Experimental design choices influence both what a scientist can discover, as well the confidence that can be placed in the final outcome. Poor experimental design decisions can also lead to wasted time and resources, as well as non-reproducible research.
The emerging area of data science for experimental design aims to develop computational strategies to design experiments in a principled fashion, exploiting data science to produce more efficient, more accurate, and more reproducible research.
In this workshop, we will explore how data science can help us design, perform, and analyse scientific experiments. The workshop will cover aspects of experimental parameter optimisation, laboratory automation, and issues around reproducibility of data analysis. It will include a discussion session about the barriers to incorporating data science for experimental design in real laboratories, led by social scientists who study the behaviour of experimental scientists.
The aim will be to bring together data scientists who work on data science for experimental design, experimental scientists/practitioners, and social scientists to examine the latest research in the field, as well as to discuss practical steps towards integrating these tools in the laboratory.