Marina Romanchikova (NPL): How good are our data? Measuring the data quality at National Physical Laboratory (School Seminar)

Event details

  • When: 12th March 2019 14:00 - 15:00
  • Where: Cole 1.33a
  • Series: School Seminar Series
  • Format: Seminar

Abstract:

From mapping the spread of disease to monitoring climate change, data holds the key to solving some of the world’s biggest challenges. Dependable decisions rely on understanding the provenance and reliability of data. Historically, only a small fraction of the generated data was shared and re-used, while the majority of data were used once and then erased or archived. At NPL Data Science we are defining best practice in measurement data reuse and traceability by developing metadata standards and data storage structures to locate and interpret datasets and make them available for sharing, publication and data mining.

The talk will shed light on the most burning issues in the scientific data management, and illustrate it with examples from industrial and academic practices. It will present several NPL Data Science projects that focus on delivering confidence in data obtained from life science imaging, medicine, geosciences and fundamental physics.

Speaker Bio:

Dr Marina Romanchikova joined the NPL Data Science team in 2017 to work on data quality and metadata standards. She obtained an MSc in Medical Informatics at University of Heidelberg, Germany, where she specialised in medical image processing and in management of hospital information systems. In 2010 she received a PhD on Monte Carlo dosimetry for targeted radionuclide therapy at the Institute of Cancer Research in Sutton, UK. Marina worked six years as a radiotherapy research physicist at Cambridge University Hospitals where she developed methods for curation and analysis of medical images.

Current interests

– Quantitative quality assessment of medical images and medical image segmentation
– Harmonisation of medical and healthcare data from heterogeneous sources
– Applications of machine learning in healthcare
– Automated data quality assurance