Data Science for Experimental Design

We are the UK’s national institute for data science and artificial intelligence.The Institute is named in honour of Alan Turing, whose pioneering work in theoretical and applied mathematics, engineering and computing is considered to have laid the foundations for modern-day data science and artificial intelligence. The Institute’s goals are to undertake world-class research in data science and artificial intelligence, apply its research to real-world problems, driving economic impact and societal good, lead the training of a new generation of scientists, and shape the public conversation around data.Click here to join our newsletter

The Alan Turing Institute

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-unreproducible 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.
Schedule Summary:
Morning session 1: Planning experiments: How can we use data science to design experiments to be as informative as possible?
Morning session 2: Conducting experiments: How can we use laboratory automation and robotics to reduce research biases?
Afternoon session 1: Analysing experiments:  How can we make research more reproducible?
Afternoon session 2: From theory to practice: What are the obstacles to integrating data-driven experimental design in real laboratories, from an ethnographic and sociological perspective?
<A HREF="https://datascienceforexperimentaldesign.github.io/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Speakers, agenda and further details are available here</A>

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Data Science for Experimental Design

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