Your go-to blog for modern lab management

In the rapidly evolving landscape of modern laboratories, the integration of a Laboratory Information Management System (LIMS) has become vital. Not only does this digital lab solution facilitate improved data management and accuracy, but it also streamlines lab operations, paving the way for improved efficiency and productivity.

Choosing the right LIMS is a pivotal decision for any laboratory looking to optimize its operations. The right LIMS streamlines workflows enhance data accuracy and provides comprehensive sample tracking and reporting capabilities. With the myriad of options available, selecting the ideal LIMS tailored to your lab's specific needs can be daunting. In this blog, we'll guide you through everything you need to make this crucial decision.

In this blog, we'll cover:

• What is a LIMS?
• Benefits of implementing a LIMS
• Differentiating between LIMS and ELN
• Key factors to consider when choosing a LIMS
• Practical tips for selecting the best LIMS for your lab
• Weighing up your options

What is a laboratory information management system?

A LIMS is a software-based digital lab solution designed to support laboratory operations by managing data, automating workflows, and ensuring compliance with regulatory standards. By integrating various laboratory functions, a LIMS enables seamless data entry, storage, and retrieval, which significantly reduces the risk of errors and enhances data integrity. 

LIMS play a central role in modern labs by handling complex data management tasks and providing a structured and efficient approach to laboratory processes. They also facilitate better resource management, such as inventory tracking and equipment maintenance, and can generate insightful reports to aid in decision-making and regulatory submissions. Overall, a LIMS is an indispensable tool for laboratories aiming to improve productivity, accuracy, and compliance in today's fast-paced scientific environment.

Benefits of implementing a LIMS

The benefits of implementing a LIMS are vast and varied, offering significant enhancements across various facets of lab operations:

• Improved data management: A LIMS ensures the integrity and accessibility of data, which is crucial for reducing errors and improving the accuracy of decision-making processes. Centralizing data storage facilitates easier retrieval and analysis, thereby supporting more informed scientific conclusions and business strategies.
• Streamlined workflows: By automating routine and repetitive tasks, LIMS frees up valuable time for research and development activities. This automation can include sample handling, data entry, and report generation, leading to increased efficiency and productivity within the laboratory.
• Enhanced sample tracking: A LIMS provides precise tracking of samples within the lab, from collection to final analysis. This improved traceability ensures accountability and reduces the risk of sample loss or contamination, which is vital for maintaining the quality and reliability of lab results.
• Inventory management: By keeping track of lab inventory in real-time, LIMS aids in resource planning and budgeting. This feature helps labs manage their supplies more effectively, avoiding shortages and minimizing waste, which can result in cost savings and more efficient operations.

LIMS can be beneficial for a wide range of laboratories, including those involved in research and development, quality control, and biobanks. In research and development labs, it supports innovation by streamlining data management and sample tracking, allowing scientists to focus on their core scientific activities. Quality control labs benefit from enhanced data integrity and regulatory compliance, while biobanks can manage large volumes of biological samples with greater accuracy and reliability. 

Overall, the implementation of a LIMS can lead to significant improvements in lab efficiency, accuracy, and productivity.

Differentiating between LIMS and ELN

In addition to LIMS, another popular digital lab solution is electronic lab notebooks (ELNs). These solutions serve distinct purposes – understanding the difference between the two is crucial when deciding on a digital platform for your lab:

LIMS (Laboratory Information Management System)

• Primary focus: Managing samples, associated data, and laboratory workflows. It tracks the lifecycle of samples from receipt to disposal and supports various laboratory processes.
• Key Features: Sample tracking, inventory management, quality control, reporting and analytics, workflow automation.
• Use Cases: Managing high-throughput laboratories, clinical diagnostics, environmental testing, and any lab requiring rigorous sample tracking and process automation. Common in clinical labs, environmental labs, and industrial testing facilities.
• User Interaction: Used by lab managers, technicians, and analysts to track and manage samples, workflows, and lab resources.
• Rigidity: LIMS are designed to enforce strict protocols and standard operating procedures (SOPs) to ensure consistency, accuracy, and compliance with regulatory standards.

ELN (Electronic Lab Notebook)

• Primary Focus: Documenting experiments, research notes, observations, and procedures. It serves as a digital replacement for traditional paper lab notebooks.
• Key Features: Templates for experimental protocols, electronic documentation and signatures, search functionality, and version control.
• Use Cases: Documenting detailed experimental procedures, capturing research data in real-time, and supporting collaborative research projects. Common in academic research, pharmaceutical R&D, and biotech labs.
• User Interaction: Scientists and researchers use ELN interactively to document their daily work and experimental details.
• Flexibility: ELNs are designed to be more flexible and user-friendly. They allow scientists and researchers to document their experiments, observations, and notes in a manner that suits their individual needs and preferences. 

The best of both worlds

SciSure's (formerly eLabNext's) ELN capabilities are a comprehensive platform that bridges the functionalities of both LIMS and ELN and is tailored to the R&D Sector. This unified system seamlessly combines sample management, workflow automation, and experiment documentation, providing an all-in-one solution for modern laboratories. Some of the top features include:

• Adaptability: Adapts to the specific needs of various laboratory environments, ensuring it can be tailored to fit unique workflows and requirements. This adaptability makes it a versatile choice for a wide range of scientific research settings.
• User-friendly interface: Boasts an intuitive design, making it easy for lab staff to adopt and integrate into their daily routines. This ease of use helps to minimize the learning curve and ensures that teams can quickly start benefiting from the system.
• Data integrity: Ensures that all data is securely stored and easily accessible. This guarantees that essential information is always protected and readily available when needed, enhancing the overall efficiency of laboratory operations.
• Collaboration and data sharing: Enhances collaboration among team members with its collaborative tools. These features facilitate better communication and coordination, allowing for more effective teamwork and streamlined research processes.

