The relationship between data is extremely important to understand and effectively turn this data into information to help with business decisions.

Even though large amounts of data are being stored this doesn’t seem to be enough, it isn’t until people can see those data points and how they correlate to other data points that it becomes of value.

In the LIMS world this can be;

1. Batches, samples and sub-samples… the genealogy! 
2. Instruments, their modules and associated components.
3. Projects, requests and corresponding samples.

Like a family tree of linkages for the scientist to make decisions.

The interactive tree view in QLIMS shows the genealogy quickly on the screen and lets users navigate directly to parent and child records by clicking on the ID’s.

Extensive state-of-the-art research and development lab with some detailed Excel spreadsheets, but the time is to embrace the era of LIMS, this is Lab 4.0 after all!

The challenge? Convincing people to let go of their spreadsheets. To convince them they’ll need the same flexibility while being controlling enough to reduce potential errors while looking good and being easy to use…

For a solution, we need to enter the Result Matrix!

1. A total overview of the workflow within a batch of samples.
2. Possibility to drill down a complete workflow into 1 or more steps.
3. See only the information you need to see with intelligent filtering options.
4. Interactive result entry within the workflow. Qualitative, quantitative and checks & balances.
5. Possibility to save commonly used filter combinations.

Now they continue their work on the QLIMS platform. Stay tuned for more exciting ‘Field Notes’! 

The customer is both knowledgeable in their field and technically capable. Strong Python scripting skills and great ideas.

The challenge is to collate data from multiple long files being generated by this instrument, perform calculations, validate the data, upload it to the sample and attach graphs generated on the fly without any manual intervention.

How will we solve this? 

We need to write a Python script for our Bridge module that ingests the files and then calculates averages based on the raw data from the instrument.

Then we can load the customer’s own Python script into the QLIMS Bridge to generate the graphs. Our own script can attach the graphs as attachments to the relevant sample records.

Problem solved, fully automated solution!

We know the technical capability of the bridge, but we learn more and more with each implementation of the power in real-world laboratory scenarios.