AnalysisGroupD

NOTES FOR GROUP D

Tim Snow Leading

Modelling 2D data

• Everyone agreed that modelling 2D data has the potential to deliver more information than analysing 1D data, however, routinely analysing 2D data is not currently done
• Fitting of 2D data requires careful thought if down sampling is required as features could be missed, conversely fitting a Pilatus 2M image will take a long time

Simulations and evolving algorithms

• Projects coupling simulation with data fitting exist, although are typically in the earlier stages of development (i.e. SASSIE)
• The next generation of algorithms are anticipated to revolve around either optimised simulation fitting or machine learning
• Both approaches require a computing resource beyond a single desktop/laptop
• Collaborations with institution HPC or national HPC are likely required
• Additionally, links to commercial HPC/machine learning could be used for data fitting

Automation

• Automation of data acquisition requires care as many variables change dynamically
  • Automatic alignment of some sample environments is challenging to human operators, let alone algorithms
  • Deducing when 'good' data has been obtained is tricky, especially if the sample is liable to beam damage
  • Determining an experimental endpoint could prove tricky too

• Automation of static scans is a good starting point
• Automatic data reduction should follow on from DAQ, however, fitting will likely require human intervention
• Automatic creation and archival of sample meta-data would prove highly useful for machine learning

Collaborative funding strategies

• Scientific funding is, as ever, extremely tight (however, also under increasingly close scrutiny as well...)
• Combining efforts and collaborating is likely to give governments / funding bodies good reason to fund projects as facilities / institutions can point towards shared (i.e. free(ish)) resources and get 'more bang for your buck'
• Finding common areas would be a positive step
  • Data analysis
  • Data fitting
  • Identification of data quality
  • Ways to automate different SAS measurements/techniques
  • Code development
  • Code maintenance/archiving
  • Experimental meta-data
  • Computing resource sharing (i.e. HPC)

• Events, such as canSAS, a good way to start such links
• Attending conferences either nationally or internationally a good way to form links with institutions, companies or funding bodies as well as other researchers