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Dotmatics
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  • Platform

    Scientific Intelligence Platform

    AI-powered data management and workflow automation for multimodal scientific discovery

    Learn More

    Capabilities

    Adaptive Workflows

    Customize, automate, and scale your lab workflows

    Artificial Intelligence

    Leverage AI and ML to accurately predict scientific outcomes

    Material & Ontology Management

    Classify materials and manage entities with full traceability

    Luma Products

    BioGlyph Luma

    Next-gen protein design for complex biologics – integrating molecular modeling, registration, and production with seamless data traceability and precision.

    Geneious Luma

    Accelerated antibody discovery for sequence analysis, construct design, and lab execution—integrating the power of Geneious Prime and Geneious Biologics with Luma’s adaptive workflows.

    Lab Connect

    Automated lab data ingestion and modeling—connect instruments, structure scientific data, and streamline lab operations with seamless integration.

  • Solutions

    The State of Chemicals & Materials

    Uncover key trends shaping the chemicals and materials industry

    Read More

    Solutions

    Antibody & Protein Engineering

    Integrated registration, lab workflow and data management

    Flow Cytometry

    Automated flow data processing and auto-gating

    Industry

    Biology Discovery

    Chemistry R&D

    Chemicals and Materials

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    R&D Software for Scientists

    Review our comprehensive portfolio of products driving scientific breakthroughs for R&D innovation and collaboration.

    Explore All

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Home pageFAQ Center

How can data science be used in lab automation?

How can data science be used in lab automation?

Most research and pharmaceutical laboratories have largely adopted lab automation hence removing the need for human input, especially in repetitive tasks/routines. With the integration of data science in lab automation, these automation systems have become ‘intelligent’.

Automated systems in labs, each tasked with performing particular routines, can be controlled from a centrally-integrated system in the laboratory. When each independent system churns data from its operations, the data is stored in a cloud-based platform. Algorithms, such as those of AI and machine learning, are then set on the big data to derive insights. The centrally-integrated systems can then utilize the insights to make the automation systems more efficient. The automated systems can even conjure up novel protocols for effectively performing lab operations hence mimicking human cognitive capabilities.

Therefore, as depicted, when data science is integrated into lab automation, the automated systems evolve from mundane to intelligent. The intelligent lab systems can even autonomously readjust operations when lab results are unsatisfactory, halt routine procedures when a mistake occurs, or call for human intervention when a lab machine is performing sub-optimally. The lab can even be left to operate overnight with only minimal human oversight.

The impact of data science analytics is even more powerful when several laboratories combine their data in one cloud-based system and utilize data management tools. These different laboratories can have a protocol for standardizing and annotating their data, before sending it to the cloud. Such collaboration from different automated labs should result in even larger data for modeling with AI and machine learning algorithms. The bigger, and more diverse, the data sets the greater the veracity of insights gathered from the analytics. 

Ultimately, lab automation relieves the human personnel in the laboratories from doing repetitively mundane tasks and shifts their focus towards tasks that require critical thinking.

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