Scientific Collaboration
Scientific collaboration is crucial for advancing research, particularly in complex fields like artificial intelligence and healthcare. Current research focuses on optimizing collaboration through data sharing frameworks (e.g., federated learning), mitigating biases like the misinformation effect from flawed AI explanations, and leveraging machine learning to predict successful collaborations and identify influential researchers. These efforts aim to improve research efficiency, enhance the quality and impact of scientific outputs, and address ethical concerns surrounding data privacy and equitable participation.
Papers
Process Mining for Unstructured Data: Challenges and Research Directions
Agnes Koschmider, Milda Aleknonytė-Resch, Frederik Fonger, Christian Imenkamp, Arvid Lepsien, Kaan Apaydin, Maximilian Harms, Dominik Janssen, Dominic Langhammer, Tobias Ziolkowski, Yorck Zisgen
Investigating Collaborative Data Practices: a Case Study on Artificial Intelligence for Healthcare Research
Rafael Henkin, Elizabeth Remfry, Duncan J. Reynolds, Megan Clinch, Michael R. Barnes