METHODS’ central goal is to make research data FAIR-compliant throughout the research cycle, not just at the final stage of data publication. This approach starts with data-generating places and their existing practices and solutions, including all areas and steps where primary research data are collected and processed. Data-generating places and laboratories are at the center of canonical workflows and play an impactful role in establishing a culture of data sharing as a bottom-up solution along with reducing the research data management workload of individual researchers.
The main focus of the Interest Group Data Generating Places lies in
- (Semi-)Automatic data documentation
- Making raw data FAIR-compliant at birth.
FAIR data principles always relate to a specific purpose. Data-generating places and laboratories strive to develop solutions to make raw data FAIR-compliant as soon as possible after collection. Doing so will prevent fraud, promote replicability, and open up new ways to reuse raw data with much richer contextual information than data collections published at the end of the research cycle. Furthermore, automatically documenting data at each workflow step reduces unnecessary data wrangling for researchers. Doing so, data providers can readily publish the already documented data with little effort. Actually, the IG consists of lab managers of laboratories of experimental economics, experimental social sciences, and laboratories in sport sciences.