Problem: Data Quality and Data Quantity Quality - Ensuring data is worth analyzing Cytometers No standardize setup methodology across cytometer to ensure inter and intra system reproducibility No standardize setup methodology across cytometer manufacturers Data Acquisition Too many variables for consistent acquisition for non-experienced Basic Analysis Too qualitative and subjective Limited connection between raw data and annotations Quantity – Figuring out what in a particular data file and across data files High content Increasing parameters make it difficult to see trends across all dimensions and extract information Increasing use of flow with other technologies to extract information Number of files Increases in productivity with automated sample prep, cytometer setup, and walk-away operation Speed Software limiting step instead of computer or data generation

My Contribution: Data integrity and more reproducible basics As a cytometer manufacturer, we should be able to better characterize cytometers and improve day-to-day performance and reproducibility Work to transform basic setup and data acquisition paradigms to deliver “good” data Mimic more mainstream analytical systems “Cradle to grave” setup and operation (i.e. manufacturing to user) Use industry accepted performance characterizations as they are defined (e.g. Q and B) Decrease the subjectivity of preliminary / basic analysis by employing… More modern data representations More automated analysis based on annotations More controlled analysis with administrative controls

Barriers to success: Industry consensus and flow pride Consensus Everyone is a flow “expert” Market is unsure of information wanted or capable out of flow Manufacturers can’t organize / standardize until users do Flow Pride Market takes “pride” in high content and difficulty to standardize data Manufacturers are technology / instrument “HAPPY” Flow industry views themselves outside of analytical system due to their “better” technology