Lab digitalization is based on research data management workflows that depend on multiple platforms, e.g. ELNs, SDMS, LIMS, etc.
Making research reproducible and FAIR
Scientific funding agencies all over the world mandate that researchers have a data management plan in place, and that research data must be organized to be FAIR – Findable, Accessible, Interoperable, Reusable.
Inevitably, FAIR data keeping introduces the first steps to lab digitalization, which ensures safe and accessible research data, reproducible experiments, increased productivity and is prerequisite for the automation of workflows.
Many lab leaders find the road to digitalization very steep
Research data is complex and difficult to track. Any one lab may work with many types of data ranging from human observations to extremely large datasets generated by equipment such as nuclear magnetic resonance, mass spectrometers, infrared spectroscopy, to name only a few. Several researchers may work on hundreds of samples over long periods of time.
Another stumbling block may be the fact that the boundaries between different research data management systems available are not clearly defined.
It may not be obvious which system a given functionality may reside in. What types of lab digitalization platforms exist, and when does it make sense to deploy which of these systems? What diferentiates an ELN from an SDMS?
Four platforms for lab digitalization
A full lab digitalization setup will usually be based on four different platforms (image 1).
Image 1 – Four lab software platforms typically used for research laboratory digitalization.
For the daily experimental work in a lab, two platforms will be responsible for safely storing the data. An Electronic Lab Notebook (ELN) for human input, and a Scientific Data Management System (SDMS) for gathering data from lab equipment.
A Laboratory Information Management System (LIMS) will keep track of samples and workflows. Finally, a platform for data analysis is needed. The last will depend on the type of research that is conducted.
In this post, we focus on the SDMS: the Scientific Lab Management System.
The role of the Scientific Data Management System
In its simplest form, a SDMS gathers data from instruments by automated upload and stores it centrally.
This functionality defined the early SDMS, where simple file synchronization transferred data from instruments so the limited storage space of the instrument could be cleared for new data. Even in the simplest form, an SDMS offers solutions to several problems, as any scientist will know, having struggled to find research data produced by a former colleague, on various samples and on different instruments.
Nowadays, the key for a state-of-the-art SDMS lies in automation and accessibility. The automated upload straight from the instruments eliminates the risk of human error and ensures data completeness for a lab. Building the SDMS as a webapplication ensures centralized accessibility from anywhere.
LOGS – a research data exchange platform
Makers of scientific instruments, for example spectrometers, are aware of the benefits of an SDMS. Many such companies have started adding SDMS capabilities into their software, but the capabilities generally only work with the manufacturer’s own data format.
Bruker IDS is designing LOGS (https://logs.sciy.com), a research data communication platform that goes beyond the traditional SDMS, and that works with data formats from multiple instrument manufacturers and allows web-based access to all data without the need for third party software.
Data kept in LOGS remains uniquely accessible, both for the individual researcher, for calling up data in day-to-day discussions internally in a research group, and for sharing data with collaborators.
Image 2 – A SDMS in its very simplest form (left) and LOGS, the SDMS designed by SciY / Bruker IDS (right).
Advantages of a modern, state-of-the-art SDMS include:
Automatic Upload of the primary data (raw data files produced by the instrument). Files produced by a finished measurement are recognized and uploaded automatically to the SDMS.
Metadata management is included along with data in LOGS. Some metadata is extracted from the original data, such as information about sample, experiment, and operator, and further metadata can be added manually by the user.Data is searchable by metadata parameters, giving researchers a new type of access to their own data and that of past and present collaborators, sorting it by sample, by experiment type, or by publication.
Searching & Filtering the SDMS content by measuring method, instrument, spectrometer operator, any text found within the measurement files, etc. allows researchers to find, group and access data simpky and quickly.
Sharing & Distributing data ro other researchers, within and outside of the local lab, ensures that data remains FAIR, even when passed on. Contextual metadata and experimental information remains with the research data, ensuring reproducibility.
The list of advantages to the researcher goes on, and is growing, as todays webtechnology offers a growing amount of options.
In summary, a general SDMS gathers and stores data centrally. At a central location, data can be backed up and accessed for analysis, for sharing with collaborators, publishers, and funding bodies. Data is searchable by multiple parameters, and truly becomes Findable, Accessible, Interoperable, and Reusable (FAIR principles) .
If you are interested in digitalizing lab workflows and FAIR research data, have a look at the research data exchange platform LOGS and get in touch.
