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Quality control (QC) scientists at a large biopharmaceutical company grappled with "human middleware" in their data workflows. These manual, labor-intensive processes slowed down batch release testing and compromised data integrity. 

In response, they partnered with TetraScience. The Tetra Scientific Data and AI Cloud™ automates their data workflows end to end through seamless bidirectional integration with LabWare LIMS and other software. The result is faster, more reliable, and fully traceable QC processes.

With TetraScience, the biopharma achieved:

• Faster batch release testing
• Higher productivity for scientists
• Enhanced data integrity
• Analytics-ready data for future chromatography dashboards

Producing cell and gene therapies is exceptionally complex, and the stakes are incredibly high. For the contract development and manufacturing organizations (CDMOs) enabling these treatments, operational efficiency isn’t just about saving time and money; it can be a matter of life and death.

Recognizing this, an industry-leading CDMO modernized its scientific data strategy for developing and analyzing cell and gene therapies. Initially, its scientists relied on paper-based records and spreadsheets to track and record data. These methods were slow and prone to human error.

The CDMO decided to implement an integrated solution combining LabVantage LIMS with the Tetra Scientific Data and AI Cloud™. The solution led to several key outcomes:

• Higher productivity for scientists
• Improved data integrity
• Future-proof, AI-ready data

Scientists at a biopharmaceutical company faced a data bottleneck in their high-throughput screens. Most could not access the screening data directly and had to rely on computational biologists to manually gather, process, and analyze it. This inefficient process slowed drug discovery.

To resolve the issue, the biopharma adopted the Tetra Scientific Data and AI Cloud™. This scalable solution automatically replatforms and engineers the screening data, making it readily available for visualization through a dashboard. Now, all discovery scientists can easily access high-quality data.

Key outcomes include:

• Rapid data search and aggregation: reduced from 1–2 days to 10 minutes 
• Higher productivity: refocused computational biologists on more valuable work
• Faster decisions in hit selection: decreased from 2 weeks to 1–2 days

Development scientists at a leading biopharmaceutical company use quantitative polymerase chain reaction (qPCR) to evaluate the impact of process changes on gene therapy products. These assays are crucial for chemistry, manufacturing, and controls (CMC). However, the team's manual-intensive data workflow significantly limited their throughput and data analysis capabilities.

The biopharma's leadership turned to TetraScience to streamline their qPCR process. The solution employs the Tetra Scientific Data and AI Cloud™ to automatically replatform and engineer the qPCR results into analytics- and AI-ready data.

As a result, the improved workflow:

• Increases assay throughput by up to 6x
• Minimizes manual steps, saving up to 1 hour per assay
• Enables meta-analyses across multiple experiments

A leading contract research organization (CRO) performs drug transporter assays for biopharma companies, providing crucial insights into the pharmacokinetics and potential drug-drug interactions of lead compounds. However, its scientists struggled with slow and error-prone data processes, impacting the scalability of these assays.

To address this challenge, the CRO partnered with TetraScience to streamline the entire workflow. The Tetra Scientific Data and AI Cloud™ automatically replatforms and engineers the data from each study, eliminating many inefficient steps.

As a result, the CRO has achieved:

• 50% decrease in manual data entry
• 80% fewer items for QA to review
• 3,000 hours per year reallocated toward increasing throughput

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How drugs bind to blood plasma proteins influences their bioavailability and efficacy in the body. At a top 25 biopharma company, scientists measure this interaction using rapid equilibrium dialysis followed by mass spectrometry (RED-MS). However, they were spending significant time on manual file preparation and statistical calculations, which slowed down their absorption, distribution, metabolism, and excretion (ADME) studies.

Discover how the Tetra Scientific Data and AI Cloud™ streamlined and enhanced their workflows by automatically preparing and processing data for RED-MS assays. The new solution:

• Cut data preparation and processing time from hours to minutes
• Centralized and contextualized data for rapid search and retrieval
• Eliminated manual steps for improved scalability and data integrity

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The flow cytometry core facility at a leading biopharma faced complex data challenges. Scientists struggled with data curation, while facility staff had difficulty monitoring instrument performance. Both groups needed more efficient ways to manage and analyze their data.

Tetra Catalysts provided a way forward. This outcomes-based offering optimized flow cytometry workflows with a science-led approach that combines the Tetra Scientific Data and AI Cloud™, deep domain expertise, and a library of scientific use cases and supporting artifacts.

With Tetra Catalysts, the biopharma was able to:

• Redirect scientists’ time spent on manual data processes to more valuable work
• Collect and engineer data automatically in the cloud for rapid search and analysis
• Visualize and trend instrument calibration data, enabling predictive maintenance
• Recover over $50,000 per year through time savings

At a top 10 pharmaceutical company, scientists developing purification methods were bogged down by inefficient data processes. They struggled to consolidate analytical data from UPLC and connect it with upstream processing data from FPLC. This hampered chemistry, manufacturing, and controls (CMC) efforts.

The Tetra Scientific Data and AI Cloud™ transformed the workflow by enabling a seamless exchange of data between chromatography data systems (UNICORN and Empower) and Signals Notebook. Scientists can now compare multiple runs effortlessly, identify anomalies rapidly, and access historical data instantly.

Find out how TetraScience helped this pharma:

• Eliminate 25 to 100 hours per week of manual data transcription
• Reduce data search times by up to 8 hours
• Streamline report preparation, saving 10 to 20 days per year
• Speed up process development by predicting optimal purification conditions to maximize yield and purity
• Reclaim $375,000 per year in scientists’ time

Predictive models can dramatically improve drug production, but they require large-scale, contextualized datasets. At a top 25 biopharma, data scientists working in chemistry, manufacturing, and controls (CMC) were stuck. They couldn't use process data (collected by PharmaMV software) to build these models because it wasn't optimized for predictive analytics.

The biopharma partnered with TetraScience to liberate this trove of valuable data. The solution uses the Tetra Scientific Data and AI Cloud™ to automatically replatform and engineer data from PharmaMV into AI-native Tetra Data.

Now, the data scientists can:

• Search and assemble contextualized data rapidly
• Access AI-native data for advanced analytics and modeling of drug production

Read the customer story to learn more.

Scientists in the Chemistry, Manufacturing, and Controls (CMC) division at a top-ten biopharma company specialize in developing methods for purifying biologics. However, they struggled with inefficient data practices. Manually assembling and analyzing heterogeneous sets of chromatography data (from FPLC and UPLC) was far from scalable.

Discover how TetraScience accelerated their development cycle by replatforming and engineering chromatography data. This analytics-ready data powers an in-house application to further streamline and enhance analysis.

The new solution enables the CMC team to:

• Redirect thousands of hours of manual data handling to model building
• Search and retrieve chromatography data faster and easier
• Streamline troubleshooting during scale-up to facilitate tech transfer
• Generate AI-native data for future AI applications

At Charles River Laboratories Hungary (SOLVO), scientists perform drug transporter assays to evaluate the absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties of drug candidates. They follow a rigid and inefficient sampling scheme to generate dose-response curves, adding cost and time to projects.

Learn how TetraScience helped SOLVO streamline ADME/Tox screening with artificial intelligence (AI). By replatforming and engineering scientific data, the Tetra Scientific Data and AI Cloud™ supports the development and validation of an in silico model to optimize sampling and improve IC₅₀ calculations.

The in silico model, built in just 5 months, will enable SOLVO to:

• Cut the number of sampling points by half while improving IC₅₀ accuracy
  
• Reduce wet lab experiments by 50%
• Decrease manual data curation by 1.5 FTE

When a large biopharma needed to digitize their batch release workflows, eliminate manual data handling, and prepare their scientific data for downstream use in analytics applications and AI, they chose TetraScience.

The Tetra Scientific Data and AI Cloud™ will enable the seamless transfer of data between Signals Notebook, LabX, and Tiamo through integrations and advanced data engineering while simultaneously preparing data for downstream use in analytics and AI applications.

This global biopharma can expect:

• 40% increase in lab productivity
• 4x reduction in error rate
• Reduction in FTEs required for QC review
• AI-native data for prediction and troubleshooting

For the full story, read our 1-page customer story.

High-throughput screening of genomic edits in mice is a complex, multi-step process. Each genetic modification requires unique reagents for detection by quantitative PCR. Read this customer story to learn how the Tetra Scientific Data and AI Cloud increased productivity through the automation and enhancement of sample records.

Challenge:
A top 20 biopharma company operates its qPCR-based genotyping workflow at 40% capacity due to limitations in sample tracking.

Solution:
The Tetra Scientific Data and AI Cloud enables per-well sample tracking by replatforming and engineering scientific data.

Result:
33% increase in genotyping throughput so far; expected to increase by 150% total soon.

A leading biopharmaceutical company was searching for a data solution that would help them accelerate biologics discovery and development, improve time-to-insight, and bring therapies to patients faster.

This case study explores how the Tetra Scientific Data and AI Cloud helped them accomplish their goals by:

• Automating CRO data ingestion
• Eliminating time-consuming manual data cleansing
• Accelerating .CZI image processing
• Streamlining HTS workflows
  

Read this case study to learn how TetraScience optimized large scale scientific data.

Flow cytometry is a powerful tool to screen and optimize protein therapeutics. But the raw data is complex and requires preparation before downstream analysis. Performed manually, this process creates a bottleneck for high-throughput approaches.

