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

In this blog, we will highlight: 

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

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

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

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

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

What are the main differences between Lab Protocols and SOPs?

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

Lab Protocols:

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

Standard Operating Procedures (SOPs):

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

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

Importance of standardization in lab procedures

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

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

Consistency and Reproducibility

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

Quality Control

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

Lab Safety

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

Efficiency and Time Management

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

Regulatory Compliance

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

Cost Efficiency

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

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

How to write an effective lab procedure

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

1. Define the Purpose

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

2. Scope and Application

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

3. List of Materials and Equipment

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

4. Detailed Steps

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

5. Safety and Compliance Notes

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

6. Quality Control Checks

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

7. Documentation and Reporting

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

8. Review and Approval

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

9. Revision History

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

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

Streamlining lab protocol creation and sharing with digital lab solutions

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

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

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

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

AI protocol generation and beyond

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

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

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

A better way to manage lab procedures?

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

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

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

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

Innovative Digital Features to Accelerate Lab Operations

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

Key eLabNext Developer Features for Lab Digitalization

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

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

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

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

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

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

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

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

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

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

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

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

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

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

New Trend: White Label Software in Big Pharma

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

White Label Software Examples

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

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

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

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

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

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

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

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

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

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

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

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

The Big But! 

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

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

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

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

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

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

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

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

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

Conclusion

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

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

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

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

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

What is Generative AI? 

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

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

Gen AI Encompasses a Wide Range of Algorithms and Approaches

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

There are several algorithms and approaches, including:

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

How Does Gen AI Work?

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

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

How is Gen AI Currently Used in Biotech?

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

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

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

What Are the Concerns About Using Generative AI in Biotech?

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

Day-to-Day Usage of AI for Lab Operations

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

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

Summary

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

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

In this blog, you will learn: 

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

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

What’s involved in lab operations?

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

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

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

Essential laboratory operations

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

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

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

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

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

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

Laboratory roles

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

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

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

Operational challenges faced by non-digitalized labs

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

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

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

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

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

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

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

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

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

How to optimize lab operations with digital tools

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

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

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1. Enhanced data management

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

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

2. Automated lab workflows

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

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

3. Improved sample and inventory management

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

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

4. Streamlined protocol and SOP management

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

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

5. Enhanced compliance and security

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

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

6. Seamless collaboration and communication

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

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

The formula for operational excellence 

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

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

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

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

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

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

Diversity of Data Types

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

Volume and Scale

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

Lack of Standardization

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

Temporal and Longitudinal Data

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

Metadata Complexity

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

Data Security and Compliance

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

Evolution of Analytical Techniques

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

User Adoption and Training

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

Integrating and Mapping the Data

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

The Solution to Data Management and Mapping Challenges

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

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

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

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

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

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

Merijn van Leeuwen, Coordinator Amazon & Atlantic Forest WWF Netherlands 

Doubling down on deforestation 

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

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

About World Wildlife Fund NL  

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

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

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

Key Features and Benefits: 

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

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

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

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

Electronic lab notebooks (ELNs) benefit both industrial and academic labs. The ability to quickly query all your laboratory activities to identify new avenues for discovery or to troubleshoot an ongoing issue is a massive advantage over traditional paper notebooks. 

However, setting up and maintaining an ELN so that all the benefits of going digital are available is not trivial. 

This is where an ELN consultant (such as Rebecca De Souza) can help set you and your lab up for success! In the blog below, we'll discuss what an ELN consultant is and the top 6 ways I've seen ELN consultants help laboratory teams achieve digitalization zen.

What is an ELN Consultant?

An ELN consultant is a laboratory professional who provides guidance and expertise in choosing, implementing, customizing, and optimizing an ELN for a laboratory. ELN consultants are crucial in helping research organizations effectively leverage digital tools, enhance collaboration, and ensure compliance with industry standards and regulations.

6 Ways an ELN Consultant Can Benefit Your Lab

#1: Take the setup burden off of your laboratory staff

Selecting and setting up an ELN often falls on the shoulders of laboratory personnel, who must balance ELN implementation with their usual lab responsibilities. This makes sense, given that they will be the primary users of an ELN. 

However, it does take away valuable time spent conducting research or other laboratory tasks. In addition, given the newness of ELNs, many lab workers don't have experience using an ELN, much less know the best path to selecting and setting one up. 

An ELN consultant works with lab professionals tasked with ELN selection and setup, providing valuable knowledge through experience. They can help your team establish your lab's needs, show how different ELNs compare when meeting these needs, create a plan for ELN rollout, identify common setup pitfalls and how to avoid them and assist with the training staff. While laboratory staff involvement is a critical component of ELN setup success, having an ELN consultant to help ensures that the laboratory team can focus more on their everyday responsibilities while still staying involved in the ELN selection and implementation process.

#2: Navigate the needs of stakeholders

Three primary stakeholders are invested in the success of an ELN:

1. The corporation or principal investigator (PI) / lab head
2. Legal and IP teams
3. Laboratory staff

Each of these entities has a vested interest in ensuring that lab data is recorded, searchable, and auditable. 

However, each party's expectations may differ or be in direct conflict. For example, the corporation or PI and legal and IP stakeholders may expect overly detailed ELN record keeping but not consider the time investment or practicality of keeping records up to date. Discordance between these three groups can result in low ELN compliance and confusion about the expectations or purpose of the ELN, which can ultimately lead to the abandonment of an ELN. 

Having navigated this trifecta of needs before, an ELN consultant can help those tasked with selecting and setting up an ELN. The consultant can directly interface with all three stakeholders or assist those in charge of the ELN implementation and maintenance with these interactions. Keeping this trifecta of stakeholders in balance will ultimately lead to the long-term success of your lab's ELN.

