the biotech industry may have different preferences and priorities regarding user experience. The flexibility offered by our Marketplace allows customers to customize the user interface, data visualization tools, reporting capabilities, and other aspects of the platform to match their preferences and workflows. This level of customization enhances user satisfaction, productivity, and overall usability, as customers can tailor the platform to meet their teams' unique needs and preferences.

Scalability

The eLabDeveloper portal allows add-on creators to create a vibrant ecosystem around the core eLabNext platform. As more developers contribute add-ons, the software's overall functionality and usefulness have grown rapidly and will continue to grow. This scalability ensures that the software remains relevant and competitive in the long term.

Laboratories in the biotech industry often experience workload and resource fluctuations due to project timelines, research initiatives, and regulatory demands. A scalable Biotech SaaS platform like eLabNext allows customers to scale up their software usage as their needs grow. This means handling larger datasets, accommodating more users, and supporting increased computational demands without compromising performance or stability.

Biotech labs may also need to expand their operations or collaborate with external partners, such as academic institutions, research organizations, or contract research organizations (CROs). eLabNext enables customers to seamlessly onboard new users, integrate additional data sources, and extend access to collaborators without significant infrastructure investments or disruptions to ongoing activities. This flexibility fosters collaboration, accelerates research efforts, and facilitates knowledge sharing across interdisciplinary teams.

Scalability also allows customers to embrace future innovations more rapidly without constraints. By investing in eLabNext, customers future-proof their operations, enabling them to stay ahead of the curve, capitalize on new opportunities, and drive continuous advancements in biotechnology.

Reduced Development Costs 

Leveraging the creativity and expertise of third-party developers can reduce the development costs associated with expanding the functionality. Instead of building every feature in-house, customers can rely on the eLabDeveloper community to contribute ideas and solutions, saving time and resources.

Instead of investing time and resources in developing software solutions from scratch, customers can select off-the-shelf functionalities that meet their requirements. This saves on development costs and accelerates the implementation timeline, allowing customers to integrate new features into their workflows quickly.

Shared Development Costs Across the Community

It also saves add-on developers money. eLabNext Developer fosters a collaborative ecosystem where multiple developers can contribute to creating add-ons. As a result, development costs are shared across the community, reducing the financial burden on individual customers. By pooling resources and expertise, customers can access a wide range of functionalities at a fraction of the cost of developing them independently.

Cost-Effective Functional Expansion Over Time

While customization is essential to meet specific business needs, developing custom solutions can be expensive for eLabNext customers. With the eLab Marketplace portal, customers can customize the platform using existing add-ons, significantly reducing the cost of expanding functions.

In-house development requires ongoing maintenance and support to keep the software up-to-date and address any issues. By relying on our Marketplace portal, customers benefit from continuous updates made by developers in eLabNext Developer and support provided by third-party vendors. This ensures that the software remains current and functional without additional investment in maintenance and support services.

Faster Innovation

Access to eLab Marketplace provides eLabNext customers with an agile way to find cutting-edge features and capabilities without waiting for lengthy development cycles. As a result, customers can stay ahead of the curve and leverage the latest advancements in biotechnology to drive innovation in their research and development activities.

The biotech industry is characterized by rapid technological advancements, evolving regulatory requirements, and shifting market dynamics. The interplay between eLabNext Developer and our Marketplace enables customers to respond quickly to these changes by adopting new functionalities and tools as they become available. By staying agile and adaptable, customers can capitalize on emerging trends, seize opportunities, and maintain a competitive edge in the market.

Just look at the emergence of voice assistants like Google Assistant and Alexa. This technology has recently been applied in laboratories and incorporated into the our Marketplace with the addition of the ASCENSCIA add-on. It provides an easy way for eLabNext users to access information and record data while working in the lab, freeing up their hands and allowing them to focus on their experiments.

Streamlined Workflows & Automation

With access to our Marketplace, customers can create custom automated workflows tailored to their specific needs. They can develop scripts, plugins, or integrations that automate repetitive tasks, such as data processing, analysis, or report generation. 

End-to-End Automation

Customers can automate entire end-to-end processes by orchestrating multiple tasks and integrations within the system. They can automate data collection, analysis, and visualization workflows, leading to faster insights and decision-making. eLabNext's integration with Elemental Machines, a temperature detection system, is a great example of how add-ons can facilitate automation in a life sciences laboratory.

Robotics Control and Integration

Robots in biotech labs often perform repetitive tasks such as pipetting, sample handling, or plate manipulation. By integrating robots with the system's developer platform, customers can automate these tasks, reducing the need for manual intervention and improving throughput and efficiency.

Workflow Orchestration

Automation allows customers to orchestrate complex workflows involving multiple instruments and robotic systems. They can develop scripts or plugins that control the sequence of operations, coordinate interactions between robots and instruments, and optimize resource allocation to maximize productivity.

Integration with External Systems

Automation facilitates integration with external systems, databases, or software platforms, enabling customers to exchange data seamlessly between different tools and environments. For example, add-on developers can develop software that interfaces with the eLabNext Digital Lab platform or other data analysis software to streamline data exchange and collaboration.

Quality Control and Assurance

Real-Time Monitoring and Feedback

Automated systems can monitor instrument performance and data quality in real-time, detecting anomalies or deviations from expected norms. Add-ons available in our Marketplace can help customers trigger alerts or notifications when issues arise, enabling immediate corrective actions to ensure data integrity and experimental reproducibility.

Standardization and Compliance 

Automation enforces standardized procedures and quality control measures, ensuring compliance with regulatory requirements, industry standards, and best practices. Customers can develop automated workflows using eLabNext that enforce validation checks, calibration routines, and data integrity controls, minimizing the risk of errors and ensuring regulatory compliance.

Audit Trails and Documentation

Automated systems maintain detailed audit trails and documentation of instrument usage, experimental protocols, and data processing steps. Customers can use the eLabNext platform to generate automated reports, logs, and documentation that comprehensively record experimental activities, facilitating traceability, reproducibility, and regulatory audits.

Conclusion

In the rapidly evolving biotechnology landscape, the convergence of automation, robotics, and data integration is paving the way for rapid advancements. With eLabNext Developer enabling add-on developers to integrate with the eLabNext platform and eLab Marketplace, enabling eLabNext customers to seamlessly expand and tailor functionality, biotech labs can harness the power of automation to streamline workflows, enhance precision, and drive innovation. 

By controlling robots and receiving real-time data from instruments, researchers can accelerate experimentation, scale effectively, optimize resource utilization, and ensure data integrity. This transformative approach increases efficiency and productivity and opens new frontiers in research and development. As biotech continues to push the boundaries of what's possible, the future undoubtedly lies in the seamless integration of automation technologies, positioning it at the forefront of scientific discovery and breakthrough innovation.

To see eLabNext's Developer or Marketplace portals and hear about how these tools have improved digitalization in the life sciences, send us a request and schedule your demonstration today.

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In today's rapidly evolving research landscape, effective sample management is fundamental to the success of laboratory operations. Laboratories, ranging from biobanks and diagnostic centers to pathology departments, must navigate the complex challenge of managing vast volumes of samples. Precision and efficiency are paramount as these facilities strive to uphold the integrity of their work and accelerate scientific progress.

The ability to meticulously track, securely store, and swiftly retrieve lab samples is not merely a logistical requirement; it directly influences the productivity, cost-effectiveness, and accuracy of research outcomes.

In this blog, we will cover: 

1. Types of lab sample
2. Common pitfalls in lab sample management
3. Digital lab sample management solutions
4. 5 top tips to improve your lab's sample management processes

Modern labs are under increasing pressure to enhance throughput and accuracy while reducing costs and minimizing errors. Traditional methods of sample management, often manual and error-prone, are proving inadequate in the face of these demands. Combatting the challenges of sample management, innovative technologies such as Electronic Lab Notebooks (ELNs) and digital lab inventories are redefining the landscape of lab sample management.

This guide delves into the challenges posed by sample management and outlines our top tips and best practices to elevate your lab's sample management regime.

Types of lab sample

Laboratories across various fields handle diverse types of samples, each presenting unique challenges in terms of storage, preservation, and management. Understanding these sample types and their specific requirements is crucial for implementing effective sample management systems.

Biological samples

(Blood, tissues, cells, DNA)

• Storage: Biological samples often require precise temperature conditions, such as refrigeration or cryogenic storage, to prevent degradation. The risk of cross-contamination is also a concern, necessitating strict protocols.
• Management: Tracking the lineage and treatment of samples (e.g., aliquoted, stained) is vital, especially in biobanking, where samples may be stored for long periods and used in multiple studies.

Environmental samples

(Soil, water, air filters, plant material)

• Storage: These samples may change over time if not stored properly, with factors like temperature, humidity, and light affecting their integrity.
• Management: Traceability and the ability to link samples back to their collection point are essential.

Clinical samples

(Patient tissues, swabs, biopsy specimens)

• Storage: Many clinical samples are sensitive to environmental conditions and must be processed or preserved quickly to retain their diagnostic value.
• Management: Clinical samples require stringent labeling to protect patient confidentiality and ensure traceability. Compliance with health regulations and standards (e.g., HIPAA, GDPR) is also mandatory.

Microbiological Samples

(Bacteria, viruses, fungi)

• Storage: These require specific growth mediums or inhibitors to maintain viability or dormancy. Controlling contamination is particularly challenging given the infectious nature of many such samples.
• Management: Ensuring that microbial strains are not mixed or contaminated during handling requires meticulous sample tracking and personnel training.

Material Samples

(Metals, polymers, semiconductors)

• Storage: Some materials may be sensitive to atmospheric conditions, such as humidity or temperature, which can alter their properties.
• Management: Tracking the batch and source of material samples is crucial, especially when these materials are used in high-stakes applications like aerospace or medical devices.

Each type of lab sample demands a tailored sample management strategy. This underscores the need for robust inventory and sample tracking systems that can accommodate the volume of samples, their tracking and labeling needs, and storage requirements. 