Facing this challenge, a leading biopharma company looked to increase the throughput of its antibody screening method by streamlining the scientific data workflow.

Read the case study to learn how TetraScience helped this company achieve:

• 25X faster data preparation, including gating and QC
• Up to 48 hours saved per screen
• Over 3X screening throughput by supporting a multiplexing approach
• Higher-quality data, centralized and searchable in the cloud
  

The outcome was faster, better lead identification without increasing instrument footprint or personnel.

Ginkgo Bioworks is pioneering new ways to design biology. The company’s innovative cell programming platform enables scientists to edit DNA and produce biological designs that can identify new therapeutics for pharmaceutical companies, enable agriculture companies to protect crops from pests, and more.

Learn how the Tetra Scientific Data and AI Cloud™ enabled Ginkgo to optimize lab operations and allowed software engineers to focus on high-value work while also helping the company prepare data for analytics and AI applications.

The clinical-stage biopharmaceutical company Prelude Therapeutics (PTx) deployed the Tetra Data Platform to eliminate manual data processing and accelerate scientific discoveries.

This case study describes:

• The challenges of manual collection, transfer, and manipulation of data for the integration of PTx instruments & software
• The benefits of scientific process automation and workflow management with the Tetra Data Platform (TDP)
  

TetraScience built a cloud-based data processing solution to automate Tecan, Titian, Dotmatics, and PerkinElmer Envision workflows with upstream data science applications. Our vendor-agnostic integrations enabled scientists to access all of their instrument data, information of informatics software, and scientific data visualization in one place. No more need to move and update files from multiple locations.

Learn how to reduce manual tasks by automating workflows, enabling scientists to focus on high-value activities like developing new cancer treatments. Read the full case study.

A leading biotech company was facing challenges with their flow cytometry workflows due to inefficient, manual processes that were time consuming, high effort and error prone.

This case study shows how this biotech organization was able to automate, simplify, and improve the quality of their flow cytometry workflows by addressing the pain points that include:

• Manual data manipulation
• High utilization of instruments
• Slow, complex, and risky data preparation processes
  

Read this full case study to learn about how to improve and accelerate flow cytometry workflows with the Tetra Scientific Data and AI Cloud™.

Surface plasmon resonance (SPR) enables scientists to measure molecular interactions between a potential drug and its target. Read this customer story to learn how the Tetra Scientific Data and AI Cloud accelerated lead characterization by replatforming and engineering SPR data.

Challenge:
A top 10 biopharma company performs manual QC of SPR data for lead characterization. The process takes up to 80% of scientists’ time, throttling assay throughput.

Solution:
The Tetra Scientific Data Cloud streamlines the entire QC process by automating data ingestion, engineering, and publishing.

Result:
Over 3x higher assay throughput and better quality data.

When Andelyn Biosciences broke ground on two state-of-the-art gene therapy CDMO facilities, they needed the optimal scientific data strategy. They decided to create a “Connected Plant” by leveraging The Tetra Scientific Data and AI Cloud™ across their entire enterprise.

Read this case study to learn how TetraScience and Andelyn successfully launched “The Connected Plant” where the Tetra Scientific Data and AI Cloud:

• Provides the data infrastructure, automation, and engineered data Andelyn requires to become “The Digital CDMO”
• Lays the foundation for AI-enabled workflows
• Powers an easy-to-use search for all assay data
• Consolidated every independent data story across the scientific organization
• Onboarded 100% of Andelyn’s legacy gene therapy data
  

Find out why Andelyn says “TetraScience is the cornerstone of 'The Connected Plant Strategy.'”

Most Contract Research Organizations (CROs) use their own data formats for standard assays, which can pose data aggregation challenges to biopharma companies. Being able to harmonize data from different CRO reports is critical to scale up absorption, distribution, metabolism, and excretion (ADME) testing of pharmacokinetics (PK) properties of drug candidates.

This case study demonstrates how TetraScience helped two clinical-stage startup companies get the most out of their CRO collaborations for ADME/PK needs.

It covers:

• The challenges of laborious manual data workflows for pharmacokinetics and pharmacodynamics (PK/PD) studies. Scientists have to manually check the reports from CROs.
• The benefits of an optimized process that connects, parses, and harmonizes ADME/PK reports from various CROs.

Harmonized data allow visualizations of structure activity relationships (SAR), data science, artificial intelligence, and machine learning (AI/ML). By automating CRO data entry, processing, and transfer, scientists save time and reduce manual errors. With automated data management, CRO outsourcing can help biopharma companies realize more cost-effective results. Read the full case study to learn more.

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A top 20 global biopharma needed to integrate 350+ scientific instruments into a centralized data environment, but each integration required five minutes of manual configuration. Learn how the company paired Atlassian Jira project management software with the Tetra Scientific Data and AI Cloud™ to automate the configuration of Tetra File-Log Agents and achieve 25x faster instrument onboarding.

As a process for separating components of a sample, chromatography is critical to modern pharmaceutical and biotech workflows. One challenge is the difficulty in aggregating data across multiple runs or across diverse in-house instrumentation. Manual data reporting and transformation between data formats are time-consuming and prone to errors.

Biopharma organizations are partnering with TetraScience to unlock the full value of chromatography data – and faster, more reliable quality control.

Our case study – Data-Driven Quality Control – describes how TetraScience helped a top 15 global biopharma to improve chromatography data analysis. You'll learn about our automated quality control workflows for:

• System suitability tests (SST)
• Column degradation checks
• Shelf-life for active pharmaceutical ingredients (APIs)

TetraScience ingested and processed high-performance liquid chromatography (HPLC) data into a FAIR format.

Researchers saved an estimated 8 hours/week in manual data gathering, visual comparison and reporting. Our client was also able to use existing runs to predict failures before they occurred while resolving inefficiencies in communication.

Enhance Your Data-driven Quality Control  

Read our case study for an in-depth look at how TetraScience has advanced chromatography data analysis in the cloud. We will review:

1. Cost savings in manual labor and consumables
2. Streamlined reporting between functions 
3. Harmonized data adhering to FAIR principles

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Alexion, AstraZeneca Rare Disease, needed a technology partner to streamline a bioprocessing purification workflow so they could advance their analytics capabilities and transform the lives of patients with rare diseases.

TetraScience successfully developed an automated, FAIR-data powered (findable, accessible, interoperable, reusable), analytics-enabled FPLC workflow that significantly improves time-to-insight and throughput for their downstream laboratory.

Read the case study to find out how TetraScience helped Alexion:

• Decrease time-to-insight from 1 week to 1 day
• Reallocate ~5000 hours per year to higher value work
• Increase FPLC throughput
• Create the lab’s first centralized, searchable, cloud data store
• Eliminate the operating costs associated with retrieving difficult-to-find data

Maximizing ROI from scientific data and workflow solutions hinges on aligning IT, scientific, and data science teams. However, many organizations struggle with limited resources, best practices, and expertise in new technologies. 

Tetra Catalysts, led by our elite team of Tetra Sciborgs, addresses these challenges by bridging gaps between departments, identifying high-impact scientific use cases, and developing strategies to optimize data utilization and AI readiness. This approach boosts the value of Tetra Scientific Data and AI Cloud™ deployments by up to 10x.

Biopharma executives need to accelerate discovery, advance better drug candidates, and deliver a more robust portfolio. This one-page guide features real-world case studies from leading biopharmaceutical companies demonstrating how the Tetra Scientific Data and AI Cloud™ has proven capabilities in:

• Solving immediate data problems and accelerating workflows
• Improving use cases across the value chain (HTS, chromatography, CDMO automation, etc.)
• Identifying hidden data problems that TetraScience can solve
• Reducing cost, improving productivity, and accelerating time to scientific insight

Fundamental breakthroughs in life sciences are possible for the biopharmaceutical organizations that fully harness their scientific data. Read our guide and discover how we can help.

The purpose of this document is to outline the Verification and Validation (V&V) strategy employed by TetraScience to ensure the Tetra Data Platform (TDP) performs as intended against user requirements. By aligning with this strategy, customers can leverage TetraScience deliverables to significantly reduce in-house validation efforts. It should be noted that a customer purchasing this package is still responsible for defining and ensuring the “last mile” validation of their own specific configuration of the platform, as well as defining sufficient standard operating procedures to operate and secure the system.

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Our comprehensive catalog of courses in TetraU, is your gateway to acquiring invaluable knowledge and expertise on the Tetra Scientific Data and AI Cloud. Whether you're a beginner seeking to explore the foundations or an experienced professional looking to enhance your skills, our carefully curated selection of courses has something for everyone.

• Dive into engaging modules led by TetraScience experts, designed to deliver practical insights and best practices.
• Utilize foundational and in-depth modules along with hands-on workshops, offering a blend of knowledge and real-world application.
• Ensure you're equipped with expertise to help you thrive while using the Tetra Scientific Data and AI Cloud.

Don't miss out on this opportunity to unlock your potential and excel in your endeavors. Review the catalog and enroll in TetraU to embark on an exciting journey of growth and success!