Our Digital Lab Platform offers LIMS and ELN capabilities, which are comprehensive solutions for automating and optimizing lab processes.

Key factors to consider when choosing a laboratory information management system

When selecting a LIMS for your lab, several key factors must be carefully considered to ensure the system supports your unique workflows and research demands. Here are some critical aspects to evaluate:

Flexibility

The ability to adapt the LIMS to your lab's unique processes is crucial for operational success. SciSure offers unparalleled flexibility, allowing labs to tailor the system to their specific workflow requirements. Whether you need to customize data fields, create specific workflows, or integrate unique lab protocols, SciSure can be configured to meet your lab's precise needs, ensuring that it grows and evolves alongside your research demands.

API and integration capabilities

A robust Application Programming Interface (API) facilitates seamless integration with other systems and instruments, enhancing data flow and operational efficiency across the lab. SciSure's API and Software Development Kit (SDK) capabilities allow for extensive customization and integration, catering to the unique needs of your lab.

User-friendliness

An intuitive interface is key to ensuring all team members, regardless of their technical expertise, can effectively interact with and utilize the LIMS. SciSure boasts an easy-to-use interface designed specifically for life science R&D, ensuring a smooth user experience. The user-friendly design minimizes the learning curve, allowing researchers to focus more on their scientific work rather than navigating complex software, thereby increasing overall productivity and user satisfaction.

Automation capabilities

Automation within a LIMS significantly reduces manual data entry, minimizes errors, and boosts productivity. SciSure offers powerful automation features, streamlining various laboratory processes such as sample tracking, data entry, and report generation. By automating repetitive tasks, labs can reduce the risk of human error, ensure data accuracy, and free up valuable time for scientists to focus on critical research activities.

Implementation timeline

A swift and smooth implementation process is essential to minimize disruption to lab operations and ensure quick adoption of the new system. SciSure provides a streamlined implementation process, ensuring your lab can quickly benefit from its features. The dedicated implementation team works closely with your lab to provide training, support, and customization, ensuring that the transition is seamless and that your team is well-equipped to utilize the system effectively from day one.

Practical tips for selecting the best LIMS for your lab

Selecting the right LIMS for your lab can be a daunting task, but these practical tips will guide you through the process. From assessing your lab's specific needs to planning for future growth, we've got you covered:

1. Assess your lab's specific needs

Understanding your lab's specific requirements is the first step in selecting the right LIMS. Consider both current needs, such as sample tracking and data management, and future growth to ensure the selected LIMS can scale with your lab. Additionally, evaluate any specialized requirements your lab might have, such as integration with existing equipment or specific compliance standards.

2. Evaluate multiple vendors

Take the time to explore and compare what different vendors offer. Request demos and trials to get a hands-on understanding of how each LIMS works. Consider feedback from existing users and look for reviews or case studies that reflect the experiences of labs similar to yours. This can provide invaluable insights into the reliability, user-friendliness, and overall performance of the LIMS from each vendor.

3. Consider the total cost of ownership

Look beyond initial costs and assess the long-term value of the LIMS. Consider aspects such as ongoing maintenance, customer support, training, and software upgrades, all of which can significantly impact the total cost of ownership. Make sure to ask vendors about any hidden costs or fees that might arise over the product's lifespan.

4. Plan for the future

Ensure the LIMS you choose is capable of scaling with your lab. Look for solutions like SciSure for Research, which offers flexibility and ongoing support to help you adapt to your evolving needs. Make sure the system can handle increased data loads, support additional users, and integrate with new technologies as they become available. Planning for future expansion will help ensure your lab remains efficient and effective as it grows.

By carefully following these steps, you can ensure that you choose a LIMS that will serve your lab effectively now and in the future.

Weighing up your options

Choosing the right LIMS is a critical decision that can significantly impact the efficiency and effectiveness of your laboratory operations. By considering the specific needs of your lab, evaluating the options available, and focusing on long-term value, you can choose a LIMS that will support your lab's success well into the future. 

SciSure offers a comprehensive, customizable solution that combines the best of LIMS and ELN functionalities, making it an ideal choice for labs of all sizes and specialties. Discover the benefits of investing in the right LIMS for long-term success and take the first step towards optimizing your lab operations with SciSure.

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This article was originally published by eLabNext prior to its integration into SciSure. SciSure was formed in 2025 through the merger of eLabNext and SciShield.

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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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 SciSure, 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. SciSure 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 SciSure 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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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 SciSure (formerly 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 SciSure 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 SciSure 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 SciSure, 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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Lab management is the backbone of any successful research environment. From coordinating lab operations, managing resources, and guiding personnel, lab managers ensure that research runs smoothly, efficiently, and safely. Effective lab management doesn’t just keep the lab organized—it drives innovation, ensures compliance with safety and regulatory standards, and enables the lab to reach its full potential.