Common pitfalls in lab sample management

Managing laboratory samples is a multifaceted task that brings a variety of challenges. When relying on traditional sample management systems, like error-prone manual labeling, these challenges become even more pronounced. Here are some common mistakes commonly made in lab sample management:

Mislabelling

Manual labeling, predominant in traditional sample management systems, is highly susceptible to human error. Incorrect, incomplete, or illegible labeling can lead to confusion in sample identification. Such errors compromise the integrity of data, undermining the reproducibility and reliability of research findings. Mislabelling can disrupt entire research projects or lead to incorrect patient diagnoses in clinical settings.

Lost or perished samples

Logbook-based sample management systems often lack robust tracking and monitoring mechanisms, making it easy for samples to be misplaced or improperly stored. Without precise, real-time tracking, samples can easily be left in non-optimal conditions, leading to degradation or spoilage. The loss or deterioration of samples can waste valuable resources, delay research progress, and result in irrevocable data loss in cases of irreplaceable samples (such as those from unique clinical trials).

Sample tracking mismanagement

As labs grow and sample volumes increase, manual tracking methods such as logbooks or spreadsheets become overwhelmed. These methods are time-consuming, prone to oversight, and often cannot be updated in real-time, creating bottlenecks in sample management. Inefficient sample tracking leads to delays in sample retrieval and can cause bottlenecks in data analysis, reducing the overall efficiency and responsiveness of research activities.

Errors in sample management can have far-reaching consequences, from compromised patient diagnoses to invalidated research findings. To minimize the incidence of these errors, many labs are turning to digital lab solutions. 

Digital lab sample management solutions

Adopting digital lab solutions is a highly effective strategy for enhancing and streamlining lab sample management processes. A number of technologies are available to support these improvements. For example, a platform that combines a Laboratory Information Management System (LIMS) with an Electronic Lab Notebooks (ELNs) centralizes sample management, storage data, study data and protocols on a real-time updated and accessible platform. Meanwhile, specialized inventory management systems automate numerous aspects of sample management and tracking, significantly boosting efficiency.

These tools not only simplify daily operations but also ensure that data is consistently accurate and easily retrievable. Digital solutions offer a range of features to streamline and strengthen lab sample management:

• Automation: Digital lab solutions such as eLabNext, as a standalone LIMS or complete Digital Lab Platform, automate sample tracking, data entry, and storage management. Automating these critical processes minimizes human error while enhancing operational efficiency. ensures that every sample action is accurately logged and standardized, leading to fewer errors and consistent handling procedures.
• Barcode labeling and tracking: By incorporating barcode systems, labs can ensure precise tracking of each sample from collection to disposal. Barcode tracking simplifies the retrieval and auditing of samples, allowing for a seamless flow of information throughout the sample's lifecycle. The use of barcode ID virtually eliminates the risks associated with manual sample labeling, drastically reducing errors in sample handling and processing.
• Enhanced documentation and reproducibility: Digital lab solutions play a crucial role in enhancing the documentation and reproducibility of experiments. By integrating an ELN, researchers can maintain detailed, time-stamped records of samples during storage and research.
• Centralized sample data: Digital platforms such as LIMS and ELNs serve as a centralized repository for all sample information, facilitating easy access and data sharing across the lab. eLabNext offers real-time sample tracking data among lab members, accessible from anywhere. Centralized systems also enhance data security and facilitate compliance with regulatory standards.
• Customizable Workflows: ELNs, LIMS, and inventory management solutions such as the eLabNext Digital Lab Platform can be customized to fit the specific needs of individual labs, optimizing sample management processes.

The adoption of digital tools in laboratory environments is not merely a trend but a pivotal shift towards more sophisticated, efficient, and error-resistant sample management. As labs continue to modernize, the integration of technologies such as ELNs and digital inventories will become increasingly essential.

5 top tips to improve your lab's sample management processes 

Implementing digital solutions is just one aspect of effective sample management. There are a number of best practices that you can adopt to optimize your processes and minimize errors. Here are our top five tips to improve your sample management system:

1. Standardize protocols and procedures

Develop and enforce standardized protocols for all sample-related activities, from collection to storage to disposal. Clear, consistent guidelines and SOPs help reduce errors and improve efficiency, ensuring that all team members follow the same procedures.

2. Implement robust training programs

Regularly train all personnel on the latest sample management practices and technologies. Comprehensive training ensures that staff are proficient in both manual and automated systems, which is crucial for maintaining the integrity and accuracy of samples.

3. Regularly audit and update your systems

Conduct periodic audits of your sample management system to identify areas for improvement. Regularly updating your processes and systems in response to new technologies, changes in compliance regulations, or internal reviews can help you stay ahead of potential issues.

4. Maintain a contingency plan

Prepare for unexpected events, such as power outages, equipment failures, or natural disasters, by having a robust contingency plan in place. This should include backup storage solutions and detailed plans for protecting and recovering samples to prevent loss and ensure continuity of operations.

5. Ensure robust labeling and tracking

Consistent, accurate sample labeling and tracking are crucial for maintaining the integrity of samples and ensuring their traceability. Digital lab solutions are the easiest way to achieve this, bringing invaluable automated labeling and tracking capabilities to eliminate human error, streamline processes, and ensure accurate data management.

These tips are applicable to labs of all disciplines and sizes. By embracing these best practices, you can be sure to improve operational efficiency and research outcomes.

A smarter way to manage lab samples

Effective sample management is a critical component of modern laboratory operations, directly impacting research outcomes, productivity, and cost efficiency. By embracing digital solutions like our Digital Lab Platform, labs can overcome the challenges of traditional sample management practices and unlock new levels of efficiency.

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We live in an incredible time where labs have access to great tools, from automation to AI. But in a world where technological advancements rapidly evolve and shape our world, scientific laboratories are often playing catch-up. The reluctance to fully accept digitization has profoundly impacted our scientific research efficiency and quality. Paper-based and manual documentation processes are one of the core methods that are outdated and hinder progress, leaving labs scrambling to catch up with the rapid pace of innovation.

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In the following blog, we discuss how electronic lab notebooks (ELNs) and other digital platforms enable labs to digitize their operations more quickly and how integration with voice assistants drives the next leap in lab efficiency.

The Era of Lab Digitization

The laboratory digitization movement has already started and is accelerating. Labs worldwide have recognized the need to modernize their operations and change how they manage their data. ELNs are emerging as vital tools for enhancing lab operations through better data management. Such tools offer scientific lab features such as structuring and tracking data, flexible collaboration between scientists, time-saving add-ons, and inventory management platforms. Labs that have embraced and continue to embrace digitization are more flexible, organized, and collaborative, putting them at the forefront of groundbreaking discoveries and allowing them to respond rapidly to biomedical emergencies like COVID-19 by developing novel vaccines and managing in-house viral testing facilities.

However, using such tools can come with a price. Adopting ELNs and a new way of doing lab work can be very challenging. Given the diversity and complexity of scientific labs, scientists may need help accessing ELNs from the lab or documenting their data instantly, creating additional burdens. For instance, scientists may need to dedicate a whole day of their week to updating their ELNs through a manual process of collecting old, scribbled notes. Such inefficiencies can impact the adoption of ELN solutions in the lab, slow the lab’s digitization process, and impede the overall research operations.

Integrating Voice Assistants with ELNs – A Game-Changer

Now, imagine taking this digitization journey a step further by incorporating a voice assistant into a lab’s ELN. This solution is now accessible through a partnership between ASCENSCIA, the creator of a voice assistant mobile app, and eLabNext, the developer behind an all-in-one digital lab platform. Through this software collaboration, scientists get access to this intermediary tool, making documenting data effortless. By offering a seamless user experience, labs can eliminate adoption barriers and revolutionize how scientists interact with eLabNext, producing higher-quality experiments. 

Here’s how:

1. More accessible data: Labs can sync the ASCENSCIA voice assistant with their eLabNext account with one click. This connection creates a two-way seamless data flow between the lab scientists and the eLabNext solution. Whether connected to the ELN or LIMS capabilities in eLabNext, scientists can access their experiments and protocols or locate their samples through simple, hands-free voice commands. Every detail of a researcher’s experiment is just one voice command away, without printed protocols or double-checking a paper lab notebook.
2. No experimental detail left behind: There are moments during experiments when things can get out of control, and a protocol needs to be paused to take notes, compromising the quality of experiments for the quality of data. With the integration of the ASCENSCIA voice assistant with eLabNext, scientists can focus solely on their experimental workflows while ASCENSCIA captures every detail in real-time. Every detail is captured in a structured format with a time stamp directly in eLabNext, leaving no work to be done later and saving your scientific team the unnecessary days of manually updating their system. Furthermore, ASCENSCIA’s voice assistant has 97% accuracy for scientific terminology, so there’s no need to correct entries manually. 
3. Fewer errors, accelerated R&D: Human errors can be costly in scientific research, leading to unnecessary repeated experiments and prolonged timelines. By leveraging ASCENSCIA voice assistant with your eLabNext solution, labs can minimize errors, streamline R&D operations, and reduce costs. 

Futurising Laboratory Operations and Empowering Scientists

In conclusion, the journey of lab digitization is not without its challenges, but the rewards are undeniable. By embracing voice technology, labs can revolutionize their workflows, making scientific research more accessible, efficient, and impactful. The rise of voice-activated labs heralds a new era of innovation, where scientists have unprecedented access to information and resources, reshaping the landscape of scientific discovery. ASCENSCIA and eLabNext invite you to be part of the future of scientific research to empower scientists to create a healthier world — one voice command at a time.

Whether you are starting your lab digitization journey or want to take it a step further, ASCENSCIA and eLabNext teams are here to guide you into the future of laboratory research.

Reach out to us at elabnext.com or book a demo at ascenscia.ai

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2024 is already shaping up to be a “highly digital” year: AI and digitalization are increasing exponentially, bringing new applications and breakthroughs. In the life sciences, including academia and biotech, labs with 30 years of legacy samples and data are experiencing pressure to digitize and organize. 

Every day, we hear stories from very reputable and historical academic, healthcare, and industry institutions asking us how they should digitize their samples because they have no idea where to start. Just yesterday, I visited a biotech lab with 14 freezers completely full. There was an average of 80,000 samples in each freezer, amounting to approximately 1.1M samples in total. The samples' owners and authors are no longer with the company, so the lab manager had to reach out to alumni to understand what is in those samples and whether they have any value. 

Imagine a world where you don’t have to do that because everything is already digitized! This process alone allowed the organization above to discard 1/3rd of these samples – 373,000 samples gone and 4.6 freezers liberated! Imagine the amount of money saved because of this. 

You didn’t need new freezers; you just needed better sample management!

The Path to Lab Digitalization

Embarking on the journey to digitize a laboratory, especially with extensive sample storage, can be overwhelming. However, breaking down the process into manageable steps and utilizing tools like eLabNext can streamline the transition. 

This 9-step actionable guide provides detailed strategies to digitize your lab effectively, one sample at a time, ensuring a smooth and systematic approach.

1. Conduct a Comprehensive Inventory Assessment

Begin by thoroughly assessing your lab's sample inventory. Document the types of samples, storage conditions, and the quantity of samples stored. Reach out to all PIs, request clarification on sample metadata, and make a master Excel file of your legacy samples. If you currently use outdated software, export everything into CSV or Excel and organize it. This is an excellent opportunity for spring cleaning: Anything that is not claimed within a month of starting this process should be discarded to save space!

2. Develop a Prioritization Framework

Establish a prioritization framework to determine which samples to digitize first. I call those samples “high-profile samples.” Consider factors such as sample relevance to ongoing research projects, frequency of use, regulatory requirements, or even the cost associated with those samples. Engage with researchers and stakeholders to gather input and consensus on prioritization criteria. Don’t be afraid to be very direct and progress-oriented in your discussions!

3. Formulate a Digitization Task Force

Create a dedicated task force responsible for overseeing the digitization process. This team should comprise members from various departments, including researchers, lab managers, IT specialists, and data analysts. Assign clear roles and responsibilities to each team member to ensure accountability. You, as the lead, will use SMART/RACI or AGILE project management methodologies to complete this, with timelines and deadlines!

4. Standardize Data Entry Processes

Develop standardized data entry processes and protocols to maintain consistency and accuracy across digitized samples. Define data fields, naming conventions, and metadata requirements in collaboration with stakeholders. Provide comprehensive training to team members on these protocols. Platforms like eLabNext have large-scale import features and a Customer Success team readily available to help you import thousands of samples at a time! This feature also allows you to convert existing data into compatible formats, such as Excel sheets, and utilize eLabNext to upload information efficiently. Conduct thorough data validation checks before importing to ensure data integrity.

5. Adopt an Incremental Approach

Adopt an incremental approach to digitisation by starting with a small subset of samples before scaling up. Begin with a single freezer, rack, or shelf, and gradually expand to larger batches. This approach minimizes disruptions to ongoing research activities and allows for iterative improvements.

6. Implement Rigorous Quality Control Measures

Implement robust quality control measures to ensure the accuracy and reliability of digitized data. Establish data validation, cross-checking, and auditing protocols to identify and rectify errors promptly. Monitor data entry processes regularly and provide ongoing training to mitigate quality issues.

7. Document Standard Operating Procedures (SOPs)

Document standardized operating procedures (SOPs) for digitization processes and best practices. Create detailed guides covering data entry protocols, troubleshooting steps, and system usage instructions. Maintain an accessible repository of SOPs for reference and training purposes.

8. Foster Continuous Feedback and Adaptation

Encourage open communication and feedback loops throughout digitization. Regularly solicit input from stakeholders, including researchers and lab personnel, to identify challenges and areas for improvement. Adapt digitization strategies based on feedback to optimize efficiency and user satisfaction.

9. Celebrate Achievements and Sustain Motivation

This is one of the most overlooked attributes of this process! Scientists are so focused on day-to-day tasks that we forget to stop and smell the roses and celebrate people who accomplish things! Celebrate milestones and achievements reached during the digitisation journey to sustain motivation and morale. Recognize the efforts of the digitization task force and acknowledge progress towards the ultimate goal of a fully digitized lab. Set new goals and benchmarks to maintain momentum and drive continuous improvement.

Summary

Digitizing a lab, one sample at a time, demands a systematic and collaborative approach. By conducting a comprehensive inventory assessment, prioritizing samples, and leveraging tools like eLabNext, the digitization process can be effectively managed. Establishing standardized processes, implementing rigorous quality control measures, and fostering continuous feedback is essential for ensuring data integrity and user satisfaction. With each step taken towards digitization, celebrate achievements and sustain motivation towards the overarching goal of an entirely digitized laboratory. Remember, it's a journey that starts with one sample, one box, one rack, and one shelf until the entire lab is seamlessly digitized.

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eLabNext, an Eppendorf Group Company and leading provider of Digital Lab Platforms that include lab information management systems (LIMS), electronic lab notebooks (ELN), and artificial intelligence (AI)/machine learning (ML) solutions for life science laboratories, has joined forces with Zifo, a distinguished scientific informatics services company specializing in data and informatics consulting and software validation. This strategic partnership aims to deliver cutting-edge digitization tools to life sciences laboratories worldwide.

As part of this partnership, Zifo will offer product selection, configuration, integration, rollout, validation, and managed services support to eLabNext and its clients across the entire ELN lifecycle. Through this alliance, customers will benefit from streamlined processes and enhanced collaboration capabilities, ensuring optimal performance of their laboratory operations.

"We're excited about this partnership as it merges Zifo's bilingual expertise in science and technology with eLabNext's innovative product," explains Kannan Jagamoorthy, General Manager, Americas at Zifo. "This collaboration will usher in a new era of digital solutions in the laboratory space."

Further, eLabNext will deploy Zifo's GxP validation package for internal and external customer use, enabling a further commitment to achieving the highest quality assurance standards. As part of this arrangement, eLabNext will manage the procurement process for clients seeking GxP compliance services from Zifo, thus ensuring seamless delivery of validation reports.

"As a company, we make decisions based on value-driven impact for our current and future customers," says Zareh Zurabyan, Head of eLabNext, Americas. This aligns with Zifo's mission and vision, and the decision to partner with Zifo was a no-brainer, given our multiple customers in common. We are especially excited to collaborate with Zifo on enacting meaningful, positive change in the digital transformation of the pharmaceutical industry and the GMP space."

This strategic partnership heralds a new era in laboratory digitization, combining the expertise of two industry leaders to deliver unparalleled solutions and services to the life sciences community. With a shared commitment to innovation and excellence, eLabNext and Zifo are poised to redefine laboratory informatics and drive transformative outcomes for their customers.

About Zifo

Zifo is a global specialist in scientific and process informatics services, operating across research, development, manufacturing, and clinical domains. With expertise in over 90 partner technologies and platforms, Zifo serves customers in pharmaceuticals, biotechnology, contract research, industrial chemicals, food & beverage, consumer goods, and oil and gas industries. Zifo's "Glocal" approach leverages remote delivery to ensure cost-effective solutions and dedicated local support, providing engagement, success, and quality.

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Everyone who has worked in a lab has encountered a lab protocol or SOP at some point, but why are these documents important? Adherence to protocol ensures efficiency, Standardisation, and safety in daily lab processes. 

In this blog, we will highlight: 

1. Key differences between lab protocols and SOPs 
2. The importance of standardization in lab procedures 
3. How to write an effective lab procedure
4. Streamlining lab protocol creation and sharing with digital lab solutions

Laboratory protocols and Standard Operating Procedures (SOPs) are fundamental frameworks that ensure the consistency, safety, and accuracy of scientific experiments.

Lab protocols are detailed step-by-step instructions designed to carry out a specific experimental task or lab process. On the other hand, SOPs provide a comprehensive set of guidelines for almost any lab procedure, from ensuring lab cleanliness to proper equipment use, safe handling of hazardous substances, and lab maintenance procedures.

Together, lab protocols and SOPs form the backbone of daily laboratory operations, enabling scientists to conduct their work efficiently, securely and in compliance with regulatory standards. They serve as a blueprint for conducting research in a consistent and optimal manner.

Read on for our quick guide to lab procedures and protocols. We'll examine different procedure types, highlight the importance of standardization, and give our top tips on how to write effective SOPs for your lab—including how to streamline protocol writing with advanced digital lab solutions.

What are the main differences between Lab Protocols and SOPs?

The terms "lab protocol" and "SOP" are sometimes used interchangeably in the context of laboratory environments, although they often have distinct meanings depending on the context and specific use:

Lab Protocols:

• Purpose: A lab protocol typically refers to a set of instructions designed to carry out a specific experiment or a testing procedure. It details the steps necessary to perform a scientific experiment, a clinical test, or any sequence of actions in a lab setting.
• Specificity: Protocols are usually very specific and can be more detailed about experimental conditions, such as concentrations, temperatures, and equipment settings.
• Flexibility: While they should be followed as written to ensure consistency and reproducibility of results, protocols might be slightly adjusted or optimized according to specific research needs or objectives.

Standard Operating Procedures (SOPs):

• Purpose: An SOP is a broader document that provides step-by-step instructions to perform any task within a lab (or other environments) consistently and correctly. SOPs are used not only for experiments but for any procedure that should follow a specific standard to ensure lab safety and compliance.
• Scope: They cover a wider range of activities beyond scientific experiments, including general lab maintenance, daily procedures, data documentation, and other operational processes.
• Regulatory Compliance: SOPs are often required to comply with industry standards and regulatory guidelines. They are designed to ensure that different individuals can perform the same task in the same manner over time, enhancing efficiency and quality control.

While there may be some overlap between lab protocols and SOPs, both are crucial for maintaining high standards of practice, though their applications and the rigidity with which they must be followed can differ.

Importance of standardization in lab procedures

Lab protocols and procedures are central to establishing standardization within daily lab operations, ensuring that every process is performed consistently and effectively. 

