In many research projects, time for innovation is short: Only after months of data discovery, data collection, integration and quality assurance, novel research can happen. Especially in data-intensive research, scientists spend between 50 and 80% of their time and effort in tasks related to data management.
Acquire data from project partners and make it accessible to all researchers
All project partners use the dataset manager to upload data from day one. Research quickly find relevant data. We support versioning and branching, and make it easy to keep up to date.
Design a shared, conceptual model
Use the explorative modelling tool to quickly reach an understanding of existing standards. Collaborate effectively to build your own conceptual models and use them directly to integrate data.
Deal with many different formats and data models
Handle more than 20 common file formats ranging from XML to JSON to Shapefiles, XLS and SQLite, plus databases and service standards.
Integrate and harmonise multiple data sets into one
Ensure you get reliable research results by harmonising multiple data sets into one consistent data set.
Understand data quality and coverage
Find out which parts of your shared models are covered by which data. Find gaps, inconsistencies and excess, and validate data against defined schemas and rules.
Provide data to new applications and existing tools
Create a transformation project to restructure and convert data as required by your existing or new research tools. Use our fast cloud transformation engine to deal with large or real-time data sets.
We typically get involved during the proposal phase of a research project. We contribute…:
During the project, we work with you to continually improve data management in a similar way a Scrum Master helps a software development team resolve roadblocks and to continually get more efficient.
We can help you to complete your research successfully through our expertise in more than 25 different projects: