Digitising Your Lab, One Sample at a Time

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

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

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

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

The Path to Lab Digitalisation

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

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

1. Conduct a Comprehensive Inventory Assessment: 

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 If you currently use outdated software, export everything into CSV or Excel and organise it.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

2. Develop a Prioritisation Framework: 

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

3. Formulate a Digitisation Task Force: 

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

4. Standardise Data Entry Processes: 

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

5. Adopt an Incremental Approach: 

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

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

7. Document Standard Operating Procedures (SOPs): 

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

8. Foster Continuous Feedback and Adaptation: 

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

9. Celebrate Achievements and Sustain Motivation: 

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

Summary

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