The Tetra Scientific Data Cloud helps accelerate discovery throughout the product lifecycle. Read this case study to see how TetraScience solved data challenges within:

• Enterprise biopharma
• Gene-therapy CDMO
• Biotech

The case study fact sheet also covers specific use cases where TetraScience integrations have catalyzed discovery and improved process efficiency. These include:

• Hamilton Microlab STAR (Sequential Transfer and Aliquot Robot) liquid handler
• ÄKTA Unicorn
• PerkinElmer Envision
• Tecan
• Titian
• Dotmatics
• Novocyte Quanteon Cytometer
• Cytoflex LX Flow Cytometer

Find out why 12 out of 25 top biopharmas trust TetraScience with their data by downloading your copy today.

Scientists face several data challenges that impede workflow efficiency and their ability to access and analyze experimental results. These challenges include:

• Scattered scientific data across instruments and applications
• Inaccessible or poor-quality experimental data
• Manual processes to move and prepare scientific data
  

This guide for scientists provides a side-by-side comparison of scientific data before and after TetraScience. You will learn how the Tetra Scientific Data and AI Cloud addresses scientific data challenges and enables you to achieve better and faster science.

Life sciences IT across R&D, Quality, and Manufacturing face several challenges that impede their ability to collect, manage, and distribute data to their teams. These challenges include:

• Scattered, disconnected silos that make accessing and utilizing scientific data difficult
• Maintaining and developing custom, point-to-point instrument and application integrations
• Time and expertise to build in-house data solutions

This guide for scientific IT provides a side-by-side comparison of data access, technology flexibility, and solution scalability before and after TetraScience. You will learn how the Tetra Scientific Data and AI Cloud addresses these scientific data challenges and enables you to achieve better and faster science.

Scientific data engineers in biopharma organizations manage significant challenges as they provide scientists and data scientists with the high-quality data they need. These challenges include:

• Consolidating data from complex workflows
• Navigating scattered, ad hoc file storage across massive companies
• Tedious, error-prone manual transcription processes
• Poor metadata attribution

This guide explains how The Tetra Scientific Data and AI Cloud™ solves these issues and empowers scientific data engineers to build and configure pipelines that enable downstream usage of informatics applications, provide centralized and searchable data storage, and power advanced analytics, visualization tools, and AI/ML.

A few years after TetraScience decided to build an Intermediate Data Schema (IDS) on JSON, Allotrope Foundation developed the Allotrope Simple Model (ASM), which also uses JSON.

If your organization is interested in adopting the ASM, you can use the Tetra Scientific Data and AI Cloud to transform your data into the ASM at scale. To fine-tune this process, TetraScience collaborated with a top 25 pharma company to integrate primary lab data into the cloud, engineer the data into our IDS, and then transform data into the ASM.

Download this fact sheet to learn how and why to transform data into the ASM by first converting files to the TetraScience IDS.

AI/ML-powered science is rapidly advancing biopharmaceutical science. However, biopharma companies have a problem. AI/ML is not possible without connected ecosystems, centralized data stores, and harmonized, enriched, analytics-ready data. In other words, biopharmas lack the data maturity AI/ML requires to function. That’s where TetraScience comes in.

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Read in this fact sheet, how TetraScience can help unlock your data and unleash your AI/ML with the Tetra Scientific Data and AI Cloud.

Following the US FDA’s 21 CFR Part 11 regulation is a critical element for FDA-regulated industries to ensure compliance. In this document we assess the different sections regarding the compliance of the Tetra Data Platform with the requirements for Electronic Records and Electronic Signatures as defined in the guidance document. It helps identify the coverage and responsibility of each of these sections.

Biopharmaceutical organizations need a robust approach to maintaining data integrity. The value, volume, and variability of their scientific data demand it.

ALCOA++ is a set of principles used in the life sciences and other regulated spaces to ensure data integrity. Learn how the Tetra Scientific Data and AI Cloud™ upholds ALCOA++ throughout the entire scientific data journey, from data collection to analysis.

Get the essentials from an expert discussion on AI featuring AWS and TetraScience.

Artificial intelligence (AI) stands on the brink of revolutionizing the biopharmaceutical industry. It promises to slash time to market, lower costs, mitigate risks, and create novel therapies. However, the main barrier to realizing these benefits is not the development of AI models but rather the quality and accessibility of the scientific data that feeds them.

The recent webinar "Scalable data strategies for scientific AI-enabled breakthroughs" featured insights from industry experts Naveen Kondapalli, Senior Vice President of Product and Engineering at TetraScience, and Himanshu Jain, Healthcare and Life Sciences Strategy Leader at AWS. They delved into the data challenges underlying scientific AI and discussed strategies to overcome them.

In this recap, we summarize four key takeaways from the conversation that will jumpstart your AI journey.

A groundbreaking research survey of 500 biopharma executives reveals why life sciences companies need to rethink their data platforms. Replatforming scientific data to an open cloud platform while transforming the data to FAIR (Findable, Accessible, Interoperable, Reusable) principles impacts three key industry drivers:

• Speed: Learning from experiments faster, reducing time to market for final molecules
• Cost: Improving productivity, increasing operational efficiency, increasing return on investment (ROI)
• Risk: Improving safety, reducing errors, improving compliance with regulatory requirements

Executives say that replatforming scientific data to an open cloud platform would accelerate time to market by 65%.

Each one-day delay in getting a drug or therapeutic to market costs biopharmas an average of $1.26 million in lost revenue and/or loss of first-mover advantage.

Slowness to market and lost revenue are not the end of the story. Cloud ‘laggards’ spend 3.7X more on inefficient infrastructure compared to cloud leaders.

Download this report to calibrate your scientific data strategy.   Learn how utilizing FAIR data in the cloud is a game changer.

TetraScience teamed up with Pharma IQ to survey biopharma industry insiders and ask about their organizations’ strategies for cloud adoption, digital labs, FAIR data, and the use of artificial intelligence and machine learning (AI/ML).

The 2022 State of Digital Lab Transformation in Biopharma Report shows the importance of digitalization in the pharmaceutical industry and makes a strong case for cloud data platform automation. Industry leaders identify this move as critical in order to improve scientific outcomes and accelerate discovery.

Key findings include:

• 87% of survey respondents claim that maintaining point-to-point, DIY integrations is burdensome.
• 74% of biopharma organizations are replatforming their scientific data to the cloud to enable scale, resilience, and faster time to market.
• 49% of bench scientists' time is spent on manual data retrieval, cleansing, and transformation.

Basic data-to-cloud replatforming is accelerating, but life sciences organizations still face obstacles. About half of the respondents of our survey say that “less than half” or “none” of their data can now be considered FAIR.

Accelerate Digital Transformation in Biopharma

A cloud-based scientific data platform can enable life sciences organizations to speed lab digitization, provide FAIR data to accelerate research and scientific outcomes– adapting to their changing business needs. Download our full report and discover why these initiatives of data automation in the cloud are gaining momentum in biopharma.

The Chromatography Insights app in the Tetra Data and AI Workspace makes it easy to track column, method, and system performance across all your chromatography systems.

In this demo, you'll discover:

• The Tetra Data Lakehouse architecture
• 3 illustrative use cases
• Scientific outcomes driven by the Chromatography Insights app

Discover how the Tetra Scientific Data and AI Cloud integrates with Waters Empower Chromatography Data System (CDS) and Veeva Vault LIMS to enable a seamless flow of data between them. The platform also replatforms and engineers the chromatography data, optimizing it for analytics and AI in downstream applications.

Watch the demo to see this workflow in action.

Discover how the Tetra Scientific Data and AI Cloud integrates with Bio-Rad's droplet digital polymerase chain reaction (ddPCR) systems and Veeva Vault LIMS to enable a seamless flow of data between them. The platform also replatforms and engineers the ddPCR data, optimizing it for analytics and AI in downstream applications.

Watch the demo to see this workflow in action.

For simple devices like balances and pH meters, capturing data and adding context (e.g., sample IDs) in an ELN or LIMS is challenging. Traditional approaches, such as manual transcription or middleware, are either error-prone or costly. Our new mobile app bridges the gap, offering both simplicity and accuracy.

In this demo, you'll see how the app captures readings with a point-and-shoot approach. Add notes just by speaking into the device. With a single click, send all the data to the Tetra Scientific Data and AI Cloud™, where it's automatically contextualized with metadata. From there, it can seamlessly flow to an ELN, LIMS, or any other target.

Discover how low-code scripting on the Tetra Data Platform streamlines data processing and boosts productivity. This feature allows scientific data engineers to easily create pipelines using Python directly within the platform.

In this demo, you'll learn how to label files using a Python script.

Explore the powerful search functionality within the Tetra Data Platform. Learn how to search, filter, and manage your scientific data with ease.

In this demo, you'll learn about:

• Indexed data: Understand what data and metadata are indexed for search within the platform.
• Tetra Data Platform user interface: Use the intuitive UI to search and filter your data.
• Querying in Snowflake: Build and execute queries within Snowflake to extract specific data.‍
• Querying with Elasticsearch: Leverage Elasticsearch and the platform's API for advanced data searches.

Discover file versioning and lineage within the Tetra Data Platform. The platform allows you to easily monitor each file's entire lifecycle. 

In this demo, you'll learn how to:

• Explore comprehensive file details: Access detailed information about each file, such as metadata and source.
• Visualize the complete file journey: Track every step, including upload, indexing, and triggered pipelines.
• Manage file versions: View, upload, and switch between different versions of files with ease.

Discover how the Tetra Data Platform empowers you with robust data access controls, ensuring secure access to your files.

