Implementing Electronic Lab Notebooks
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 Foundation2
- Documenting Experiments3
- Enabling Collaboration4
- System Integration5
This article discusses the fifth and last core area: Research Management.
The ELN Advantage for Research Management
The final ELN implementation phase focuses on laying the groundwork for gathering and leveraging the data collected by and stored in the ELN—this includes critical data that measures and monitors the efficacy of research activities. For example, information about project and experiment lifecycles enables research managers to evaluate research campaigns as they are executed. Likewise, historical data is useful in modeling the planning and execution of future research campaigns.
The ability to make such measurements is what sets an ELN apart from paper-based systems. After implementing an ELN, organizations gain improved insight into their research practices; they can adjust or establish new standard operating 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.
Compiling All Available Information
As scientists document their work in the ELN, experiment data is entered into ELN documents and organized in forms, tables and other sections of the experiment. At the same time as scientists create their experiments, the ELN software is recording information about the experiment and the processes being executed.
Information useful for creating metrics can be categorized as:
- Background Information
- Experimental Parameters
Background information entered by the scientist provides the 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
- Project Name/ID
- Target Area
- GxP level
- Lot/Batch Number
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:
- Inventory Numbers/Asset Tag Numbers
- Model Numbers
- Lot/Batch Numbers
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.
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
- 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 captures all of the above data facilitates data mining and the creation of useful metrics reports. As well as providing insight into ongoing activities, generated metrics can serve as predictors when planning future campaigns. Research managers can mine this information for quantitative answers to common research management questions in several areas of experiment creation and resource planning, which are outlined below.
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.
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.
Experiment source – The source of each experiment is recorded. Counting the unique sources quantifies which experiment templates are most often used.
Average time in a workflow stage – Measuring the time experiments stay in stages such as ‘In Progress,’ ‘Waiting for Witness’ or ‘Pending QA Review’ can identify bottlenecks in completing experiments.
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.
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.
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.
Enhancing Experiment Execution, Today and Tomorrow
Armed with this information, research management can begin to build a statistical database tracking the efficiency of research activities based on a clear understanding of research phases, 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.
This is the final article in a six-part series on implementing ELNs. You can find the preceding articles at the web adresses below.
Bennett Lass is the Director of ELN Services at Accelrys Inc. He may be reached at editor@ScientificComputing.com.