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SUMMARY:Machine-Actionable Data Interoperability for Chemical Sciences (MA
 DICES): Bridging experiments\, simulations\, and machine learning for spec
 tral data
DTSTART;VALUE=DATE:20220207
DTSTAMP:20260407T064155Z
UID:7ddbc38570ad3154dba6a0f666b893d5a56566380080ef91ff730aeb
CATEGORIES:Conferences - Seminars
DESCRIPTION:Recent advances in the computational sciences allow us to simu
 late many spectra (e.g.\, X-ray absorption\, infrared/Raman\, NMR) in sil
 ico. In principle\, this could open up unprecedented possibilities for the
  interpretation of experimental data. \nExperimental data\, however\, com
 es in various\, often undocumented or proprietary formats. In recent effor
 ts\, this experimental data is being recorded in electronic lab notebooks 
 and archived with open data formats\, aiding and automating crucial metada
 ta capture. However\, most of these lab notebooks have no mechanisms to ex
 change data between each other and even less so with our simulation tools\
 , and typically\, exporting data from such notebooks again requires lossy 
 conversion to a chosen file format.\nStandardization is an arduous process
 \, and for a wide enough domain\, it is infeasible. Nevertheless\, without
  significant effort\, there is a danger that we will not escape the local 
 minima of “★★★/★★★★★” linked open data (as defined b
 y Tim Berners-Lee).\nIn the case of the interoperability between experimen
 tal and computational data\, there is the additional difficulty that compu
 tational systems are completely described\, idealized systems with implici
 t assumptions\, whereas for experimental systems parameters are ill-define
 d\, unknown\, or uncertain.  Moreover\, we also often miss a link between
  spectra data and the (meta) data contextualising the sample and its histo
 ry.\nHow and where can we be interoperable in this setting? How can we mak
 e sure that experimental data can readily be consumed by computational too
 ls\,  and vice versa\, from the bottom-up? How can we share\, contextual
 ise and disseminate analysis (e.g.\, post-processing\, peak assignment) in
  a reproducible way (on platforms such as MaterialsCloud or the Chemoti
 on repository)? What new paradigms could such interoperability enable?\nAt
  the CECAM MADICES workshop\, we will bring together developers\, scientis
 ts\, and data specialists to discuss the hurdles and opportunities of data
  interoperability in the context of the chemical and materials sciences. 
  We will strive for general technical recommendations\,  with  X-ray abs
 orption spectroscopy as the first prototype use case.
LOCATION:
STATUS:CONFIRMED
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