Keeping your research and development (R&D) efforts running smoothly depends on a lot of things — teams working in unison, processes being streamlined, errors being reduced, and data remaining accessible. Below are just a few of the critical lab errors and inefficiencies you can avoid to save time and resources.

1. Redundant Efforts

All too often, teams work in isolation, with siloed data and little knowledge of each other’s work. Efforts may be redundant, insights are unshared and failures are often repeated. Labs need to harmonize R&D efforts to avoid this cycle of redundancy.

With all teams using the same system, your organization’s scientific project data are united into a single source of truth. When solutions can support chemistry, biology, and chemicals & materials teams, whether they’re performing highly standardized experiments or ad hoc research, teams can easily pass data back and forth, fine-tune workflows and permissions, and share their insights. With your teams working better together, your organization is positioned to meet its end goals faster.

2. Nonrepeatable Experiments

Because experiments are often improperly recorded, tests aren’t easy to repeat or audit. This can threaten progress and even intellectual property. One way to address this issue is by creating a template for the experiment, including the process for data collection.

An electronic lab notebook (ELN) lets you create a library of experiment templates across scientific disciplines and captures the intricacies of all types of experiments occurring throughout your entire R&D pipeline. Form-based entry keeps data intake consistent in highly structured experiments, and data collection can be standardized for ad hoc exploration. An ELN should also feature tab-based organization to help organize the many details of multi-step workflows, as well as signing and countersigning capabilities to help validate work.

3. Duplicate Registrations

Registration tools often do not have the scientific wherewithal to determine uniqueness for all entity types, causing duplicate registrations. It is essential to utilize registration tools that support single or batch registration of diverse types of entities, including chemical structures, biological entities, and multi-component mixed entities. Systems can be set up to crosscheck new registrations against existing registration databases and only register unique entries. If a registration already exists, a duplicate should not be created; instead, the existing registration will simply be supplemented with the new data.

4. Data-entry Errors

Data-entry errors or omissions can make your experiments and results unsearchable or even worse, invalid.

With a reliable ELN, forms-based entry helps ensure experiments are recorded with all required entries. When it’s time to upload large datasets, the notebook can be set up to pull raw data, results, and metadata directly from laboratory instruments, high-throughput analysis tools, corporate databases, inventories, internal libraries, external sources, etc. This reduces data-entry errors and eliminates time-sinks related to manual uploading and processing. Researchers can create automatic checks-and-balances upon data upload to alert you to missing data or erroneous entries. The system and ELN should meticulously track your experiments and results with clean, reliable data collection.

5. Unsearchable Data

Even with electronic storage of your data, it’s easier to get data into the system than it is to get data out. This often makes the task of searching and parsing through your valuable data cumbersome or impossible, and insights are isolated and ultimately lost. A better system can only improve this error.

A reliable ELN helps you efficiently and cleanly input data by facilitating a variety of upload options, including error-reducing forms entry, drag-and-drop file input, and automatic upload from instruments, inventories, and both internal external databases. Full use of metadata and proper naming conventions helps correlate data back to specific users, compounds, sequences, projects, CROs, etc., which in turn impacts searching.

All data coming into the ELN are parsed and federated into a highly-searchable master data source. Your ELN should handle all types of data—chemistry files (SD files, SMILES), sequence files (e.g., FASTA, FASTQ), materials data (polymers, formulations, samples, physical characterization data), text, images, videos, etc. This scientific acumen, combined with powerful search capabilities, means that no matter what type of data you’re working with, you can get it out of the ELN just as easily as it went in.

6. Security or Confidentiality Breaches

A top priority for all systems should be data security and confidential collaboration. Your system should unite scientific project data coming from different teams and standardize them against common data models by delivering its robust collection of applications on a single secure platform. All internal and external multidisciplinary teams can work within the same system. All experiments can be recorded within the same ELN. All data synchronized. All actions captured in an audit trial. But even in this remarkably open and collaborative research environment, security and confidentiality remain protected. How? First, behind the scenes, there should be encrypted data transfer on a secure, dedicated AWS instance. Or you can host on-site. Within your ELN, you should have the ability to finetune users’ access to different data sets and functionality, define signing and countersigning requirements, encrypt reports, and assign project codes and aliases when working with CROs. The result? Your teams can work together in real-time, without compromising security or confidentiality.

7. Stalled Projects

In a collaborative research environment, responsibilities often fall across teams. Those teams need a way to efficiently communicate their requests or else projects may stall. For example, one team may need another team to perform a particular assay or analysis before they can push forward with their project. A reliable ELN helps facilitate such scenarios by providing direct access to tools for managing requests, assigning tasks, and monitoring their progress. With full visibility into cross-team requests, projects can progress without delay. 

8. Inaccurate Instrument Data

The accuracy of your analyses hinges on your instrument data being precise. But many issues can arise, from collection errors, to processing mistakes, to missing metadata, to undetected instrument drift. The automation of the acquisition, parsing, and transfer of instrument data into your ELN, saves you time and ensures that you have clean data for your analysis tools and AI/ML models. Effortlessly collect hundreds of terabytes of data from all your lab tools, including mass spec, HCS, bioreactors, and more. Your system should let you collate your instrument files, log files, parsed data, metadata, experiment results, and analysis calculations directly into one united data lake, so you have a complete, fully searchable, drill-down dataset at your fingertips.

9. Missed Connections

When teams work in isolation, important insights can be missed. An integrated collection of R&D tools for scientific data collection, instrument integration and data processing help teams work together to efficiently make and test new products. But true collaboration isn’t just about optimizing day-to-day lab workflows. It’s about sharing data to garner insight and guide decisions.