Mass Spectrometry & Spectroscopy
The State of Analytical Data Management Sanji Bhal, ACD/Labs
Over the past decade, we’ve experienced immense change in R&D. From new instruments and software to the rise of the digital lab, the landscape of analytical chemistry data has evolved. At ACD/Labs, we need to keep a fi nger on the pulse of analytical data management (ADM).
That’s why we launch a comprehensive survey every few years to uncover the latest trends and preferences regarding analytical chemistry data and its management.
In this survey we heard from academia (30%), industry/manufacturing (26%), biotech/pharma (13%), government (11%), nonprofi t research (9%), contract service providers (5%), and consultants (3%).
Here’s what we found…
Analytical Data is Managed in Multiple Applications and Shared Haphazardly
The diversity of analytical data means that it’s stored and managed in many different applications and systems for most organisations.
Data Diversity is a Real Problem and a Necessity
Data is the backbone of all scientifi c research projects. Analytical data is primarily collected to ensure the identity and quality of materials to specifi cally:
1. Understand the structure or composition of materials and processes by which they’re made
2. Evaluate the performance of experiments, materials, or processes
It is often necessary to run several different analytical experiments to answer these questions (e.g. LC/MS and NMR). Analytical labs are equipped with a variety of instruments so that analysts can choose the best instrument for the answers sought. Since we want to execute the best science, diversity of analytical data is necessary. In addition, many research teams use instruments made by multiple vendors, which leads to fi le compatibility issues.
Unsurprisingly, our survey found that over 92% of respondents collect data on numerous instruments, use multiple techniques, and rely on diverse software for processing analytical data. To break this down further, 45% typically use 2-4 analytical techniques; 37% use 2-4 different instruments to collect data (33% use 5-9 instruments!); and 54% use 2-4 software applications to process their data.
Microsoft applications are still the most popular way to manage and share analytical results, selected by 80% of respondents. Whether its Excel spreadsheets, PowerPoint presentations, or email, ubiquitous access to these applications makes them an easy choice even though they are neither designed nor best-suited for scientifi c data sharing and management.
Instrument software was the second most popular choice at 70%. While instrument software is restrictive to only processing and analysis of the data collected on that instrument, it is designed for it. It was surprising to learn that many organisations are still using software developed internally to manage and share analytical data, even with the development and maintenance overhead required. Many other systems deployed in R&D are also used to house and share analytical data ELNs, LIMS, CDSs, SDMSs, archives and more.
These systems represent different activities and are often used in combination throughout the lifecycle of an analytical data fi le:
1. Stored in a raw data archive to affi rm quality and accuracy
2. Processed and stored in vendor software to extract results and retain the processed data fi le
3. Results shared with scientists via LIMS or Email may include images of spectra, confi rmation of expected structure/material composition, and text results (MW, peak tables, retention times, etc.).
4. Decisions based on those results may be recorded in a scientist’s ELN along with the image of a spectrum, peak table(s), confi rmation of expected structure with the scientist’s notes. Decisions may also be presented in an internal meeting via PowerPoint and subsequently stored on SharePoint, or shared in a report
5. Stored in the CDS or SDMS to conform to FAIR/ALCOA principles and fulfi ll regulatory requirements
INTERNATIONAL LABMATE - FEBRUARY 2023
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