This diary entry from R&D software provider, IDBS, looks at how individuals, laboratories and external collaborators can make their work more accurate with the help of cloud-based software.
Reproducibility crisis
According to a recent poll by Nature,1 70% of scientists polled have failed to reproduce at least one other scientist’s experiments. Even more shockingly, more than half of scientists have failed to reproduce their own experiments. So, what can be done to address the reproducibility crisis in laboratories?
Quality of data
The failure to replicate past experiments can be understandably frustrating for scientists who want a solid foundation of research to build upon. While no two labs are exactly the same, and a number of factors could affect the results of a reproduced experiment, there are also many factors scientists can control. The most important? The quality of their data.
In a laboratory environment, increased data quality accelerates workflows, enhances analysis and ensures the delivery of a higher quality of products and results. When data is of a poor quality, whether because of multiple errors or a disorganised format, businesses will be unable to gain insight into the project or experiment details. However, errors can and do go unnoticed and the findings of flawed experiments often end up published in reputable journals — meaning they could be repeated by unknowing parties later.
Recording data accurately
Although one or two data errors may seem like a relatively minor issue, they can escalate into much larger problems further down the line. For scientists repeating experiments, even the smallest of errors could mean you’re wasting both time and money trying to replicate the impossible. The best solution is making sure scientists have the tools to record data accurately in the first place.
With the sheer volume of data being created each day, many businesses are seeking alternatives to the traditional paper-based recording methods to keep up with today’s technology. One of these alternatives is an electronic laboratory notebooks (ELN). Digital solutions, like ELNs, reduce the time-consuming process of recording data into laboratory notebooks. They use sophisticated technology, which can eliminate the manual transcription of data and maintain information integrity and quality when recording experiment information.
The results of in-house experiments will often form the basis of multiple follow-up experiments too and, in some cases, one experiment will form part of a group of 15 or 20 separate projects that combine to form one set of results. In this situation, there could potentially be hundreds of different people all working simultaneously with the same information, so the accuracy of this information is key — and using an ELN is only part of the solution, especially when collaborating with external organisations.
A cloud-based collaboration platform
The continued use of external providers, such as contract research organisations (CROs) has increased the likelihood that experiments will need to be repeated by third parties — potentially working in different regions, countries and time zones.
Collaboration projects can cause all kinds of logistical problems — with various stakeholders needing to coordinate activities and transfer data and reports. Results need to be easily searchable, dated and timestamped. Other contextual information should also be digitally recorded, meaning colleagues trying to repeat experiments have as much information as possible before replicating an experiment.
The best way to ensure data integrity when collaborating is using a cloud-based platform. This enables organisations to create and share templates with external collaborators and, when a study is completed, data can be seamlessly transferred back to your organisation’s internal systems with just a few clicks.
The benefits of the cloud
Using the cloud provides you with both the flexibility and security you need to get your projects moving, saving you both time and money.
By using the right platform, collaborators — both internal and external — can enter their data directly into your system, eliminating the need for disparate data files and formats, and reducing the chances of errors creeping into work by standardising the data formats used.
No more misunderstandings and no more ambiguity: all data can be recorded and viewed as originally intended, giving anyone reproducing an experiment the best possible chance of success.
Reference