#3: ELN organization

While the name ELN suggests that it is a simple replacement for a traditional paper notebook, it's more akin to a filing cabinet than a notebook. As this analogy suggests, an ELN (depending on which one you're using) is more like an organizational system allowing data input and storage. 

Most ELNs have a built-in organizational structure. For example, the eLabNext Digital Lab Platform has the Project>Study>Experiment structure, with additional options to create project groups as an extra layer of organization. 

It can be tempting to allow each individual or team in your lab to determine how to use the built-in organizational tools; however, this can lead to confusion when searching for specific data. How a corporation or lab decides how to structure its ELN organization will depend on their individual needs, and an ELN consultant can use their experience to help guide that decision and harmonize the organization structure across team members. If you already have an ELN and need to restructure and re-organize, an ELN consultant can also help with this!

#4: Generating workarounds and increased efficiency

If carefully selected, the ELN you choose should be capable of fulfilling most of your lab's needs — emphasis on the "most," as there are often lab-specific scenarios that no software developer could preconceive. ELN consultants can help devise workarounds to cover these unforeseen situations, whether they are suggesting alternative approaches or working with a software developer on your behalf to create a solution. They can also assist by using their experiences working in a lab with an ELN to assess the impact of required ELN tasks on day-to-day laboratory workflows and where streamlining can be applied to increase efficiency.

#5: Better training and documentation

After selecting an ELN, deciding on its organizational structure, and setting it up, it must be rolled out to the larger team or organization. This involves creating an ELN handbook or usage policies and providing training. 

An ELN consultant can assist by directly creating these documents and training sessions/videos. They can also work with your designated laboratory, "Super Users," to help train current and new staff or with additional training when new ELN features become available. This can reduce your staff's training burden so they can focus on their other research-focused objectives.

#6: ELN compliance

An ELN is only as useful as the data recorded in it. As such, ELN compliance is vital for success. Many of the topics discussed above can impact ELN compliance, and an ELN consultant can assist with avoiding common compliance pitfalls. 

Suppose your organization or lab has known non-compliance issues or needs help identifying compliance issues. An ELN consultant can assist you with assessing the current degree of compliance, identifying barriers to compliance, and creating strategies for increasing compliance.

Experience the Benefits of an ELN Consultant

An ELN consultant can provide valuable assistance as your organization or lab navigates the ELN landscape. They can reduce the burden of setting up and maintaining an ELN, allowing your team to return to what matters most: their next discovery! 

If you need an ELN consultant, contact me at rebecca.a.g.desouza@gmail.com or connect with me on LinkedIn.

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What is a Health Check?

A health check is a checkup on the health of the lab's digital operations and progress using impact-driven metrics.

• The Purpose: Constant productivity improvement – on all fronts!  
• Impact-driven metric examples:
  • % of samples digitized within a period of time (e.g., two freezers by Q2)
  • % of SOPs digitized within a period of time (e.g., 20 legacy SOPs)
  • % of Electronic Lab Notebook entries and reports digitized within a period (e.g., 30% of ELN entries and reports digitized by the end of the year)

The Importance of Healthchecks at eLabNext

If we at eLabNext do not know how our customers are using the system, what features they are underutilizing, which features they wish they had, how effective system deployment has been, and how implementation has impacted the internal lab culture, we are not doing our job! 

The reality is that customers define many of the new features we develop, and we take pride in understanding their needs through conversations and having an actual methodical approach to building our community of innovative and creative users and proactively seeking their opinions. In the world of Customer Success, this process is called a "Health Check." At the same time, we cannot ensure our customer's success if we do not establish responsibility and accountability to implement the system sustainably.

Health checks are similar to going to your Primary Care Physician for a physical or taking your car for an oil change. In the tech world, a health check is a periodic check that examines your lab's/business' technological and usability health. Depending on the nature of the company and the industry, these technical health checks can cover a wide range of assets, applications, policies, technology, people, or business operations.

When we talk about a Customer Success Health Check at eLabNext, we refer to a comprehensive assessment of your Technical and Usability Health. This allows us to better facilitate successful usage, implementation, and customer satisfaction.

Impact Goals Assessments

Upon contract signature with eLabNext, our Customer Success and Lab Digitization Specialists will meet with key stakeholders to understand the lab's short- and long-term goals. Specifically, they focus on:

1. Digital Lab Strategy: What is the organization's Digital Lab Strategy, and what are the short—and long-term goals?
   • How will you assess your success, and what are your internal key performance indicators (KPIs)?
   • What are your one-month, three-month, six-month, nine-month, and twelve-month goals? What percentage of your lab's operations and physical items would you like to have digitized or automated?
2. Lab Assets: How many storage units, equipment, and supplies should be tracked within eLabNext's Digital Lab Platform (DLP)?
3. Sample Migration and Strategy: How many legacy samples should be digitized immediately, and what is the lab's long-term Sample Strategy?
4. Sample Automation: How automated should sample management workflows be, and what does your data structure and standardization look like?
5. SOP Management: How many and which SOPs need to be digitized, edited, and used with version control in the short- and long-term?
6. Project and Report Management:
   • How many projects/programs do you need to track currently?
   • What is the structure and cadence of your Lab Reports?
   • How will the results be tracked and accessed?
7. Automation, Integration, and Customization: What is your digital tool development strategy, what integrations would you like to have, and are there any specific workflows for which you'd like to create customizations?

