Before: Files and analysis are on local PCs, data entry and file moving is manual
Instruments dump files onto local PCs, and scientists run analysis on the data locally too. Results are then data-entered into ELNs or Excel manually. What problems does this cause?
Metadata is lost
- Metadata such as instrument settings, last date of service, which user performed the analysis, and experiment time is not tracked
Raw data is lost
- Raw data is lost; analyses cannot be modified or re-run without substantial effort. Lack of traceability makes meta-analyses difficult.
Data entry is time-consuming
- Even just entering the primary analysis results becomes a drag on scientists' productivity.
After: No human required. Ganymede saves your data, analyzes it, and pushes it to apps
Raw data is saved in the cloud and analysis is automated, with traceability in between. Data can then go into ELNs/LIMS, Excel, analysis apps, pipelines - anything. We also build a data lake of this as we go.
Sync everything into the cloud automatically
- All your raw data, analyzed data, metadata, and even the internal data from your intergrated apps is saved forever in a single cloud data lake.
Do any analysis, add any metadata, push results anywhere
- Run analyses automatically and add metadata automatically. Push results into any app or pipeline, or even back to instruments for control.
Compliance is a snap with universal versioning
- Go beyond audit logs. Ganymede saves all data and code it ever sees, making GxP easy: rewind time to any point and see who changed things.
