Implementing Electronic Lab Notebooks
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.
Collaborative efforts can be defined as sharing of information between one or many persons within a research organization. By focusing on the data, we can identify a wide range of collaboration needs or modes within an enterprise. Each collaborative mode puts different needs and constraints on how data and information are documented, shared and used.
Reusing information from previous experiments can significantly increase the productivity of the scientist. Leveraging information from previous projects focuses research efforts either on previously successful work or on areas that have not yet been investigated. It also shifts effort away from areas which were previously determined to be of little or no benefit to the overall goal of the research program.
Finding and using previous experimental data can eliminate the need to perform experimental work. A significant amount or research work is often undertaken simply because it is easier to repeat the work than try to find paper documents stored in archive sites. The ELN helps to minimize unnecessary re-work by providing an easy way to find data in current and archived experiments.
Another common mode of collaboration requires scientists to record data in separate experiments and then share the information. This is typically done when a scientist submits samples for analysis or characterization. The work associated with creating and analyzing the sample constitutes two distinct activities. The analyst needs to understand the history of the sample and its preparation to optimize the analytical work. The submitting scientist needs to incorporate the analytical results into his or her own experiment to draw conclusions and plan future activities.
A similar collaborative mode is found when groups within an organization prepare reagents, cell lines, etc. which are then consumed by other research groups within the organization. Scientists using these materials in their experiments need to document how and when they were prepared.
A fourth collaborative mode covers multiple investigators recording their data into a single document or experiment, as often occurs when experimental activities extend past a single work shift. Documenting animal studies or conditions in a bio-reactor are typical examples of this type of 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
Improving collaboration is one of the primary reasons for deploying an ELN. The ELN is designed to overcome two of the biggest obstacles identified for collaboration -- 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.
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.
Besides resolving many of the collaborative challenges presented by a paper notebook, an ELN deployment provides the following additional functionality to facilitate the sharing and reuse of data:
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 communication about experiments. Such notes enable reviewers to comment on research without being in physical proximity to those who created the experiment, encouraging dialogue between members of globally distributed research teams.
Referencing: An instant two-way link between experiments gives researchers a persistent link between scientifically related experiments. Referencing creates valuable traceability between, for instance, those preparing reagents and those using them, or those requesting tests and those performing them. This not only streamlines collaboration but can actually help mitigate errors. For example, if scientists notice an error in sample prep, they can rapidly notify those who have referenced the experiment.
Cloning: Often the basic information recorded in an experiment, i.e., purpose, materials, equipment, procedures, etc., do not change or, if they do, the changes are minimal. Cloning enables scientists to begin new experiments by electronically copying existing experiments. The basis for the cloned experiment can be any experiment in the ELN. Thus, the ELN offers significant efficiency improvements in enabling scientists to share experiment parameters as well as time savings in documenting similar experiments.
Virtual Notebooks: 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 might have relevant information; then you need to identify all the notebooks used to record the data. Finally, you need to confirm the location of the notebooks to copy and compile the data. With an ELN, there is no need to know the who, when or where. Simple searches bring together all the information in a matter of seconds. Built-in ELN reporting capabilities enable the formatting and editing of final reports just as quickly.
Work request integration: This capability relies on systems integration. Work request integration offers the ability to manage the “information round trip” for samples submitted for analysis. Rather than throwing a request over the wall for someone else to work with, work request integration gives analysts and requesting researchers’ visibility into the complete workflow, so that work gets done faster and more efficiently.
ELN-enabled collaboration has a multiplicative impact on the data recorded by the individual scientist and the research effort it supports. The central document repository enables all scientists to access any document independent of the research site. The ELN eliminates physical barriers to sharing data and collaborating in the creation and documentation of experimental work.
Bennett Lass is the Director of ELN Services at Accelrys Inc. He may be reached at editor@ScientificComputing.com.