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SUMMARY:One format to rule them all? How to generate high quality data for
  research and industry: notes from the National Physical Laboratory. - Mar
 ina Romancikova - NPL
DTSTART:20180308T130000Z
DTEND:20180308T143000Z
UID:TALK101020@talks.cam.ac.uk
CONTACT:James Fergusson
DESCRIPTION:Have you ever found your own project file and wondered what th
 ese data are and how you generated them? You are not alone. Scientists sto
 re the research results in a multitude of formats and locations. The termi
 nologies that describe the data are poorly defined and may vary even withi
 n a single phrase\; more variations are found across individuals\, teams\,
  departments and organisations. The data documentation typically requires 
 a considerable amount of human input and is performed on an ad hoc basis. 
 None of it is made easier by the plethora of ever-changing proprietary for
 mats used by scientific equipment vendors. The result are countless workin
 g hours spent on “data archaeology” and generation of “data cemeteri
 es” rather than “data lakes”.\n \nIn the world of medical imaging\, 
 these issues have been alleviated by the use of a single data exchange sta
 ndard between different devices. In other research domains such remedy is 
 still to be found. National Physical Laboratory (UK)\, National Institute 
 of Standards and Technology (USA)\, AstraZeneca and GlaxoSmithKline have j
 oined the efforts to make scientific data available\, discoverable and und
 erstandable.\nThe Cancer Research UK Grand Challenge initiative “Google 
 Earth of Cancer” provides an ideal platform for this undertaking. The in
 itiative encompasses all existing instruments for a novel cancer imaging t
 echnology called Mass Spectrometry Imaging (MSI). Researchers from the par
 tner organisations will define a “minimum metadata standard” for MSI\,
  and the equipment vendors will be actively encouraged to implement it\, l
 eaving scientists to do the science.\nMSI data will be stored in Object St
 ores as self-describing data objects that can be exchanged between organis
 ations and tagged with features of interest using machine and deep learnin
 g. We will extend the approach to include other scientific data types as t
 he project progresses.
LOCATION:Kavli Large Meeting Room\, Kavli Building
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