Professor Susanna-Assunta Sansone leads the Data Readiness Group, in this 1 minute video she describes her work.
Susanna has worked since 2001 in the areas of data interoperability and reproducibility, research integrity, and the evolution of scholarly publishing, and she collaborates with researchers, service providers, journal publishers, library science experts, funders and learned societies in academic, commercial and government settings alike.
With her team of data engineers (research software and knowledge engineers) she researches and develops new methods and tools to make digital research objects (including data, software, model and workflows) Findable, Accessible, Interoperable and Reusable, in one other word FAIR for humans as well as for machines. Her team also builds interoperability standards, and run informative, educational registries to enable data quality and readiness, essential in Data Science.
Thanks to the amount of data, which is increasingly available in the public domain, we start to see the rise of scientific discoveries that are made using other people’s data. However, the vast majority of data that is in the public domain is still not reusable, mainly because data is poorly described for third party use.
Governments, funders and publishers expect greater transparency and reuse of research data, as well as greater access to and preservation of the data that supports research findings. The 2019 UKRI Research and Innovation Infrastructure report on “Opportunity to grow our capability” places the implementation of the FAIR Principles as enabler in today’s data-driven era. It also highlights that more detailed assessment of the implementation requirements for FAIR data within each discipline is needed. The report also states that the conceptual design, R&D and prototyping to improve existing or create new data infrastructures are significant research activities in their own right; and to meet the ambition of data-intensive science, the education and career development of research software engineers and research data professionals is critical.
I strive to enact the technical, cultural and policy changes necessary to motivate and reward researchers for share richly described, high-quality data, to maximize the reuse; and ensure data quality for use by machines in all areas of data sciences, such as AI and machine learning, where decisions are make with minimal human intervention.
She completed a Diploma (1997) and PhD (2000) in Molecular Biology in the Faculty of Medicine, St Mary’s Hospital, of the Imperial College of Science, Technology and Medicine in London.
In 1999, she joined an Imperial spin off (Microscience Ltd. now Emergent BioSolutions, Inc.) to work as a Senior Scientist on the molecular characterization of a vaccine strain. In 2001, she moved to the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI, Cambridge) where she worked as a Project and Team Coordinator and Principal Investigator in research data management.
Susanna moved to Oxford in October 2010 as Principal Investigator at the Oxford e-Research Centre, and in 2013 she was appointed to her current position of Associate Director. Since 2012 she is also a consultant for Nature Research Group at Springer Nature, and the founding editor of its Scientific Data journal.
I am currently looking for motivated DPhil/PhD students to join my group. If you have an interest in my areas of activity, please get in touch with your CV and project proposal.
Below are some guidelines for you.
I am interested in research proposals in any disciplines and at the intersection of data and software engineering that fit under the Departmental Information Engineering theme.
The research proposals should respond to the needs for delivering step-changes in the ability of researchers to utilise existing large and complex data types, and offer the much-needed learning opportunities in research data readiness. The FAIR Principles provide a high-level guidance to improve data (re)use by machine, however, there is no elucidation on the technical, social and policy implications necessary to make data FAIR or FAIRer.
The research proposals should be designed to deliver novel conceptual and methodological contributions to advance the practices and the infrastructure for research data management necessary to use data at scale in a way that is not possible now. For example, the research proposals should define (and prototype) how to move from the current manually-focused, time-consuming and error-prone operations to a streamlined, unambiguous and AI-ready framework, using objective metrics to drive the advancements and demonstrate the project’s impact on the researchers.
Beyond science, the research proposals can also contribute of the nascent body of knowledge around ‘research on research’, opening up the whole way of thinking how we discover, access, reuse extant data or create, curate and share new scholarly knowledge; and how we enact the cultural changes that motivate, reward and credit researchers for disseminating high-quality, FAIR data.
Since the early 2000s, I have delivered over 200 plenary lectures and keynotes (many of which are available here), and members of my group also deliver educational seminars, training and teaching material, to events worldwide.