implementing
electronic
lab notebooks
a six part series on best practices
in Electronic Lab notebook (ELN)
implementation.
Bennett Lass Ph.D., PMP
Accelrys, Inc.
In the past ten years, the electronic lab notebook
(ELN) has moved from nascent technology to a
mainstream laboratory chemical informatics platform.
Deploying an ELN has a transformational effect on
the way research is performed. The ELN touches
more than just the laboratory. Anyone who creates,
reviews, mines or manages research and research
data will eventually use the ELN and the information it contains. As such, planning and deploying
an ELN must address the disparate needs of many
potential users. This requires a methodical approach
in planning and executing the deployment project.
The following six articles are based on my experiences in planning and managing ELN deployments in large and small research organizations.
The articles outline a methodology to
• Analyze stakeholders and end user needs
• Leverage ELN functionality to make
research more efficient
• Integrate the ELN into the electronic lab environment
• Use metadata to evaluate and plan research activities
© 2011 Scientific Computing, Advantage Business Media. All brands or product names may be trademarks of their respective holders.
Table of Contents
1 how do you define and manage success?
4 building the foundation
7 documenting experiments
10 enabling collaboration
13 system integration
16 research management
how do you define and manage success?
implementing electronic lab notebooks
Bennett Lass Ph.D., PMP
Introduction
In the past five years, the electronic lab notebook (ELN) has moved from nascent technology deployed mainly by
early adopters and technology enthusiasts to a mainstream laboratory chemical informatics platform. Atrium Research
and Consulting estimates that over 25% of the potential market for ELNs has either deployed or is in the midst of deploying an ELN, with larger organizations of over 500 users comprising the bulk of these deployments.1
The deployment of an ELN has a transformational effect on the way research is performed. As such, plans to deploy an ELN
must address the needs of scientists who create experiments, groups and departments that make use of the experiments and
data created by scientists and business sponsors who finance the projects. These groups may see the goals and success criteria
for the project differently. Technical sponsors generally look at factors that make scientists more efficient in documenting
experiments. Business sponsors tend to focus on streamlining system costs and lower total cost of ownership.
What makes an ELN
implementation successful?
The answer varies depending on who you ask and
how they define success. Understanding the different
needs and expectations of the stakeholders can help
you make the best case for implementing an ELN.
The demands of the technical side are somewhat straightforward.
Scientists and technical managers ultimately want to do their
jobs faster, with as little disruption as possible. A successful ELN
implementation lets scientists quickly create and edit experiments and, more importantly, share and reuse experiments they
or others have created. Integration with applications essential
to their work—registration, metrology, LIMS, and inventory
systems—results in less time spent cutting and pasting results.
Technical managers gain visibility into research to track experi-
measured by return on investment (ROI), the measure of money in
(expenses) vs money out (savings) over a specified period of time.
Expenses in an ELN implementation include not just the cost
of purchasing hardware and software, but configuring systems,
training users, and maintaining systems over time (often referred
to as total cost of ownership). Savings are easy to identify, but
may be harder to quantify. Factors influencing ROI include:
• Project efficiency (“Are we conducting more experiments”)
• Scientific efficiency (“Am I spending more time doing science
and less on cutting and pasting data into my notebook?”)
• Cycle time within projects (“How quickly are we submitting
samples or passing work to the next stage?”)
• Rework (“How often are we repeating
or duplicating experiments?”)
be able to use the ELN without extensive training or support.
• Information gathering processes (“Is it easier
to review research or collect information for
intellectual property and regulatory filings?”)
The business case for an ELN, however, can be harder to make.
• Systems used (“Has the implementation enabled
us to consolidate or decommission systems?”)
Business sponsors view ELN deployment as an investment intend-
• Time (“Are we able to initiate any of the above activities faster?”)
ments in progress and gauge efficiency. And both groups should
ed to improve R&D efforts and process efficiency. Thus success is
1
how do you define and manage success?
Obviously, business sponsors define success by how well an
implementation minimizes expenses while maximizing savings,
as well as the time factor (savings today are more valuable than
savings tomorrow). Organizations can make a business case for an
ELN by looking for streamlined configurations, support for critical
document and scientific workflows, cost-effective outsourcing
options, integration with or elimination of existing systems,
Experiment Documentation
This second layer focuses on evaluating how individual disciplines
(e.g., process chemistry, analytical chemistry, or discovery) work to
create and document experiments. Analysis focuses on creating
and optimizing the ELN to accommodate the scientific workflow
associated with creating and documenting experiments.
and strategies for achieving lower total costs of ownership.
Inter-Department Collaborative Work
Managing Success
Though the measure of success for an ELN deployment project
can be measured by ROI, business and technical sponsors will
have different opinions about what factors impact ROI the most.
But both camps will agree that monitoring the impact of ROI
The third area looks at how different groups within the organization can maximize collaborative activities and gain research efficiency. For instance, studies might investigate the movement of
samples and analytical results between process or development
groups and corresponding analytical/characterization laboratories.
factors through five core areas will have the greatest influence
on overall project success and ultimately ROI valuation.