In this demo, you'll learn about:

• Metadata collection and how it helps to define access groups
• Components of data access controls
• Setting up access groups
• Impact of access groups on search results

Event subscriptions allow you to programmatically notify downstream systems of key events within the Tetra Data Platform (TDP). By subscribing to specific TDP events, you can automatically trigger custom apps and business processes to accomplish follow-on actions.

In this demo, you'll learn about:

• Supported event types
• Examples of use cases
• Creating event subscriptions

Discover how the Tetra Scientific Data and AI Cloud™ enhances data management and search capabilities with advanced labeling and contextualization.

In this demo, you'll learn how to apply:

• Static labels: Automatically applied to incoming files based on source information (e.g., laboratory number and site).
• Dynamic labels: Extracted from file names, paths, or metadata (e.g., well locations and instrument info).
• Third-party labels: Added from external sources like CSV files or databases (e.g., sample ID).

Explore these features to streamline your data management and make data searching more efficient.

Data is at the heart of scientific decisions. The Tetra Scientific Data and AI Cloud places the scientist's user experience at the forefront. With its intuitive search and filter features, scientists can easily access, organize, and analyze data.

This demo (Part 3) introduces a new feature called Projects, which helps you quickly organize and access data associated with specific labs, experiments, instruments, or any other type of project. Projects are organized by user-defined attributes and automatically update with new data when relevant files are uploaded to the platform.

Watch the other videos in the series: 

• Part 1 – Search Page
• Part 2 – Saved Searches

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Data is at the heart of scientific decisions. The Tetra Scientific Data and AI Cloud places the scientist's user experience at the forefront. With its intuitive search and filter features, scientists can easily access, organize, and analyze data.

In this demo (Part 2), you'll discover how to store and share search criteria for reuse.

Watch the other videos in the series: 

• Part 1 – Search Page
• Part 3 – Projects

Get a 5-minute recap of our webinar on how a modern, cloud-based platform and laboratory information management system (LIMS) can transform QC to meet the demands of expedited product releases. 

This webinar discussed how to:

• Move from a legacy LIMS to an advanced, future-proof, and compliant LIMS in the cloud
• Build a foundational data strategy for digital QC, enabling easier compliance and improved quality
• Empower data-driven decision making through dashboards and AI
• Accelerate batch release with improved right-first-time test execution
• Reduce total cost of ownership while increasing decision speed and confidence using a holistic approach to quality 

Watch the entire webinar on demand.

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This video demonstrates how structured UNICORN data can be analyzed and compared using the FPLC Data Explorer with the Tetra Data and AI Workspace. The Tetra Data and AI Workspace integrates data and data apps in a single environment, so scientists can analyze data intuitively and more efficiently than ever before.

With UNICORN data, scientists can gain valuable insights by visualizing, comparing, and analyzing chromatograms and peak alignments. Secure data storage, automation, and integration allows users to access data globally and significantly reduce non-value-adding activities.

This innovative approach has proven to decrease time to insight from 1 week to 1 day, accelerate basic tasks like overlaying chromatograms from 1 day to 5 minutes, and improve scientific workflow efficiency allowing the reallocation of about 5,000 hours per year of valuable resources.

Learn about the paradigm shift in scientific data management as organizations worldwide adopt modern strategies for analytics and scientific AI. This video introduces the shift from on-premises to cloud-based storage, the transformation from file-based to engineered data, and the move to an event-driven processing model. Learn how TetraScience's automated workspace and endpoint-agnostic data cloud empower multi-company collaboration, enabling scientists to transform data into insights that fuel advanced analytics, machine learning, and AI. Discover the future of data management today.

Data is at the heart of scientific decisions. The Tetra Scientific Data and AI Cloud places the scientist's user experience at the forefront. With its intuitive search and filter features, scientists can easily access, organize, and analyze data.

In this demo (Part 1), you'll discover how to:

• Navigate the platform’s search page
• Employ hierarchical data views
• Utilize advanced filtering and bulk operations to streamline your workflow

Dive in and discover how our search features can transform your scientific data journey.

Watch the other videos in the series: 

• Part 2 – Saved Searches
• Part 3 – Projects

The Tetra Data and AI Workspace transforms how scientists interact with their data. Applications can now be deployed in a cloud environment alongside your scientific data. The Data and AI Workspace supports applications from popular software providers, custom apps built with industry feedback, and innovative Scientific AI use cases developed by TetraScience.

In this demo, we'll showcase FlowJo in the Data and AI Workspace. You'll see how to easily import flow cytometry data into FlowJo, perform analysis in the cloud, and automatically send the results to an electronic lab notebook.

With the Data and AI Workspace, you can:

• Centralize your scientific data securely
• Reduce manual scientific labor
• Ensure data integrity at every step
• Automate workflows efficiently
• Analyze your data from anywhere in the world
• Integrate seamlessly with downstream targets using API and SQL

Our new AI-powered tool transforms how scientists analyze their data. With simple conversational prompts, they can retrieve data and generate visualizations—in just seconds. Tetra Scientific AI will help scientists save time, eliminate errors, and gain deeper insights into their data.

This demo shows how a scientist can quickly aggregate and analyze data from over 500 samples in a gene editing experiment, without manual data handling or processing.

How efficiently cultured cells can produce a therapeutic protein depends largely on the media. However, developing an optimal formulation is a complex challenge. The permutations of media components and their concentrations are immense. Scientists often have to screen dozens to hundreds of formulations in the lab to identify the most effective ones. Now, AI can drastically speed up this process.

The Tetra Media Formulation Optimizer uses an advanced in silico model to predict the best formulations for a given cell line. This innovation has the potential to reduce wet lab experiments by up to 88 percent.

Lead clone selection is usually a time-consuming and resource-intensive phase in cell line development. However, AI is poised to transform this process.

Introducing the Tetra Lead Clone Selection Assistant, powered by Tetra Scientific Data and AI Cloud™ and Google Cloud Vertex AI. This application can predict the most promising lead clones in silico, enabling faster and better decision making. As a result, it has the potential to shorten lead clone selection from five months to just one month.

Discover how combining the Tetra Scientific Data and AI Cloud™ with AWS HealthOmics streamlines and enhances the analysis of next-generation sequencing (NGS) data. This solution provides a comprehensive framework for metadata management, facilitating the analysis of properly contextualized datasets.

Biopharma scientists and IT struggle to ensure a seamless flow of data between an electronic lab notebook (ELN) and a chromatography data system (CDS). They often resort to inefficient manual processes. Consequently, only a fraction of the chromatography data and context is captured, leaving the rest siloed. To make matters worse, most CDS data is trapped in proprietary formats, making it inaccessible.

Bidirectional data workflows using the Tetra Benchling Connector and Tetra Chromeleon Agent solve these challenges and bring round-trip lab automation into scientific workflows. See the tools in action in this video.

For more information, read our blog post.

Learn how the Tetra Data Platform collects files from scientific instruments and centralizes them in the cloud. This demo covers the following topics:

• Automated data collection
• Automated house cleaning
• Centralized scientific data cloud
• Automated notifications

Discover how the Tetra Data Platform collects data from applications, including chromatography data systems, ELNs, and LIMSs. This demo highlights:

• Automated data collection
• Leveraging of vendor APIs/SDKs
• Contextualization with metadata
• Advanced search

Explore how the Tetra Data Platform contextualizes data to make it findable and accessible. This demo shows how the platform enables users to enrich primary data with metadata through various methods, such as

• Manual addition
• Agent configuration
• Extraction from file names
• Retrieval from registries or applications
• API

Biopharma organizations continue to spend time and money managing scientific data scattered across disconnected silos, ad hoc file shares, and legacy solutions. Why not future-proof with one open platform for all scientific data?

The Tetra Scientific Data Cloud™, purpose built for scientific data, enables IT to say goodbye to countless hours spent maintaining dozens or hundreds of instruments and informatics software applications. With open and centralized data in the cloud, scientists are able to search faster and more easily, create new workflows, and gain insights from analytics, visualizations, and AI/ML. Unlock innovation with the Tetra Scientific Data Cloud.

Watch the video to see how the Tetra Scientific Data Cloud simplifies data management and unlocks innovation.

See how Tetra Data is pushed into Benchling Notebook. The TetraScience integration to Benchling is powered by Tetra Pipelines, utilizing DataWeave as the simple transformation scripting language. DataWeave scripts enable scientific data engineers to configure pipelines that match their data schemas in Benchling without worrying about the underlying code or infrastructure of the integration mechanism.

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Presented by Ian Cooper, Integrations Architect, this video is part of the Tetra Product Short Cuts series, which shows what TetraScience does for your scientific data—in short order.

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Learn more about the Tetra Benchling Integration.

Explore some of the new features and enhancements in Tetra Data Platform v3.6 designed to benefit scientific data engineers. This video shows how low-code DataWeave scripting allows engineers to build data pipelines with fewer developer resources than building custom protocols. It also demonstrates how bulk data processing makes it easier to reprocess files after changes to pipeline functionality.

To learn more about the new features and enhancements in v3.6, read the full release notes.

See how the pluggable connector framework provided with Tetra Data Platform v3.6 enables faster integration of scientific instruments and applications with the Tetra Scientific Data Cloud. Teams can take advantage of newly released connectors without having to wait for a platform upgrade. This video shows how a user can install a new connector in the cloud, configure the connector, and then later upgrade the connector to a new version as soon as it’s available.

To learn more about the new features and enhancements in v3.6, read the full release notes.