But what exactly does lab management involve, and how can it elevate a lab from good to great? In this guide, we’ll break down the key elements of lab management and share practical tips, including how digital lab solutions can streamline and optimize your operations.

In this blog, we will highlight: 

1. What is lab management?
2. What does a lab manager do?
3. The benefits of digital lab solutions for lab management 
4. Essential Lab Management Tools and Technologies
5. 5 tips for mastering lab management

‍What is lab management?

Lab management encompasses a wide range of activities crucial for a laboratory's smooth day-to-day operation. These activities range from managing a diverse team of scientists and technicians to overseeing the procurement and allocation of resources and maintaining up-to-date lab protocols. 

Additionally, effective lab management involves maintaining an organized and well-equipped workspace conducive to research productivity. Lab management extends to less tangible but equally important tasks, such as fostering a positive lab culture and facilitating communication among staff members.

What does a lab manager do?

A lab manager plays a crucial role in ensuring the smooth operation of a laboratory. They are the backbone of the lab, responsible for balancing scientific research with daily administrative tasks. Lab managers oversee everything from staffing and scheduling to equipment maintenance and compliance. Some of the key responsibilities of a lab manager include:

Overseeing lab operations‍

Lab managers are responsible for coordinating day-to-day lab operations. This includes managing experiments, monitoring timelines, and making sure everything runs smoothly. They act as the central point of communication for the team, ensuring that everyone is aligned with the lab’s goals and processes.

Managing lab resources and inventory‍

One of the most important roles of a lab manager is managing the lab’s resources, from reagents and consumables to specialized equipment. They need to keep an accurate inventory, reorder supplies when necessary, and ensure that all lab materials are available and in working condition. Without proper inventory management, experiments could face costly delays due to missing or expired materials.

Ensuring compliance and safety‍

Lab safety and compliance with industry regulations are top priorities for lab managers. They are responsible for ensuring that safety protocols are followed, equipment is regularly inspected, and the lab meets regulatory requirements. This includes managing documentation for audits and certifications, which is critical for maintaining the lab's credibility.

Managing the lab’s finances‍

Lab managers also handle budgeting and financial oversight, which includes purchasing new equipment, managing supplier contracts, and ensuring that the lab operates within its financial limits. They need to optimize spending, often negotiating with vendors and making informed decisions about resource allocation.

Scheduling, supervising, and training staff‍

A lab manager is responsible for recruiting, training, and supervising lab staff. They ensure that team members have the skills they need to perform their tasks and encourage ongoing development through training programs. A collaborative and well-trained team is essential for maintaining a productive lab environment.‍

Implementing new technologies and digital tools

‍As scientific technologies evolve, so must the lab. Lab managers play a key role in integrating new technologies and digital tools, such as Laboratory Information Management Systems (LIMS) or Electronic Lab Notebooks (ELNs). These tools help streamline data management, improve workflow efficiency, and enhance collaboration among researchers.

The benefits of digital lab solutions for lab management 

In today's fast-paced research settings, digital tools have become indispensable for streamlining lab management processes. Digital lab platforms offer transformative benefits in overcoming the main challenges and pitfalls of traditional lab management. By integrating tools such as ELNs, LIMS, and Inventory Management Systems, labs can significantly enhance accuracy and efficiency.

Platforms like SciSure combine the features of ELN, LIMS, and much more to deliver comprehensive features that streamline every aspect of lab management, from protocol management to sample tracking and team collaboration.

Centralized lab data management

Managing large volumes of lab data is a complex task, especially when dealing with research findings, experiment results, and sample records. SciSure offers a centralized solution for lab data management, providing lab managers with a streamlined and organized approach:

• Centralized data repository: Store all experimental data, protocols, and sample information in a single, accessible platform. This eliminates fragmented data silos, ensuring that all team members can quickly access the latest information when needed.
• Search and retrieval: Advanced search functionality makes it easy to locate specific datasets, experiments, or samples within the platform, saving valuable time and reducing the risk of lost data.
• Real-time data syncing: Data entered or updated within the platform is instantly synced across the system, allowing lab managers and staff to work with real-time, accurate information.
• Data Structuring: Easily organize and categorize datasets according to project, experiment, or researcher, creating a structured and navigable system for all lab data.

Centralizing lab data helps labs maintain organized, easily accessible records, improving workflow efficiency and preventing data loss or mismanagement.

Protocol and SOP management

Effective lab protocol and SOP (Standard Operating Procedure) management is crucial for ensuring consistency, compliance, and accuracy in a lab setting. SciSure's digital lab platform allows for seamless creation, updating, and sharing of life science and biotechnology protocols across teams, offering:

• Version control: Automatically track and manage multiple versions of protocols, ensuring that all staff are using the most up-to-date procedures.
• Customizable templates: Create protocols tailored to specific experiments or workflows with customizable templates that simplify the setup process.
• AI-Generated Protocols: Utilize AI-powered add-ons to generate initial protocol drafts based on brief descriptions, speeding up protocol creation and ensuring accuracy.
• Remote access: Provide all team members with instant, remote access to protocols, reducing delays and preventing miscommunication over which SOPs to follow.

These features help labs maintain compliance with regulatory standards and foster consistent practices across all research activities.

Sample and specimen management

Managing lab samples and lab specimens is often a time-consuming task, but SciSure's platform offers powerful tools to simplify and automate this process:

• Barcode sample tracking: With automated barcode and RFID integration, the platform tracks every sample from collection to disposal, reducing the risk of misplaced specimens and ensuring accuracy in experiments.
• Centralized sample data: Store detailed information about each sample, including storage location, collection date, and experiment results, in a centralized digital system for easy access and retrieval.
• Expiry and condition monitoring: The platform includes automated alerts for samples nearing expiration or those stored under specific conditions, helping labs prevent the use of expired or compromised specimens.

By digitizing specimen management, SciSure ensures that labs can maintain accurate records, avoid costly errors, and optimize the use of valuable research materials.

Communication and collaboration

In a lab setting, effective communication and collaboration are critical for ensuring project success. SciSure's digital lab platform enhances team coordination with its robust communication features:

• Centralized data sharing: SciSure allows for seamless sharing of experimental data, protocols, and results in a centralized platform, ensuring everyone is on the same page.
• Real-time collaboration: Teams can work together in real-time, accessing the same datasets and making updates collaboratively, no matter where they are located.
• Task and project management: The platform includes built-in tools for assigning tasks, tracking project progress, managing timelines, and ensuring lab activities remain organized and on track.
• Discussion threads and comments: Enable researchers to provide feedback or ask questions directly within the platform, reducing the need for endless email chains and improving clarity.

These communication tools foster a collaborative and transparent lab environment where team members can work together efficiently and share insights with ease.

Lab security

In any laboratory setting, the security of sensitive data and specimens is paramount. SciSure's Digital Lab Platform provides robust lab security features to protect valuable lab assets, ensuring compliance with industry standards and safeguarding research integrity:

• Data encryption: All data stored within the platform is encrypted, ensuring that sensitive information is protected from unauthorized access or breaches.
• Role-based access control: Assign specific access levels to different users based on their roles within the lab, ensuring that only authorized personnel can view or edit certain data or protocols.
• Audit trails: Every action performed within the platform is tracked, providing a comprehensive audit trail that lab managers can review to ensure accountability and transparency. This is essential for both security and compliance with regulatory standards.
• Regular backups: SciSure provides automated backups of all lab data, ensuring that crucial information is never lost, even in the event of system failures or accidents.
• Compliance with data protection regulations: The platform is built with data protection regulations in mind, including GDPR and HIPAA, ensuring that labs meet the necessary standards for data security and privacy.

With SciSure's security features, lab managers can confidently manage their labs, knowing that sensitive data and specimens are protected against breaches and data loss.

By simplifying everyday operations and boosting security, SciSure's Digital Lab Platform enhances lab productivity, minimizes administrative burdens, and helps lab managers run their labs more efficiently while maintaining high standards of safety and compliance.

5 tips to master lab management

As we have seen, managing a laboratory efficiently is crucial for fostering innovation and maintaining high standards in scientific operations. Whether you're looking to improve workflow, boost team morale, or ensure consistency in your operations, these 5 top tips will provide you with the tools necessary for success.

1. Leverage Technology: Digital lab platforms like SciSure can streamline lab processes, improve data management, and enhance collaboration. By automating manual tasks and centralizing information, these tools can help labs work more efficiently and effectively.

2. Develop Standard Operating Procedures (SOPs): Establishing clear SOPs for common lab tasks ensures consistency, reduces errors, and helps onboard new team members quickly. SciSure offers templates and a generative AI add-on to help boost your SOP writing.

3. Foster a Culture of Collaboration: Encourage open communication and knowledge sharing among lab members. Regular meetings, journal clubs, and team-building activities can help create a supportive and collaborative environment.

4. Invest in Training and Development: Provide ongoing training and professional development opportunities for lab staff. This can help improve skills, boost morale, and retain top talent.

5. Continuously Monitor and Improve: Regularly assess lab performance using key metrics and seek feedback from team members. Use this information to identify areas for improvement and implement changes to optimize lab operations.

By embracing technology, refining processes and promoting a culture of collaboration and continuous improvement, you can not only optimize current operations but also pave the way for future successes. Remember, the goal of effective lab management is not just to maintain the status quo but to continuously seek ways to innovate and improve.

‍Essential Lab Management Tools and Technologies

Effective lab management often relies on a suite of tools designed to streamline processes, ensure compliance, and improve team collaboration. Here’s an overview of some essential tools for any modern lab manager:
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‍Lab management done right

Effective lab management is essential for the success of any scientific research organization. By understanding the key components of lab management, leveraging digital tools, and developing strong leadership skills, lab managers can create a thriving research environment that drives innovation and discovery.

Digital lab platforms like SciSure can be powerful allies in this pursuit. By streamlining processes, improving data management, and enhancing collaboration, these tools can help labs work more efficiently and effectively. As the scientific landscape continues to evolve, embracing digital transformation will be key to staying ahead of the curve and achieving research excellence.

Want to explore how SciSure could transform your lab management processes? Book a free personal demo today.

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This article was originally published by eLabNext prior to its integration into SciSure. SciSure was formed in 2025 through the merger of eLabNext and SciShield.

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.

SciSure (formerly 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. 

SciSure 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, SciSure 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 SciSure 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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Biomedical research is critical in advancing healthcare and understanding biological processes. This work often involves handling potentially hazardous biological agents, making biosafety a critical aspect of laboratory operations. As research continues to innovate and explore new frontiers, the need for robust biosafety programs remains a priority.

