The lab is at the heart of science. It is fundamental for finding and creating new materials and medicines, developing the products and testing them. This is why many companies are concentrating on making their labs more efficient and productive, minimizing potential errors and ensuring high quality of data and outcomes. In all these attempts, organizations often do not focus enough on the most critical part of the lab – the human, the scientists.
The Scientists
Scientists are the ones who execute the work and make the decisions. They schedule the work, assign tasks and resources, create and manage methods, prepare samples, execute tests, synthesize novel compounds, work on new chemistry processes and more. They document their work, analyze the results, collaborate and review the work of peers.
Scientists make decisions about assigning lab work, how to improve a formulation or a manufacturing process, what to test or synthesize next, or simply approve the release of a batch. They need to decide which data and knowledge they would like to leverage and build on. Their decisions need to be based on data. Therefore, scientists must to be able to access, share and re-use this data and knowledge. Any technology to support the scientists should help them to easily access and use the data they need.
An environment that helps scientists to do more and better science is a scientific ecosystem that brings all the data to them. This means making data accessible and reusable regardless of what scientific “language” it is (biology, chemistry, formulations, etc.), where it was captured, whether it is from internal our outsourced partners, or where the scientists are at any given moment when they need the data.
The Complexity
Scientists are typically working in a complex digital environment. They move between many different systems to complete routine work, taking more time on the computer than for lab work itself. As current labs are often disconnected, data is locked in silos, not transferable and not easily analyzed. It results in manual, error-prone and inefficient activities. It is also difficult to search and find relevant previous work, and to make sure work is not being duplicated amongst colleagues.
Scientists often work with a patchwork of overlapping, loosely integrated point solutions (ELN, LES, LIMS, MES, etc.) on different, often proprietary “platforms” that are specific for each instrument vendor. This results in overlapping, similar functionality in multiple systems like multiple method authoring and execution systems. These solutions deliver an inconsistent user experience and forces scientists to be constantly logging into different systems. They also deliver data in different formats, mainly due to lack of consistent data model.
The Collaboration
Scientists collaborate with each other to drive science. Today they move between in-person, virtual and hybrid workspaces to do so. Moving between different dimensions can be cumbersome and frustrating when not supported adequately. The recent pandemic increased the urgency for a cloud-based environment that helps to control data access and allows scientists to better collaborate with peers or across teams. Scientists expect instant data access whether inside the lab or remotely, anywhere and anytime.
Collaboration should also be safe. Emailing sensitive information is not secure and prone to cyber-attacks. Cloud-based technologies help bolster security and protect IP. Established cloud providers run continuous backups and allow for fast recovery of data in case of an incident and they maintain compliance with ISO27001 for information security. Then scientist know the data they are collaborating on is safe without any additional efforts.
The Data
Scientists across the organization use different terms and words for the same thing – standardization is necessary to communicate, collaborate, share and leverage data. Laboratories need to ensure master data and dynamic data across the lab is organized in a standardized way, define parameters for vocabularies and the units associated with them and apply ontologies to these, so that all scientific data is harmonized and searchable.
The framework of the AllotropeTM Foundation for example, is setting taxonomy and ontology standards. Another framework for standardization is the S88 standard of the ISA, which is a standard for batch process control to create consistency in process design and execution by using common libraries of process elements. Standards like these help to ensure data remains consistent, easily traceable regardless of which lab did the work, which equipment was used or who did perform and record the work.
Organizations should also look into parameterizing data very early on in the Electronic Lab Notebook (ELN) used in Research and Development to avoid unstructured documents and “paper on glass”. This parametrization of the data encompasses all pieces of the experiment, the material, the recipe, the method, the equipment, and the results connecting all through a database underneath the ELN.
The Scientific Notebook
Focusing on the scientists and leveraging decades of experience, BIOVIA has created a new, cloud-based ELN – BIOVIA Scientific Notebook. It has a user-centric, mobile-friendly design. Scientists can leverage dynamic user-based templates, integrated materials management and powerful search functions. It supports collaboration with always-on data access in the cloud.
Instead of a traditional, document-centric approach, it is fully data-centric. Scientific Notebook uses a knowledge graph to interconnect each piece of data, so scientists can re-use their data for advanced analytics and decision-making. Each piece of lab data has a variety of associated metadata, and is connected to other related information. The result is a much deeper level of connection between lab data, enabling greater scientific insights and knowledge management.
Scientific Notebook includes specialized tools for collaboration like ideation funnels, project tracking and maturity states, configurable communities, communications and notifications. It provides direct access to experimental data and results and integrates directly with other lab informatics or business solutions from Dassault Systèmes or 3rd party vendors. This reduces the complexity of laboratory and collaboration workflows.
BIOVIA Scientific Notebook ensures scientists can do science better and faster in an unprecedented effortless way.
To learn more, download our datasheet here!
Stephen Hayward is a PhD in Chemical Engineering, with a background in laboratory informatics, chemical modeling, and environmental analysis. Originally from Toronto, Canada, he currently leads product marketing efforts for informatics solutions at Dassault Systèmes BIOVIA in San Diego.