Learn how the multi-byte character support introduced with Tetra Data Platform v3.6 empowers scientists to use Japanese language in their workflows. See in this video how scientists now can:

• Ingest scientific data with multi-byte characters
• Enrich data with multi-byte characters in file labels
• Search files that use multi-byte characters
• Use labels with multi-byte characters as trigger conditions for pipelines

To learn more about the new features and enhancements in v3.6, read the full release notes.

See how the file journey timeline capabilities in Tetra Data Platform v3.6 enables platform administrators to visualize file activity in various stages of processing. Using health monitoring, file information, and events timeline pages, users can track Tetra Data creation at any moment and trace files to confirm data integrity.

To learn more about the new features and enhancements in v3.6, read the full release notes.

Discover how the introduction of Intermediate Data Schema (IDS) Entity Relationship Diagrams (ERDs) in Tetra Data Platform v3.6 offers a more intuitive way to interface with schemas via SQL. This video shows how ERDs provide a visual map of how all SQL data tables are related to each other. ERDs can help streamline the process of building analytics and AI applications using engineered scientific data.

To learn more about the new features and enhancements in v3.6, read the full release notes.

Learn how Tetra Data Platform v3.6 streamlines the process of creating custom pipelines that transform raw scientific data into AI-ready Tetra Data. This video provides two examples of custom pipelines: one adds labels to data; the other also harmonizes, further enriches, and then pushes data to a third-party application. For both examples, the video shows how newly introduced protocol.yml files help simplify pipeline development.

To learn more about the new features and enhancements in v3.6, read the full release notes.

Learn how Tetra Data Platform v3.6 makes it easier for scientists to find their data on the TetraScience platform. In particular, this video shows how scientists can more easily query for multiple terms. It also shows how they can select elements within Intermediate Data Schema (IDS) files, search for those terms, and find all IDS and raw files that contain those elements, providing greater visibility into raw data than before.

To learn more about the new features and enhancements in v3.6, read the full release notes.

Discover how biopharma teams can automate data processing and unlock new data-driven insights with the Tetra Scientific Data Cloud. Through a plate reader demo, this video describes how the Tetra Scientific Data Cloud harmonizes and enriches raw instrument data. It can then automatically correlate sample information with scientific results, eliminating the time-consuming, error-prone process of copying and pasting data.

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The video also explains how teams can translate scientific data into insights using visualization tools built on the Tetra Scientific Data Cloud. In the demo, a visualization tool transforms plate reader information into heatmaps and IC50 curves, which can be used to accelerate discovery of unique drug candidates through data science analysis.

Learn how the Tetra Scientific Data Cloud can help organizations simplify data processing while employing electronic lab notebooks (ELNs), instrument control software, and visualization tools. Using a small-scale fermentation experiment as an example, this video highlights how the Tetra Scientific Data Cloud can help an upstream processing team reduce manual steps as they create an experiment in an ELN, add metadata, trigger a run, and then automatically capture results as harmonized data back in the ELN. Harmonized data can then be used in a visualization dashboard or other downstream analytics or AI applications.

With the SIMPLO (stability, instrument and method performance, and lab optimization) dashboard, powered by the Tetra Scientific Data Cloud, you can easily visualize all your chromatography data.

This demo focuses on method performance, which is relevant to lab managers and quality control teams. By using this dashboard, you can quickly identify the most common culprits of method performance issues. Benefits include reduced downtime and rework, enhanced data integrity, and superior operating performance.

With the SIMPLO (stability, instrument and method performance, and lab optimization) dashboard, powered by the Tetra Scientific Data Cloud, you can easily visualize all your chromatography data.

This demo focuses on stability, which is relevant to scientists in quality control, formulation, and development. The SIMPLO dashboard streamlines analysis of stability studies, enabling you to focus on deriving insights from the data instead of aggregating the data.

With the SIMPLO (stability, instrument and method performance, and lab optimization) dashboard, powered by the Tetra Scientific Data Cloud, you can easily visualize all your chromatography data.

This demo focuses on system usage, which is relevant to lab managers and purchasing decision makers. By tracking the usage of all your chromatography systems, you can obtain a comprehensive view of fleet utilization and make more informed decisions on capital expenditure.

With the SIMPLO (stability, instrument and method performance, and lab optimization) dashboard, powered by the Tetra Scientific Data Cloud, you can easily visualize all your chromatography data.

This demo focuses on instrument performance and system suitability, which is relevant to analysts and lab technicians. The SIMPLO dashboard streamlines analysis of system suitability testing, enabling you to quickly identify trends from multiple injections or across multiple instruments.

With the SIMPLO (stability, instrument and method performance, and lab optimization) dashboard, powered by the Tetra Scientific Data Cloud, you can easily visualize all your chromatography data.

This video provides an overview of six dashboards within the application, the context for their use, and the benefits provided to the user. Learn how SIMPLO empowers chromatography analysts to quickly and easily analyze their data, leading to faster and better scientific outcomes.

For a deeper dive, watch our SIMPLO dashboard demos:

• System Usage
• Instrument Performance
• Method Performance
• Stability
• Column Utilization
• Compliance

With the SIMPLO (stability, instrument and method performance, and lab optimization) dashboard, powered by the Tetra Scientific Data Cloud, you can easily visualize all your chromatography data.

This demo focuses on column utilization, which is relevant to chromatography scientists and lab technicians. By tracking the history of each chromatography column in detail, you can ensure the right column is used for any given experiment and retire old columns before they cause out-of-specification events.

Meet Katja and Tal, two of our Sciborgs currently helping customers accelerate scientific outcomes with the Tetra Scientific Data and AI Cloud™.

In this video, you'll learn how they improved bioassay development and high-throughput screening for two biopharma customers.

Sciborgs are at the heart of Tetra Catalysts. They are an elite team of Scientific Business Analysts and Scientific Data Architects embedded in your organization who focus on business outcomes and identify high-impact areas to maximize ROI.

For a detailed look at our Sciborgs, read our blog post on how Sciborgs Drive Better Science.

With the SIMPLO (stability, instrument and method performance, and lab optimization) dashboard, powered by the Tetra Scientific Data Cloud, you can easily visualize all your chromatography data.

This demo focuses on compliance metrics, which are particularly useful to quality control and quality assurance teams but are also relevant to lab scientists who use chromatography data systems. By proactively tracking this information, you can make data-driven decisions for risk management and prevent out-of-specification events.

See how to build custom workflows with Self-Service Tetra Data Pipelines. TetraScience has a large and growing library of productized pipelines to collect, enrich, harmonize, and publish data. However, at times, you may need to expand the platform with customized capabilities. That's where Self-Service Tetra Data Pipelines come in. They're easy to code, written in Python, and scalable.

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Presented by Jeremy Karnowski, Principal Technical Trainer, this video is part of the Tetra Product Short Cuts series, which shows what TetraScience does for your scientific data—in short order.

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Learn more about Self-Service Tetra Data Pipelines.

Meet Katja and Tal, two of our Sciborgs currently helping customers accelerate time-to-value with the Tetra Scientific Data Cloud™.

In this video, you will learn about their background, what makes scientific business analysts and data architects unique, and why having Sciborgs work directly with scientists brings so much value to customers.

Sciborgs, like Tal and Katja, are at the heart of Tetra Catalysts. They are an elite team of classically trained scientists with technology expertise and industry leading experience in:

• Speeding time-to-value when deploying the Tetra Scientific Data Cloud
• Identifying new opportunities to improve the use of your scientific data
• Augmenting science by leveraging industry best practices

Watch the video to meet our Sciborgs.

For a detailed look at our Sciborgs, read our blog about how Sciborgs Drive Better Science.

Too many biopharma organizations spend excessive resources manually transferring data among instruments and applications, or constructing rigid point-to-point data connections. They waste significant time curating and preparing data for advanced analytics and AI. 

Learn how the Tetra Scientific Data Cloud can help you harness the full value of your scientific data and improve scientific outcomes by accelerating the scientific data journey, enabling end-to-end scientific workflows, and capitalizing on the power of AI/ML. 

This video provides a walkthrough of the Tetra Scientific Data Cloud, highlighting how it enables organizations to:

• Automatically collect and centralize data for easy data access
• Contextualize data with rich metadata for immediate data retrieval
• Standardize and harmonize data and metadata for comparison and visualization
• Prepare data for advanced analytics and AI

Get a quick recap of our webinar exploring how dispersed scientific data can easily be accessed, enriched, and harmonized for analytics and AI/ML with the Tetra Scientific Data Cloud™.

Featured case studies include:

• High-throughput screening
• Collaboration with contract research organizations (CROs)
• Batch release
• Instrument utilization

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Watch the entire webinar on demand.

Learn how the Tetra Scientific Data and AI Cloud streamlines data processing workflows, eliminating the tedious manual steps of transferring data from scientific instruments to electronic lab notebooks (ELNs). This demo video shows how teams can automatically transfer raw data from instruments to the Tetra Scientific Data Cloud; transform data into the standardized JSON-based Tetra Data format; and then easily initiate the automatic transfer of harmonized files to IDBS E-WorkBook using an integrated connector from TetraScience.