Understanding Biosafety Levels (BSLs)

The Centers for Disease Control and Prevention (CDC) define biosafety levels through the Biosafety in Microbiological and Biomedical Laboratories (BMBL) guidelines. The BSL framework is intended to categorize laboratories based on the risk posed by the biological agents they handle. This framework outlines four biosafety levels (BSL-1 to BSL-4) with increasing stringency in safety practices and containment measures.

• BSL-1: This is the basic level for handling minimal risk agents, suitable for work involving well-characterized agents not known to cause disease consistently in immunocompetent adult humans. Basic practices such as hand washing, the use of personal protective equipment like lab coats and gloves, and good laboratory hygiene are typically sufficient.
• BSL-2: This level applies to laboratories working with moderate-risk agents that can cause human disease but are typically spread through direct contact. Additional precautions beyond BSL-1 include controlled access to the laboratory, specific training for personnel, and the use of appropriate personal protective equipment.
• BSL-3: This level is for handling high-risk agents that can cause serious or fatal diseases via inhalation or exposure to bodily fluids. In addition to BSL-2 controls, BSL-3 facilities have additional engineering controls such as specialized ventilation systems to prevent the release of infectious aerosols.
• BSL-4: This is the highest biosafety level for laboratories working with dangerous and exotic agents that pose a high individual risk of aerosol-transmitted infections and life-threatening disease that are frequently fatal and for which there are no vaccines or treatments. This level is required for related agents with unknown risk or route of transmission. Most stringent safety and containment measures including complete isolation from the outside environment through multiple airlocks and highly specialized ventilation systems.

Key Components of Biosafety

Effective biosafety controls include several key components:

• Facility Design: The laboratory design itself plays a critical role in biosafety. Well-designed facilities incorporate physical barriers like self-closing doors, airlocks, sealed walls, and HEPA-filtered ventilation systems to control airflow and prevent the escape of pathogens. The layout of the lab should also minimize the risk of contamination by separating areas with different biosafety levels.
• Engineered Equipment: Safety equipment plays a critical role in biosafety. Primary barriers in biosafety are physical containment measures (e.g., biological safety cabinets (BSCs), centrifuge safety cups) that protect laboratory personnel and the environment from exposure to infectious agents. They are the first line of defense against splashes, spills, and aerosols that can contain harmful biological agents.
• Safe Work Practices, Training, and Procedures: Safe lab practices minimize the risk of exposure to infectious agents by ensuring aseptic techniques, having established protocols for handling biological materials, and ensuring proper disposal of biohazardous waste.
• Risk Assessment: A biosafety risk assessment is a systematic process that identifies, evaluates, and mitigates risks associated with the use of biological agents. It aims to:
  • Identify procedural hazards and hazardous characteristics of biological agents that are handled.
  • Classifies biological agents into risk groups according to their degree of risk of infectivity, virulence, pathogenicity, availability of preventive measures and effective treatments, and potential damage to the environment.
  • Determine the appropriate biosafety level for controls and restriction understanding.
  • Consider biosecurity which focuses on the prevention of theft, loss, and misuse of hazardous biological agents and toxins, equipment, and/or valuable information.
  • Identify and implement controls to minimize the risk of exposure to biological agents for workers, the environment, and the community.
  • Ensure regulatory compliance with local and national biosafety regulations.

Learn how to create your own biosafety risk assessments in our guide.

• Personal Protective Equipment: Personal protective equipment (PPE) like gloves, gowns, and respirators are the last line of defense and provide additional protection. This combination minimizes the risk of exposure to infectious agents during laboratory procedures.

Maintaining Biosafety in the Workplace

Improving biosafety hazard identification, evaluation and control can be attained by doing the following:

• Form a Biosafety Committee: A dedicated committee with expertise in biosafety and laboratory operations should oversee the implementation and monitoring of biosafety research and operations. This committee, composed of laboratory personnel, EHS professionals, and operational stakeholders should oversee all aspects of biosafety within the facility.  Their responsibilities include reviewing proposed research activities for EHS concerns, approving the use of biological agents, and ensuring compliance with biosafety requirements.
• Conduct Regular Risk Assessments: Employers must conduct biosafety risk assessments to determine which employees have exposures from work activities or conditions that are reasonably anticipated to elevate risk of contracting a disease caused by an infectious agent. Laboratories that perform procedures with biohazardous materials that contain transmissible pathogens are likely to have occupational exposure to these agents. A biosafety risk assessment evaluates risks, so the appropriate control measures are implemented to prevent illness. Regularly assess the risks associated with the biological agents being handled and adjust protocols as needed.
• Develop Emergency Preparedness and Response Plans: Emergency preparedness and response plans must cover procedures to handle spills and exposures, an exposure control plan, accessing emergency contact information, and how to report an incident involving biohazards. Facilities where human blood or other potentially infectious materials (OPIM) exist as an occupational hazard should have a specific exposure control plan. The purpose of the exposure control plan is to describe practices to prevent or minimize exposure to human blood, bloodborne pathogens (BBP), or other potentially infectious materials (OPIM).
• Training and Competency: The priority of biosafety training is to reduce the risks of biological agents through awareness and work practices. Do the following in biosafety training:
  • Communicate containment measures associated with biological risk groups and biosafety levels.
  • Educate laboratory personnel on safe practices for handling biological materials and the prevention of laboratory acquired infections.
  • Ensure compliance with regulations.

