Adopting a Data-First Scientific Informatics Platform

Lab of the Future Guide

Adopting a
 Data-First Scientific Informatics Platform

Why making data accurate, accessible, and secure is the first step toward building a lab of the future.

As the buzz around the “lab of the future” has swirled over the past several years, the concept has been discussed as a future vision. An ideal. Perhaps even a prophecy.

But as labs today increasingly rely on advanced technologies, such as automation, highperformance cloud computing, and machine learning, it’s clear “the future” is now.

Why do these factors matter so much?

Data Accuracy

The data pouring in from R&D efforts need to be collected cleanly and quickly, which can be incredibly challenging given the diversity of a company’s research programs and the many research tools used in those endeavors. To improve data accuracy, manual data handling and wrangling should be replaced with less error-prone acquisition methods, including automated assay data collection, instrument integration, and standardized and flexible ELN protocol templates with error-proof entry. Additional checks-and-balances, such as missing-and-erroneous-data warnings, registration system cross-checking, license and compliance checks, and signing and countersigning requirements can help further guarantee data accuracy.

Data Accessibility

All data should be accessible. It should not matter when, where, or how they were derived, nor should it matter whether they live in a corporate repository or third-party data source. Achieving this is complicated when companies have scores of data producers who are working with complex and varied data types. To make data accessible, companies must find a platform that lets them create a master data source that breaks down data silos, standardizes on a common data model, and provides control over

the naming conventions and experimental metadata collection that will help data become searchable and re-usable. When data are accessible to both humans and machines alike, researchers can build off established knowledge, avoid redundant efforts, and leverage advanced analytics like machine learning and artificial intelligence. It is also important to note that just because data are accessible, doesn’t mean they are automatically open for all to use. That’s where data security comes into play.

Data Security

Innovation hinges upon collaboration. Therefore, researchers both within an organization and at its partner locations need to be able to share data in real-time, without compromising security or confidentiality. This requires safeguards both behind the scenes and within the user-facing aspects of the platform. Companies may want the choice of on-site hosting with their own security schemes or a dedicated cloud instance. RESTFul API can help ensure data transfers are automatic, secure, and lossless. Permission controls and web-based user access can make it easy to define users’ access to different data sets, projects and functionality (e.g., entity registration), create signing and countersigning requirements, capture complete audit trails to protect IP, encrypt reports, and assign project codes and aliases to maintain confidentiality.

Not surprisingly, obtaining data that are accurate, accessible, and secure requires a solution that seamlessly brings together an incredible amount of tools, resources, and users.

Vetting a Scientific Informatics Platform

Discovering materials or biological agents with novel properties and capabilities requires extensive research and development. Companies likely have multiple teams working in diverse domains, at scattered locations, and with huge volumes of disparate data. Each of these teams requires not only the specialized lab instruments and software needed to innovate, but also the behind-the-scenes infrastructure needed to securely capture, analyze, store, find, and share data.

The term “platform” is often used to describe a solution that facilitates this multifaceted endeavor.

BOTTOM LINE

While the word platform may conjure up the image of an all-encompassing and supportive research solution, the term is also inherently quite vague and it has been poorly defined in the past. This has left a lot of room for interpretation. Many vendors have taken advantage of this ambiguity when touting their “platform.” In fact, the term has sometimes been used as shorthand to plug conceptual gaps, instead of detailing required functionality. Finding a suitable scientific informatics platform can be incredibly challenging when a company is undertaking complex research and development efforts.

What should a scientific informatics platform ideally deliver? Let’s consider this question in the context of the reality facing researchers today. In the last twenty years, a lot has changed in the lab and in the IT departments that support them. For example, there has been a dramatic deepening in scientific understanding and a corresponding increase in the kinds of modalities of treatment open to investigation (RNA-based therapies, cell therapies, more targets for vaccines, etc.).

There has likewise been a revolution in basic IT technologies available to create user-friendly, scalable solutions. The vendor and IT-support community must harness the power of these capabilities to deliver scientists better software that is more commensurate to the needs of today’s scientific context. They must provide solutions that cover a huge scope and support multiple dimensions of organizational complexity.