System Integration
Although the ELN can have a transformative effect on research
Foundation
This area focuses on evaluating and preparing the general
environment within the research organization. Work in this
area is primarily concerned with ensuring that the ELN is
deployed to the optimal groups with minimal disruption to
operations, it is only a single component of an overarching
electronic lab environment within the research organization.
The work associated with this fourth area focuses on how
to integrate the ELN with other informatics systems such
as LIMS, CDMS, etc. to obtain multiplicative benefits.
overall organization. Work also focuses on harmonizing scientific
workflows and making sure the needs of auxiliary groups,
Research Management
(e.g.: archivists and legal) are met with the ELN deployment.
The top of the pyramid focuses not on the creation
of experiments in the ELN but the metadata generated in the ELN system as experiments are created
and managed. Typical metadata includes:
• Number of experiments created
• Average time to complete an experiment
(creation to witnessed date)
• Name/number of scientists creating experiments
Although this information is useful in and of itself, it can become even more beneficial when
combined with experiment data such as:
Fig 1: Core areas in an ELN deployment
2
how do you define and manage success?
• Target Compound
• Department
• Development Phase
The intersection of these data provides valuable insight into
the research activities, helping to answer questions such as:
• How many experiments did it take to complete
a formulation of compound A?
• How many scientists were involved in
developing compound A?
• How long did it take to complete the process
development stage for compound A?
The effort to collect this data is minimal. The value of
the mined data in evaluating ongoing work and planning new projects can be invaluable.
Summary
This article presents an overview of the success factors the business and technical stakeholders use when evaluating the success
of an ELN deployment project and the impact on ROI. The five-tier
approach provides a methodology that evaluates, prioritizes and
manages these factors to ensure a successful ELN deployment.
In future articles I will explore in more detail the application of each tier to the definition and management of a successful ELN implementation.
Bennett Lass is the Director of ELN Services at Accelrys Inc.
He may be reached at [email protected].
References
1. Elliot, Michael H., “Electronic Laboratory Notebooks Enter Mainstream Informatics” Scientific Computing http://www.scientificcomputing.
com/Electronic-Laboratory-Notebooks-Enter-Mainstream-Informatics.
aspx?terms=ELN
3
building the foundation
implementing electronic lab notebooks
Bennett Lass Ph.D., PMP
Introduction
This is the second article in a series on best practices in implementing an Electronic Laboratory Notebook (ELN). The previous article1
identified five core areas which, when used to guide the management of an ELN implementation, will optimize return on investment (ROI) and ensure a successful ELN deployment. This article discusses the first core area: Establishing a Solid Foundation.
Before installing and deploying an ELN within the research organization, you need to establish a solid foundation upon which to install and
grow the ELN. As an enterprise software package, the ELN not only impacts the daily routine of bench scientists; it also changes the way
supervisors and support organizations act on and interact with ELN documents and the information stored in documented experiments.
Establishing a firm foundation for an ELN implementation begins with asking fundamental questions about why your organization is implementing an ELN for R&D and what benefits you expect to achieve. The questions can seem trivial on the surface, but as
you work through the answers, you will see that exploring them requires a deep dive into your fundamental work processes.
The Ten Questions
In the process of planning the ELN installation, the discussions
amongst stakeholders and key users should include the following ten questions. The ensuing discussion and ultimate
answers will generate information and insight into the work
processes that need to be addressed when working in the ELN.
The information will also define aspects of the ELN such as
how data is produced and documented, what users do with
experiment data, how data is used by other groups and how
it supports daily activities. Additionally, the analysis examines
formulators, etc., have different needs when documenting
their experimental work. Understanding what scientists
are using the system will guide how the ELN is configured
to meet discipline-specific scientific workflows.
In addition to scientists, other groups such as legal, quality,
compliance, etc. also access experiments and data in the ELN.
The needs of these groups are vastly different from the needs
of the scientists who create the experiments. For the ELN
deployment to be successful within the enterprise, the needs
of these groups also need to be identified and fulfilled.
how experiments and data are shared amongst groups,
where handoffs need to be managed and what integrations
with other systems can facilitate document creation.
What scientific workflows will be documented in the ELN?
Workflows differ depending upon the scientific disciplines
using them, and each workflow puts different demands on the
Who will use the system?
Although the first response may be, “The Scientists,” the
analysis should identify which scientists. The various scientific
ELN deployment. After identifying the research groups who
will use the ELN, you will need to document and analyze their
daily routines. This analysis should highlight items such as:
disciplines, e.g., discovery biologists, process chemists and
4
Building the foundation
• Type of data recorded, e.g., text, tabular,
images, external files, etc.
• Amount of data
• Data sources
Will the system be deployed locally or globally?
Knowing where researchers work when accessing the system and
the infrastructure available to support the transport of data are
two key items that need to be addressed prior to ELN installation
• Methods used for data analysis
and deployment. Insufficient bandwidth between sites can
• Format of results
result in frustratingly slow performance, diminishing system
• Reporting requirements
use and reducing the data available for mining and analysis.
• Extent of interdisciplinary collaboration
Workflow analysis will drive the number and configuration of
templates used to document experiments. Having a template
that matches the work and data flow of the scientist will make
the system easier to use which will result in faster adoption.
What are the impacts of workflows on other groups?
