Register training material Upload training material to Zenodo
2 materials found

Author: Darren Spruce  or Audun Skau Hansen 


Self-evaluation Photon and Neutron RIs for FAIR data certification

This ExPaNDS project deliverable describes a FAIR self-assessment undertaken by the ten ExPaNDS partner Photon and Neutron Research Infrastructures (PaN RIs) over the three-month period July – September 2022. After reviewing selected examples of existing FAIR evaluation frameworks designed to...

Keywords: FAIR, metadata, expands, European Photon and Neutron facilities , wp2-ExPaNDS

Resource type: Document

Self-evaluation Photon and Neutron RIs for FAIR data certification https://pan-training.eu/materials/self-evaluation-photon-and-neutron-ris-for-fair-data-certification This ExPaNDS project deliverable describes a FAIR self-assessment undertaken by the ten ExPaNDS partner Photon and Neutron Research Infrastructures (PaN RIs) over the three-month period July – September 2022. After reviewing selected examples of existing FAIR evaluation frameworks designed to enable assessment at different levels (dataset, repository, and organisation), the report describes the evaluation approach adopted for the ExPaNDS FAIR self-assessment. As no existing framework met our specific need to focus on FAIR workflows and processes in PaN RIs, it was necessary to select, combine, and adapt existing frameworks. Supported by four underlying guiding principles, our approach drew heavily on the FAIR Principles, the RDA FAIR Data Maturity Model, and FAIRsFAIR’s CoreTrustSeal+FAIRenabling framework. Post-evaluation feedback from ExPaNDS partners indicated that they found the FAIR self-assessment a useful and valuable exercise for understanding current levels of FAIRness at their facilities and for articulating what implementations they have in progress or planned to support FAIR in future. A key output of the ExPaNDS FAIR evaluation is the collected self-assessment reports from the ten partner facilities. These reports are published openly and in full as part of the deliverable. In addition, the self-assessments are supplemented with some high-level observations on the state of the FAIR journey across the ExPaNDS facilities. FAIR, metadata, expands, European Photon and Neutron facilities , wp2-ExPaNDS PaN Community
Machine learning in electronic-structure theory

Machine Learning approaches are increasingly being used across many fields of science, with electronic structure theory being no exception. They offer novel approaches to age-old problems. In recent years they have been used in construction of trial wavefunctions, in computing Hamiltonians, and...

Keywords: machine learning, electronic-structure theory

Resource type: video

Machine learning in electronic-structure theory https://pan-training.eu/materials/machine-learning-in-electronic-structure-theory Machine Learning approaches are increasingly being used across many fields of science, with electronic structure theory being no exception. They offer novel approaches to age-old problems. In recent years they have been used in construction of trial wavefunctions, in computing Hamiltonians, and in direct calculation of properties and forces. These methods are highly versatile and computationally efficient, yet many questions regarding their interpretability and ability to extrapolate information remains unanswered. How are they being used in electronic structure theory today, and how do they fit into the bigger picture? Would electronic structure theory have looked anything different if it was conceived in the age of machine learning? This seminar seeks to answer these kinds of questions, and was originally given as a trial lecture at the Hylleraas Centre of Quantum Molecular Sciences at the University of Oslo. machine learning, electronic-structure theory