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Table 1. High-level advantages and defi ciencies of applications and systems typically involved in the analytical data lifecycle
Application/System
Instrument vendor software
Microsoft Applications (PowerPoint, SharePoint, Teams, Outlook, Excel etc.)
In-house developed software
LIMS ELN Advantages
• Store raw and processed data • Available for (re-)processing and posterity
• Widely available Defi ciencies
• Lacks experimental context • Requires access to specifi c versions of software
• Scientifi c context is not supported • Data abstracted to text & images
• Fit-for-purpose • Sample-centric storage
• Requires dedicated staff for maintenance
• Lacks experimental context • Data abstracted to text & images
• Experiment-centric storage • Inability to review and reprocess data • Data abstracted to text & images
CDS
• Assemble multi-vendor data • Data is “locked down” for regulatory-readiness
• Limited ability to review and reprocess analytical data • Data lacks experimental context
• Data abstracted to text & images
SDMS
• Assemble multi-vendor data • Data is “locked down” for regulatory-readiness
• Limited ability to review and reprocess analytical data • Data lacks experimental context
• Data abstracted to text & images
Scattered data makes assembly, a crucial step in decision-making, diffi cult as it forces scientists to search multiple locations for answers. When there are many possible locations for data, the path of least resistance is often to repeat the experiment or request the data from a colleague, which wastes time, materials, and can cause frustration.
Scattered Data Makes Reporting Time-Consuming
Reports are a key way to share information within an organisation or with external partners. Only 18% of respondents said they rarely (or ever) collate analytical reports with data from different instruments and techniques, and 40% do so weekly or daily. How much time is wasted, then, in moving from system to system to collect all of
Data access is especially challenging when it involves data collected by others, in larger/scattered organisations, or when a team member leaves or joins the organisation.
Reasons for Needing Access to Data from Past Experiments
To appropriately address these hurdles to data access, it’s essential to consider why scientists need to access data from past experiments.
Across all R&D sectors, the top three reasons for accessing data from past experiments and reports are: 1. To compare with new results 2. To reprocess or reanalyse for new information 3. For publication purposes
And, the top 3 reasons for Pharma/BioPharma are:
1. To compare with new results 2. For regulatory purposes 3. To reprocess or reanalyse for new information
Accessing past data for regulatory purposes was the second most important factor for Pharma/Biopharma, while it was understandably insignifi cant for academia and non-profi t organisations. Other than this variation, the reason why data from past experiments needs to be accessed was consistent across the R&D sectors.
the relevant data to compile these reports? By fully implementing an ADM solution, scientists can collate reports by simply linking to data.
Analytical Data is Mission-Critical but Diffi cult to Access and Share
Nine out of ten respondents note that they need NMR, LC/MS, GC/MS, or other analytical data daily to make decisions. Seven out of ten agree that sharing data and interpretations within their organisation is important. However, for an element that is mission-critical to their job, it’s not easy to access or share that data with others; 50% agree that searching for data in their organisation is a challenge, while 68% say it’s hard to access and share with others.
25% of respondents accessed old data to replace data that was lost or misplaced. Properly managed, accessible data can deliver signifi cant savings of time and effort. Especially when old data cannot be found, the alternative is to re-run experiments!
Only 18% of respondents accessed old data for data science projects.
While academic and non-profi t groups may focus less on data management than other R&D sectors, this could be an opportunity to improve productivity. From my own days in the lab several research projects would pass from one student to another. Finding data from within the group, even from current colleagues, was challenging.
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