The complexity of today's research environment means that companies need a diverse range of solutions to innovate, including various specialty tools and applications, databases, and systems. Researchers need to pass data between these solutions to make decisions and advance projects. This is often easier said than done.

Small companies may need to pass data between their ELN, LIMS, and perhaps even Excel files and paper notebooks. Mid-size biotechs and large pharmas may also need to link in additional systems, such as registration or inventory systems. More often than not, the connectivity between these R& tools and systems is lackluster, ranging from complete disconnection and full reliance on manual data transfers, to cobbledtogether, overly complicated infrastructures that rely on a delicate balance of mappings and transfers.

Figure illustrates how a scientific informatics platform can solve this challenge, freeing researchers’ time to focus on science, not data transfers. In the figure

The top three boxes of the graphic show the current status quo, where the various tools and systems needed by R& teams range from disconnected to loosely integrate

The lower box illustrates how a unified platform provides a comprehensive behind-the-scenes foundation to truly connect the various R& tools and systems a company uses and eliminate the need for manual data transfer between them

Why is interoperable componentry so important to data accuracy, accessibility, and security?

Interoperability is key to data accuracy, accessibility, and security.

Different groups in large organizations will have unique needs based on their complex research processes, specialty instrumentation and software, external research partnerships, etc. As such, interoperability between the platform components, as well as functions both internal and external, are essential to supporting secure, collaborative, data-driven research.

In essence, the magic happening behind the scenes—data standardization, merging and mapping, communication and security protocols—makes it possible for all internal users and partners to have quick access to the reliable data they need to make decisions, while keeping data to which they don’t have access secure.

A scientific operating platform should enable companies to configure a solution that supports diverse R&D workflows, facilitates clean data collection, and enables secure and seamless data sharing between both internal and external research groups. As illustrated in Figure 1, interoperable components might include:

Electronic Laboratory Notebook:

Ensure data accuracy by helping both internal researchers and CROs quickly record experiments with a library of reusable protocol templates that help standardize the detailed steps of highlystructured experiments or capture the varied steps of ad hoc experimentation.

Lab Execution System:

Track individual process steps.

Decision Support:

Unite corporate and third-party data in a secure master data source that harmonizes all R&D data, while also providing permission controls for fine tuning user access to different projects and datasets.

Registration System: Register unique materials or entities, including, biologics, small molecules, and formulations, using aliases as needed to protect privacy and limiting registration capabilities to certain user profiles as needed.

LIMS:

Track testing and support data accuracy by automating data collection directly from instruments, such as NMR, mass spec, flow cytometry, cellular imaging, gene panels, etc.

Key Benefits of a Scientific Operating Platform

Workflow

Efficiency via Accurate, Accessible, and Secure Data

Science is intensively iterative and collaborative. In the make-test-decide innovation cycle, potential new products are made, tested, and then optimized in an effort to bring new discoveries to market both efficiently and safely. The results of each cycle iteration are evaluated, in context, to decide what to do next. The faster this cycle of innovation goes, the faster new discoveries are made. Collecting, managing, and delivering data created in the innovation cycle is an onerous task; anything that can be done to automate processes and reduce the likelihood of human error will not just improve workflow efficiency, but will also improve data accuracy. Making collected data accessible to users in their preferred software and tools will continue to support both workflow efficiency and decision making. Establishing permission controls will help maintain security. A scientific operating platform can do these things. It can unite the tools and software that facilitate individual workflow tasks, while working behind the scenes to structure, and standardize and secure data, flow data between steps, and feed data into decision support systems. As a result, end users spend more time on science and less time on formatting, mapping, and moving

data throughout the innovation cycle. It sounds simpler than it is of course. The scope of requirements is large given the sheer complexity of the undertaking, advances in instrumentation and software, and the fact that the underlying science gets increasingly complex as our appreciation for the intricacies of nature grows.

