In most research projects time for innovation is short.
Novel research can only happen after months of data discovery, data collection, integration, and quality assurance. In data-intensive research, scientists can spend up to 80% of their time on data management tasks alone. We can change that.
wetransform typically gets involved during the proposal phase of a research project in one of two ways:
During the project, we continually improve data management. Functioning similarly to a Scrum Master in software development, we resolve roadblocks and improve efficiency wherever possible.
From day one, all project partners use the data set manager to upload data. This enables the researchers to find relevant data swiftly. We support versioning and branching, which makes it easy to stay up-to-date.
The explorative modelling tool is used to reach an understanding of existing standards quickly. Custom conceptual models are built through collaboration and used to integrate data.
We can handle more than 20 common file formats, ranging from XML to JSON to Shapefiles, XLS and SQLite, plus databases and service standards.
Reliable research results are ensured by harmonising multiple data sets into one consistent data set.
We find out which parts of shared models are covered by which data. This also enables us to find gaps, inconsistencies and excess, and validate data against defined schemas and rules.
We create a transformation project to restructure and convert data as required by existing or new research tools. Our fast cloud transformation engine deals with large or real-time data sets.