Standardization in lab procedures is crucial for several reasons, particularly in scientific research, diagnostic testing, and any setting where precise and reproducible results are necessary. Here are some key aspects of why standardization is so important:

Consistency and Reproducibility

Standardization ensures that experiments or tests conducted in a laboratory yield the same results, irrespective of who performs them or when they are performed. This consistency is crucial for validating experiments and confirming results across different studies or even different laboratories.

Quality Control

Standard procedures help maintain high quality and reliability in experimental results. By following a standardized protocol, labs can minimize variability that might arise from different methodologies or operator errors, ensuring the data's accuracy and reliability.

Lab Safety

SOPs include detailed safety guidelines that are crucial for handling hazardous materials, operating complex machinery and ensuring general laboratory safety. Standardisation in these procedures helps prevent accidents and ensures the safety of lab personnel.

Efficiency and Time Management

Having a set of well-defined, standardized procedures reduces the time needed for training and briefing. New personnel can quickly learn and adapt to established methods, which increases the overall efficiency of the laboratory. It also helps to manage resources better by eliminating unnecessary variations in the use of materials and equipment.

Regulatory Compliance

Many laboratories operate under strict regulatory conditions imposed by governmental and international bodies, especially in the pharmaceutical, medical, and food industries. Standardized procedures ensure that labs comply with these regulations, helping to maintain certifications and avoid legal or ethical violations.

Cost Efficiency

By reducing errors and reiterations of experiments, standardised procedures can lead to significant cost savings. Consistent protocols minimise the waste of expensive reagents and samples, which can have a substantial impact on lab budgets.

In summary, the standardization of lab procedures is integral to the scientific process, ensuring that results are accurate, reliable, and verifiable. This not only boosts the credibility of the scientific findings but also enhances operational efficiency within laboratories.

How to write an effective lab procedure

When writing a lab protocol or SOP, it can be useful to follow a stepwise process. Follow this guide to start writing effective lab protocols or SOPs for your lab:

1. Define the Purpose

Start by clearly stating the purpose of the protocol or SOP. Specify what the procedure will accomplish and why it is important. This sets the context and ensures all users understand the intent behind the procedure.

2. Scope and Application

Outline the scope of the protocol or SOP. Explain when and where it should be used and by whom. This section should also list any prerequisites, such as necessary qualifications, training, or conditions required before performing the task.

3. List of Materials and Equipment

Provide a detailed list of all materials, equipment and software needed to complete the procedure. Include specific models, settings and any safety gear required.

4. Detailed Steps

Break down the procedure into clear, numbered steps. Each step should be concise and describe precisely what needs to be done. Use bullet points for clarity and ensure that the steps are in the correct order.

5. Safety and Compliance Notes

Include any safety instructions related to the procedure. Highlight critical steps, potential hazards, and emergency response actions. Ensure all compliance guidelines are met and documented within the SOP.

6. Quality Control Checks

Integrate checkpoints or quality control measures to ensure the procedure's output meets the required standards. Specify how results should be evaluated and recorded.

7. Documentation and Reporting

Detail how the results should be documented, who should receive the reports, and the format for these reports. Ensure the process aligns with data integrity and record-keeping requirements.

8. Review and Approval

Before finalizing, the protocol or SOP should be reviewed by senior staff or peers. This review ensures accuracy and practicality. Once reviewed, it should be approved by the required authority.

9. Revision History

Keep a revision log at the end of the document to track changes. Include dates, the nature of revisions, and who approved the changes.

Adopting a structured approach such as this helps create comprehensive and reliable lab protocols and SOPs, enhancing the efficacy of daily laboratory operations.

Streamlining lab protocol creation and sharing with digital lab solutions

Whether your lab relies on paper-based lab protocols or stores key protocol documents in an online file-sharing system, your lab could benefit from a comprehensive digital lab solution tailored to creating, sharing, and accessing key lab protocols and SOPs.

eLabNext for Protocol and SOP Management is an advanced digital system for creating and sharing dynamic and interactive protocols and SOPs. The platform allows lab personnel to easily access necessary protocols anywhere, anytime, reinforcing compliance and facilitating seamless collaboration through a shared protocol library and integrated communication tools.

This powerful digital lab solution offers a user-friendly interface, making it easy for labs to generate step-by-step interactive protocols that guide laboratory processes. Every change made to a protocol is tracked, ensuring transparency and accuracy throughout your work processes. In addition, our platform comes equipped with a wide range of templates for common lab protocols, saving you time when creating protocols. 

The eLabNext Digital Lab Platform can enhance adherence, lab safety, and collaboration within your team via a shared library for protocols and SOPs.

AI protocol generation and beyond

In addition to the robust protocol creation and sharing that can be achieved with our platform, eLabNext has recently developed an AI-powered Protocol Generation Add-On – an innovative tool designed to transform and streamline your lab's protocol and SOP creation experience.

Powered by advanced generative AI software, the add-on overcomes the tedious task of manually creating protocols, automating the generation of comprehensive protocols from a brief description of your experiment, significantly cutting down your time and effort.

Available as an add-on, the generative AI tool eases the burden of documentation. Beyond protocols and SOPs, the tool can also assist in swiftly producing detailed lab reports and experiment summaries, allowing you to dedicate more time to research and less to paperwork.

A better way to manage lab procedures?

Understanding and utilizing lab protocols and SOPs is not just about maintaining standards; it's about enhancing the efficiency, safety, and efficacy of scientific research.

Adopting digital tools can greatly enhance the creation, management and adherence to these crucial documents. Whether you're drafting your first SOP or looking to refine existing procedures, adopting digital solutions from eLabNext can not only streamline the protocol creation process, but also ensures that all procedures are up-to-date, consistent and accessible, reducing human error and increasing productivity.

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A Digital Lab Platform (DLP) helps streamline complex and time-consuming tasks by automating, integrating, or optimizing lab operations and workflows. This allows scientists to focus on research rather than administrative tasks, resulting in more efficient use of resources and fewer manual errors. Through an open ecosystem design that seamlessly integrates with existing tools and services, eLabNext Developer extends the digital lab platform capabilities to provide a scalable and future-proof solution for life science research and development laboratories. 

In contrast to the one-dimensional nature of traditional ELNs (Electronic Laboratory Notebooks) and LIMS (Laboratory Information Management Systems) systems, the foundation of eLabNext serves as a unifying force, connecting various laboratory operations and making the platform multi-dimensional. This truly transformative platform is designed to improve efficiency, speed of innovation, and seamless integration of digital tools within the laboratory in the world of life science research and development. As a result, eLabNext Developer is changing the way scientists and developers work together to improve the way laboratories operate and is leading the way in a new era of digitization for scientists around the world. 

Innovative Digital Features to Accelerate Lab Operations

An open ecosystem is a powerful tool for facilitating sharing and collaboration within and across organizations. By enabling easy integration with a wide range of tools and services, a digital lab platform contributes to a more connected and collaborative research environment. eLabNext Developer is built to provide a broad range of tools to users to customize features for a digital laboratory environment. The laboratory information management platform is designed as an open ecosystem that seamlessly integrates with various tools and services, which increases the system’s flexibility and capabilities. This includes a powerful Software Development Kit (SDK) and an Application Programming Interface (API), which allow developers to innovate, automate, and improve their workflows and system operation. Together, these features empower developers with everything they need to access their next-generation all-in-one Digital Lab Platform capabilities. 

Key eLabNext Developer Features for Lab Digitalization

• The first-to-market developer hub for the life science R&D lab platform with a comprehensive, all-in-one platform for access to developer tools for building, deploying, testing, and releasing add-ons to the eLabNext marketplace. 
• eLabNext Digital Lab extension or customization of workflows and functionality in eLabNext, or extend and integrate existing IT and information systems using our Software Development Kit (SDK) and API. Further increase efficiency, data consistency, and process automation in the lab. 
• Device, product and service providers can use eLabNext to connect their products and services to a digital lab used by over 60,000 scientists worldwide to enable the complete digitisation of the laboratory. 
• As a truly scalable digital lab platform, eLabNext Developer will empower R&D laboratories and organizations to adapt to changing market needs and new products by easily integrating software tools, products, and services as they become available. 

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In-House Buildouts vs. Using Readily-Available Software and the Path towards Digital Sustainability

When considering whether to develop in-house software or purchase an off-the-shelf solution for your biotech applications (including lab operations, data analysis, or protein platform analysis), it is important to look at the history of the trends. 

A Brief History of Software as a Service (SaaS) in the Life Sciences

Early Development (Pre-Internet Era - Before the 1990s): Before the widespread adoption of the Internet in the 1990s, life science and pharma labs predominantly developed their own in-house software solutions to meet specific research needs. Custom-built software was standard, tailored to the unique requirements of each lab. Development timelines were lengthy, and costs were high due to the need for specialized expertise and infrastructure.

Proliferation of Commercial Software (1990s - 2000s): With advancements in computing technology and the availability of commercial software, many life science and pharma labs began adopting commercial off-the-shelf (COTS) software solutions in the 1990s and 2000s. These solutions offered standardized features, functionalities, and workflows, reducing the need for extensive in-house development efforts and providing cost-effective alternatives to custom-built software.

Emergence of SaaS Models (Early 21st Century—2000s): The early 21st century saw the rise of Software as a Service (SaaS) models, coinciding with the growth of the Internet and cloud computing. Labs started transitioning from on-premises software to cloud-based SaaS solutions in the 2000s to streamline research workflows, reduce IT overhead, and access specialized features and expertise. SaaS offerings provide advantages such as rapid deployment, scalability, and subscription-based pricing models.

Adoption of Biotech SaaS Solutions (Recent Years - 2010s onwards): In recent years, there has been a growing trend towards adopting specialized biotech SaaS solutions tailored to the unique needs of the life science and pharma industries. Labs increasingly recognize the benefits of leveraging ready-to-use biotech SaaS solutions, such as cost savings, rapid deployment, continuous updates, and compatibility with existing systems. This trend has accelerated since the 2010s, with widespread adoption across the industry.

