Fully automated solutions to acquire, secure and analyze laboratory instrument and CRO data
In many labs manual data processes are common. These are invariably time-consuming and negatively impact lab efficiency. Manual data-wrangling also invites error, causing problems when the data is later used for AI/ML modeling and decision support.
The Dotmatics BioBright solution allows for full end-to-end data automation.
With the solution in place, scientists are freed from mundane, error prone data processing. Instead, they can concentrate on data analysis and critical decision making to help bring critical therapies and materials to market.
Or do your scientists lose hours a week collecting files from instruments, manually moving files around the network, and storing and organizing files before they can even start to analyze them?
Or do your scientists exchange valuable results via email and SharePoint?
Do they spend hours finding and organizing CRO reports and understanding the results from irregular formats?
How much time is lost in your research cycle waiting for data to send, sychronize or receiving results?
How much valuable time do your scientists spend extracting the data and metadata from a wide variety of proprietary instrument output types?
Is your team forced to maintain an ever growing and shifting ecosystem of proprietary formats? Analysis pipelines breaking all the time
Are errors being introduced in the data’s journey from files, to data/metadata to analysis and experimental results only to be detected late in the process, invalidating experiments?
High throughput labs can generate terabytes of files per day.
Can your scientists and their existing systems keep up, or are your processes and storage solutions becoming overwhelmed with the sheer volume of files and data?
Is your data secure end-to-end? Are the files encrypted both in transit from the instrument and at rest in the data repository? Is it secured all the way to the visualization or analytics layers?
Who is responsible for securing the data? Is that overhead being delegated to the scientists? Who is responsible for keeping up with state of the art protections and stewardship of your data?
Can you see trends in equipment performance in real-time across extended time periods (e.g Can you spot drift in mass-spec)?
Are manual data capture processes and analytics too slow or soloed to allow proactive management, costing you time and money when action is delayed?
Can you serve new views of scientific data and high volume instrument outputs to your researchers, as fast as they develop and adopt new methods?
Are your scientists dependent on manually processing data into Excel and unsustainable scale? Are they hindered in fully exploiting their scientific advances due to a lack of relevant and labeled data for analytics?
Can you see raw instrument data, experimental protocols and results all in one place?
Is it difficult to drill down from an interesting or questionable result back to the raw data and instrument outputs?
A Mass-Spec core facility at a large pharma company implemented the BioBright solution to capture and process the 700Gb - 1Tb of files they were producing per week. They replaced a process that required scientists to manually load their data into a data store and then manually analyse it – taking each scientist 2-3 days per week
With the Biobright solution
A high content screening lab at a leading biotech had a legacy HCS data processing infrastructure that was slow, lossy and manual
The legacy infrastructure was replaced with the fast, lossless and insightful Dotmatics BioBright solution
A large pharma’s bioreactor scale up facility used manual collection methods to gather data from their reactors. researchers used Excel to aggregate data across the various sizes of reactor to make decisions about reactor set-up during the scale up process. It took 2 to 3 days to gather up all the data, analyze it and make decisions.
The aggregated data became available fast enough to allow previously impossible analytics. For example, an AI model of dissolved oxygen (DO) crashes was applied to the readily available data, and predicted the likelihood of a crash up to two hours in advance. This gives the operator time to adjust the bioreactor to mitigate the potential problem.
We continuously add support for new formats.
Do you have a particular data format you need for your workflows?
Reach out to us!
We will analyze the file, show you the data that can be extracted, and how you can leverage it with DarwinSync