Data sharing for integrated computational methods


Research and development are facilitated by sharing knowledge bases and collaborative efforts that involve the collective utilization of data. We are in a transition phase of breaking data silos and creating collaborative data sharing between data producers and users. Improving our understanding of complex phenomena requires integrating diverse data sets and algorithms. Computational approaches based on the use of information from past experiments can be, example given, vital to understanding fundamental processes in disease. Thereby, use cases are presented based on publicly available resources in cancer research and artificial intelligence methods to find answers to detailed questions. Several tools and programming approaches can be employed for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We support and promote the idea of data sharing to be paramount for making progress in research. 

About Claire Jean-Quartier

Claire Jean-Quartier is one of the data stewards at TU Graz. Focusing data science she has been engaged in interdisciplinary topics related to wet and dry biomedical research next to the aspect of sustainability. Her fascination for natural and technical sciences lead to basic research involving all data steps from data generation, mining, processing to sharing and quality assurance. These experiences demonstrated the challenge for open science and the necessity of RDM.”