Data-Driven Decisions via Accurate, Accessible, and Secure Data

When vetting research informatics platforms, there is often a natural temptation to prioritize workflow over data flow. This is understandable. After all, the workflow produces the data. And there are also obvious benefits to facilitating workflow: improved efficiency, elimination of tedious low-value tasks, pretty new interfaces. But, workflow-based solutions that fail to address the complexities of behind-thescenes data flow will also fail to facilitate datadriven research. Helping record data is one thing; helping ensure data are accurate, accessible, secure, and ready-to-use is something else altogether. Figure 2 helps compare a solution based on an ELN/ workflow strategy versus one based on a data-first strategy. Do you want a solution that prioritizes only efficiency, but not better decisions?

It’s easy to get lost in the complexities of platform requirements and lose sight of the ultimate goal: empowering the lab of the future. Let’s look at the key benefits an R&D organization can anticipate from adopting a comprehensive scientific operating platform.

Oftentimes, there is the promise of better decisionmaking with ELN/workflow-oriented solutions. It may seem like an easy promise to deliver. After all, the data required for good decisions will always be captured by ELN and will therefore be both accurate and easily accessible to all users who have proper permissions. Won’t they? Perhaps? Sometimes? But what happens when work is performed external to sponsor-provided systems? What happens to data when those sponsorprovided systems are no more than a loosely linked patchwork of different software? Accurately capturing data is hard. Mobilizing it and making it securely accessible for users to quickly and effectively make better decisions is even harder. This is where ELN/ workflow- based approaches fall short. It is better to invert the problem and focus on building out a data management platform that can ingest and organize all the data your organization produces. Choosing a solution that prioritizes data flow, and not just workflow, will ultimately empower researchers to make better informed decisions using data that both are accurate and accessible. And, by prioritizing the data problem first, workflow efficiency will automatically follow suit because the difficult, behindthe-scenes work has been done.

Lower Technical Debt and Total Cost of Ownership via a Scientific Operating Platform with Interoperable Components

It’s not just researchers who suffer when an R&D infrastructure is built by stringing together rigid, singlepurpose lab systems, legacy software, and different team’s preferred research tools. The IT staff implementing, supporting, and budgeting for these solutions suffer as well. Cobbled together R&D solutions create constant pressure to keep pace. IT staff face one lengthy and costly integration project after another, and they are often left with results that are cumbersome and inflexible. On top of this, an absence of genuine connectivity and a lack of unimpeded data flow across the research cycle impacts not just efficiency, but also insight—something that can make or break collaborative, data-driven research organizations.

A scientific operating platform with interoperable components can help unite all the tools and data researchers need to innovate. Researchers will be able to quickly capture, find, share, and use data, without wasting time switching between applications and transferring data. IT managers will be able to reduce the time and cost of implementing and supporting disconnected solutions, while also reducing technical debt by working with a single vendor whose solution spans the entire full make-test-decide innovation cycle.

Data management systems are often expensive and difficult to procure or buy. They usually require a significant investment in coding, which is both expensive and slow. Creating an effective R&D data management solution is difficult because of the inherent diversity and scale of scientific data. A straightforward way to think about this problem is that decision support systems are exceptionally good at adding rows of data (scale), but not so good at adding columns of data, nor in defining the relationships between them.

New ways of organizing scientific data are required to make handling diversity easier and quicker. Self-service capabilities to organize data closer to the scientists doing the work are needed. Luckily, innovative technologies exist to make handling the diversity deluge much more tractable. While these technologies are evolving rapidly, there are organizations that have heavily invested in understanding and implementing these novel approaches.

Start with the data. Find a partner that has invested heavily in using and deploying them. Go from there.

Start with the data.

Find a partner that has
 invested heavily in using and deploying them. Go from there.

Why is managing scientific data so difficult?

Dotmatics Scientific Informatics Platform

Dotmatics has built the most powerful scientific informatics platform in the world. By connecting best-in-breed applications on an open and flexible data-driven platform, Dotmatics delivers accessible, accurate, and secure data, streamlines workflows, and ultimately accelerates discovery.

Applications

Access best-in-breed software applications from a scalable end-toend R&D platform.

Insights

Transform deep, contextualized data into rapid, actionable insights.

Data

Unite and prepare all data produced across workflows, teams, domains, and locations.

Workflows

Optimize workflows via improved processes and better collaboration.

Two million scientists
 can’t be wrong.

Join Dotmatics to become a true lab of the future.

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