Having answered these questions, our team helps the customer define a series of impact goals to achieve their lab's digitization vision. This approach creates an objective framework for measuring the success of the customer's digitization efforts and identifying underutilized areas of the platform from which the customer can achieve greater value. With each health check call, Customer Success looks at the progression of these goals across three timeframes:

PAST

• What pain points was the customer's team previously experiencing?
• What digitization impact goals were previously set to address these pain points?
• Were any additional steps or calls scheduled with the eLabNext team to support these goals:
  • Key user training sessions with Digital Lab Consultant
  • New feature releases/demos
  • Activation of add-ons from the Marketplace
  • Workflow implementation walkthroughs with Customer Success
  • Import templates to migrate Inventory data into the platform

PRESENT

• How well does the customer feel those goals were met:
  • Subjectively: How satisfied are users with their Digital Lab Experience compared to before the last health check call? Have they received new value from the system? Are workflows more streamlined than before?
  • Objectively: How frequently are users logging in to engage with the platform? Are they creating more samples, protocols, or experiments in the system than before? How many experiments have been signed off and completed?
• Are the impact goals still as relevant today as they were when they were set?
• Are there new pain points that need to be addressed?

FUTURE

• What new impact goals does the customer want to pursue going forward? What existing goals still need to be achieved?
• How will the customer prioritize their revised impact goal statement?

It helps to think of your lab in this context: If you take your lab to the doctor, would they say it is healthy or notice symptoms that need to be addressed?

Tools and technologies are great, but they're only as good as their implementation and the desired success they generate. Leveraging eLabNext's health check framework, we plan to ensure our customers' success. To align, reach out to your Lab Digitization Specialist and/or Customer Success Specialist.

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In the lab software space, the smoke is starting to clear.

And what I've seen isn't pretty; it's the aftermath of failed implementation of electronic lab notebooks (ELN) or laboratory information management systems (LIMS) that don't fit the needs of Biotech and BioPharma laboratories.

On one extreme, I see classic Silicon Valley tech software organizations focused more on pretty user interfaces (UI) than truly valuable lab workflow management. Conversely, I see function-heavy, extremely technical, but non-user-friendly software, limiting adoption and use. The rise of AI/ML in drug discovery has further complicated the landscape, with scientists' attention diversifying, adding additional difficulties in the decision-making process of which laboratory software platform to use.

Overall, these problems are related to a common issue: the lack of a holistic approach to a lab's core challenges.

The Solution: A Sample and Digital Strategy

We at eLabNext are prepared with a solution, a new concept and approach called "Sample and Digital Strategy."

A sample is the focal point of any lab, whether cell lines, antibodies, plasmids, blood, DNA, RNA, protein, or a mouse colony. Everything starts with a sample! This sample has metadata attached to it and file outputs from your instruments, all of which can amount to millions of datasets, also known as a deep data lake.

If you do not have a strategy for efficiently managing this data and making it accessible to all of your departments, you are at risk of data loss and potential loss of IP, a victim of poor business strategy decisions. In other words, Sample and Digital Strategy is foundational in defining the lab's business strategy and ultimately picking the right software to fit your lab's needs.

5 Easy Steps to Implementing a Sample and Digital Strategy

If all of this sounds a little too familiar, it's likely time to transition away from your current software solution and one that allows the implementation of a Sample and Digital Strategy. Here's a step-by-step process for identifying a better solution for you.

Step #1: Define your Sample Strategy

Get your Sample Strategy in order. That means:

• Consolidate your freezers and samples and use this time as an opportunity for spring cleaning, both physically and digitally. You probably have random unlabeled samples lying around; claim or toss them.
• Clean up your Excel sheets!
• Export the data out of old-school software or the currently problematic ones! Organize it, make your data structured, and prep it for import into a new system!
• Consider following the guidelines provided by our #Sample360 initiative!

Step #2: Define your Digital Lab Strategy

We talk about Business Strategy. We discuss Research and IP strategy. But we hardly discuss Digital Strategy.

These days, you cannot have sustainable operations and a sustainable lab workflow if there isn't a robust digital strategy defined right from the beginning for the lab. Within the next five years, AI and ML will completely revolutionize how we analyze our data, and if you do not start structuring your data now, you'll fall behind. We can help you with defined steps on how to centralize your data and develop naming conventions, search, and accessibility prompts to structure your data and grow into your workflow rather than outgrow it as soon as the number of samples increases.

Find out more about how Bayer is doing this now with eLabNext!

Step #3: Prepare for Technical Transition

Prepping technically means not just diving in and releasing new software on your staff. Take a logical and strategic approach:

First, identify the tech-savvy champions on your team that will lead this project.

1. First, identify the tech-savvy champions on your team that will lead this project.
2. Prepare and organize the list of your storage units (e.g., freezers, shelves, racks, etc.) and equipment (e.g., balances, HPLCs, mass specs, etc.).
3. Prepare a list of all your samples and supplies.
4. Make sure your Excel files are standardized and clean.
5. Define a project/program and experiment naming convention if you haven't already. 
6. Identify all other software currently used in the lab for potential integrations.

Step #4: Prepare for Training

Prepare your team for Digital Strategy Training. That means doing the following:

• Assess the existing skillset and identify the folks that have expertise in implementing new technology, and most importantly, those that are resistant to change.
• Develop a training plan.
• Choose the right trainers and make sure that your Digital Lab Consultant (if you've hired one) is someone you enjoy working with!
• Provide hands-on experience and lead by example! You can't promote lab digitization if you aren't digitized yourself.
• Foster a culture of continuous learning. Once the training is done, learning doesn't end. With software and digital solution implementations, there are constantly new updates, new features, and creative ways to optimize your physical lab workflows. Find out more about efficient implementation here.
• Measure your lab's progress and outcomes.

Step #5: Set Deadlines for Implementation and Transition

Set deadlines, or else it'll be a never-ending project. Digital and Sample Strategies need a strong foundation, but it also needs to be cultivated constantly. For the initial kickoff of implementing new ways of doing science, it is necessary to set expectations for the whole lab and the company, communicate them effectively, and execute professionally. This will help ensure adoption success.

Spring has Sprung: A Spring Cleaning Announcement

We are currently offering a special Spring Cleaning discount for anyone who wants to transition from their problematic ELN/LIMS. This special includes:

• 1,000 sample free Import
• Free Import of all Equipment
• Free Import of all Supplies
• Free Digital and Sample Strategy consultation and training
• Start-up discount for labs that have less than ten people

If you're interested, contact us here.

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As technology continues to evolve, it's crucial for researchers and institutions to stay adaptable. With the rise of digitization in the business world, the debate over paper documents versus digital or electronic lab notebooks persists.

So, let's take a deeper look at both approaches to understand the value each could bring to your research. The integration of ELNs represents a step toward a more interconnected, efficient, and collaborative future in scientific exploration. The use of electronic laboratory notebooks in laboratories is growing as laboratories strive for higher quality due to the volume, complexity, accessibility, and security requirements.

Whether recorded on paper or in pixels, the pursuit of knowledge remains at the forefront, driving laboratories to innovate in their record-keeping practices.

Let's delve into the common reasons behind the continued use of paper notebooks and explore why many laboratories are making the shift to electronic solutions in the modern era.

The Charm of Paper

Simplicity and Tangibility

There's a perceived satisfaction in flipping through pages and jotting down notes with a pen. Paper notebooks offer a straightforward and tangible way to record experimental details.

Minimal Learning Curve

Researchers, especially those accustomed to traditional methods, may find it easier to stick with paper due to its minimal learning curve — no need to adapt to digital interfaces.

Security Concerns

Some scientists express reservations about the security of digital data. Paper notebooks are perceived as less susceptible to cyber threats, providing a sense of control over sensitive information.

Universal Accessibility

A paper notebook doesn't rely on electricity or devices. It's universally accessible, which can be advantageous when technology is not readily available.

Efficient Organisation and Searchability

ELNs are based on FAIR Principles (Findable, Accessible, Interoperable, Reusable), which are recognized by the research community. It empowers researchers with tools for efficient data organization and searchability. Finding specific experiments or data becomes a breeze, saving valuable time in the research process.

Collaboration and Sharing

Digital notebooks facilitate seamless collaboration. Researchers can share data in real-time, transcending physical boundaries. This interconnectedness enhances teamwork and accelerates the pace of scientific discovery.

An ELN facilitates global collaboration; this is especially true where outsourcing agreements have been set up so that different laboratory capabilities can be made use of. Integrating ELNs with a multivendor informatics architecture will streamline data capture and analysis workflows, thus enhancing the efficiency and accuracy of data management. An ELN enables real-time collaboration in research projects because it draws on the different expertise of laboratories and scientists. It sets clear and standardized communication parameters - usually through a real-time platform. This ensures all communications regarding an experiment are kept in context and are always linked to their source. Because it allows you to connect all interactions and notes to their relevant data, an ELN contains the 'story' behind the information and protects it for easy retrieval in the future.

Integration with other research applications

ELNs can be integrated with secure cloud-based communal repositories like Mendeley, eLabNext AI Protocol Generator, and other applications, making publishing, information accessibility, and the research process easier.

This integration streamlines data capture and analysis workflows.

Workflow automation

Workflow automation makes it convenient for scientists to stay on top of their assignments, automating, securing, and linking critical files to an experiment. In addition to pre-populated standard operating procedures (SOPs) templates, users can create complete experiment templates to save time starting from scratch.

Our LIMS capabilities helps you to store data using inventory tracking systems. It can also be used to automate tasks related to inventory control, such as logistics, ordering, and shipping so that labs can stay on top of material usage during their many experiments or production processes. These features enhance the efficiency of ELNs and help reduce costs.

Version control and data integrity

ELNs often come equipped with version control features, ensuring that every iteration of an experiment is documented. This helps maintain data integrity and provides a clear audit trail for all research activities.

Digital documents don't get damaged and lost

Unlike paper documents, electronic lab notebooks do not wear or fade with time; you can actually read the text and are not reliant on expert handwriting analysis. Most importantly, ELNs do not occupy physical space and cannot get easily misplaced or somehow 'lost in transit' between two points or between individuals.

Better record-keeping and compliance

ELNs automatically record each entry's user name, date and time, providing an audit trail of project progress and enabling compliance with regulatory requirements. Removes insecure transmissions

Rather than sending documents via email with the associated risk of security breaches, the ELN offers an online portal storing documents on a secure website.

Environmental Considerations

In an era where sustainability is a priority, opting for electronic solutions reduces paper and cardboard usage. This aligns with broader efforts within the scientific community to adopt eco-friendly practices. Although there is an initial investment in implementing ELN software, it leads to long-term cost savings by reducing the need for paper, ink, printers, storage space, and administrative resources associated with paper-based notebooks.

Striking the Balance

In the end, the choice between paper and electronic lab notebooks isn't a one-size-fits-all decision. Laboratories must strike a balance that aligns with their specific needs and the preferences of their researchers. While some may hold onto the nostalgic charm of paper for a little longer, others recognize the undeniable advantages that digital solutions bring to the modern laboratory.

Ultimately, the decision between a paper and electronic lab notebook depends on the specific needs and preferences of the research team, as well as considerations such as budget, security requirements, and institutional policies. Many modern research institutions are increasingly adopting electronic solutions due to their enhanced collaboration capabilities and integration with other digital tools.

Overall, electronic lab notebooks offer a modern, efficient, integrated, documented, and secure solution for managing research data, fostering collaboration, and advancing scientific discovery in various fields.

Find out how the eLabNext Digital Lab Platform can benefit your lab by scheduling a free demo today!

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Barcoding biological samples and integrating this information with laboratory sample management software offers a more efficient means for tracking a wide range of biospecimens. The blog below discusses sample barcoding and its advantages over traditional sample tracking methods.

What is Sample Barcoding?

Biological sample barcoding assigns a unique identifier to an individual biospecimen, analogous to barcodes used on consumer products. Barcodes provide a reliable means of cataloging and tracking the location and application of samples used in a laboratory, particularly when integrated with sample management software. 

Before Barcoding: Relying on Manual Methods for Sample Management

Before barcoding was applied in laboratories, researchers employed various traditional methods to track biological samples, often relying on manual and time-consuming processes. One method still used by many labs is documentation in a lab notebook, where detailed records, handwritten notes, and labels are meticulously maintained. This method, however, is prone to human error and could lead to misinterpretation or loss of crucial information. Even when spreadsheets are used to manage and track sample inventories, there is still room for human error and mistakes. 

Physical tagging systems, such as numbered or color-coded labels, are often implemented to distinguish samples. While these methods are better than relying solely on written records, they still have limitations, especially when dealing with large-scale studies or when long-term storage is required. The absence of a standardized and universally applicable system makes data sharing and collaboration challenging. 

The Benefits of Barcoding

Sample barcoding helps provide solutions for these challenges. While not all scientists transitioned from paper records or disjointed software solutions, there are some key reasons for the growing interest in sample barcoding and its integration with comprehensive lab information software.

Sample Identification and Tracking

Barcoding provides a unique identifier for each biological sample, reducing the chances of errors in sample identification. Integration with laboratory sample management software enables real-time tracking of sample locations, movements, and usage history. This metadata provides a more comprehensive view of a sample collection compared to manual sample management methods.

Efficient Data Management

Combining barcodes with sample management software allows for efficient and accurate data entry. Researchers can quickly scan barcodes instead of manually entering sample information, reducing the risk of transcription errors. It streamlines data management by providing a centralized platform for storing and retrieving sample-related information.

Automation and High Throughput

Barcoding facilitates automation in sample-handling processes. Automated systems can scan and process samples more quickly and accurately than manual methods. This is particularly important in high-throughput laboratories where large numbers of samples need to be processed efficiently.

Sample Integrity and Reproducibility

Barcoding helps maintain the integrity of samples by reducing the likelihood of mix-ups or contamination. By integrating with sample management software, researchers can ensure the reproducibility of experiments by accurately documenting and tracking sample conditions and parameters.

Compliance and Quality Control

Barcoding and software integration help laboratories adhere to regulatory and quality control standards. They also enhance traceability and auditability, which is crucial for compliance with various industry and research regulations.

Time and Cost Savings

Streamlining sample management processes through barcoding and software integration can save time and reduce operational costs. Automation and efficient data handling contribute to overall workflow optimization, allowing researchers to focus more on the scientific aspects of their work.

Data Integration and Analysis

Integration with sample management software enables seamless integration with other laboratory systems, facilitating comprehensive data analysis. Researchers can correlate sample information with experimental results, helping them draw meaningful conclusions from their data.

Collaboration and Data Sharing

Barcoding and sample management software facilitate collaboration by providing a standardized and easily shareable format for sample information. Researchers from different labs or institutions can more effectively share data, fostering collaborative efforts and accelerating scientific progress.

Conclusion

Combining barcoding and laboratory sample management software improves the efficiency, accuracy, and overall management of biological samples in research lab settings. 

If you’re considering the eLabNext platform or are a current eLabNext customer who hasn’t taken advantage of sample barcoding yet, explore the Biobanking section of the Marketplace or check the ZPL Printer add-on or FLUICS PRINT add-on. eLabNext can also provide a list of printers and scanners supporting sample barcoding. If you are a ZPL, Brady, or FLUICS customer looking to make your next steps on your digital journey and want to be the with the ‘Easiest to Use’ and ‘Best Value’ ELN provider, then request a demo or jump straight to your free 30-day trial of eLabNext.

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There was a time not too long ago when the only biotech incubators out there were LabCentral and BioLabs.

But a lot has changed. The life science industry, including big pharma, has become more flexible, allowing young, ambitious, and innovative companies to flourish and grow. Incubators have been pivotal in creating this thriving ecosystem, and there are hundreds of facilities and coworking spaces that cater to the unique needs of biotech startups, including  SmartLabs, CIC, Alexandria LaunchLabs, Harvard Innovations Lab, Greentown Labs to InnoLabs, Cure Innovations Lab, MBI, and more. Large pharma has done the same with Bayer’s Co.Lab, BMS’s Thomas O. Daniel research incubator and collaboration center, and J&J’s JLABS.

With so many players in the biotech incubation scene, it can be challenging for newcomers to distinguish themselves. That said, one incubator that spun out of MIT, The Engine, has experienced significant success by focusing its efforts on companies in the “Tough Tech” industry. In this area, cutting-edge science is deployed to solve the world’s most significant problems. This niche concentration has enabled The Engine to attract and nurture some of the most fascinating disruptors in the industry, all under a single roof.

To get a glimpse “under the hood” of The Engine, I sat down with their Business Development Manager and hardcore Boston Celtics fan, Hayden McFarlane, to learn more about what makes them unique and how they foster the next generation of Tough Tech disruptors. 

Q: What is the difference between a traditional biotech coworking space and The Engine accelerator? What makes The Engine special and unique?

A: At The Engine Accelerator, we focus on being the home of  “Tough Tech”. This is reflected in how we’ve structured our spaces, where we have 3D printing labs, electronics labs, machine shops, dry lab spaces, as well as BSL-2 biology and chemistry labs.

What makes us different is that we sit at the convergence of Science and Engineering. This means that teams could be in the labs pipetting in the morning, then in our fabrication space doing 3D printing over lunch, and by afternoon, they’re fabricating prototypes with our Super Mini Mill.

The machine room is a shared space with 3D printers, allowing companies to reserve equipment time for creating necessary pieces without outsourcing or investing in rarely used equipment.

The Engine comprises biology and chemistry labs, along with a range of shared lab spaces and lab suites available for rent. These facilities are equipped with diverse equipment to meet the research needs of various companies.

Q: Why would a young start-up want to be part of an incubator instead of getting their own lab/office space?

A: It boils down to making the most of your money early in your Tough Tech journey. As a true accelerator, we provide equipment, permitting, utilities, facilities, etc. This makes it easier for the startup to concentrate on de-risking their science or tech as quickly as possible.

Q: Is there a limit to how long a company can remain at The Engine or how big their group can get?

A: We built the space with the growth of our residents in mind. Teams can lease a single wet lab bench and grow into a private 60-bench lab over their growth cycle. Because of this, we do not impose time limits. Part of the rationale behind that is that we are operating in the Tough Tech world, where teams are tackling the toughest challenges in the world. It’s impossible to put a time limit on when a company will solve something like that.

Q: How did the pandemic shape the incubator business model generally and The Engine’s culture specifically?

A: Teams are now more interested in the hybrid model and letting staff work from home when they can. Obviously, that's impossible for lab workers, and with that in mind, we structured our building to be 66% lab space so if another pandemic were to hit, our most usable space would still be in play.

Q: What are the top 5 industries or sciences represented at The Engine, and is there a particular type of resident you’re looking for?

A: Climate change, human health, and advanced systems and infrastructure are the primary 3 industries our residents work in. We have some residents who work in food or agri-tech, but they have some overlap with climate change and human health. We typically search for founders whose breakthroughs will make impactful changes in society and those who may disrupt their industry over the next 30-plus years.

Q: So you are looking for industry disruptors! What defines a disruptor, and how does The Engine ecosystem promote or support this?

A: A disruptor is typically the founder with industry-changing ideas. It's easy to focus on the tech, but the person driving the tech and the team behind it is the disruptor. We find the disruptors through various onsite programming, including our Blueprint Program aimed at postdocs and research scientists who have developed great ideas. We have created a massive ecosystem over the last 7 years that tends to attract these types of founders. The Engine Accelerator’s infrastructure, programs, and network uniquely help disruptive founders bridge the gap from their breakthrough to commercial viability and scale.

Q: What is Tough Tech? How does it differ from biotech, and how will it transform the industry and the world?

A: Tough Tech is a transformational technology that will change the world for the better but requires time and complex solutions to reach commercial viability, potentially spanning the course of years or even decades. Things like fusion energy, carbon capture, battery tech, cell therapies, quantum computing, and so much more. All of these things will help slow down some of the global issues, such as climate change, we are collectively dealing with.

Q: What are the challenges in running The Engine?

A: I think one of the biggest challenges is raising awareness about what The Engine offers. Many people mistakenly think you have to come from MIT or be invested in by The Engine Ventures to be part of it. However, 750 Main St is a home for ALL tough tech founders. Additionally, teams need to move quickly but with precision. Often, the infrastructure needs they had when they moved in have changed. We work with teams to ensure they can effectively scale their operations without losing time or capital.

Q: What is the screening process for new residents like?

A: We have a process for evaluating if a team is truly a “Tough Tech” project, and from there, it's a relatively streamlined process of EHS forms, etc. The process starts with filling out our Space Inquiry Form and can take 2 -6 weeks for our team to review and get the company into the space, depending on the team's infrastructure needs.

Q: In 3 words, how would you describe The Engine?

A: Three words is an injustice for a place that houses 90+ teams working on world-changing technologies! But I would say - Inspiring, Dynamic, & Transformative.

Q: How should people get in touch with The Engine if they want to take space here?

A: They can apply on the website www.engine.xyz or email me at hayden@engine.xyz.

The Factors that Keep The Engine Running

I’ve spent countless hours at The Engine for panel discussions, fun events, informative demonstrations, training, or pitching, and 3 unique threads make it such a special place!

First, the infrastructure, interior design, and architecture perfectly balance privacy and collaboration. You can isolate yourself, concentrate and power through complex projects, and at the same time, find a moment to brainstorm with colleagues and other companies to extract a deeper understanding of your tasks.

Second, the ecosystem is genuinely inspiring due to the extremely high application standards, which attract some of the most fascinating companies and talents into the space. See what it means to “Turn tough tech breakthroughs into Tough Tech startups” here.