Deployment to multiple sites may also require analysis of
security policies regulating access to the stored data. Although
the ultimate goal for an ELN deployment is total access by all
users to all data, local requirements may require limiting access
to some data stored within the notebook to specific groups
Analyzing scientific and document review workflows highlights
Will the system be deployed in a validated
(GxP) environment?
the various actors who either impact or are impacted by these
The deployment of an ELN may extend to groups working
workflows. Scientific workflow analysis points out collaborative
interactions between groups, i.e., process chemists and analysts
who share information when documenting experiments. Document review analysis reveals the needs of quality groups, legal
and other non-scientific groups who access ELN documents.
in validated environments. If any of these groups have been
identified as potential users of the ELN, then validation plans
and scripts need to be developed and implemented according
regulatory agency requirements and the company’s policies.
Understanding these dynamics and including them in your plan-
How will documentation practices be harmonized?
ning will help build a solid foundation for your ELN deployment.
When working with paper notebooks, it is very easy for different
users or groups who perform the same or similar procedures
What integration will be needed with other systems?
An advantage of an electronic environment is its ability to support
integration among different systems. For example, direct connectivity with hardware such as balances allows for automatic recording of values, eliminating transcription errors. Integration with
Laboratory Information Management Systems (LIMS), Scientific
Data Management Systems (SDMS) and other Document Management systems supports direct sharing of data, which can eliminate time-consuming cutting and pasting into paper notebooks.
A survey of the other software commonly used in the research
to develop divergent ways of documenting the data collected
during experiments. For example, groups operating bioreactors
have developed both Excel and Access applications for recording data. In an ELN environment, harmonizing procedures in
both experiment execution and data recording minimizes costs
and standardizes procedures for searching and mining data.
Prior to deploying the ELN to different groups doing
similar work, it is a good idea to examine your existing
SOPs, execution practices and data recording procedures and harmonize them to a single standard.
environment will highlight the systems where direct integration can save time and reduce data recording errors.
5
Building the foundation
How will experimental data be used?
Summary
The advantage of an ELN compared to a paper notebook
The ten questions listed in this article will guide project manag-
is the ease with which data can be shared and/or mined.
ers and stakeholders through an important analysis process
ELN documents can be viewed by anyone with access to
which identifies the needs and functionality required in the
the system regardless of their physical location. Data can
ELN deployment. Understanding users and their needs creates
be extracted into third-party applications for advanced
a solid foundation upon which to build, configure and deploy
data analysis and presentation. Understanding how the
an ELN for use by multiple groups within the enterprise.
data will be used will guide template design and ultimately
how the data is recorded and indexed in the database.
Bennett Lass is the Director of ELN Services at Accelrys Inc.
He may be reached at [email protected].
Who will fulfill ELN-specific roles?
An ELN implementation creates new roles for people involved
in installing and maintaining the system. These positions
include template editors to create document templates,
system administrators to create user accounts and notebook
folders and super users to provide local support. This is in
References
1. Lass, Bennett D., “Implementing Electronic Lab Notebook: How do you Define and Manage Success?” Scientific Computing, June 2, 2011. http://www.scientificcomputing.com/articles-IN-Implementing-
Electronic-Lab-Notebooks-060211.aspx.
addition to more traditional roles for network applications
such as network, server and database administrators.
What is the governance model for managing the ELN?
Another aspect in establishing the foundation is setting governance and security expectations. Organizations should identify
infrastructure, system and scientific workflow administrators
and determine a governance model that specifies how the
organization will manage change. Who can make changes? What
can be changed? When can these changes be made? Where will
the changes be made, tested and documented? Such decisions are particularly critical in validated environments, though
governance is essential for any organization that expects the
ELN to support intellectual property claims or regulatory filings.
6
Documenting experiments
implementing electronic lab notebooks
Bennett Lass Ph.D., PMP
Introduction
This is the third article in a series on best practices in electronic lab notebook (ELN) implementation. The previous article identified five core
areas that need to be managed to ensure a successful ELN deployment. This article discusses the second core area: documenting experiments.
A driving force for deploying an ELN is improving the scientist’s work in the laboratory. This is generally focused on improving efficiency in executing and documenting experiments. To accomplish this, first analyze how specific scientific groups (i.e., biology,
process, discovery, analytical, etcetera) document their day-to-day activities. Understanding these processes and their impact on
the configuration and deployment of the ELN is the next step in planning and managing a successful ELN implementation.
Analyzing scientific workflows
Scientific workflow analysis focuses on understanding and mapping the process(es) used by scientists in target groups when conducting and documenting their daily work. This analysis should
be agnostic to the mechanics of creating and documenting the
queried or mined. The advantage of an electronic record is the
ability to find documents based on data queries and the ability
to mine and combine data from multiple experiments to turn
data into information. This analysis will impact how and where
data is stored and the ease with which it can be accessed.
experiment. The analysis should focus on the specific needs created by the scientific workflow. In particular, the analysis should
follow the data flow, i.e., data sources, data formats and data use.
The analysis should start with an understanding of the data
collected when executing an experiment. This includes not
only the data used to record the experiment setup, but also
the result data collected during experiment execution.
