Ensuring data compliance in laboratories is increasingly complex due to evolving regulations and data standards. New technologies, particularly artificial intelligence (AI), offer solutions by automating compliance processes and enhancing data handling. Colin Thurston from Scimcon highlights two main compliance challenges: maintaining accurate data capture during experiments and effectively reusing massive datasets generated in biosciences. To address these, labs should adopt standardized data formats like XML and focus on maintaining data that meets FAIR principles—interoperability and reusability. AI can improve data analysis and reporting, but researchers must tape negative findings to avoid bias. Furthermore, regulatory bodies like the FDA are emphasizing strict guidelines for AI use. As data volumes increase, labs must streamline compliance practices to adapt effectively, ensuring that both technology and procedures work together efficiently to foster data integrity and regulatory adherence. By prioritizing these strategies, labs can navigate the complex landscape of data compliance more effectively.
Source link
Share
Read more