Many Scispot customers often ask to start processing data based on a sample management workflow. This has been a common practice to run workflows programmatically in Lab Information Management Systems. However most modern LIMS don’t support this workflow.
Over the last year, Scispot built an embedded Jupyter server to solve for this use case.
Scispot allows scientific data analysts to write Python scripts on top of its scientific tables, Labsheets, using Embedded JupyterHub.
The Jupyter server is already included in Scispot Lab Operating System, so you can start working right away without setting up Python on your computer. This saves you time and effort.
You don't need to worry about configuring your own Python environment. Just open the Jupyter server and begin your work. Link your data analysis workflow directly with your ELN and LIMS.
Jupyter Notebooks become part of your electronic lab notebook to facilitate unified user experience.
Scispot customers follows these simple steps to automate their LIMS workflow without leaving Scispot platform:
- Utilize Scispot's embedded JupyterHub server to write Python scripts.
- Convert the scripts into executables for reuse across different triggers.
- Create a trigger with a specific rule. For instance, you can configure the Python script to run automatically when the sample status changes to "processing".
- Configure alerting mechanisms to receive notifications based on the output generated by the scripts.
Here are the most popular use cases that scripts solve using Scispot LIMS workflows:
1. Automated Sample Processing
- For your lab data, you can create a scheduling workflow for sample processing. This framework automatically starts a Python script when it registers a new sample in the LIMS. The Python script will initiate the sample processing workflow.
- Set up a trigger in the Scispot LIMS system. The trigger will run a Python script. This will occur every time a new sample record is added.
- This Python program can assign tasks to lab folks, update sample statuses, and begin instrument analysis.
- A Python script can get information about a new sample from the LIMS database. It can assign resources like lab equipment and staff according to set rules. It can also change the sample status to "processing" in the LIMS.
2. Quality Control Checks
- After analyzing a sample, you can set up a Python script to automatically check the data quality. This script will help ensure that the data meets certain standards. By automating this process, you can save time and reduce the risk of errors. This will allow you to quickly identify any issues and make necessary adjustments.
- Set up a trigger in the LIMS system to execute a Python script once the analysis of a sample is finished.
- This Python script can check the quality of data before processing or reporting.
- The Python script can retrieve analysis results from the LIMS database. It can then compare these results to set limits or reference values. Any differences will be highlighted for lab staff to review.
3. Inventory Management and Instrument Maintenance
- Scispot LIMS lets you set up a Python script to control inventory levels and schedule maintenance for lab instruments automatically.
- Set up triggers in the LIMS system to execute Python scripts when certain conditions are met. For instance, trigger Python scripts when inventory levels are low or maintenance is scheduled.
- You can simplify inventory management by using Python scripts. These scripts can automatically create purchase orders for restocking. You can also schedule maintenance tasks for instruments.
- For example, a Python script could regularly monitor the inventory levels in the LIMS database. If certain items drop below a set threshold, the script can automatically create purchase orders to restock them. Similarly, the script could analyze instrument usage data to schedule preventive maintenance tasks for instruments, ensuring their optimal performance.
In the Scispot LIMS environment, each use case triggers a Python script. These scripts automate tasks for sample management, quality control, and inventory/instrument management, and many more use cases. As a result, you can save 1000s of hours focussing on building your proprietary data on top of LIMS rather than worrying about tactical workflows for your day to day operations.
Next Step? We are introducing natural language prompts to run these scripts right within the Scispot platform. More to follow soon...
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