Next, you need to identify the process(es) used to analyze or
manipulate data. These can be data transformations, calculations, spectral integrations, etcetera. Pay particular attention
to categorizing input data, output results and the processes
used. Note whether the data is contained solely within the
confines of the experiment or combined with other data.
Finally, the analysis focuses on identifying data that will be either
Harmonization
During the scientific workflow analysis, it might be discovered
that groups doing the same work in different locations
developed divergent ways of recording experiment data. This
is not unusual when using paper notebooks. The isolating
nature of paper notebooks creates procedure silos. The collaborative nature of an electronic environment makes ongoing
support of disparate processes not only expensive vis-a-vis
total cost of ownership, but also works contrary to needs for
searching and reuse of data recorded in the experiments.
Where disparate methods are identified, organizations should
work to harmonize the processes as well as associated items, such
as vocabularies and common terminology to make searching
more intuitive and the data more cohesive for mining information.
7
Documenting experiments
Migrating from paper
After processes are mapped and similar methodologies
are harmonized, the process of migrating from a paper to
an electronic lab notebook begins. “Paper on glass” recreates existing paper notebooks in the ELN. The benefit of
this approach is maintaining familiarity for end users. Direct
transcription of forms and other paper-based mechanisms
for recording data preserves existing processes and is
least disruptive when transitioning users to an ELN.
Although the processes are familiar, they may not be the most
efficient way to record experiments. Paper on Glass does
not leverage the new functionality available in an electronic
based system. Thus, Paper on Glass should be seen as a way
station and not a final destination in the ELN deployment.
“Must Have” items should meet two key criteria. First, there should
not be a way to accomplish the need by either adjusting the
process or using a combination of out-of-box functionality.
“Must Haves” also should be items which significantly increase
user efficiency in either time to document experiments or data
integrity. If the need passes these tests, the items should be
addressed through custom services engagements. Electronic
lab notebooks have a software development kit (SDK) that
can be used to extend functionality using standard programming languages. These enhancements to the notebook can
be done either by the customer or the ELN provider. Once
all “Must Haves” are addressed, deployment can begin.
As the deployment plan is executed, the functionality provided
by the ELN and the customization activities are aggregated
into experiment templates that provide the functionality
Beyond paper on glass
To move beyond paper on glass, the documented processes need
to leverage the functionality available in the ELN so scientists
can move past the two-dimension physical page. To guide
data entry, forms can be created that include drop-down lists
of values. This speeds up data recording and standardizes data
entry to improve searching. Material lists and reaction schemes
can be linked. Specialized sections for entering information
about solution preparation or pharmaceutical formulations can
and guidance needed to effectively document experiments.
Template sections provide general capabilities to record text or
embed files, or specific capabilities to draw chemical structures
and reactions, calculate solution concentrations and create
design of experiment (DOE) data. These sections, along with
the customizations, create templates that can either be loosely
configured to allow users to select the required sections to meet
their changing needs (e.g., discovery activities) or more closely
controlled to address GxP work (e.g., GMP GLP, etcetera.).
automatically calculate amounts, concentrations and other values.
The process to leverage these capabilities starts with mapping the scientific processes to ELN out-of-box functionality. The result is an ELN deployment plan that includes a
roadmap for maximizing out-of-box functionality and a
gap analysis of needs to be addressed for most efficient
use of the ELN. The gap analysis should include an impact
assessment grouping items into three categories:
• Must Have
• Like to Have
• Nice to Have
Document workflow
In contrast to scientific workflow, which dictates the way scientists
record experiment data in the ELN, document workflows direct
and facilitate the review and signoff of experiments as they evolve
from works in progress to reviewed and archived intellectual
property. With paper notebooks, this was done by physically
passing the notebook. Document workflows in the ELN include
pathways to the various groups within an organization that must
review and sign-off on experiments. The electronic document is
“virtually” passed for review and signing. Because the document
remains within the ELN during this process, there is no limitation
on the location of users or groups who can be part of this process.
8
Documenting experiments
Revolution vs. evolution
References
If not done correctly, moving from paper to an ELN will be
1. Lass, Bennett D., “Implementing Electronic Lab Notebooks: How do you Define and Manage Success?” Scientific Computing, June 2, 2011. http://
www.scientificcomputing.com/articles-IN-Implementing-Electronic-Lab-
Notebooks-060211.aspx.
2. Lass, Bennett D., “Implementing Electronic Lab Notebooks: Building the foundation” Scientific Computing, June 21, 2011. http://www.
scientificcomputing.com/articles-IN-Implementing-Electronic-Lab-
Notebooks-Part-2-062111.aspx.
perceived by scientists as a revolutionary activity. When the
outlined process is followed, daily routine will be fully mapped
to the ELN functionality, enabling scientists to continue
documenting their experiments with minimal interruption. The
movement to the ELN will be evolutionary and not revolutionary.
The evolutionary process should not stop after the initial
deployment. Over time, scientific workflows will change and
new functionality will be introduced in the ELN. Each time this
happens, the analysis needs to be revisited so that scientists’
needs and ELN functionality remain synchronized and the
evolutionary growth of the ELN is sustained in the enterprise.