Discover how the Tetra Scientific Data and AI Cloud can simplify data management for high-throughput screening (HTS). This demo video shows how the Tetra Scientific Data and AI Cloud eliminates manual data collection and transformation, as well as the time-consuming process of constructing point-to-point integrations. Using the example of liquid handler and plate reader files, the video shows how the Tetra Scientific Data and AI Cloud automatically:

• Collects instrument files
• Adds relevant contextual information
• Harmonizes files into the vendor-agnostic Tetra Data format
• Distributes files to an ELN

Organizations can then easily reuse Tetra Data in additional analytics, data visualization, and AI applications.

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While hot air balloons and regulatory compliance may seem worlds apart, this video explores how the preparation, testing, and validation required for ballooning provide a compelling metaphor for ensuring compliance.

Learn precisely how TetraScience uses a structured process for quality and compliance controls. This includes: 

• A quality management system that determines if TetraScience can safely contain, hold, and manage data that is intended for GxP use downstream (ISO 9001)
• A software development lifecycle that ensures our business operates both appropriately and ethically and enables us to build our cloud services in a standardized, repeatable way
• A verification suite for function, unit, and downstream testing that ensures “fit for purpose” 
• End-to-end use case testing where TetraScience runs data through simulations of field usage (GAMP 5

Learn how the release of Empower Agent v5.2 by TetraScience improves agent performance, reliability, and scalability as you ingest data from the Waters Empower Chromatography Data System (CDS) into the Tetra Scientific Data Cloud™. New capabilities provide scientists with significantly faster access to data and reduce the likelihood of processing errors—even as organizations scale the volume of chromatography projects.

This video highlights key features and enhancements, including:

• Horizontal scalability
• Project prioritization
• Maximum number of projects per scan
• Agent connectivity monitoring
• Ingestion of project and user access permission data

Learn how the Tetra Scientific Data Cloud streamlines scientific workflows by automating the bidirectional movement of data between informatics systems and chromatography data systems.

This video shows how the TetraScience platform collects data from an electronic lab notebook (Revvity Signals), transforms that data into a standardized format, and writes a new sample set method, which can be used to run an experiment in a chromatography data system (Waters Empower). The platform then collects experiment results and harmonizes the scientific data so it can be easily integrated back into the ELN.

Learn more about Tetra Integrations and see a full list of current and upcoming releases.

Accelerating biotech and biopharma research, development, and manufacturing requires a purpose-engineered platform for ingesting and transforming data, making it Compliant, Harmonized, Liquid, and Actionable.

Andelyn Biosciences is a biopharmaceutical CDMO, pioneering solutions that turn hope into reality™.

In this short video, Bryan Holmes, Vice President of Digital and Technology Solutions at Andelyn, discusses how despite advances in technology, many biopharmaceutical companies still rely on manual data processing.

See how Andelyn is solving this challenge by streaming data to the Tetra Scientific Data Cloud™. As a result, Andelyn can aggregate data faster, provide better insights, and accelerate development timelines and bring more treatments to more patients.

Learn how the TetraScience partnership with PerkinElmer Informatics accelerates scientific insights for biopharmaceutical customers. This partnership connects the full PerkinElmer Informatics Signals Research™ Suite to the Tetra Scientific Data Cloud™, creating a single source for all laboratory information that benefits scientists and IT departments. By focusing on solving customer challenges, this partnership demonstrates how two leading companies work together to maximize scientific data for improved outcomes.

Discover how organizations can eliminate manual steps during data processing through integration of scientific instruments and applications with the Tetra Scientific Data and AI Cloud. This video shows how scientists can create an experiment in the Benchling electronic lab notebook (ELN), send information to instrument control software, start a run, and then automatically extract experimental data. The Tetra Scientific Data and AI Cloud harmonizes data and passes it back to the ELN. Team members can track data movement in real time within the Tetra Scientific Data and AI Cloud interface.

Learn how the Tetra Scientific Data and AI Cloud uses automation to eliminate error-prone, inefficient manual transfer of information between instruments and informatics systems. Using a titration experiment workflow as an example, this video shows how the Tetra Scientific Data and AI Cloud automates transfer of sample information, weight data, and titration experiment results between Signals Notebook and both LabX and tiamo instrument software.

Learn how the Tetra Scientific Data and AI Cloud can help streamline upstream bioprocessing by automating the collection, transformation, and publication of data. As this video explains, the Tetra Scientific Data and AI Cloud can reduce data management burdens and help ensure data integrity while also facilitating file searches and enabling the consumption of data in a variety of endpoints, including analytics and AI tools. The video shows how a real-time TIBCO Spotfire dashboard built on the Tetra Scientific Data and AI Cloud can help scientists visualize and analyze experimental results to generate new insights rapidly.

As scientific data grows in complexity and volume, deriving insights from it becomes increasingly challenging. TetraScience’s lakehouse architecture offers a transformative solution, seamlessly bridging the gap between data lakes and data warehouses. 

With this architecture, you can:

• Efficiently query and transform scientific data, unlocking advanced analytics and Scientific AI.
• Manage datasets for both data lake and data warehouse use cases with SQL and Delta tables.
• Create datasets optimized for specific scientific use cases.
• Share datasets across platforms like Databricks without incurring additional governance burdens or costs from data duplication.

Droplet digital polymerase chain reaction (ddPCR) is increasingly being used in the development and manufacturing of gene therapies. The technique can measure the quantity and quality of gene therapy vectors as well as evaluate cells treated with the therapy.

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In this solution brief, we explore the applications of ddPCR in gene therapy and the data challenges faced by scientists who routinely use the technique. We also show how the Tetra Scientific Data and AI Cloud™ streamlines and enhances ddPCR workflows, leading to faster and better scientific outcomes.

Biopharmaceutical organizations that want to speed therapeutics development and reduce costs through Scientific AI must bridge the gap between their scientific raw data and value-generating AI applications.

The Tetra Scientific Data and AI Cloud offers the only path to closing that gap. It is the factory for producing large-scale, liquid, compliant Tetra Data, which is purpose engineered for science and AI. The platform enables organizations to fully harness their data for Scientific AI, accelerating and improving scientific outcomes.

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Discover more in our solution brief.

Building and maintaining your own scientific data management solution is fraught with hidden costs and risks. Eliminate challenges with a purpose-built data platform for biopharmaceutical science.

TetraScience employs a comprehensive, 3 pillared approach to regulatory compliance—adhering to essential industry guidance and standards, including:

• Good Manufacturing Practices (GMP)
• Good Laboratory Practices (GLP)
• Good Automated Manufacturing Practices (GAMP 5 2nd edition)
• Food and Drug Administration (FDA) 21 CFR Part 11
• FDA Computer Software Assurance (CSA) Guidance
• EU Annex 11

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Learn how TetraScience can accelerate your journey to quality and compliance by:

• Reducing validation effort by 80%
• Increasing ROI by 2-3x
• Slashing deployment time by two-thirds
• Simplifying change management

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In order to help our customers maximize the value of the Tetra Scientific Data and AI Cloud™, TetraScience created an offering that combines a library of deployment-ready, Scientific AI use cases, supporting artifacts and deliverables, and best practices—all derived from our industry-leading expertise—to accelerate and improve scientific discovery, development, and manufacturing . Tetra Catalysts will help customers to improve lab productivity through data automation and Scientific AI, derive new insights and optimize and speed the deployment for specific scientific outcomes, based on best practices and industry trends.

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Read this solution brief to understand how TetraScience’s combined expertise in science, data, and technology can minimize the burden of your teams while maximizing value of your data through Scientific AI as well as the ROI of your deployment.

Learn how Tetra Scientific Data and AI Cloud takes data management beyond traditional approaches by centralizing, enriching, harmonizing, and curating scientific data in a flexible cloud-native platform.

Biopharma organizations use high-throughput screening (HTS) to parallelize experiments to identify higher-quality drug discovery and development leads more quickly. 

HTS currently relies on manual, error-prone process to collect and transform scientific data into formats capable of being used within informatics applications or analytics, delaying downstream drug development.

In this solution brief, you will learn how the High-Throughput Screening Tetra Scientific Application brings cohesion to your HTS workflows by:

• Increasing screening throughput through end-to-end automation
• Eliminating error-prone, manual updates to inventory and registration systems
• Harmonizing data across multiple formats to enable analytics and visualizations, such as SAR and in-silico evaluations
• Providing a centralized historical record and scientific context to scientific data and results

The biopharmaceutical industry is undergoing a seismic shift in how scientific data is managed and analyzed. Emerging technologies like AI are driving the change, offering the potential to bring better therapies to market faster and at lower costs. To achieve these goals, organizations must fully leverage their scientific data. 

Yet, by and large, these datasets are trapped in silos and proprietary formats. Biopharma leaders need to embrace data-centric strategies and frameworks to unlock the full value of their scientific data. Otherwise, they risk failing to capitalize on Scientific AI and falling behind the competition.

In this white paper, you'll learn:

• The inherent challenges of scientific data
• Why traditional data management is no longer sufficient
• 8 modern strategies to maximize data value

Despite its immense value, scientific data in chemistry, manufacturing, and controls (CMC) remains largely untapped. As a result, biopharma companies are struggling to accelerate timelines and reduce costs during late-phase development. A group of industry experts explored the root causes and how best to address them. This white paper summarizes their key findings.

