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.

Introduction

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.

However, an essential precursor to creating the lab of the future—a powerful scientific informatics platform—is not often the highlight of any “lab of the future” vision. Perhaps this is because data management is not as flashy as technologies like robotics or artificial intelligence.

Or, maybe it’s because creating a strong data foundation is so daunting when companies are undertaking ambitious research endeavors involving complex workflows, large volumes and varieties of data, and diverse teams scattered across domains and locations.

In any case, the benefits of the “lab of the future” will likely elude companies who fail to invest in infrastructure and processes that help make data accurate, accessible, and secure.

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Why do these factors matter so much?

Data Accuracy

the naming conventions and experimental The data pouring in from R&D efforts need to be

metadata collection that will help data become

collected cleanly and quickly, which can be

searchable and re-usable. When data are

incredibly challenging given the diversity of a

accessible to both humans and machines alike,

company’s research programs and the many

researchers can build off established knowledge,

research tools used in those endeavors. To

avoid redundant efforts, and leverage advanced

improve data accuracy, manual data handling

analytics like machine learning and artificial

and wrangling should be replaced with less

intelligence. It is also important to note that just

error-prone acquisition methods, including

because data are accessible, doesn’t mean they

automated assay data collection, instrument

are automatically open for all to use. That’s

integration, and standardized and flexible ELN

where data security comes into play.

protocol templates with error-proof entry.

Additional checks-and-balances, such as

Data Security

missing-and-erroneous-data warnings, Innovation hinges upon collaboration. Therefore, registration system cross-checking, license and researchers both within an organization and at its compliance checks, and signing and partner locations need to be able to share data in countersigning requirements can help further real-time, without compromising security or guarantee data accuracy.

confidentiality. This requires safeguards both behind the scenes and within the user-facing

Data Accessibility

aspects of the platform. Companies may want the choice of on-site hosting with their own security

All data should be accessible. It should not schemes or a dedicated cloud instance. RESTFul matter when, where, or how they were derived, API can help ensure data transfers are automatic, nor should it matter whether they live in a secure, and lossless. Permission controls and corporate repository or third-party data source. web-based user access can make it easy to Achieving this is complicated when companies define users’ access to different data sets, have scores of data producers who are working projects and functionality (e.g. , entity with complex and varied data types. To make registration), create signing and countersigning data accessible, companies must find a platform requirements, capture complete audit trails to that lets them create a master data source that protect IP, encrypt reports, and assign project breaks down data silos, standardizes on a codes and aliases to maintain confidentiality.

common data model, and provides control over

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.

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Vetting a Scientific Informatics Platform

Discovering materials or biological agents with

What should a scientific informatics platform

novel properties and capabilities requires extensive

ideally deliver? Let’s consider this question in the

research and development. Companies likely have

context of the reality facing researchers today. In

multiple teams working in diverse domains, at

the last twenty years, a lot has changed in the lab

scattered locations, and with huge volumes of

and in the IT departments that support them. For

disparate data. Each of these teams requires not

example, there has been a dramatic deepening in

only the specialized lab instruments and software

scientific understanding and a corresponding

needed to innovate, but also the behind-the-scenes

increase in the kinds of modalities of treatment

infrastructure needed to securely capture, analyze,

open to investigation (RNA-based therapies, cell

store, find, and share data.

therapies, more targets for vaccines, etc.).

There has likewise been a revolution in basic IT

The term “platform” is often

technologies available to create user-friendly, scalable solutions. The vendor and IT-support

used to describe a solution

capabilities to deliver scientists better software

that facilitates this

that is more commensurate to the needs of today’s

multifaceted endeavor.

B O T T O M

community must harness the power of these

scientific context. They must provide solutions that

L I N E

cover a huge scope and support multiple dimensions of organizational complexity.

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.

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Key Features of a Modern Scientific Informatics Platform 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&D 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 1 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&D 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&D tools and systems a company uses and eliminate the need for manual data transfer between them

Small

Common

Large

Point Solutions

Integrated Apps

Integrated Suite

Informatics ELN

Setups Across the Industry

ELN

LIMS

LIMS

Excel

Paper

Reg.

LIMS

Reg.

ELN

etc.

Suite

Lab of the Unified Platform

Future

ELN

LIMS

Reg.

etc.

Platform

Figure 1: Tight integration of R&D tools and systems on an unified informatics platform eliminates manual data handling and frees time for researchers to innovate.

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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:

R g e

istration System:

Ensure data accuracy by helping both internal

Register unique materials or entities, including,

researchers and CROs quickly record experiments

biologics, small molecules, and formulations,

with a library of reusable protocol templates that

using aliases as needed to protect privacy and

help standardize the detailed steps of highly-

limiting registration capabilities to certain user

structured experiments or capture the varied steps

profiles as needed.

of ad hoc experimentation.

IM

L Lab Execution System:

Track individual process steps.

S:

Track testing and support data accuracy by automating data collection directly from instruments, such as

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.

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R, mass spec, flow

cytometry, cellular imaging, gene panels, etc.

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Key Benefits of a Scientific Operating Platform

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.

Workflow Efficiency via Accurate,

data throughout the innovation cycle. It sounds

Accessible, and Secure Data

simpler than it is of course. The scope of

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

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.

iteration are evaluated, in context, to decide what to do next. The faster this cycle of innovation goes, the

Data-Driven Decisions via Accurate,

faster new discoveries are made. Collecting,

Accessible, and Secure Data

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

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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?

Oftentimes, there is the promise of better decision-

Lower Technical Debt and Total Cost of

making with ELN/workflow-oriented solutions. It may

Ownership via a Scientific Operating Platform

seem like an easy promise to deliver. After all, the data

with Interoperable Components

required for good decisions will always be captured by

It’s not just researchers who suffer when an R&D

ELN and will therefore be both accurate and easily

infrastructure is built by stringing together rigid, single-

accessible to all users who have proper permissions.

purpose lab systems, legacy software, and different

Won’t they? Perhaps? Sometimes? But what happens

team’s preferred research tools. The IT staff

when work is performed external to sponsor-provided

implementing, supporting, and budgeting for these

systems? What happens to data when those sponsor-

solutions suffer as well. Cobbled together R&D

provided systems are no more than a loosely linked

solutions create constant pressure to keep pace. IT staff

patchwork of different software? Accurately capturing

face one lengthy and costly integration project after

data is hard. Mobilizing it and making it securely

another, and they are often left with results that are

accessible for users to quickly and effectively make

cumbersome and inflexible. On top of this, an absence

better decisions is even harder. This is where ELN/

of genuine connectivity and a lack of unimpeded data

workflow- based approaches fall short. It is better to

flow across the research cycle impacts not just

invert the problem and focus on building out a data

efficiency, but also insight—something that can make

management platform that can ingest and organize all

or break collaborative, data-driven research

the data your organization produces. Choosing a

organizations.

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.

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.

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Why is managing scientific data so difficult?

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.

B O T T O M

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L I N E

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

Insights

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

Transform deep, contextualized data into rapid, actionable insights.

Data

Workflows

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

Optimize workflows via improved processes and better collaboration.

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Two million scientists can’t be wrong.

Join Dotmatics to become a true lab of the future.

Learn more Request a demonstration today to see for yourself how Dotmatics delivers a scientific informatics platform like no other.

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