This process also looks at items previously identified as “Like
to Have” or “Nice to Have” to see if they can be addressed.
Summary
During this phase of the ELN deployment, organizations should
start by evaluating their current processes to understand
how data is created, recorded and used by the scientific community. Once the processes are mapped, the requirements
are overlaid with the ELN’s capabilities. Gaps are identified and
evaluated, and items deemed as “Must Haves” are addressed
via customization activities using the ELN’s SDK. Document
templates are created with the functionality needed to guide
and support the end user scientists in their daily workflows.
Ongoing evaluation of scientific workflow and ELN
functionality will ensure that the scientific community and the product evolve together, optimizing the
ELN’s positive impact on scientists’ daily work.
Bennett Lass is the Director of ELN Services at Accelrys Inc.
He may be reached at [email protected].
9
enabling collaboration
implementing electronic lab notebooks
Bennett Lass Ph.D., PMP
Introduction
This is the fourth entry in a series on best practices in Electronic Lab Notebook (ELN) implementation. The first article identified five core
areas which need to be managed to ensure a successful ELN deployment. This article discusses the third core area: collaboration.
The last article focused on analyzing and meeting the needs of different R&D groups who use the ELN to document daily
laboratory work. Improving the efficiency of the scientist is a major driving force for deploying an ELN into the laboratory. The
analysis took a silo approach, looking at each group individually and showing how the ELN optimizes the individual’s work.
, simply focusing on the individual scientist however, does not address all the benefits offered by the ELN. The benefits
gained by the scientist can be multiplied through improved information-sharing and collaboration via the ELN.
Collaboration Modes
Collaborative efforts can be defined as sharing of information
Another common mode of collaboration requires scientists to
between one or many persons within a research organiza-
record data in separate experiments and then share the informa-
tion. By focusing on the data, we can identify a wide range
tion. This is typically done when a scientist submits samples for
of collaboration needs or modes within an enterprise. Each
analysis or characterization. The work associated with creating
collaborative mode puts different needs and constraints on
and analyzing the sample constitutes two distinct activities.
how data and information are documented, shared and used.
The analyst needs to understand the history of the sample and
its preparation to optimize the analytical work. The submitting
Reusing information from previous experiments can sig-
scientist needs to incorporate the analytical results into his or her
nificantly increase the productivity of the scientist. Leveraging
own experiment to draw conclusions and plan future activities.
information from previous projects focuses research efforts
either on previously successful work or on areas that have
A similar collaborative mode is found when groups within an
not yet been investigated. It also shifts effort away from
organization prepare reagents, cell lines, etc. which are then
areas which were previously determined to be of little or
consumed by other research groups within the organiza-
no benefit to the overall goal of the research program.
tion. Scientists using these materials in their experiments
need to document how and when they were prepared.
Finding and using previous experimental data can eliminate the
need to perform experimental work. A significant amount or
A fourth collaborative mode covers multiple investigators
research work is often undertaken simply because it is easier to re-
recording their data into a single document or experiment, as
peat the work than try to find paper documents stored in archive
often occurs when experimental activities extend past a single
sites. The ELN helps to minimize unnecessary re-work by provid-
work shift. Documenting animal studies or conditions in a
ing an easy way to find data in current and archived experiments.
bio-reactor are typical examples of this type of collaboration.
10
enabling collaboration
Limitations of the Paper Notebook
The needs created by collaborative work are hampered by the “functionality” of paper notebooks.
Sharing information recorded in separate notebooks requires a
scientist to copy the information and send it to the requesting
scientist. This takes time and effort and distracts the scientist from
his primary work. It also creates issues for the scientist receiving
and using the information. How does one record and preserve the
audit trail for the information contained within the copied pages?
Paper notebooks do not lend themselves to easy searching. Although external databases can correlate notebook
numbers to users, the content of paper-based experiments
is not indexed for searching. Thus, it is not possible to find
experiments that used a specific target compound or all
experiments using a specific pathway for synthesis.
Paper notebooks also present issues when recording
information in the same experiment or notebook requires
physical proximity of the scientists. This limits where
scientists can collaborate and who can collaborate. It also
forces scientists to record information one by one. Signing
and witnessing can also cause issues, if it is not clear who
was responsible for specific entries within an experiment.
Advantage of the ELN for
Collaborative Work
Finding and using experiment data recorded in an ELN is
easy. Templates used to create experiments are optimized
for searching. Key data can be marked as mandatory so
experiments cannot be created without the data. Indexing of experiment data facilitates targeted searching based
on specific values, key phases, chemical structures, etc.
Scientists can execute targeted searches to find experiments
that meet defined criteria. Typical searches include:
• Compound names, IDs or structures
• Chemical lot numbers or equipment ID
• Scientist name, or experiment date(s)
• Location, project name, study phase
Since all experiments in the ELN are stored in a central repository,
the search returns links to all experiments that meet the search
criteria. Access to the information is then just a click away.
For scientists who submit samples to a central lab for
analysis or characterizations searches can find the relevant
experiments. For the analyst, the search finds experiments
documenting how and when materials were created. For
the scientist submitting the sample, the search finds the
experiment(s) for submitted samples. In both cases, electronic
hot links can be created between the experiments. Copies
of relevant data can be copied between experiments so
scientists and reviewers have complete access to all data.