Finally, the access to instruments and digital technologies and support from the staff make The Engine a valuable place for startups. The Engine stands out in the bustling world of biotech incubators by embracing 'Tough Tech' and providing a dynamic space where disruptive ideas and groundbreaking science collide, all in an inspiring, dynamic, and transformative ecosystem.

eLabNext is proud to partner with The Engine. Our collaboration underscores our commitment to supporting innovative startups as they navigate the challenges of launching their ventures. Together, we've worked closely to ensure that startups have access to the tools, resources, and expertise they need to succeed, fostering an environment where ambitious entrepreneurs can thrive.

To learn more, visit engine.xyz.

AOByte recently started a new partnership with eLabNext, an all-in-one lab management software. eLabNext solutions help to improve the quality of the research by providing all-around tools for any lab. Due to its rapid expansion, eLabNext has decided to release an SDK, allowing developers to create new add-ons that other users can install on the eLabNext dashboard. Add-ons allow users to integrate 3rd party software into dashboards, software such as Dropbox, Google Drive, etc. Add-ons also enable users to add functionality to their dashboard without waiting for their desired functionality to be released by eLabNext.

Our company is proud to accompany eLabNext on its journey. Seeing a growing demand, we’ve decided to share part of our journey of creating custom add-ons. This article is a good place to start if you’re interested in eLabNext add-on development.

Starting Add-on Development

To start add-on development, you must first turn on Developer mode from settings. Navigate to Account Settings > Developer. Developer mode is turned on by simply toggling the switch. In turned-on Developer mode, the SDK will attempt to inject an add-on JavaScript file from the “Add-on script URL” on the page load. A single JavaScript file will be loaded at runtime on page load each time you browse the eLabNext dashboard.

Now, let’s try to create a simple add-on. Before jumping into coding, here are two valuable resources: eLabNext SDK documentation and eLabNext REST API documentation.

Use the Download Template from the Developer settings page to create an empty add-on. This is a working sample add-on, which can be fed to the SDK via an HTTP server of your choice. Our team is using a NodeJS-based http-server for development purposes. The add-on below achieves a simple task of displaying the tasks table in the dashboard. It also allows users to create and delete tasks.

/*

@rootVar: SAMPLE_ADDON

@name: Sample

@description: Sample addon

@author: Stepan Smbatyan

@version: 1.0.0

*/

var SAMPLE_ADDON = {};



((context) => {

context.init = (config) => {

$(() => {

context.SampleAddon = new context.SampleAddon(config);

});

};



context.SampleAddon = new Class({

Implements: [Options, Events],

Extends: eLabSDK.Base,

options: {},

initialize: function (config) {

// Store a reference to the function's context

var self = this;

// Set the options for the application using the provided configuration

self.setOptions(config);



$(document).ready(() => {

const currentPage = Helper.History.get('pageID');



const pageID = currentPage || new URLSearchParams(window.location.search).get('pageID');



renderTaskPage();



if (pageID === 'tasks') {

getTasks().then(({ data }) => {

renderTaskTable(data);



addDeleteBtnListener();

});

}

});

},

});



// #TODO: remove context.init() when upload as add-on to marketplace

context.init();

})(SAMPLE_ADDON);



// ======================================= DOM =======================================



/**

* Renders the task list UI by updating the browser history, creating a button and table,

* filling the table with task data, and updating the main content section with the table container.

* @param {Event} e - Optional event object. If provided, prevents the default action.

*/

const renderTaskTable = (data) => {

const button = createAddTaskButton();

$('#main-content')

.html('<section id="tableContainer"></section>')

.prepend(button.render());



const table = createTaskTable();

table.data = data;

table._renderHTML();

};



/**

* Creates a custom page for tasks using eLabSDK.

* This function initializes a new CustomPage object with specified configurations.

* @returns {CustomPage} A CustomPage object representing the task page.

*/

const renderTaskPage = () => {

return new eLabSDK.CustomPage({

rootVar: '.nav-main-level',

pageID: 'tasks',

mainMenu: 'Tasks',

subMenu: 'Task list',

});

};



/**

* Creates a button element using the eLabSDK.GUI.Button constructor.

* The button is configured with a label, CSS class,

* and an action to show a dialog for updating tasks.

* @returns {eLabSDK.GUI.Button} - A button element configured to add a new task when clicked.

*/

const createAddTaskButton = () => {

return new eLabSDK.GUI.Button({

label: 'Add New Task',

class: 'addNewTaskBtn',

action: () => showDialog(DIALOG_CONFIGS.CREATE, createTaskAction),

});

};



const addDeleteBtnListener = () => {

$('.deleteBtn').on('click', (e) => {

const id = e.currentTarget.getAttribute('_dataId');



showDialog(DIALOG_CONFIGS.DELETE, () => deleteTaskAction(id));

});

};



/**

* Creates a table element using the Helper.Table.create method.

* The table is configured with specified target container, data

* and columns for displaying task information.

* @returns {HTMLElement} - A table element configured to display task information.

*/

const createTaskTable = () => {

return Helper.Table.create({

target: 'tableContainer',

caption: null,

data: {},

columns: [

{

name: 'Full Name',

key: 'fullName',

width: '20%',

cellRender: ({ creator }) => `<b>${creator.fullName}</b>`,

},

{

name: 'Title',

key: 'title',

width: '20%',

cellRender: ({ title }) => `<span>${title || '-'}</span>`,

},

{

name: 'Description',

key: 'contents',

width: '45%',

cellRender: ({ contents }) => `<span>${contents || '-'}</span>`,

},

{

name: 'Created',

key: 'created',

width: '10%',

cellRender: ({ created }) => `<span>${created.split('T')[0]}</span>`,

},

{

name: 'Action',

key: 'actions',

width: '5%',

cellRender: ({ taskID }) => `

<p class='deleteTranslationIcon deleteBtn' _dataId="${taskID}">

<i class='fa fa-trash-alt _actionIcon' title='Delete translation'></i>

</p>

`,

},

],

});

};



// ======================================= MODAL =======================================



/**

* Initiates the deletion of a task identified by its taskId asynchronously.