5 Benefits of Subscribing to SaaS Rather Than Building In-House Software

1. Cost and Time Savings: Off-the-shelf biotech SaaS solutions eliminate the need for extensive development efforts and associated costs, such as hiring specialized developers, infrastructure setup, and ongoing maintenance. By leveraging pre-built solutions, organizations can significantly reduce upfront investment and time-to-market, allowing them to allocate resources more efficiently and focus on core research activities.

2. Access to Specialised Expertise and Features: Off-the-shelf biotech SaaS platforms are often developed by specialized vendors with domain expertise in life sciences and biotechnology. These solutions typically offer advanced features, functionalities, and workflows tailored to specific research needs, providing access to state-of-the-art technologies and methodologies that may be challenging to replicate in-house. By utilizing specialized expertise, organizations can benefit from best practices, industry standards, and cutting-edge innovations without requiring extensive internal development efforts.

3. Rapid Deployment and Scalability: Off-the-shelf biotech SaaS solutions are designed for rapid deployment and scalability, allowing organizations to rapidly implement and scale their research workflows as needed. These platforms typically offer cloud-based infrastructure, automated provisioning, and flexible pricing models, enabling seamless scalability to accommodate growing data volumes, research projects, and user requirements. By leveraging SaaS solutions, organizations can respond more effectively to changing research needs and market demands, ensuring agility and competitiveness in the dynamic biotech landscape.

4. Continuous Updates and Maintenance: Off-the-shelf biotech SaaS solutions are continuously updated and maintained by the vendor, ensuring access to the latest features, security patches, and performance optimizations. By outsourcing software maintenance and updates to the vendor, organizations can minimize the burden on internal IT teams and avoid disruptions to research workflows caused by outdated or unsupported software versions. Continuous updates also enable organizations to stay ahead of regulatory requirements, industry trends, and emerging technologies, ensuring the long-term relevance and sustainability of their research infrastructure.

5. Compatibility and Integration: Off-the-shelf biotech SaaS solutions are designed to be compatible with existing research tools, laboratory equipment, and data management systems, facilitating seamless integration and interoperability. These platforms often offer standardized data formats, APIs, and integration capabilities, enabling organizations to consolidate and streamline their research workflows across multiple applications and platforms. By leveraging compatible SaaS solutions, organizations can maximize the value of their existing investments, improve data accessibility and collaboration, and enhance overall research productivity and efficiency.

New Trend: White Label Software in Big Pharma

The trend of big pharma purchasing white-label software and using it internally reflects a strategic shift towards maintaining control over proprietary data and processes while leveraging external technology solutions. Rather than relying on third-party vendors for software development and data management, big pharma companies customize and deploy white-label software solutions internally, enabling them to safeguard sensitive data, streamline operations, and maintain a competitive edge. 

White Label Software Examples

Customized Laboratory Information Management Systems (LIMS): Big pharma companies may purchase white-label LIMS software and tailor it to their specific laboratory workflows and data management needs. By deploying customized LIMS internally, they can securely manage and track experimental data, samples, and workflows without relying on external vendors.

In-House Clinical Trial Management Platforms: Rather than outsourcing clinical trial management to third-party vendors, big pharma companies are investing in white label clinical trial management platforms that can be customised to meet their unique requirements. This allows them to control trial data, patient records, and regulatory compliance while streamlining the clinical trial process.

Proprietary Data Analytics Platforms: Big pharma companies often require advanced data analytics capabilities to analyze large-scale biological datasets, identify potential drug targets, and optimize research strategies. By purchasing white-label data analytics platforms and customizing them internally, they can leverage powerful analytics tools while protecting proprietary data and intellectual property.

Internal Collaboration and Communication Tools: To facilitate collaboration and communication among research teams, big pharma companies may adopt white-label collaboration platforms, project management tools, and communication software. These internal solutions enable secure collaboration, document sharing, and real-time communication while ensuring data privacy and confidentiality.

Regulatory Compliance and Quality Management Systems: Big pharma companies must adhere to stringent regulatory requirements and quality standards throughout drug development. By internally deploying white-label regulatory compliance and quality management systems, they can ensure compliance with regulatory guidelines, track quality metrics, and manage audit trails without exposing sensitive data to third-party vendors.

The trend of big pharma purchasing white-label software and using it internally underscores the importance of data privacy, security, and control in the highly regulated pharmaceutical industry. By customizing and deploying internal software solutions, big pharma companies can harness the benefits of external technology while safeguarding proprietary data and maintaining compliance with regulatory standards.

Digital Sustainability's 3 Main Pillars: Expand, Integrate, and Support!

To ensure sustainability (that is, maintaining optimal performance in the long term) when implementing digital tools, particularly when purchasing SaaS solutions, biotech companies should focus on the following:

Scalability: Choose SaaS solutions that offer scalability to accommodate the company's growth and evolving needs. Prioritise platforms that can seamlessly scale resources, such as storage, computing power, and user licenses, as the company expands its operations, increases data volumes, or introduces new products and services. Regularly assess scalability requirements and adjust subscription plans or configurations to support ongoing growth and innovation.

Integration and Customisation: Select SaaS solutions that offer robust integration capabilities and customization options to align with the company's existing systems, workflows, and unique requirements. Ensure that the chosen platforms support open APIs, data interoperability standards, and flexible configuration settings, allowing seamless integration with internal databases, laboratory equipment, and third-party applications. Collaborate closely with the SaaS vendor to tailor the solution to the company's specific needs, workflows, and business objectives, leveraging customization features, workflow automation tools, and professional services as needed.

Maintenance and Support: Prioritize SaaS solutions that provide reliable maintenance, support, and updates to ensure ongoing performance, security, and compliance. Choose vendors with a proven track record of delivering timely software updates, patches, and enhancements, as well as responsive customer support services and technical assistance. Establish clear service-level agreements (SLAs) and communication channels with the vendor to address any issues, resolve technical challenges, and provide training and support to end-users. Regularly review and optimize software configurations, monitor performance metrics, and conduct user feedback sessions to identify improvement areas and ensure the digital tools' long-term sustainability.

By implementing these strategies, a biotech company can scale up its operations, integrate and customize digital tools to meet its specific needs and ensure ongoing maintenance and support for sustainable digital transformation. This approach enables the company to leverage the benefits of SaaS solutions while maximizing efficiency, innovation, and competitiveness in the rapidly evolving biotech industry.

The Big But! 

If your company has a unique workflow or requires a platform or solution that cannot be fulfilled by off-the-shelf SaaS options and decides to build software in-house, integrating it with existing SaaS workflows presents a unique challenge. However, there are several steps the company can take to ensure successful integration:

1. Identify Integration Points: Analyse the in-house software's functionality and determine where it intersects with existing SaaS workflows. Identify integration points where data exchange or interaction between the in-house software and SaaS platforms is necessary.

2. Standardize Data Formats and Protocols: Establish standardized data formats, protocols, and APIs for data exchange between the in-house software and SaaS platforms. Ensure compatibility with common data standards and industry-specific formats to facilitate seamless integration and interoperability.

3. Implement Middleware or Integration Tools: Use middleware or integration tools to facilitate communication and data exchange between in-house software and SaaS platforms. Implement APIs, web services, or middleware solutions that can translate data between different systems, synchronize data in real time, and orchestrate workflows across multiple platforms.

4. Customize SaaS Platforms: Work with SaaS vendors to customize their platforms or APIs to accommodate the unique requirements of the in-house software. Collaborate closely with vendors to develop custom integrations, plugins, or extensions that enable seamless interoperability with the in-house solution while leveraging the scalability, reliability, and features of the SaaS platforms.

5. Develop Custom Connectors or Plugins: Build custom connectors, plugins, or adapters to facilitate integration between the in-house software and SaaS platforms. Develop custom scripts, APIs, or middleware components that bridge the gap between different systems, allowing data to flow bi-directionally and workflows to be synchronized effectively.

6. Implement Data Governance and Security Measures: Establish data governance policies, access controls, and security measures to protect sensitive information and ensure compliance with regulatory requirements. Implement encryption, authentication, and authorization mechanisms to secure data transmission and access between the in-house software and SaaS platforms.

7). Test and Validate Integration: Conduct thorough testing and validation of the integration between the in-house software and SaaS platforms to ensure reliability, accuracy, and performance. Test data exchange workflows, error handling mechanisms, and system interactions under various scenarios to identify and address any issues or discrepancies.

8. Provide Training and Support: Offer training and support to end-users to familiarise them with the integrated workflows and software interfaces. Provide documentation, tutorials, and user guides to help users navigate the integrated environment effectively and maximize productivity.

Conclusion

By following these steps, life science companies can effectively integrate their in-house software with existing SaaS workflows, enabling seamless data exchange, collaboration, and workflow orchestration across the organization. This approach allows the company to leverage the benefits of in-house and SaaS solutions while optimizing efficiency, innovation, and competitiveness in its operations.

However, if you're beginning your digital journey, there are many benefits to choosing a SaaS over building an in-house platform. 

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Biotech R&D has birthed some of the most impressive innovations, from recombinant DNA to genome editing. While the road to commercialization has always been challenging, many fundamental barriers to innovation have grown bigger. Currently, there’s an overload of unstructured data and ideas. Translating these into world-changing ideas presents a huge organizational and logistical challenge.

With the rise of generative AI in the past year, a solution to some of these issues is on the horizon. Generative AI can promote divergent thinking, challenge the bias of experts, evaluate and refine ideas, and facilitate collaboration across niche research areas. It’s also streamlining the data lifecycle and changing the creative aspects of biotech lab operations, such as automating and improving the quality of content, from lab notebooks to published scientific literature. 

In the following blog, we look at what generative AI is, how it works, and its applications in biotech and the broader life sciences.

What is Generative AI? 

Generative AI, or Gen AI, is a class of artificial intelligence techniques and algorithms that generate new data samples or content, including audio, code, images, text, simulations, and videos. 

Unlike discriminative models that focus on classification or prediction tasks based on existing data, generative models learn the underlying patterns and structures of the data to generate new instances that are statistically similar to the training data.

