In the era of big data, efficient data management is crucial for the success of scientific research. From mapping existing tools in your ecosystem and evaluating the latest technology, to implementing and training your team on new investments, the effort to improve management practices of newly-created and legacy data is time consuming and complex. Because of this, the decision on when and what to invest in for scientific teams is a process with numerous stakeholders within an organization. Digital lab assistants are a newcomer to the ecosystem and are playing an increasingly important role in helping scientists streamline their data management practices. These practices aim to improve the organization, tagging, and retrieval of data generated in laboratories.
Digital assistants come equipped with several capabilities that streamline data management and make them a logical investment during the initial stages of a digital transformation. By using digital lab assistants to replace pen and paper data collection methods, scientists can create a digital audit trail, not having to worry about finding and sifting through binders during audits.
Another one of these capabilities is the implementation of business rules that direct captured data to the appropriate locations - ELN entries, folder drives, SDMS tools, etc. This ensures that all information is organized systematically, making it easier for researchers to locate specific data points, experiment outcomes, and sample history when needed. Regardless of how sophisticated a company’s digital ecosystem or how well established their data management strategy, a digital lab assistant can be leveraged to make measurable improvements.
An example of how digital lab assistants upload assay results to the right place within the ELN
Digital assistants are able to tag files and notebook entries with important metadata. Metadata is crucial for making data findable and accessible, as it provides context and information about the data itself. The scientist is often burdened with incorporating metadata not generated by the instrument software. By automatically tagging files and entries with project IDs, data & time stamps, or impacted studies, digital assistants save researchers time and effort while improving data organization.
Digital assistants can also standardize free-text answers, which is particularly useful in the context of scientific research. Standardizing these answers allows for easier comparison and analysis of data across different experiments and researchers, helping to fuel AI/ML initiatives.
Combining many of the aforementioned benefits means that digital lab assistants can help companies adhere to FAIR (findable, accessible, interoperable, reusable) and ALCOA (attributable, legible, contemporaneous, original, and accurate) principles. For example, digital lab assistants can automatically tag files and notebook entries with important metadata, implement business rules that ensure that data is stored in the appropriate locations, and standardize free-text answers. They can also track who created the data and when, format data in a way that is easy to read and understand, capture data at the time of the experiment, prevent data from being modified or corrupted, and validate data against known standards.
The benefits of using digital assistants to help with managing data are numerous and can be realized at any point in an organization’s digital transformation. Improved data collection methods lead to more accurate and reliable results, while more efficient data management processes save time and resources. Furthermore, the ability to easily locate data during audits, when creating reports, or when determining if an experiment has already been conducted minimizes the risk of duplicate efforts and ensures that research is always moving forward.
The integration of digital assistants in laboratories is impacting the way scientists manage their data. By using digital lab assistants to adhere to an organization’s data practices, researchers can optimize their workflows, improve data organization, and ultimately, advance scientific discovery.
Interested in learning more about our platform and how it can help with your data management goals? Request to speak with LabVoice here: info@labvoice.ai.