Inside, you'll learn:

• The top 3 business challenges facing CMC and their underlying causes
• Key areas for future development within CMC
• The industry's readiness for electronic regulatory submissions
• Opportunities for using AI in CMC and their potential impact

Biopharma leaders today are poised to capitalize on the power of artificial intelligence (AI) to speed delivery of new therapeutics and drive down fast-rising costs. But for many biopharma companies, there is a gap between the vision for AI and the present reality. They struggle to launch AI initiatives or capitalize on previous efforts.

Why? The answer centers on data. The vast volumes of data that biopharma companies generate and collect are not ready for advanced analytics, AI, or machine learning applications. Moving forward will require a revolutionary scientific data paradigm.

In this white paper, you’ll learn:

• The promise of Scientific AI in biopharma
• 3 primary obstacles to Scientific AI
  
• How to close the AI gap

In response to increasing demands on biopharmaceutical manufacturing facilities, many companies are looking toward “smart” factory, or Pharma 4.0, solutions. However, most biopharmas lack the data maturity to achieve smart factory capabilities.

The artificial intelligence (AI), advanced analytics, and enterprise interconnectivity that enable Pharma 4.0 principles cannot function without a data infrastructure that provides large-scale, liquid, purpose-engineered, and compliant data.

In this white paper, we review why data maturity is no longer an option for biopharmaceutical manufacturers and how the four data tenets of Pharma 4.0 enable data maturity, which enables manufacturing plant maturity. We then discuss precisely how mature data feeds into numerous smart factory use cases, including:

• Optimized tech transfer
• Collaborative data supersets
• Advanced process controls
• Continuous manufacturing
• Continued process verification
• Digital quality monitoring
• Recursive data exchange
• Real-time release testing
• Agile manufacturing
• Predictive maintenance

If you want to know how TetraScience can help you achieve data maturity, contact our team or request a demo today.

Leading biopharma organizations are reimagining research, development, and delivery of life-saving therapeutics through automated data acquisition, wrangling, and transformation.

This white paper — Paving the Way to Automated Data Acquisition and Transformation — describes how this world of data science automation has become a reality.

You will learn:

• The benefits of findable, accessible, interoperable, reusable (FAIR) data in the cloud
• The importance of multi-point productized integrations
• The steps to automate the scientific data lifecycle

Why End-to-end Data Lifecycle Automation Is Critical

With an automated end-to-end data lifecycle, harmonized scientific data flows freely throughout the biopharma data pipeline to accelerate innovation and support quality. Decoupling instrument control from data publishing enables scaled-up data science automation.

We discuss these benefits in detail in our white paper:

• With an automated and remote control interface to interact with instruments, organizations don't need to worry about collecting data with the heterogeneous and fragmented nature of lab data systems.
• With automated data acquisition across different instruments and lab systems, scientists no longer have to manually locate, transfer, and clean the data.
• With data pipelines for automated processing and interactive analysis, the data is automatically prepared, analyzed, and presented to the researcher, driving faster decision-making.

The Tetra Scientific Data and AI Cloud automates the collection and transformation of data in a format that is compliant, harmonized, liquid, and actionable. See what a modern-day end-to-end data pipeline looks like with TetraScience. Read our entire white paper now.

Findable, Accessible, Interoperable, and Reusable (FAIR) data helps life science organizations to drive innovation. This process requires collecting, transforming, and standardizing scientific data to make it searchable, ready for analytics, and able to flow freely among instruments and applications.

This ebook — Unlock the Full Potential of Your Scientific Data — describes how harmonized scientific data in the cloud can help biopharma companies make the most of their data.

You'll learn:

• The challenges of legacy data
• The steps in the scientific data journey
• The essential properties of Tetra Data

Download your copy today to gain a deeper understanding of how Tetra Data eliminates manual data processes and can help your organization gain insights to accelerate ground-breaking treatments.

The biopharmaceutical industry is racing to harness advanced analytics and AI to improve drug discovery, development, manufacturing, and quality control. However, a significant barrier remains: the current state of scientific data. Despite the massive volume of available data—measured in exabytes—much of it is not yet accessible or optimized for advanced analytics and AI/ML applications. Bridging this "data utility gap" is essential for speeding up scientific breakthroughs and delivering life-changing therapies to patients more efficiently and cost-effectively.

Databricks and TetraScience have partnered to overcome these challenges, unlocking the full potential of scientific data. Together, we are revolutionizing the way scientific data is utilized, driving innovation across a growing number of Scientific AI use cases throughout the biopharmaceutical value chain.

Join us to explore:

• ‍‍How scientific data must be replatformed and engineered into large-scale, compliant datasets optimized for analytics and AI
• The utilization of AI-native datasets across the pharmaceutical value chain, delivering novel insights and enabling data-driven decision-making

As a practical example, learn how Databricks and Tetrascience enable the consolidation and comparison of data from Chromatography Data Systems into a single, interactive dashboard. This empowers teams to assess performance variability, identify root causes, and track trends of this critical analytical technique in real-time, leading to faster, more informed decisions across labs.

Register now to secure your spot and start transforming your scientific data.

The world’s most important data is biopharma’s scientific data. We’ll deep dive into how to revolutionize CMC data workflows, scientific use cases, and biopharma and CxO collaboration. Gain insights into optimizing your data strategy to drive CMC efficiency and innovation.

Join our panel of CMC biopharma executives as we explore the path to harnessing the power of liquid data and its groundbreaking impact on furthering scientific outcomes.

A new paradigm that accelerates product releases with confidence

Today, quality control (QC) in biopharma faces several critical challenges. Due to outdated methodologies and disconnected systems, QC teams grapple with error-prone processes, data silos, and limited insight into quality trends. 

This webinar explores how a modern, cloud-based platform and laboratory information management system (LIMS) can transform QC to meet the demands of expedited product releases. We’ll discuss how purpose-engineered, large-scale, compliant, and liquid datasets enable automated workflows and analytics/AI-driven insights. Plus, we’ll show an intuitive interface that unifies quality assurance (QA) and QC to streamline critical quality processes. 

During this webinar, you'll learn how to:

• Move from a legacy LIMS to an advanced, future-proof, and compliant LIMS in the cloud
• Build a foundational data strategy for digital QC, enabling easier compliance and improved quality
• Empower data-driven decision making through dashboards and AI
• Accelerate batch release with improved right-first-time test execution
• Reduce total cost of ownership while increasing decision speed and confidence using a holistic approach to quality 

Creating scientific data for analytical development

Heterogeneous data formats prohibit advanced analytics and AI

The pharmaceutical industry is experiencing a surge in scientific data due to the growing complexity of new therapeutic modalities and the urgency to bring improved therapeutics to market more efficiently. However, this valuable scientific data is often trapped in on-premise instrument control and acquisition software, compounded by the challenge of dealing with diverse vendor-specific data formats, thus inhibiting the potential of AI.

Is engineered scientific data the force multiplier for AI?

In this webinar, we will explore a use case involving a leading pharmaceutical company that aims to provide its scientists and engineers with analytics and AI-ready data across multiple laboratories, departments, and facilities.

By making this data available, scientists can leverage prior work to inform their actions and improve scientific outcomes. Moreover, they can identify and accelerate the most promising scientific strategies , leading to substantial time and cost savings.

This webinar will address several crucial aspects of maximizing the value of scientific data in the biopharmaceutical industry, including:

• Centralizing and harmonizing scientific data
• Improving collaboration and data sharing
• Leveraging Semantic AI in biopharma
• Managing diverse taxonomies efficiently
• Automating data preparation and engineering

Don’t miss this opportunity to discover how your organization can maximize the value of your data and accelerate scientific advancements through AI.

Watch this webinar with Andelyn Biosciences, a full-spectrum CDMO, to learn about their critical initiative of a Connected Plant that will enable biopharma to replatform their scientific data and workflows to the cloud. This innovative connected approach represents a generational movement transforming the way therapeutics are developed, validated, and brought to market.

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The Waters Empower Data Science Link (EDSL), powered by TetraScience, enables scientists and engineers to do more with their valuable experimental data. From enabling chromatography fleet management to deducing new trends across experiments or injections - making lab data FAIR and usable accelerates the move to a digital lab.

This presentation will cover how the data science link helps support Allotrope’s mission (universal formats, interoperability, simplified IT and system integration) and promotes improved data science while using existing resources. This session will focus on:

• High-throughput, automatic acquisition and conversion of data into a vendor-agnostic and data science-compatible format in the cloud, (which can be easily converted to the Allotrope Data Format (ADF) through third-party tools)
• How bidirectional communication between chromatography instrumentation, software, and tools enables sample set method creation, eliminates manual transcription, and increases data integrity
• Easy query of converted data based on sample ID, method name, and other fields using common data science tools
• TetraScience’s membership in the Allotrope Partner Network and how it informs analytics on chromatography data system (CDS) data
  

Scientific data is the most valuable asset of a biopharmaceutical company. Yet, there are still many roadblocks and misunderstandings of how best to realize the scientific and business potential of data. By using modern cloud platforms combined with the right strategy, a connected lab is achievable today and can serve as the foundation for advanced analytics, AI and ML.