Improving collaboration is one of the primary reasons
Besides resolving many of the collaborative challenges presented
for deploying an ELN. The ELN is designed to overcome
by a paper notebook, an ELN deployment provides the following
two of the biggest obstacles identified for collabora-
additional functionality to facilitate the sharing and reuse of data:
tion—physical location and data searching.
In the paper world, finding information on what colleagues are
doing means quite literally finding the colleagues and leafing
through their notebooks. This is plausible only when the colleagues are nearby, but impossible if the colleagues are across
the country or across the ocean. Finding the data becomes
even more difficult if you don’t know who created the experiment or the notebook has been archived to a remote site.
Messaging
ELNs foster collaboration by providing easy mechanisms to alert
colleagues about points of interest in experiments. For instance,
scientists might send information to peers through experiment
“hyperlinks” or add annotations to provide insights on experiments that they have modified or tests they have performed. ELNs
also offer “sticky note” comments to facilitate informal, unaudited
11
enabling collaboration
communication about experiments. Such notes enable reviewers
might have relevant information; then you need to identify all
to comment on research without being in physical proximity
the notebooks used to record the data. Finally, you need to
to those who created the experiment, encouraging dialogue
confirm the location of the notebooks to copy and compile the
between members of globally distributed research teams.
data. With an ELN, there is no need to know the who, when or
where. Simple searches bring together all the information in a
Referencing
matter of seconds. Built-in ELN reporting capabilities enable
the formatting and editing of final reports just as quickly.
An instant two-way link between experiments gives researchers
a persistent link between scientifically related experiments.
Referencing creates valuable traceability between, for instance,
Work request integration
those preparing reagents and those using them, or those
This capability relies on systems integration. Work request
requesting tests and those performing them. This not only
integration offers the ability to manage the “information
streamlines collaboration but can actually help mitigate errors.
round trip” for samples submitted for analysis. Rather than
For example, if scientists notice an error in sample prep, they
throwing a request over the wall for someone else to work
can rapidly notify those who have referenced the experiment.
with, work request integration gives analysts and requesting researchers’ visibility into the complete workflow, so
Cloning
Often the basic information recorded in an experiment, i.e.,
that work gets done faster and more efficiently.
purpose, materials, equipment, procedures, etc., do not change
Summary
or, if they do, the changes are minimal. Cloning enables scientists
ELN-enabled collaboration has a multiplicative impact on the
to begin new experiments by electronically copying existing
data recorded by the individual scientist and the research effort it
experiments. The basis for the cloned experiment can be any
supports. The central document repository enables all scientists
experiment in the ELN. Thus, the ELN offers significant efficiency
to access any document independent of the research site. The
improvements in enabling scientists to share experiment param-
ELN eliminates physical barriers to sharing data and collaborat-
eters as well as time savings in documenting similar experiments.
ing in the creation and documentation of experimental work.
Virtual Notebooks
Bennett Lass is the Director of ELN Services at Accelrys Inc.
He may be reached at [email protected].
Information stored in personal notebooks, whether paper
or electronic, creates information silos. Bringing together
related experiments from multiple notebooks adds a new
dimension to the recorded data. Searching with parameters
such as compound ID or study phase finds and displays a
list of experiments with related information independent of
the notebook in which the data was recorded. This virtual
notebook provides a new dimension in data analysis.
Consolidated reporting
Aggregating information for a comprehensive report can be
a daunting task. First, you need to know all the scientists who
12
systems integration
implementing electronic lab notebooks
Bennett Lass Ph.D., PMP
Introduction
This is the fifth article in a series on best practices in Electronic Lab Notebook (ELN) implementation. The first article on “How
do you define and manage success?” identified five core areas that need to be managed to ensure a successful ELN deployment
with subsequent articles focusing on Building the Foundation, Documenting Experiments, and Enabling Collaboration.
This article discusses the fourth core area: System Integration.
Scientists consume and generate reams of data in their daily lab work. On the consumption side, online databases provide a plethora of information, ranging from reaction schema to spectral data to available chemicals. The ability to use this data during daily work can greatly increase the efficiency and productivity of the scientist.
Documenting this information along with data generated from the experiment presents special challenges when working in a paper notebook. How can the data from disparate systems be incorporated into an experiment with 100 percent fidelity? One of our customers recently
summed up the value of an ELN nicely: “When everything else you’re doing is already electronic, an electronic notebook makes a lot of sense.”
ELNs leverage the electronic laboratory environment by allowing data, images, files, etc., to be directly embedded and visualized in
documents, thus eliminating the need to transcribe or cut and paste data from one system to another. ELNs make this process even
more effective, enabling searching and sharing of the data through well-designed templates created during initial deployment.
Data Sources
Depending on the scientific discipline, the type and variety
of data that needs to be incorporated into an experiment can
vary dramatically. The interaction between the ELN and data
sources is bi-directional with a need to both read and write
data. The following table provides examples of integrations.
