Capitalizing on FAIR Data from Your Voice-powered Laboratory Assistant
November 14, 2024
According to the 2021 State of Open Data report (the most recent year in which a question was asked about FAIR data), 54% of respondents (scientific researchers) thought their data was at least somewhat compliant with the FAIR data principles—Findable, Accessible, Interoperable, and Reusable. This obviously leaves room for improvement. FAIR data is a key component of achieving the lab of the future and Industry 4.0, no matter what your lab’s specialization is. This blog post will explore how voice-powered laboratory assistants can support the generation of FAIR data.
Understanding the Role of FAIR Data in Your Lab
Structuring data to be Findable, Accessible, Interoperable, and Reusable is the first step toward making better use of that data with machine learning and artificial intelligence. Making data FAIR consists of six key activities: documentation, file formats, metadata, access to the data, persistent identifiers, and data licenses. It can be much easier to apply these activities to data that will be generated rather than to existing data, but it is possible to retroactively apply data FAIRification, and there are compelling reasons why you should. Data that is FAIR leads to enhanced collaboration and data sharing, not only within your lab but across the organization. FAIR data also improves the reproducibility and integrity of original research, a key pillar of the scientific method.
How Voice-powered Laboratory Assistants Contribute to FAIR Data Generation
In previous blog posts, we have covered the different types of voice-activated lab assistants and what they can do in your lab. A voice-activated lab assistant can improve data capture and enhance GxP compliance. A voice-activated lab assistant will automatically structure data and interface with your laboratory information management system so that it is properly archived. These features contribute to making your data findable, accessible, interoperable, and reusable.
Specifically, a voice-powered laboratory assistant like LabTwin supports FAIR data generation in the following ways:
Findability
- Facilitate easy data access and retrieval
- All captured data is immediately searchable
- Voice commands tie to metadata
- Data is structured automatically
Accessibility
- Ensure data is accessible to diverse stakeholders
- Provide user access controls to ensure data integrity
- Support multiple users with voice recognition and user-friendly interfaces
- Provide training and user guidelines for effective voice interaction
- Each user chooses a different activation word so that LabTwin recognizes who is speaking
Interoperability
- Integrate with existing databases and systems
- Standardize data formats and protocols
- Allow alternative forms of input for contemporaneous information
Reusability
- Encourage data reuse through comprehensive documentation and annotation
- Prompt for necessary context and metadata during data capture
How LabTwin Overcomes Challenges to Voice-powered FAIR Data Generation
LabTwin is designed to overcome the common challenges in voice-powered laboratory assistants. LabTwin has been trained with a large language model (LLM) of pharmaceutical, life sciences, and chemical terminology. The underlying model can be pretrained with terminology specific to your lab. Artificial intelligence (AI) and machine learning (ML) have been applied in LabTwin to enable it to recognize accented English.
Predictive text was formerly rigid and unable to accurately predict the third word in a sequence from the first two words. This approach has been replaced by word vectoring and context-based predictions. LabTwin understands that contextual information such as “cell line” is often followed by a sample ID. This rigorous training eliminates most of the issues related to voice-recognition accuracy and understanding technical jargon.
LabTwin addresses data privacy and security concerns with robust data governance frameworks. User access controls ensure that all data maps to the correct originator. Each user chooses a unique activation word to avoid misattribution.
Future Trends for FAIR Data in Laboratories
Voice-powered technology will continue to make advances in laboratory settings, which will drive efficiency and innovation. Adding AI and ML capabilities will someday enable voice recognition in any language. Improved data capture and automatic data structuring will increase the production of FAIR data at the source, all while reducing human errors. These trends will continue to improve data management in scientific research, enabling its reuse and improving accessibility.
How could you use voice-powered technology to support your lab’s generation of FAIR data?