Challenges and Solutions in Biosafety Management

Challenges to managing effective biosafety programs include:

• Keeping Up with Emerging Threats: New and emerging infectious diseases can pose unforeseen risks.
  • Regular biosafety program reviews and updates are critical to address evolving threats. Design biosafety programs with the ability to adapt to new threats.  
  • Encourage collaboration and information sharing between researchers, institutions, and public health agencies to stay informed about emerging pathogens.
  • Regularly review risk assessments to categorize biological agents and adjust biosafety levels as needed.

• Resource Limitations: Implementing and maintaining stringent biosafety measures can be resource-intensive, especially for smaller labs. Biosafety programs require investment in proper facilities, equipment, training, and qualified personnel.
  • Explore grant opportunities and public-private partnerships to secure funding for biosafety infrastructure and training.
  • Focus resources on areas with the highest risk profiles.
  • Technology like hazardous substance inventory software can help address some of these challenges by streamlining processes, improving data management, and facilitating regulatory compliance.
• Training Needs: Ensuring continuous training for all laboratory personnel requires ongoing commitment and resources.
  • Implement training programs to keep staff up-to-date on the latest biosafety protocols and techniques.
  • Conduct regular assessments to evaluate staff proficiency in biosafety procedures.
  • Best practices like sharing resources and collaborating with institutions or industry groups can optimize training programs and cost-effectiveness.

Biosafety Regulatory Bodies and International Standards

Organizations like the CDC, NIH, and WHO play a crucial role in establishing and promoting global biosafety standards. Their guidelines offer a framework for national regulations and promote international collaboration in research.

• Centers for Disease Control and Prevention (CDC) - In collaboration with the National Institutes of Health (NIH), the CDC publishes the Biosafety in Microbiological and Biomedical Laboratories (BMBL) guidelines.  This document serves as the primary reference for safe laboratory practices in the US, outlining recommendations for containment measures, biosafety levels, and safe work practices for various biological agents.

• National Institutes of Health (NIH) – In addition to the BMBL guidelines mentioned above, the NIH sets biosafety requirements for institutions that receive NIH funding for research involving biological agents.

• World Health Organization (WHO) - The WHO issues biosafety guidelines that provide recommendations for laboratory practices in resource-limited settings and other international contexts.

Conclusion

In conclusion, biosafety controls are crucial during the design of laboratories and operational stages. Engineering controls, safe work practices, risk assessments, biosafety stakeholder engagement, and PPE will assist in mitigating risks to as low as reasonably achievable. By proactively identifying potential and emerging hazards associated with biological agents – including storage, use, and modification – and including the appropriate cross-functional interested parties in management of biosafety, organizations can establish a strong foundation for continual improvement of biosafety programs.

Materials that are hazardous due to their biological or infectious properties are called biohazardous materials or simply biohazards. Research laboratories work with a variety of biological agents including recombinant or synthetically derived nucleic acid, blood, tissues, body fluids, cell lines, bacteria, viruses, viral vectors, plasmids, fungi, prions, or parasites that cause disease in humans, animals, or plants. Recognizing these hazards and their associated risks is essential for implementing effective health & safety controls.

Understanding Biological Hazards

To ensure the safety of people, the environment, and the public, it’s crucial to have proper biosafety measures in place. Certain workplace safety laws require employers that are exposed to transmissible infectious pathogens to have effective written safety plans and controls in place. Laboratories that perform procedures with biohazardous materials that contain transmissible pathogens are likely to have occupational exposure to these agents.

Here are some common biohazard examples found in laboratories:

• Blood and Other Potentially Infectious Materials (OPIM): Human and animal blood, along with other bodily fluids, can transmit various pathogens.
• Laboratory Cultures and Samples:Working with cultured microorganisms and biological research materials carries the risk of exposure to infectious agents.
• Contaminated Sharps: Used syringes, needles, and other sharp instruments contaminated with biohazardous materials pose a significant risk of puncture wounds and potential infection.
• Fungal Growth: Mold and yeast can thrive in laboratory environments, causing respiratory problems and allergic reactions.
• Waste from Animals: Animal waste or contaminated bedding can harbor harmful pathogens.

Risk Groups and Biohazard Safety Levels

Biological agents are classified according to their risk level when considering infectivity, pathogenicity and availability of preventive measures and treatments for the corresponding disease. The National Institute of Health has established classification of biological agents into four risk groups (1-4).

The Centers for Disease Control and Prevention (CDC) define biosafety levels through the Biosafety in Microbiological and Biomedical Laboratories (BMBL) guidelines. The BSL framework is intended to categorize laboratories based on the risk posed by the biological agents they handle. This framework outlines four biosafety levels (BSL-1 to BSL-4) with increasing stringency in safety practices and containment measures. See our Biosafety Guidelines guide for more information on these risk groups and BSLs.

5 Opportunities to Prevent Biohazards Through Effective Biosafety and Biosecurity Governance

1. A single, centralized system for biological registration approvals

There's a growing demand for stricter regulations regarding oversight. Organizations in the US receiving federal funding have to comply with NIH Guidelines, and the White House OSTP recently introduced a comprehensive Policy for Oversight of Dual Use Research of Concern and Pathogens with Enhanced Pandemic Potential (DURC), effective from May 6, 2025. Similarly, the EU has its own directives concerning exposure to biological agents.

The emphasis is shifting from considering robust registration and approval systems for work involving biological agents as optional to regarding them as essential. Having a single, centralized system for biological registration approval ensures an organization adheres to high standards for biosafety, regulatory compliance, and internal processes with efficient oversight of projects, biological materials, and potential exposures.

2. Biosecurity - Who is working on what? Where?

Understanding who is working on what and where within a biomedical lab presents a pivotal opportunity for enhancing overall biohazard prevention strategies. By implementing comprehensive biological registration and authorization systems, organizations can gain real-time visibility into project activities, including tracking material, personnel, and space exposure levels.

Streamlining registration management processes reduces approval times and facilitates efficient communication, ensuring robust oversight and risk management throughout the project lifecycle.

Furthermore, driving consistency in materials management through standardized biomaterials libraries enables organizations to establish controls over materials usage, personnel, and locations, thereby enhancing biosecurity and internal compliance processes. This holistic approach to understanding lab activities fosters a culture of accountability and transparency, ultimately mitigating risks associated with biohazard incidents and safeguarding the well-being of personnel and the broader community.

3. Institutional Biosafety Committee Oversight for Biologicals Projects

Expanding the requirement for institutional biosafety committees to encompass all institutions engaged in high-containment laboratory activities presents a significant opportunity to enhance effective biosafety and biosecurity governance. Currently, only federally funded institutions conducting recombinant or synthetic nucleic acid research are mandated to have such committees, leaving privately funded institutions exempt from compliance. This discrepancy in regulatory oversight underscores the need for a level playing field, ensuring that all institutions conducting similar high-risk activities adhere to standardized review processes and oversight mechanisms.

By mandating a universal requirement for local oversight bodies, potential risks associated with laboratory-acquired infections and biological incidents can be mitigated. This approach not only ensures a consistent level of review and approval across all institutions but also addresses public concerns regarding the oversight and input of risky research conducted within communities. While there are challenges such as additional bureaucracy for non-compliant organizations and the need to establish and operate biosafety committees, the benefits of standardized oversight outweigh the disadvantages, promoting transparency, safety, and community trust in biosafety practices.

4. De-risking the Organization and Personnel with Training and Assessment of Biohazards

In high-containment laboratories like BSL-3 and BSL-4 facilities, personnel handle indigenous or exotic agents capable of causing severe or fatal diseases. Training in the safe handling of such pathogens is crucial for personnel, who must be supervised by knowledgeable scientists. However, the consistency and rigor of this training vary across institutions, highlighting the need for standardized assessment and guidance to ensure personnel proficiency and risk awareness.

Establishing minimum education and training requirements for personnel in high-containment laboratories offers several benefits for effective biosafety governance. It fosters confidence in the competence of individuals working with hazardous pathogens, promoting trust within the community. Moreover, by ensuring that personnel understand and can manage risks effectively, such standards enhance overall laboratory safety. However, implementing new requirements may pose challenges, particularly in adapting regulations across diverse research operations and ensuring compliance among existing workforce members. Despite these obstacles, standardized training protocols are essential for safeguarding personnel, institutions, and the wider community from biohazard risks.

5. Inspections and Medical Surveillance

By implementing robust biosafety management protocols with software such as SciSure for Health & Safety (formerly SciShield), organizations can identify personnel handling biological agents and mitigate exposure risks effectively. Leveraging Learning Management Systems (LMS) presents an opportunity to automate training assignments tailored to individual staff members' risk exposures, ensuring they receive necessary medical clearances, vaccinations, and skill enhancements. Simultaneously, optimizing equipment management processes ensures the availability and functionality of safety gear, bolstering biosecurity measures.

Strategic biosafety inspections of laboratory spaces provide organizations with a chance to ensure compliance with safety protocols, promptly addressing vulnerabilities. Embracing medical surveillance systems enables proactive tracking and management of personnel health, facilitating timely interventions such as medical clearances and vaccinations. By capitalizing on these opportunities, organizations can fortify their biosafety and biosecurity frameworks, creating safer environments for personnel and the wider community.

Managing Biological Hazards with Software

Implementing a robust biosafety program is essential to protect yourself, your colleagues, and the environment from biohazards. Investing in biological software solutions can significantly enhance your biohazard management practices, promoting safety, regulatory compliance, and overall lab hazard awareness.

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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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, the Developer Hub 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, the Developer Hub 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 SciSure 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 Marketplace. 
• SciSure 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 SciSure 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, the Developer Hubwill 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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What are Safety Data Sheets (SDS)? And what is the Globally Harmonized System of Classification and Labeling of Chemicals (GHS)?

How do chemical manufacturers, suppliers and users communicate chemical hazards and handing information to each other across the globe?  The Globally Harmonized System of Classification and Labeling of Chemicals (GHS) is a United Nations led international system provides a consistent approach to classifying and communicating the hazards of chemicals ensuring that chemicals are evaluated using the same criteria regardless of country, resulting in standardized labels and Safety Data Sheets (SDS).  