Write
Read
Internal
• Preparation
Recipes
• Data Modeling
Tools
• Work Request
• Registration
• Sample
Management
• LIMS
• Preparation
Recipes
• Metrology
• Work Request
• ERP
• Sample
Management
• LIMS
System
• Document
Management
• CDMS
• SDMS
• LIMS
• Document
Management
• CDMS
• SDMS
• LIMS
• Instruments
Internal
The internal integration points are generally proprietary databases
and data stores within the enterprise. They may be based on commercial products that provide functionality for metrology, sample
management, resource planning, etc. Other internal integration
points may be homegrown systems tailored to manage proprietary information such as formulations, recipes, instrument setup
parameters, etc. In all cases, the stored data has direct impact
on the daily work within the enterprise—easy access, recording
and updating of information are key to an efficient and effective
research effort.
External
Table 1: System Integration Points
• Available
Chemicals
• Reaction
Planning
13
Systems integration
System
project names and numbers in systems requiring this information.
Integration with SDMS systems ensures fidelity when recording
The ELN is not the only electronic system in the laboratory.
sample sequences, peak integration and spectral data. Consistent,
Today’s laboratories and pilot plants are almost entirely digital.
accurate data input expedites searching. Plus, the single interface
Balances, bioreactors and analytical instruments all report
to data through the ELN means scientists spend less time
their information digitally, either as discrete readings or as
learning and using multiple systems during their daily routine.
file-based output. File-based data is often integrated with
software packages such as Chromatography Data Management Systems (CDMS) or Scientific Data Management Systems
Reduced documentation time
(SDMS). These software packages not only provide manage-
In a fully integrated electronic lab, the ELN consolidates informa-
ment of the data, but also varying levels of data analysis.
tion from multiple systems and eliminates many paper-based
documentation tasks. The “arts and craft” sessions common in
Whether discrete values, experimental parameters or
many paper-based labs that manipulate paper-based data for
spectral data, direct access to this information is essen-
inclusion into paper notebooks, can consume upwards of four
tial for full documentation of experiment work.
hours/week per scientist. Eliminating this work can result in
substantial savings. The example below shows the ROI associ-
External
ated with saving one hour/week can exceed 3 million dollars.
Commercially available data sources have become more
• Number of users: 500
prevalent in recent years. Databases to find available chemicals
• Hourly Rate: $125
and to plan synthetic pathways are all available via the Internet.
Integration of such databases with the ELN provides invaluable
• Time Saved: One hour/week over 50 weeks
500*$125*1*50 = $3.125 million
information to the chemist in planning and executing campaigns.
As several speakers at the Symyx Symposium (now Accelrys)
noted, the ultimate goal is for tasks to be “self-documenting,”
Advantages of ELN integration
The electronic lab environment should be designed with the
never requiring users to step outside of the work they are
doing to tell a notebook or a system that they are doing it.
ELN as the central integration point. In this architecture, ELNs
connect to and integrate with the veritable alphabet soup of
Reduced time for review
systems: sample management (LIMS), analytical instrumenta-
With increased integration, issues associated with external data
tion (CDS) and scientific data (SDMS) systems; modeling and
simulation programs; chemical inventory systems and scientific databases; and business-level systems such as document
management systems or SAP. The Electronic Lab Environment
(ELE) provides a number of advantages for the scientist.
recording such as transcription errors are virtually eliminated.
Review of experiments no longer have to focus on whether
data is recorded correctly, but can focus instead on whether
the data has been properly analyzed and if the conclusions are
scientifically sound. Savings similar to “reduced documentation time” can also be achieved in the review process.
Consistent data input
Integration provides direct access to data and thus avoids
transcription errors and ensures what is stored in one system
is populated automatically in another. For instance, Enterprise
Resource Planning (ERP) systems can be utilized to populate
14
Systems integration
More efficient research
Systems integration ensures that the information scientists
need to do their work is where they need it, when they need
it. Experiments are easier to plan and execute, not just because
scientists have access to required systems, but because they
have the ability to search past work and build on what colleagues have already accomplished. Well-integrated ELNs speed
the work researchers do today, while ensuring that they don’t
have to repeat experiments or do extra work tomorrow.
In the past, paper notebooks collected everything scientists did.
Today, the ELN is no different. In fact, some of our customers have
referred to the ELN as “the center of the universe” for scientists.
Done right, integration through an ELN can provide a portal to
all the systems and data that scientists need to do science.
Summary
Integrating the ELN with both local and global resources
can have a profound impact on enterprise research.
Successful integration of the ELN with other electronicbased systems in the laboratory greatly multiplies the
effectiveness and ROI of the ELN deployment. Substantial
time reductions can be achieved when creating and reviewing experiments while data integrity is improved.
By providing access to a vast array of information and data, the
ELN keeps scientists apprised of the latest information affecting
their research as well as the availability of resources needed to
execute campaigns. In this way, the ELN brings a new dimension of efficiency to the research effort, enabling scientists
to get the answers they need in the least amount of time.
Bennett Lass is the Director of ELN Services at Accelrys Inc.
He may be reached at [email protected].