Gen AI Encompasses a Wide Range of Algorithms and Approaches

Gen AI has applications across various domains, including image generation, text synthesis, music composition, drug discovery, and content creation. It enables the generation of realistic and diverse data samples, facilitates data augmentation for training machine learning models, and fosters creativity and innovation in AI-driven applications.

There are several algorithms and approaches, including:

• Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator and a discriminator, which are trained simultaneously in a competitive manner. The generator learns to generate realistic data samples, such as images, text, or audio, while the discriminator learns to distinguish between real and generated samples. Through adversarial training, GANs produce high-quality synthetic data that closely resembles the distribution of real data.
• Variational Autoencoders (VAEs): VAEs are probabilistic generative models that learn to encode and decode data samples into a lower-dimensional latent space. By sampling from the latent space and decoding the samples back into the original data space, VAEs can generate new data samples that capture the variability and structure of the training data. VAEs are commonly used for generating images, text, and other complex data types.
• Autoregressive Models: AutoRegressive models, such as autoregressive neural networks (ARNs) and autoregressive moving average (ARMA) models, generate data sequences by modeling the conditional probability distribution of each data point given the previous observations. By iterative sampling from the conditional distribution, autoregressive models generate sequences of data samples, such as time series data, speech, or text.
• Transformers: Transformers are a class of deep learning architectures that have achieved state-of-the-art performance in natural language processing (NLP) tasks. Transformers, particularly variants like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers), can generate coherent and contextually relevant text by modeling the relationships between words or tokens in a sequence.

How Does Gen AI Work?

Gen AI uses various techniques – including neural networks and deep learning algorithms – to identify patterns and generate new outcomes based on them. The training process for a generative model involves feeding it a large dataset of examples, such as images, text, audio, and videos. While traditional AI aims to perform specific tasks based on predefined rules and patterns, gen AI goes beyond this limitation and strives to create entirely new data that resembles human-created content. A language model is an example of gen AI, using a probabilistic model of a natural language that can generate probabilities of a series of words based on text corpora in one or multiple languages it was trained on. Large language models, in their most advanced form, are a combination of feedforward neural networks and transformers.

In the area of natural language processing, there is also a neural network, a method in artificial intelligence that teaches computers to process data in a way inspired by the human brain. Deep learning is a type of machine learning process that uses interconnected nodes or neurons in a layered structure that resembles the human brain. These algorithms can take different data inputs and be used for speech and voice recognition.

How is Gen AI Currently Used in Biotech?

Gen AI is increasingly utilized in biotech and life sciences across several applications, leveraging its ability to generate realistic and diverse data samples. Some key areas where Gen AI is currently being used in biotech and life sciences include:

• Drug Discovery and Development: Gen AI generates novel molecular structures with desired properties for drug candidates. Generative models like GANs and VAEs can generate new chemical compounds with specific pharmacological properties, helping identify potential drug candidates and accelerating the drug discovery process.
• Protein Design and Engineering: Gen AI techniques are employed to design and engineer proteins with enhanced functionalities or specific biological activities. Researchers can design novel enzymes, antibodies, or therapeutics for various applications, including enzyme engineering, drug delivery, and immunotherapy, by generating protein sequences or structures with desired properties.
• Biological Image Synthesis: Generative models synthesize realistic biological images, such as microscopy images of cells, tissues, or organisms. These synthesized images can be used to augment training datasets for image analysis algorithms, improve the generalization of machine learning models, and generate data for virtual screening and testing of algorithms in silico.
• Omics Data Generation: Gen AI techniques are applied to generate synthetic omics data, including genomics, transcriptomics, proteomics, and metabolomics data. Synthetic omics data can be used to supplement real experimental data, simulate biological processes, and validate computational models, enabling researchers to explore complex biological systems and discover biomarkers or therapeutic targets.
• Text and Literature Generation: Generative models generate text-based content, such as scientific articles, literature reviews, or drug interaction reports. These generated texts can assist researchers in literature mining, knowledge discovery, and data summarisation, facilitating literature-based research and biotech and life sciences decision-making.
• Biomolecule Design and Synthesis: Gen AI techniques are used to design and synthesize novel biomolecules, such as peptides, aptamers, or nucleic acids, with specific functions or properties. By generating sequences or structures with desired characteristics, researchers can develop biomolecules for diagnostics, therapeutics, and biosensing applications.

Overall, Gen AI is revolutionizing biotech and life sciences by enabling the generation of novel data samples, molecules, and biological entities, fostering innovation, and accelerating research and development efforts in various domains. As the field continues to advance, Gen AI is expected to play an increasingly pivotal role in shaping the future of biotechnology and life sciences, including clinical research. Gen AI can help identify which patient cohorts will respond best to specific drugs, developing more personalized medicine. On the operational and marketing sides, generative AI can optimize the supply chain, manufacturing processes, and marketing and advertising strategy. 

What Are the Concerns About Using Generative AI in Biotech?

• Data Quality and Bias: Gen AI models rely heavily on the quality and representativeness of training data. In biotech and life sciences, datasets may suffer from biases, inaccuracies, or limited diversity, impacting performance and generalization ability. Biases in training data can lead to the generation of biased or unrealistic samples, hindering the reliability and validity of generated results.
• Interpretability and Trustworthiness: Many Gen AI techniques, such as deep neural networks, are complex and opaque, making it challenging to interpret and trust the generated outputs. In critical applications like drug discovery and biomolecule design, it is crucial to understand how and why generative models generate specific outputs. Lack of interpretability can impede the adoption of Gen AI in decision-making processes and regulatory approval, limiting its utility in real-world applications.
• Mapping Data: Mapping data in biotech and life sciences presents challenges due to the complexity and heterogeneity of biological systems and the diversity of data types and sources. Integrating and mapping heterogeneous data types requires harmonizing data structures, ontologies, and metadata to ensure data consistency across datasets. Challenges in mapping data can affect the accuracy and reliability of Gen AI models trained on such data, impacting their performance and applicability in real-world scenarios. Addressing issues related to mapping data is essential to ensure the quality and reliability of data inputs for Gen AI models in biotech and life sciences. 

Day-to-Day Usage of AI for Lab Operations

Above, we’ve listed the broader research applications in which Gen AI can be used. However, in day-to-day lab operations, the average life sciences and biotech scientist can utilize Gen AI in several ways. Gen AI can replace manual and repetitive tasks, freeing up resources for more complex and creative tasks. 

• Data Analysis and Interpretation: Gen AI can assist scientists in analyzing and interpreting experimental data more efficiently. By employing machine learning algorithms, scientists can train models to recognize patterns, identify correlations, and extract meaningful insights from complex datasets, such as omics data, biological images, or high-throughput screening results. Gen AI can automate data analysis tasks, streamline data interpretation processes, and provide actionable insights to guide experimental design and decision-making in the lab.
• Experimental Design and Planning: Gen AI can aid scientists in designing and planning experiments by generating hypotheses, optimizing experimental conditions, and predicting outcomes. By leveraging predictive modeling techniques, scientists can simulate experimental scenarios, predict experimental outcomes, and identify optimal experimental parameters to achieve desired objectives. Gen AI can assist in experimental design optimization, resource allocation, and risk assessment, helping scientists make informed decisions and maximize experimental efficiency in the lab.
• Literature Mining and Knowledge Discovery: Gen AI can assist scientists in literature mining and knowledge discovery by analyzing scientific literature, extracting relevant information, and synthesizing knowledge from diverse sources. Natural language processing (NLP) techniques can extract key concepts, identify relationships between scientific entities, and summarise findings from research articles, patents, and databases. Gen AI can automate literature review processes, facilitate literature-based research, and accelerate knowledge discovery in specific research areas, enabling scientists to stay up-to-date with the latest advancements and make informed decisions in the lab.

Summary

Integrating generative AI into the biotech industry brings significant benefits and notable concerns. By leveraging generative AI, researchers can tackle the challenge of navigating through vast amounts of unstructured data and ideas, fostering divergent thinking, and facilitating collaboration across niche research areas. Moreover, generative AI streamlines various aspects of biotech lab operations, from automating content generation to improving the quality of scientific literature. However, concerns regarding data quality and bias, interpretability, trustworthiness, and data mapping remain pertinent. Despite these challenges, the day-to-day usage of generative AI in lab operations holds promise, enabling scientists to enhance data analysis and interpretation, optimize experimental design and planning, and accelerate literature mining and knowledge discovery. As generative AI continues to evolve, its role in revolutionizing biotech research and development is poised to expand, shaping the future of precision medicine, drug discovery, and other critical areas within the life sciences.

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Managing lab operations effectively is no easy feat. It requires attention to detail, standardized processes, and a deep understanding of scientific workflows. Thankfully, digital solutions like eLabNext’s Digital Lab Platform are transforming the way in which modern labs manage their daily operations, making them more streamlined, automated and collaborative.

In this blog, you will learn: 

1. What’s involved in lab operations?
2. Operational challenges faced by non-digitalized labs
3. How to optimize lab operations with digital tools

In today’s fast-paced labs, maintaining control over data, samples, workflows, and communication is critical. Digital lab platforms like eLabNext have become essential for improving operational efficiency, ensuring compliance, and maintaining data integrity. Read on as we explore the intricacies of lab operations and provide actionable insights on optimizing workflows using cutting-edge digital tools.

What’s involved in lab operations?

Lab operations encompass a variety of tasks, such as sample tracking, data management, inventory control, compliance monitoring, and result analysis. These tasks must be carried out with precision to ensure accurate results and maintain quality standards. 

Every laboratory operation—from quality control to sample management and data analysis to reporting—relies fundamentally on precision and efficiency. Traditional manual methods of managing these processes can be time-consuming, error-prone, and challenging to scale. From manual record keeping to paper-based inventory logs, to hand-written labeling, traditional lab management systems can be a barrier to streamlined lab processes and operations

In light of this, digital tools are swiftly becoming essential for efficient lab management. Let’s take a look at some of the procedures and roles that contribute to everyday lab operations.

