The database lists currently:
- Datasets - 20,
- Internal Datasets - 2,
- External Datasets - 18,
- Scientists - 4,
- Workpackages - 10,
- Exports - .
- Imports - 17.
This web platform supports different features for managing and organising data.
Please be aware that this framework is still under development. If you encounter problems or have
suggestions how to improve feel free to leave us a
message.
You can find a tutorial how to work with Mellite
here .
Displayed below is a graphical representation providing a concise overview of how data flow is modeled within the system.
The primary focus is on illustrating the journey of data from external sources into the project, where it undergoes processing to generate new data. This newly created data can then circulate back to the data source. External data sources, such as cohorts, play a vital role in this process.
The import process delineates how external data is acquired within the project. An illustrative example is obtaining biomaterial for omics from the 'imagen' cohort. The model encapsulates various data creation devices, including digital apps, enrichment software, and omics tools.
Within the project, the distinction between external and internal data is defined by the data creation process. For instance, biomaterial provided by 'imagen' is considered external data, while omics data generated using these samples within the project is classified as internal data.
The export function facilitates the transmission of internal results back to their sources. Taking the 'imagen' example, omics data created is exported to the source, where it is amalgamated with other 'imagen' data. This enriched 'imagen' data can then be re-imported into the project for further analysis.
The model aims for simplicity, having evolved from more complex versions based on usability analysis. We believe this streamlined model sufficiently addresses the project's needs. However, we remain open to suggestions for continuous improvement.