Harnessing the power of data science to support experimental research
We are delighted to bring together laboratory scientists, data scientists, statisticians, and social scientists who study the behaviour of experimental scientists at the Alan Turing Institute.
We will discuss how new technologies can make experimental science more informative, more cost-effective, and more reproducible.
How can we use data science to design informative laboratory experiments?
How can we optimise experiments to save time and money?
How will robotic laboratories will shape the future of experimental research?
You can send a message to the organisers using the form below. Thank you for getting in touch!
Dr Stefanie Biedermann
Optimal designs for experiments – why, how, and where are the bottlenecks?
Dr Ozgur Akman
Reduced models of circadian systems
Prof Ross D King
Reduced models of circadian systems
A Robot Scientist is a physically implemented robotic system that applies techniques from artificial intelligence to execute cycles of automated scientific experimentation. A Robot Scientist can automatically execute cycles of hypothesis formation, selection of efficient experiments to discriminate between hypotheses, execution of experiments using laboratory automation equipment, and analysis of results. The motivation for developing Robot Scientists is to better understand science, and to make scientific research more efficient. The Robot Scientist ‘Adam’ was the first machine to autonomously discover scientific knowledge: both form and experimentally confirm novel hypotheses. Adam worked in the domain of yeast functional genomics. The Robot Scientist ‘Eve’ was originally developed to automate early-stage drug development, with specific application to neglected tropical disease such as malaria, African sleeping sickness, etc. We are now adapting Eve to work with on cancer. We are also teaching Eve to autonomously extract information from the scientific literature.
Ross D. King is Professor of Machine Intelligence at the University of Manchester, UK. His main research interests are in the interface between computer science and biology/chemistry. He has published 140 peer-reviewed publications and has an H index of 55 (Google Scholar). He is the only computer scientist with first author papers in both Nature and Science, and one of few PIs in a Computer Science Department anywhere in the world with a ‘wet lab’. His research won the British Computer Society, Machine Intelligence Prize, 2007; He was nominated for the World Technology Award (software) in 2004 and 2006; and his research was named the 4th most significant scientific advance of 2009 by Time magazine. He is also very interested in DNA computing, NP problems, computational economics, and computational aesthetics.
Dr Vishal Sanchania
Antha, enabling The complex biological design, build and test cycle using computer aided biology
Dr Sebastien Besson
The Image Data Resource: a platform for publishing, integrating and mining biological imaging data at scale
Much of the published research in the life sciences is based on image datasets that sample 3D space, time, and the spectral characteristics of detected signal to provide quantitative measures of cell, tissue and organismal processes and structures.
To address this challenge, we have built a next-generation imaging resource, the Image Data Resource (IDR; https://idr.openmicroscopy.org) - an added value resource that combines data from multiple independent imaging experiments and from many different imaging modalities, integrates them into a single resource, and makes the data available for re-analysis in a convenient, scalable form. IDR provides, for the first time, a prototyped resource that supports browsing, search, visualisation and computational processing within and across datasets acquired from a wide variety of imaging domains. IDR stores, publishes and integrates >50 TB of super-resolution, high content screening, timelapse and histological whole slide imaging data with metadata related to experimental design, image acquisition, downstream analysis and interpretation. Data from >40 studies are available for search and query through a user-friendly web interface. Wherever possible, we have mapped the reagents, methods and phenotypes to published common ontologies. IDR is available at https://idr.openmicroscopy.org.
By mining IDR data, we have demonstrated that control of cell elongation involves the Set1B histone methyltransferase in fission yeast and human cells, suggesting a conserved mechanism controlling cell shape based individual cells. To promote re-use of IDR data, we have built a virtual analysis environment using JupyterHub and Docker, so researchers can analyse and mine IDR data and metadata using cloud-based computational resources. Notebooks for mining IDR metadata and image features are available at https://github.com/IDR/idr-notebooks. Users can deploy their own IDR using Ansible-based tools available at https://github.com/IDR/deployment.
The IDR project was funded by the BBSRC (BB/M018423/1 to J.R.S., A.B. and R.E.C.S.) and Horizon 2020 Framework Programme of the European Union under grant agreement 688945 (Euro-BioImaging Prep Phase II to J.R.S. and A.B.).
Sebastien Besson joined the The Open Microscopy Environment (OME) team as a developer in March 2012. Originally trained as a physicist, he received his PhD from the Unversite Pierre at Marie Curie in Paris. He then carried out postdoctoral research at the interface between physics and biology at Harvard University. In 2011, Sebastien joined the Danuser lab to convert in-house image analysis into turn-key software packages for the cell biology community. He became part of the main Dundee OME team in 2015.
Dr Rachael Ainsworth
Reproducibility and Open Science
Making research results more accessible and reproducible can contribute to better and more efficient science, however widespread adoption of open research practices has not yet been achieved. Funding agencies (such as the European Commission Horizon 2020) are increasingly requiring research products (such as data and publications) to be made openly available in order to make results more accessible, transparent and reproducible. Recent studies have also shown that open research practices are associated with benefits to the researcher such as increases in citations, media attention, potential collaborators, job and funding opportunities. In this talk I will discuss the different aspects of Open Science, the barriers we face to practicing openly, how to 'open' up your research workflow using open and transparent data and software services in order to reap the benefits associated with open research practices, and highlight current open projects in STEM.
Dr. Rachael Ainsworth is a Research Associate and Open Science Champion at the University of Manchester. She observes jets from young stars with next-generation radio telescopes to investigate the physical processes that assemble stars like our Sun. She is a Mozilla Open Leader, fuelling the Internet health movement through training and mentorship of working open best practices. She organises HER+Data MCR, a meetup group that brings together women who work with and love data - to support one another, inspire each other, share experiences and talk data. She is interested in promoting openness, inclusivity and well-being in science and technology.
Dr Sarah Abel
Expert discussant for Theory into Practice session
Sarah Abel is a fellow of the CitiGen project, funded by HERA, and a post-doc in anthropology at the University of Iceland, where she is researching social uses of DNA in the context of personal and population histories. Her PhD thesis, defended in 2016 at the EHESS in Paris, looked at the scientific construction and political uses of genetic ancestry data in the USA and Brazil. Her dissertation was developed through the EUROTAST network, a Marie Skłodowska-Curie Actions project that focused on the history and contemporary legacies of the transatlantic slave trade. Prior to this, Sarah completed an MPhil in Latin American Studies and a BA in Modern Languages at the University of Cambridge.
Dr Louise Bezuidenhout
Expert discussant for Theory into Practice session
Louise Bezuidenhout is a research fellow of the Institute for Science, Innovation and Society at the University of Oxford. She currently works on the Changing Ecologies of Knowledge and Action (CEKA) project. Louise’s research interests are broadly centred on data sharing issues within the life sciences. Her work involves a strong empirical component, including a number of ethnographic studies in laboratories in the UK, USA, Kenya, Uganda and South Africa. Trained in both the life and social sciences, Louise holds a PhD in Cardiothoracic Surgery from the University of Cape Town (South Africa, 2007) and a PhD in Sociology from the University of Exeter (UK, 2014). She also holds an MA in bioethics from KU Leuven (Belgium, 2008). She has previously worked as a research fellow at the University of Exeter and the University of Notre Dame (USA). In addition to her work at InSIS, Louise remains an honorary lecturer at the Steve Biko Centre for Bioethics at the University of the Witwatersrand (South Africa) and is active in various initiatives advocating for better education on Open Data for scientists.