15
Research Management
implementing electronic lab notebooks
Bennett Lass Ph.D., PMP
Introduction
This is the sixth and final article in a series on best practices in Electronic Lab Notebook (ELN) implementation. The first article1 “How do you
define and manage success?” identified five core areas that need to be managed to ensure a successful ELN deployment. Subsequent articles
focused on Building the Foundation, Documenting Experiments, Enabling Collaboration, and System Integration
This article discusses the fifth and last core area: Research Management.
The ELN Advantage for
Research Management
Compiling All Available Information
As scientists document their work in the ELN, experiment data is
The final ELN implementation phase focuses on laying the
entered into ELN documents and organized in forms, tables and
groundwork for gathering and leveraging the data collected by
other sections of the experiment. At the same time as scientists
and stored in the ELN—this includes critical data that measures
create their experiments, the ELN software is recording informa-
and monitors the efficacy of research activities. For example,
tion about the experiment and the processes being executed.
information about project and experiment lifecycles enables
research managers to evaluate research campaigns as they
Information useful for creating metrics can be categorized as:
are executed. Likewise, historical data is useful in modeling
• Background Information
the planning and execution of future research campaigns.
• Experimental Parameters
The ability to make such measurements is what sets an ELN
• Metadata
apart from paper-based systems. After implementing an
ELN, organizations gain improved insight into their research
Background Information
practices; they can adjust or establish new standard operating
Background information entered by the scientist provides the
procedures for planning experiments and projects, allocate
resources more effectively and improve efficiency.
The ELN provides a rich source of information for managing
research efforts. This includes not only experiment data, but also
the metadata generated within the system as each experiment is
created, executed and completed.
context for the experiment. Depending upon the industry
and discipline, the information will vary. In the pharmaceutical
industry, where experiments are conducted on both large and
small molecules, background information might include:
• Compound Name/ID
• Cell Lines
• Study Phase
• Location
• Matrix
• Project Name/ID
16
Research Management
• Target Area
into ongoing activities, generated metrics can serve as predic-
• Analysis/Protocol
tors when planning future campaigns. Research managers can
• GxP level
mine this information for quantitative answers to common
• Lot/Batch Number
Experimental Parameters
Parameter data enables scientists to track items such
as equipment or reagents used in execution of the
experiment. Generally recorded in equipment or material tables, the recorded information can include:
• Equipment
–– Inventory Numbers/Asset Tag Numbers
–– Manufacturers
–– Model Numbers
–– Locations
• Reagents
–– Names
–– Lot/Batch Numbers
–– Manufacturers
Metadata
In addition to storing experiment data, the ELN also
automatically generates a large amount of metadata that
is stored with the experiment data in the database.
research management questions in several areas of experiment
creation and resource planning, which are outlined below.
Experiment Creation
Number of users
Using metadata value “created by,” it is possible to capture the total number of active users. Combining this value with background
information such as site, department, etc., provides increases
the granularity and potential usefulness of the information.
Number of experiments
The number of experiments provides the most insight
into research activities when combined with other
parameters and/or date ranges. Examples include:
• Number of experiments for a specific compound in a
specific phase − e.g., discovery, formulation, etc.
• Number of experiments in a specified time period
Campaign duration
Based on creation dates, the time from the first to last experiment for a specific compound and phase can be determined.
Typical metadata includes information about:
• Who created the experiment
• When it was created
• What workflow stage the experiment passed through
• How long it took to witness experiment documents
Most referenced experiment
Unique references can be counted to find the top
25 experiments that are referenced and thus have
the greatest impact on current research.
• How many times the experiment was returned from witnessing
• How much time passed from experiment creation to archiving
Improving Research Metrics
Designing and executing experiments using an ELN that
Experiment source
The source of each experiment is recorded. Counting the unique sources quantifies which experiment templates are most often used.
captures all of the above data facilitates data mining and the
creation of useful metrics reports. As well as providing insight
17
Research Management
Measuring the time experiments stay in stages such as
Enhancing Experiment Execution,
Today and Tomorrow
‘In Progress,’ ‘Waiting for Witness’ or ‘Pending QA Review’
Armed with this information, research management can begin
can identify bottlenecks in completing experiments.
to build a statistical database tracking the efficiency of research
Average time in a workflow stage
activities based on a clear understanding of research phases,
Number of experiments returned from review
Counting experiments that passed through the review
process more than once can identify experiments
that are returned from witnessing and help determine the root cause for reworking experiments
targets, compounds, therapeutic areas, etc. In addition to
identifying areas where inefficiencies are costing time and money
today, this database can also be used to predict timetables
for future research efforts derived from past experiences.
Bennett Lass is the Director of ELN Services at Accelrys Inc.
He may be reached at [email protected].
Number of cloned experiments
Insight into the value of cloning can be achieved by
comparing the number of experiments created by cloning during a specific time period with the total number
of experiments created during the same period.
Resource Planning
Besides metrics on experiment creation and research activities,
experiment data also provides metrics on resource utilization.
Equipment and material tables provide minable information about resources needed to execute research plans.
For example, combining a count of samples with equipment
ID tags over a given time period offers insights into utilization.
Linking this information to additional experiment parameters
such as compound, research phase, etc. can broaden and deepen
the understanding of operational dynamics. In a similar manner,
information about what chemicals are used in what amounts can
be used to plan inventory levels for key solvents and reagents.
18