The project „Financial Data Infrastructure“ aims to solve two fundamental problems of empirical research in the financial sector. First, the lack of research-ready pan-European financial data sets implies that many financial researchers analyze US data sets instead. The findings of their studies are therefore not easily transferable to the European financial markets. Second, empirical financial researchers often use proprietary data sets, and their incentives to share such data sets with other researchers are low, which weakens empirical research in the financial sector. Therefore, our team builds the Financial Data Infrastructure. We generate new research-ready European financial data sets and enrich them to put them into the context of research. We focus on innovating on our platform to create incentives for our users to share research data or metadata to fulfill the FAIR principles (Findable, Accessible, Interoperable, Reusable) best.
- Data Production
- Data Enrichment
- Data Visualization
- FAIR Principles
Description of ETL Process
We implement Extract-Transform-Load (ETL) processes to automatically collect, process, publish and update research data. Our flexible ETL processes cope with unstable links and various data formats and standardize research data to provide the best access to our users.
Description of Data Visualization
Our users can put research data and articlesinto context using our network visualization, displaying how articles and data are connected. They can also use this visualization to search for research data and articles via adjusting the metadata used to build the network and the number of nodes (Co-occurrences). Finally, they can download all metadata of the research data and articles in the network to get a fast overview of the essential articles in a specific research area.
Description of Data Visualisation
Our users can put research data and papers into context using our network visualization, which displays how papers and data are connected. They can also use this visualization to search for research data and papers via adjusting the metadata, which are used to build the network, and the number of nodes (Co-occurences). Finally, they can download all metadata of the research data and papers in the network at once to get a fast overview of the most important papers in a specific research area.