* Upon successful deletion, closes any open dialogs, reloads the page to reflect the changes.

* @param {string} taskId - The ID of the task to be deleted.

* @returns {Promise<void>} - A Promise that resolves after the task deletion and page reload.

*/

const deleteTaskAction = async (taskId) => {

await deleteTask(taskId);

Dialog.closeWait();

window.location.reload();

};



/**

* Adding a new task with the provided title and description,

* closing the dialog window, and reloading the current page.

* @returns {Promise<void>} A promise that resolves once the actions are updated.

*/

const createTaskAction = async () => {

const title = $('#title').val();

const description = $('#description').val();



await addTask({ title, description });

Dialog.closeWait();

window.location.reload();

};



/**

* Displays a dialog window with specified configuration options and a custom button,

* calling the provided callback function when the custom button is clicked.

*

* @param {Object} config - The configuration object for the dialog window.

* @param {string} config.title - The title of the dialog window.

* @param {number} config.width - The width of the dialog window.

* @param {string} config.btnOk - The label for the OK button.

* @param {string} config.btnCancelLabel - The label for the Cancel button.

* @param {string} config.content - The content to be displayed in the dialog window.

* @param {string} config.customButtonLabel - The label for the custom button.

* @param {string} config.customButtonStyle - The style for the custom button.

* @param {Function} callback - The callback function to be called when the custom button is clicked.

* @returns {void}

*/

const showDialog = (config, callback) => {

const {

title,

width,

btnOk,

btnCancelLabel,

content,

customButtonLabel,

customButtonStyle,

} = config;



Dialog.show({

title,

width,

btnOk,

btnCancelLabel,

content,

customButtons: [

{

label: customButtonLabel,

style: customButtonStyle,

fn: callback,

},

],

});

};



// ======================================= CONSTANTS =======================================



const DIALOG_CONFIGS = {

DELETE: {

title: 'Delete Task',

width: '550',

btnOk: false,

btnCancelLabel: 'Close',

content: '<p>Are you sure you want to delete this task?</p>',

customButtonLabel: 'Delete Task',

customButtonStyle: 'background:#fe810',

},

CREATE: {

title: 'Add New Task',

width: '550',

btnOk: false,

btnCancelLabel: 'Close',

content: `

<section>

<input id="title" type="text" placeholder="Title" />

<textarea id="description" placeholder="Description" style="padding-top: 8px;"/>

</section>

`,

customButtonLabel: 'Add Task',

customButtonStyle: 'background:#fe810',

},

};





// ======================================= API =======================================



/**

* Retrieves tasks by making a GET request to eLabSDK.

*

* @returns {Promise<Array>} A promise that resolves with an array of tasks upon successful retrieval, or rejects with an error response.

*/

const getTasks = () => new Promise((resolve, reject) => {

new eLabSDK.API.Call({

method: 'GET',

path: 'tasks',

onSuccess: (xhr, status, response) => {

resolve(response);

},

onError: (xhr, status, response) => {

reject(response);

},

}).execute();

});



/**

* Adds a new task with the provided title and description by making a POST request to eLabSDK.

*

* @param {Object} task - An object containing the title and description of the task.

* @param {string} task.title - The title of the task.

* @param {string} task.description - The description of the task.

* @returns {Promise<Object>} A promise that resolves with an array of tasks upon successful retrieval, or rejects with an error response.

*/

const addTask = ({ title, description }) => new Promise((resolve, reject) => {

const data = {

assigneeID: 0,

title,

contents: description,

};



new eLabSDK.API.Call({

method: 'POST',

path: 'tasks',

pathParams: {},

onSuccess: (xhr, status, response) => {

resolve(response);

},

onError: (xhr, status, response) => {

reject(response);

},

}).execute(data);

});



/**

* Deletes a task with the specified ID by making a DELETE request to eLabSDK.

*

* @param {string} id - The ID of the task to be deleted.

* @returns {Promise<Object>} A promise that resolves with an array of tasks upon successful retrieval, or rejects with an error response.

*/

const deleteTask = (id) => new Promise((resolve, reject) => {

new eLabSDK.API.Call({

method: 'DELETE',

path: `tasks/${id}`,

onSuccess: (xhr, status, response) => {

resolve(response);

},

onError: (xhr, status, response) => {

reject(response);

},

}).execute();

});

One of the crucial things to remember while creating an add-on is prioritizing using SDK and API methods over custom code. A good example will be rendering buttons or making HTTP requests. By using the methods provided by SDK, you can be assured, e.g. buttons will have correct styling, or all the necessary headers will be appended to your HTTP request.

More Complex Add-on Development

Obviously, most of the add-ons that will be created will be more complicated than this example. Naturally, while delivering more complex features, developers would like to use the power of breaking code into modules, minimizing code for production, writing test cases for their code, and using all the other advantages of modern web development. While working on the add-ons, we’ve created a boilerplate add-on, allowing users to achieve a project structure, packaging, testing, etc. The project can be found on GitHub.

Remember that the eLabNext SDK is gaining momentum; thus, the documentation needs to be completed. Please contact our team if you find yourself in a situation where help might be required. Our team will continue writing about the eLabNext add-on development process. We will cover topics like submissions of add-ons to eLab Marketplace, tips and tricks of eLabNext add-on development, talking about more complicated features development, and so on.