The GHS is the culmination of more than a decade of work. There were many individuals involved, from a multitude of countries, international organizations and other stakeholders. Their expertise spanned a wide gamut of subject matter expert areas including toxicology and fire protection. The GHS improves safety for workers around the world by ensuring they understand the risks associated with the chemicals they handle. GHS also simplifies international trade by eliminating the need for companies to re-label chemicals for different countries. There is currently a GHS Sub-Committee that is responsible for maintaining the GHS, promoting its implementation, and providing additional guidance as needs arise. The GHS guidance document is regularly revised and updated to reflect best practices and regulations. The latest version of the GHS guidance can be found here.

Safety Data Sheets (SDS) are critical for anyone who handles chemicals, from scientists in chemical laboratories to house cleaners using disinfectants. An SDS provides important information about a chemical's properties, hazards, and safe handling practices. In short, it's your guide to using chemicals safely and effectively.

What is the composition of a Safety Data Sheet?

An SDS is a comprehensive guide for storing and handling a chemical. Here's a breakdown of the key sections:

• Identification: This section gives you the chemical or product name, its intended uses, and the supplier contact details including emergency phone number. It also clarifies any restrictions on how the chemical should be used.
• Hazard(s) Identification: This section details the hazards of the chemical, including its classification (flammable, toxic, etc.), pictograms that illustrate these hazards, and specific GHS hazard statements and standardized codes that communicate the risks of exposure to the user.
• Composition/Information on Ingredients: This section reveals the ingredients that make up the chemical, including synonyms and chemical formulas. It highlights any substances that pose risks.
• First-Aid Measures: Here's where to find critical information on what to do in case of accidental exposure to the chemical, including inhalation, skin contact, ingestion, and eye contact.
• Fire-Fighting Measures: If a fire breaks out, this section provides guidance on the best way to extinguish it, what specific hazards to be aware of from the burning chemical, and the recommended protective actions and gear for firefighters.
• Accidental Release Measures:  This section outlines the steps to take in case of an accidental release, including how to protect yourself, the environment, and how to clean up the spill safely.
• Handling and Storage: Learn how to handle the chemical safely during everyday use and how to store it properly to prevent incidents or degradation.
• Exposure Controls/Personal Protection: This section details the occupational exposure limits (OELs) for the chemical and recommends appropriate engineering controls or personal protective equipment (PPE) to minimize exposure risks.
• Physical and Chemical Properties: Familiarize yourself with the chemical's basic properties like appearance, odor, boiling point, and flash point in this section.
• Stability and Reactivity: This section explains how stable the chemical is and what conditions could trigger hazardous reactions. It also identifies incompatible materials to avoid storing near the chemical.
• Toxicological Information: This section includes details on the potential health effects of exposure to the chemical, including both acute (such as irritation) and chronic (such as carcinogenicity) effects.
• Ecological Information: This section focuses on the chemical's impact on the environment, including its toxicity to aquatic and terrestrial organisms, its persistence, and how it moves through the soil.
• Disposal Considerations: This section provides guidance on proper waste treatment methods and how to handle contaminated waste containers.
• Transport Information: If you need to transport the chemical, this section provides information on UN identification numbers, shipping classifications, and any special precautions for transport.
• Regulatory Information: This section details relevant safety, health, and environmental regulations governing the specific chemical.
• Other Information: This section might include the date of the latest SDS revision, additional safety advice, and references for further information.

The GHS uses a set of pictograms to visually communicate hazards.

Taken from: https://www.osha.gov/sites/default/files/publications/OSHA3491QuickCardPictogram.pdf

Conclusion

The SDS should provide comprehensive information about a substance or mixture for use in workplace chemical control regulatory frameworks. Both employers and workers use it as a source of information about hazards, including environmental hazards, and to obtain advice on safety precautions. This information is a reference for the management of hazardous chemicals in the workplace. An SDS should be produced for all substances and mixtures which meet the harmonized criteria for physical, health, or environmental hazards under the GHS and for all mixtures which contain ingredients that meet the criteria for carcinogenic, toxic to reproduction or specific target organ toxicity in concentrations exceeding the cut-off limits for SDS specified in the GHS guidance.  SDSs are often mandated by regulations, and having them on-hand ensures compliance.  The information in an SDS is valuable for risk assessments and training programs, promoting an overall safer work environment. Other factors to manage with SDSs at your organization are:  

• Accessibility and Understanding: Many employers provide SDSs electronically in a centralized system that is accessible to all employees and/or they keep paper copies. Familiarize yourself with how to access and read an SDS. SciSure can help you manage and access SDS documents electronically, thereby ensuring you always have the most up to date SDSs onhand. Learn more about our solutions on the Chemical Safety Software page.

• Changes to an SDS: Regulations and toxicological understanding of chemicals can evolve. It's crucial to ensure the Safety Data Sheets in your inventory are up to date. Suppliers should also periodically review the information on which the SDS for a substance or mixture is based, even if no new and significant information has been provided. This requires searching chemical hazard databases for new information. Typically, suppliers should review SDS information every 3 to 5 years.
• Creating and Managing SDS: If you're a chemical manufacturer or distributor, there are resources to create and manage your SDSs.

Schedule a demo with us here to learn more.

This article was originally published by eLabNext prior to its integration into SciSure. SciSure was formed in 2025 through the merger of eLabNext and SciShield.

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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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 SciSure (formerly eLabNext), enable researchers to take a comprehensive approach to data structuring, integration, and management. Contact us today to learn more!

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