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Speakers

Kim Forte, Principal | Life Sciences, Deloitte

Mike Loos, Vice President Solutions Architecture, TetraScience

Anil Verma, Life Sciences GTM Leader, Amazon Web Services (AWS)

Learn how the Tetra Scientific Data and AI Cloud augments the capabilities of the Waters Empower chromatography data system (CDS)—as well as other scientific instruments and applications. This webinar video explains how the Tetra Scientific Data and AI Cloud can:

• Automatically collect data from Empower
• Contextualize data
• Harmonize data into vendor-agnostic, AI-ready Tetra Data
• Verify data quality and enrich data
• Simplify searches for particular files and enable exploration of SQL tables
• Allow users to analyze data with tools such as TIBCO Spotfire
• Publish data to other data science, visualization, and AI applications

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Artificial Intelligence (AI) has the potential to transform the biopharmaceutical value chain. However, its utility across the enterprise is hampered not by the science but by the data – its quality, quantity, and accessibility. As the volume and complexity of scientific data surge, and with the arrival of generative AI and large language models (LLMs), it has become even more challenging to reduce the gap between raw scientific data and AI-enabled outcomes that are accurate, reliable, and unbiased.

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In this webinar, we’ll show how a cloud-native platform, purpose-built for science, and powered by AWS, creates large-scale, liquid data sets that can fuel generative AI models. Migrating raw scientific data to the cloud is insufficient for the meaningful utilization of AI. Only by replatforming and engineering data at scale are biopharma organizations able to deploy accurate LLMs that can radically accelerate and improve scientific outcomes. We’ll also share real-world use cases where generative AI has created novel breakthroughs.

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IN THIS WEBINAR YOU’LL LEARN HOW TO:

• Assess the generative AI readiness of your data architecture
• Replatform and engineer scientific data in the cloud using a centralized or federated strategy
• Enable rapid consumption of engineered scientific data while optimizing cloud performance, scale, and cost
• Securely develop and deploy generative AI models to derive scientific insights across multiple use cases such as integrated omics, protein binding, and formulations
• Utilize large datasets with LLMs for predictive analyses in toxicology, and in silico ADMET
• Ensure privacy, security, and transparency through generative AI services

High-throughput screening (HTS) offers the promise of identifying higher-quality leads faster and bringing ground-breaking drugs to the market. Unfortunately, the HTS processes at many companies rely on manual, error-prone processes to collect data sets, add critical metadata, and transform data to be usable by informatics applications and analytics.

You will learn:

• How scientific data challenges reduce the effectiveness of high-throughput screening workflows
• Ways the Tetra Scientific Data and AI Cloud™ automates processes, improves quality, and enables insights
• High-throughput screening scientific data best practices

This webinar features a product demo where you can see first hand how the Tetra Scientific Data and AI Cloud can help your organization with its HTS workflows.

Hear how the Tetra Scientific Data and AI Cloud can help organizations derive more value from their chromatography data. This webinar video shows how organizations can use the Tetra Scientific Data and AI Cloud to help create a system suitability dashboard with TIBCO Spotfire to identify instrument issues and avoid misleading experimental results. It also shows how the Tetra Scientific Data and AI Cloud can simplify the search and analysis of data generated by chromatography data system (CDS) instruments, such as Waters Empower.

TetraScience is joined by Clustermarket and WattIQ for a webinar discussing how to optimize resources with harmonized scientific data in your lab

Learn how Sciborgs identify the most painful processes for scientists and design solutions with the Tetra Scientific Data and AI Cloud™. Watch this webinar to learn how Sciborgs:

• Support you in the identification of suitable use cases to uncover new, high-impact insights
• Help you achieve faster, better implementation of scientific data solutions that enable all stakeholders (scientists, data scientists, data engineers, IT staff)​​​​​​​
• Guide you through replatforming to replatform scientific data to the cloud and leverage it for improved scientific outcomes

Our speakers explore how science-based organizations are producing more data than ever before, and why digital transformation strategies to leverage this data, like AI and machine learning, require the right infrastructure of connected, searchable, and harmonized data.

The presentation features real-world case studies in high-throughput screening (HTS), contract research organization (CRO) data management, batch release and testing, as well as the digitalization of a contract development and manufacturing organization (CDMO).

Security and safety underlie cloud compliance. With the recent release of ISPE’s GAMP5 (v.2), biopharma companies are moving ever more towards continuous verification and risk-based approaches. If you manage your lab’s data integrity and traceability, tune in to learn best practices for GxP data automation, including how to:

• Build critical thinking from the GAMP5 (v.2) foundation
• Automate risk monitoring
• Collect FAIR data for governance in specific workflows
• Incorporate new modalities easily

Collaboration is commonly the basis for scientific innovation. Collaboration between life science vendors is less common.

The success of the biopharmaceutical industry depends upon leveraging scientific knowledge derived from the best instrumentation and software available, which produce a multitude of disconnected data formats.  Biopharma customers are now demanding that the vendor industry work collaboratively to connect their scientific workflows.

This webinar will provide valuable insights for biopharmaceutical scientists, lab directors, IT professionals, and life science vendors who want to learn:

• How an open network of life-science vendors accelerates scientific outcomes
• Before and after impact on scientific workflows, such as protein purification and cell profiling by flow cytometry
• The cloud and data engineering technologies that enable data liquidity independent of instrument modality
• How life science vendors are replatforming, moving from a closed to open ecosystem model focused on maximizing the value of scientific data

A new research survey of 500 biopharma executives, commissioned by TetraScience and AWS, reveals why life sciences companies need to rethink their data platforms. Replatforming scientific data to an open cloud platform while transforming the data to FAIR (Findable, Accessible, Interoperable, Reusable) principles significantly impacts three key industry drivers:

• Speed: Learning from experiments faster, reducing time to market for final molecules.
• Cost: Improving productivity, increasing operational efficiency, increasing return on investment (ROI).
• Risk: Improving safety, reducing errors, improving compliance with regulatory requirements

‍The speakers will also share insights and updates from the Bio-IT World Conference. Whether you’re just getting started or are developing the next phases of your replatforming strategy, this webinar will provide you with essential information and key insights.
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‍Learning Objectives:‍

• Competitive advantages to developing a scientific data cloud strategy
• Best approaches to getting started with replatformed scientific data in the cloud
• Long-term advantages, including moving into ML, AI, and advanced analytics

Capitalizing on the power of AI for biopharma requires a new approach to data. In this webinar presented by TetraScience and Körber, hear how your company can collect scientific data from all sources across the product life cycle and then transform it into AI-ready data. And you’ll hear how a top 25 global biopharma company is leveraging scientific data in the Tetra Scientific Data and AI Cloud to gain real-time process intelligence to understand sources of variability.  ‍

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You’ll also learn how to:

• Generate new insights by visualizing and analyzing data across multiple teams
• Employ advanced analytics to define a scientifically sound chemistry manufacturing and controls (CMC) strategy
• Find the root causes of process deviations by implementing process design and modeling best practices
• Automatically generate reports to facilitate new drug application (NDA) or biologics license application (BLA) filing, good manufacturing practices (GMP), and continued process verification (CPV)

For a few highlights from the webinar, read this Q&A blog post with industry experts and webinar participants Prof. Dr. Christoph Herwig, senior scientific advisor, Körber Business Area Pharma, and Ken Fountain, vice president, scientific applications, at TetraScience.

Within the extreme complexity of biopharmaceutical manufacturing and quality, it might appear that there are many paths to produce next-generation performance. However, without first getting grounded in identifying the value of a solution to solve business problems, enormous amounts of time and money can be wasted with little impact. In this webinar, TetraScience and Deloitte share the four levers that make theSmart Factory a reality and how to master them using a digital maturity model.

You'll learn about the four levers and how to leverage data to produce the right analytical outputs in each:

• Human Performance - reducing errors
• Process Performance – increasing yield
• Asset Performance – maximizing uptime
• Network Performance – optimization outcomes

How to calibrate your current state using a digital maturity model

How to create a foundation that supports unrestricted digital innovation

How to maximize the value of your scientific data and analytics for competitive advantage

Are data silos, reduced scientific agility and hindered collaboration keeping companies from accelerating development and delivery of new therapeutics?

While R&D organizations increase their investments in generating and managing data to fuel new drug development, the systems and processes they’ve inherited or implemented often impair critical innovations. Can organizations do more by enabling digital labs?

In this webinar, attendees will hear from AWS and TetraScience how life sciences organizations can break down data silos and do more with less by making management of scientific data easier, less costly and less labor-intensive.

View this webinar to learn about creating connected digital labs and managing scientific data towards therapeutic development.

The biopharmaceutical industry stands at a crossroads. R&D costs are rising. Sales are falling. And the process from discovery to commercialization remains long and complicated. Many biopharma leaders believe artificial intelligence (AI) will reverse these trends, potentially revolutionizing the entire value chain. They’re placing big bets on AI through investments in technology, people, and partnerships.

Explore the latest trends in Scientific AI by the numbers.

Co-sponsored by:

We spoke with 500 biopharma executives to discover how industry leaders plan to deliver better therapeutics to patients faster. Their answer was clear. Maximize the value of scientific data with cloud-enabled FAIR data to improve three key industry drivers.

• Speed: Learning from experiments faster, reducing time-to-market for final molecules
• Cost: Improving productivity, increasing operational efficiency, increasing return on investment (ROI)
• Risk: Improving safety, reducing errors, improving compliance with regulatory requirements

Explore our key findings by downloading this infographic or see the full research report.

Digital transformation only matters if it delivers results. It takes the right combination of technology and know-how. Enter Tetra Catalysts. Learn how this end-to-end, "cloud and Sciborg" solution benefits biopharma throughout the entire value chain.

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Read the solution brief for a deeper dive.