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Today scientists are solving complex problems that impact people across every industry. Central to this workflow is the Laboratory Information Management System (LIMS), which allows researchers to organize and manage their data. These systems are exceptional at collecting and storing data, but analysts and data scientists often have to work outside of the laboratory system to identify meaningful insights and discoveries. This disconnect can lead to delays in project timelines and tax already limited resources. Adding a data science tool stack to the LIMS environment will enable scientists to discover the next best solution, whether it be in the commercial or public health industries.
Early this year LabWare acquired Data Science company CompassRed (LabWare Analytics) to help develop the functionality that enables data scientists to support laboratory scientists in the LabWare LIMS environment. Below we describe how data science tools can activate scientific data and discuss what functions we are adding to the LIMS environment.
The Value of Scientific Data
Data is increasingly valuable for companies creating superior products. In general, data can be used to identify trends, optimize processes, and inform decisions. In the lab, data can help spot anomalies, identify hidden relationships, and optimize laboratory functions. Ultimately, data is valuable because it allows organizations to make informed decisions that grant them a competitive advantage. Within a lab that is collecting scientific data with a LIMS system, a variety of tools can help bring that data to life and create a competitive advantage for the organization.
Activating scientific data is difficult due to the advanced analytics and technologies involved in making sense of the raw data. Data activation converts it into a format that can be easily understood and used to draw meaningful insights. This can be done in a variety of ways, such as (1) organizing the data so it is easier to analyze, (2) creating visualizations to make it easier to understand, and (3) using machine learning and artificial intelligence to develop predictive models. Other methods of activating scientific data include developing algorithms to automate information processing and analysis, developing tools to facilitate data sharing, and creating digital tools to help researchers understand the data.
Why bring Data Science tools into the lab?
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LabWare Analytics believes that scientists should be able to activate the scientific data collected within LIMS systems by incorporating data science tools into the laboratory. With best in class tools scientists can more quickly and accurately interpret their data, identify patterns and trends in their process, and gain a more comprehensive understanding of their experiments. This can lead to more effective and efficient research processes, as well as more sophisticated, accurate, and reliable results.
The following are some of the most common data science tools scientists use that we bring to the LIMS system:
1. Data Visualization Tools: Data visualization tools allow scientists to graphically represent their data in a meaningful way, providing a comprehensive understanding of the data. These tools can be used to create interactive data visualizations, such as line graphs, scatter plots, and histograms, as well as maps and other interactive visuals. By bringing a modern visualization package into the LIMS system we believe scientists will be able to do their work more effectively.
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2. Statistical Analysis Tools: Statistical analysis tools allow scientists to analyze data and draw insights from it. General-purpose programming languages like R and Python can be used for statistical analysis. These tools can improve scientists’ ability to identify relationships between data points, test hypotheses, and calculate the probability of certain outcomes within the LIMS system.
3. Machine Learning and AI Tools: Machine learning and AI tools allow scientists to develop models and algorithms that can analyze large amounts of data and identify patterns, trends, and correlations that may not be visible to the human brain. These tools can enable scientists to run ML models that make predictions, identify anomalies, and automate data processing and analysis.
4. Data Sharing and Collaboration Tools: Data sharing and collaboration tools allow scientists to easily share data and collaborate on projects. These tools allow for secure data sharing and can be used to store, organize, and analyze data. These tools will help validate results and improve the reproducibility of a scientific study.
5. Data Management and Storage Tools: Data management and storage tools allow scientists to store and manage their data in a secure and organized way. These tools can be used to store data in the cloud, back up data, and access data from any device. Scientific organizations are often utilizing many systems. Improving the data management and storage capabilities can enable an organization to focus more on scientific methods and discovery without accumulating technical debt.
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A Science and Engineering Approach
By integrating data science tools into the laboratory, scientists can more quickly and accurately interpret their data, identify patterns and trends, and gain a more comprehensive understanding of their experiments. To do this our data scientists and data engineers are focused on bringing the foundational tools into the Labware LIMS system. This starts with enabling the use of R within workflows and general calculations. This will fundamentally improve the data visualization functionality of the LIMS client, and shape improved data models for analytics and data science work.
Who is Labware Analytics?
At the beginning of 2022, Labware acquired the Data Science consulting firm CompassRed. By doing so, they acquired a team of highly skilled and experienced data engineers, scientists, and analysts. The CompassRed team comes with a variety of experience, such as building and productionalizing machine learning models, developing advanced analytics data pipelines, and building data visualization and BI tools. The CompassRed team has spent the last nine months listening to LIMS consultants and clients, deep diving into how the LIMS system works, and attending LabWare Customer Education Conferences (CEC) and other industry conferences. Through these experiences, we have developed an opinion that a Data Science solution needs to be added to the LabWare LIMS system.