Essential laboratory operations

Sample management is one of the core functions of a laboratory and involves tracking samples from receipt to final analysis. Without proper sample management, there is a risk of cross-contamination, mislabeling, or loss of samples, which can result in inaccurate results and compromise data integrity. 

Lab protocols make sure that scientific procedures are carried out consistently and in compliance with regulatory and industry standards. Proper protocol management involves the creation, approval and regular updating of SOPs (Standard Operating Procedures) that guide lab personnel in carrying out experiments accurately. Effective protocol management not only minimizes errors but also ensures reproducibility across experiments.

Data management is another pivotal operation within laboratories. It involves the systematic collection, storage, and analysis of data generated from experiments and testing. Effective data management ensures that data is accurate, retrievable, and secure. This is critical not only for the integrity of research findings and test results but also for maintaining compliance with regulatory standards.‍

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Inventory control plays a vital role in lab operations, as it ensures that all necessary reagents and equipment are available when needed—without interruption. Proper inventory management helps to avoid unnecessary downtime that can delay experiments, as well as to prevent the wastage of resources and ensure cost efficiency.

Lab data security is essential for protecting digital assets within the lab environment. This includes the safeguarding of sensitive data, such as research findings, intellectual property, and personally identifiable information (PII), as well as the physical protection of lab samples, equipment, and reagents. Implementing strong data encryption, role-based access controls, and automated backups are key measures to ensure lab data security. 

Together, these operations form the backbone of effective and reliable lab management, ensuring that laboratories can fulfill their critical role in scientific discovery and industry quality assurance with efficiency and integrity.

Laboratory roles

Roles within a laboratory are as diverse as lab operations themselves. Technicians, scientists, data analysts, and managers work in concert to propel the lab's mission forward. A close-knit collaboration, paired with seamless communication, formulates the essence of dynamic laboratory operations—each role proving pivotal to the collective endeavor. 

• Laboratory managers oversee the laboratory’s operations, ensuring compliance with regulatory standards, managing budgets, and coordinating the efforts of the lab team. They play a key role in strategizing and ensuring that the laboratory's objectives are met efficiently and effectively.
• Technicians are the hands-on workforce, conducting experiments, maintaining equipment, and ensuring the smooth execution of daily tasks. Their meticulous attention to detail is crucial in the accurate execution of lab procedures.
• Laboratory scientists drive the laboratory's research and development efforts, formulating hypotheses, designing experiments, and analyzing data to advance scientific knowledge. They are pivotal in steering the lab’s focus toward innovative territories.
• Data analysts specialize in interpreting complex datasets extracting meaningful insights from experiments. Their expertise in statistical analysis and data visualization supports decision-making processes and underscores the integrity of research outcomes.
  

Each of these diverse lab roles contributes to the effectiveness of lab operations. Their specialized skills and collaborative efforts are key to managing the complexities of laboratory work, ensuring that every aspect of the laboratory's function is carried out with precision and care.

Operational challenges faced by non-digitalized labs

Achieving flawless lab operations is a multifaceted challenge, involving every aspect of daily tasks. Before we share some of our top tips and best practices for managing this, let’s consider the hurdles. Traditional lab management systems—such as paper lab records and physical folder-based inventory logs—can be a hindrance to various aspects of laboratory operations, compromising lab efficiency and efficacy.

• Data management and integrity: Managing the vast amounts of data generated in laboratories with paper-based records is not only cumbersome but prone to errors. These systems make it difficult to track changes, leading to potential compromises in data integrity. When managed in disparate platforms, data silos form, making study data challenging to integrate, analyze and share.

• Inventory management: Manually managing inventory is time-consuming and often results in errors such as incorrect stock levels or expired reagents. This can lead to delays in experiments and affect the accuracy of test results.

• Compliance with regulations: Adhering to the myriad of regulations in the scientific and quality assurance fields requires meticulous record-keeping and process validation. Manual record-keeping systems, which lack real-time updating and comprehensive audit trails, make compliance more challenging and time-consuming.
  

What are the legal implications if your lab is not digitized? Read our whitepaper

• Efficient use of resources: Traditional management systems like manual inventory logs often lead to inefficiencies in the allocation and use of resources, including personnel, equipment, and time. Manual scheduling and inventory management are prone to errors, resulting in wastage and delays.

• Sample tracking and management: Tracking samples manually increases the risk of mislabeling, loss, or contamination. This can compromise the validity of experiments and necessitate the repetition of work, leading to increased costs and delays in project timelines.

• Collaboration and communication: In the absence of a centralized digital system, collaboration and communication within the lab and with external partners can be inefficient. This affects the timely sharing of data and results, slowing down progress and decision-making.
  

Reliance on non-digital methods or legacy systems gives rise to a range of operational challenges, impacting efficiency, compliance, and the overall reliability of your lab outputs.

How to optimize lab operations with digital tools

In today’s rapidly evolving scientific landscape, labs are handling increasingly complex workflows, vast amounts of data, and stricter compliance requirements. Managing lab operations manually can quickly become inefficient, prone to errors, and costly. This is where digital tools, like eLabNext’s Digital Lab Platform, can transform your lab operations, offering a more streamlined, efficient, and secure way to manage everyday tasks.

Here are some key benefits of adopting digital solutions for optimizing lab operations:‍

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1. Enhanced data management

Effective data management is at the heart of any successful laboratory. eLabNext’s digital lab platform centralizes all your lab data into one secure digital hub, providing easy access and retrieval while eliminating data silos. This ensures that all lab personnel can view, update, and analyze the most current data at any time, enhancing collaboration and accuracy.

• Centralized data repository: Store experiment results, protocols, inventory logs, and compliance records in one easily accessible place.
• Advanced search and retrieval: Quickly find specific datasets, samples, or documents, reducing time wasted sifting through physical or fragmented digital records.
• Real-time data access: All authorized users can instantly access and update data, ensuring everyone works with the most up-to-date information.
  

2. Automated lab workflows

Automation is revolutionizing laboratory operations by reducing manual interventions and minimizing errors, ultimately boosting productivity and precision. eLabNext’s digital lab platform incorporates powerful automation tools that streamline repetitive tasks and integrate seamlessly with lab equipment, allowing lab personnel to focus on more complex scientific activities.

• Automated sample handling: eLabNext’s platform automates sample tracking and handling from receipt to disposal, reducing the risk of mislabeling, loss, or contamination. This ensures that samples are always correctly labeled and stored, streamlining workflows and preventing costly mistakes.
• Workflow automation: Automate routine lab workflows, such as experiment scheduling, protocol execution, and data entry. This removes the need for manual monitoring and reduces human error while also speeding up the pace of research and testing.
• Integration with lab equipment: eLabNext integrates with lab instruments and equipment to automate data collection and analysis, reducing the need for manual data entry and ensuring consistent, accurate results. This integration also enables automatic calibration checks and real-time monitoring of experiments, optimizing lab efficiency.
  

3. Improved sample and inventory management

Tracking samples and managing lab inventory manually can lead to errors, wasted resources, and disruptions in workflow. With eLabNext’s digital lab platform, sample management and inventory tracking are automated, reducing human error and improving efficiency.

• Barcode tracking: Barcode and RFID technology ensures precise sample tracking from receipt to disposal.
• Inventory automation: Automated inventory systems send alerts when stock levels are low, ensuring that supplies are always available and eliminating overstock or stockout situations.
• Waste reduction: Efficient resource tracking helps labs avoid wasting reagents, materials, and time.
  

4. Streamlined protocol and SOP management

Standardizing and managing lab protocols and SOPs (Standard Operating Procedures) is crucial for ensuring consistency and compliance. eLabNext’s protocol management features enable seamless creation, editing, and sharing of protocols, helping labs maintain up-to-date procedures.

• Version control: Automatically track protocol changes and ensure all team members are using the latest versions.
• Approval workflows: Set up approval processes to ensure SOPs are reviewed and authorized before being used in lab operations.
• Real-time access: Share protocols digitally with all relevant staff to reduce delays and prevent inconsistencies in how procedures are followed.
  

5. Enhanced compliance and security

Maintaining compliance with industry regulations and ensuring data security is a top priority for labs. Digital solutions simplify compliance management by automating record-keeping, providing audit trails, and securing sensitive information.

• Audit trails: Easily generate and track comprehensive logs of lab activities for regulatory audits.
• Role-based access controls: Limit access to sensitive data and samples, ensuring that only authorized personnel can view or modify critical information.
• Data encryption and backups: Ensure that all data is securely encrypted and automatically backed up, protecting it from unauthorized access and loss.
  

6. Seamless collaboration and communication

Collaboration is essential in a laboratory setting, and digital tools make it easier than ever for teams to work together, even remotely. eLabNext’s platform enhances communication by providing centralized access to data, protocols, and real-time project updates.

• Team collaboration: Enable multiple users to work on the same datasets and protocols in real-time, ensuring all team members have access to up-to-date information.
• Project tracking: Manage lab workflows by assigning tasks to staff, tracking progress, and setting deadlines to ensure smooth project execution.
• Shared resources: Share important documents, results, and protocols seamlessly across teams, improving communication and reducing the risk of miscommunication.
  

The formula for operational excellence 

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Effective lab operations are the foundation of successful scientific research. However, the complexity of managing data, samples, inventory, protocols, and compliance can create significant challenges, particularly for labs relying on manual processes or legacy systems. By adopting digital solutions like eLabNext’s digital lab platform, labs can streamline their workflows, improve data integrity, and reduce the risk of errors.

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From enhanced data management and sample tracking to protocol standardization and automation, digital platforms optimize every aspect of lab operations. These tools empower lab managers and staff to focus on innovation and research by reducing administrative tasks and improving efficiency.

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Ready to explore how automation and digitalization could revolutionize your lab’s day-to-day operations? Book a free personal demo today!

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In life science and biotech laboratories, “big data” has gotten more significant than ever before and shows no signs of stopping. The data pool in most labs is highly diverse (think ‘omics, imaging, etc.), large-scale, and ever-increasing.

This massive amount of diverse data requires constant wrangling. When properly orchestrated, this diverse data can be fully harmonized according to FAIR guidelines, delivering insights that drive scientific breakthroughs. Yet, there are notable challenges and unique hurdles to managing and shaping the challenging topography of the data landscape.

In the following blog, we’ll discuss the impact of these various challenges and provide a solution.

Diversity of Data Types

• The Challenge: Life science laboratories are prolific generators of diverse data types, including genomics, proteomics, metabolomics, and imaging data. The intricate challenge lies in seamlessly integrating and structuring this heterogeneous data into a cohesive framework. Furthermore, the complexity and heterogeneity of this data create issues with integration.
•  The Impact: The incompatibility between various data types acts as a stumbling block, impeding comprehensive analysis and hindering the extraction of profound insights from these multifaceted datasets.

Volume and Scale

• The Challenge: The relentless generation of data in life science and biotech experiments, fueled by advancements like high-throughput technologies, introduces an overwhelming volume that can surpass the capacities of traditional data structuring methods. Genomics alone will generate 2 to 40 exabytes in 2025.
•  The Impact: The sheer magnitude of data becomes a resource-intensive burden, slowing down the analysis process and potentially creating bottlenecks in accessing critical information. As a result, there’s a “data storage crisis” looming over the industry.

Lack of Standardization

• The Challenge: The absence of standardized data formats and structures across laboratories and research institutions presents a formidable challenge, introducing hurdles in data interoperability.
•  The Impact: The resultant lack of harmony in data standards complicates data sharing and collaborative efforts, which is now required by all labs receiving NIH funding. Researchers grapple with integrating and deciphering datasets produced under disparate standards, impeding seamless collaboration and insights extraction.

Temporal and Longitudinal Data

• The Challenge: Longitudinal studies and time-course experiments introduce a temporal dimension, necessitating the structuring of data points across different time intervals.
•  The Impact: The intricate task of structuring temporal data becomes pivotal. Misalignment or improper representation of time-dependent data compromises the accuracy of analyses and poses challenges in identifying dynamic patterns critical for scientific interpretation.

Metadata Complexity

• The Challenge: Many laboratory scientists, particularly those who manage samples using manual, paper-based record-keeping, find capturing and organizing metadata, including experimental conditions, sample details, and procedural information, challenging.
•  The Impact: The completeness and consistency of metadata emerge as linchpins for contextualizing experimental data. Incomplete or inconsistent metadata creates hurdles in reproducing experiments and comparing study results.

Data Security and Compliance

• The Challenge: Ensuring data security and compliance with stringent regulatory requirements, such as GDPR in Europe or HIPAA in the United States, adds additional complexity.
•  The Impact: Unfortunately, cyberattacks, natural disasters, and other calamities can threaten your data. The consequences of inadequate data security measures loom large, with potential breaches jeopardizing the confidentiality of sensitive information and compromising adherence to regulatory standards.

Evolution of Analytical Techniques

• The Challenge: The rapid evolution of analytical techniques and technologies outpaces existing data structures, rendering them outdated.
•  The Impact: Laboratories struggle to adapt data structuring methodologies to accommodate emerging analytical approaches. The lag in adaptation results in inefficiencies and missed opportunities to harness the full potential of cutting-edge technologies.

User Adoption and Training

• The Challenge: Researchers may resist the adoption of standardized data structuring practices due to unfamiliarity or a lack of training.
•  The Impact: The consequential inconsistencies in data structuring hinder collaborative efforts, impede effective data sharing, and disrupt the implementation of standardized analyses. Bridging this gap demands targeted training initiatives and a cultural shift towards embracing structured data methodologies.

Integrating and Mapping the Data

• The Challenge: Mapping biological knowledge from data involves representing complex biological concepts, relationships, and processes in a computationally tractable format. Developing interpretable and semantically rich knowledge representations requires domain expertise, ontological frameworks, and natural language processing techniques to effectively capture and formalize biological knowledge.
•  The Impact: Resolving challenges in mapping life sciences data leads to increased efficiency in data analysis, standardization of methodologies, and improved access to diverse datasets, fostering accelerated scientific discovery and collaboration.

The Solution to Data Management and Mapping Challenges

A strategic blend of technological solutions, standardization efforts, and targeted training initiatives becomes imperative to tackle these challenges. Only through meticulous data structuring can laboratories unlock the full potential of their research endeavors, paving the way for new horizons in life science and biotechnology. This comprehensive approach ensures that data in life science and biotech laboratories is structured optimally, fostering meaningful interpretation, collaboration, and innovation.

Digital lab platforms, such as those offered by eLabNext, enable researchers to take a comprehensive approach to data structuring, integration, and management. Contact us today to learn more!

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eLabNext is proud to announce it is now a Business Supporter of the World Wildlife Fund Netherlands (WWF-NL). This supporting collaboration has facilitated the protection of an area of the Atlantic Rainforest equivalent to eight football fields from deforestation over the past year. This contribution underscores eLabNext's commitment to sustainability and the tangible results of its collaboration with the WWF. For every new team member who joined the company last year, eLabNext donated €2,500 to the WWF.

In the world of scientific research, the shift towards more sustainable operations is crucial. eLabNext is at the forefront of this transformation, dedicated to modernizing life science R&D labs by transitioning from traditional pen and paper to a digital research environment. Our aim is to foster an eco-friendly approach to scientific work, leveraging our DLP to enhance efficiency and reduce environmental impact. "This partnership is a big part of how we're trying to apply our green values and motivating the broader scientific community to join in on making an impact," says Bastiaan Spijk, Head of Business Operations & People at eLabNext.  

At the heart of eLabNext's sustainability ethos are its digital solutions designed to minimize environmental impact. By championing digital sample management and reducing waste, eLabNext helps labs transition toward more sustainable operations. These initiatives are part of a broader strategy to promote eco-friendly practices, including optimizing resource use and enhancing energy efficiency, thereby contributing to a greener planet. For instance, the eLabNext Digital Lab Platform enables researchers to manage their samples more efficiently, reducing the need for physical storage. This not only supports environmental goals but also improves lab productivity and data reliability, showcasing how sustainable practices can enhance scientific outcomes. 

In many places, the Atlantic Rainforest shows what happens when you deforest: animals unique to the area are on the brink of extinction, the climate is becoming drier and hotter, and water shortages are starting to develop. But it is not too late! Together with the indigenous and local peoples, we will restore and protect the forest and ensure that a bright future dawns again for people as well as animals.

Merijn van Leeuwen, Coordinator Amazon & Atlantic Forest WWF Netherlands 

Doubling down on deforestation 

eLabNext has broadened its dedication to environmental sustainability by partnering with Trees For All and One Tree Planted, in addition to our original collaboration with WWF. To involve the scientific community in efforts to combat deforestation, we have introduced a referral program. This initiative invites individuals from the life sciences community and beyond to recommend new potential customers to eLabNext. As a token of our appreciation, we offer both a monetary incentive and an equal donation to our environmental charity partners in the name of the referrer. Additionally, participants receive a certificate to acknowledge their contribution and dedication to environmental preservation. 

"Our referral program is more than just a way to grow our business; it's a testament to our dedication to sustainability and helping labs go paperless," says Hovik Torkomyan, Head of Global Marketing at eLabNext. "By offering our clients and the wider life science community the opportunity to support reforestation efforts directly, we're not just rewarding them; we're also making a tangible impact on the environment. When a referrer chooses to support Trees For All or One Tree Planted through our program, doubling the donation in their name, it's a powerful statement of shared values and collective action toward a greener future. 

About World Wildlife Fund NL  

The World Wildlife Fund (WWF) started as a protector of animals like the panda. Our challenge is now much greater. Nature loss and climate change affect all life on earth today. That is why WWF is on a mission: We will make our world' Nature Positive'. 

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eLabNext, renowned for its pioneering Digital Lab Platform (DLP) that offers an all-in-one Electronic Lab Notebook (ELN) with Lab Inventory Management (LIMS) features such as sample, equipment, and SOP management for life science laboratories, is proud to announce the launch of eLabNext Developer. This groundbreaking platform is designed to democratise digital lab tool development and enhance the life sciences industry's research and development experience. Set to revolutionise laboratory operations, it offers unprecedented adaptability, extendibility, and a future-proof ecosystem. For instance, it allows Artificial Intelligence (AI) and Machine Learning (ML) capabilities to enhance eLabNext. 

eLabNext Developer represents the first-ever comprehensive and democratic developer experience within the Life Science R&D community. This platform allows eLabNext customers to extend or adapt the eLabNext software functionality according to their specific needs. Moreover, it welcomes companies in the life sciences to leverage eLabNext by connecting their products or services to digital labs. This is achieved through the development and commercialisation of add-ons via the eLabNext Marketplace. 

Key Features and Benefits: 

• First-to-Market Developer Hub: A unique, all-in-one platform that provides access to developer tools for building, deploying, and releasing add-ons. 
• Adapt and Extend the eLabNext Digital Lab: Enhance current workflows or integrate with existing IT systems using the Software Development Kit (SDK) and Application Programming Interface (API) for increased efficiency and automation. 
• Access to the Digital Lab: Enables suppliers of laboratory devices or products, as well as service providers in laboratories, to bring their solutions to the digital lab used by over 60,000 scientists worldwide. 
• Future-Proof and Scalable: The platform's design accommodates evolving market needs, allowing for the easy integration of new tools and services. 
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A Call to Innovation

eLabNext invites the global life science community to embark on this transformative journey. By leveraging eLabNext Developer, users can unlock the full potential of their laboratories, ensuring they remain at the forefront of scientific research and development. 

"We are thrilled to launch eLabNext Developer, a platform that embodies our commitment to innovation and collaboration in the life science R&D sector," said Wouter de Jong, Co-founder and Managing Director of eLabNext. "Our platform is designed to empower users to customise their research environment like never before, marking a significant step towards the future of lab digitalisation." 

eLabNext Developer is not just a platform; it represents a movement towards creating a more connected, efficient, and innovative research community. By facilitating the easy development and deployment of add-ons, eLabNext ensures that the life sciences R&D community is well-equipped to meet both today's challenges and those of the future.