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Magnetic Resonance Imaging (MRI) exams provide imaging data of high importance to various medical fields including that of neuro-oncology [1]. Beyond the technological aspect of this imaging technique, the assessment of this data is crucial and specialists are necessary. Recent trends aim at developing software systems for the automation of certain tasks [2]. The advances of artificial intelligence and in specific that of neural networks allowed for the automation of processes like that of segmentation based [3] on modern mathematical instruments, their software implementations and deployment in suitable hardware. Due to their complexity such systems are hard to be accessed by groups of specialists not belonging to the original research team and affiliated institutes. Even at premature stages such systems could provide useful insights to the broader medical community. This is especially the case when cross-facility experiments are necessary and novel approaches to research are implemented like virtual trials [ref]. This short manuscript communicates the design and development of a web-based system [Fig.1 and Fig.2] that allows for easier access to advanced methods of automated MRI assessment [4] while it allows for data management and feedback collection for further research. The system permits the inclusion of multiple imaging methods including systems that provide quantitative information [5] for the automatically segmented data. Last but not least, the outcome of this project suggests that such systems can be prototyped fast and render important radiomic advances [6] available to a larger audience. While funds allow it a publicly available beta is in www.rmn.ai

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.

The advancement of quantum technologies is expected to pave the way for what we refer to as practical Quantum Computing (pQC). pQC should provide solutions to certain problems, superior to those offered by the classical computational paradigm. The QC field is in development but has not taken its final form yet. In an attempt to define requirements for its practical use, this research reviews the current state of QC services as provided by IBM, Google, Microsoft , Rigetti and D-Wave, and proposes potential applications in the synchrotron radiation facility Elettra Sincrotrone Trieste. This research has concentrated its focus on potential applications that may benefit from using QC for Optimization problems even if it acknowledges other uses in fields like those of simulation modelling and information theory. In specific, this work starts by hinting on the optimisation needs for accelerator control till beamline end-station uses in scientific applications like X-ray Fluorescence data fitting and Ptychography reconstructions. The connection of QC to specific practical optimisation problems in a synchrotron and a Free Electron Laser may serve as a partial guideline for the future development of the field.

Code and data for a publication on a fast tomography alignment algorithm. Raw data have been kindly provided by Prof. Dr. Francesco Brun (University of Trieste, Elettra Sincrotrone Trieste)

Two dataset for XRF alignment experiments. Contents: dataset 1 (raw signals, manual alignment, automatic alignment) dataset 2 (raw signals , manual alignment, automatic alignment) &nbsp

The report summarises the views expressed by the Italian Computing and Data Initiative (ICDI) in response to the open consultation for the EOSC Strategic Research and Innovation Agenda (SRIA), closed on the 31st of August. It provides insightful input and suggestions about the current draft of the SRIA document shared with the wider EOSC community, with the aim of helping to shape the future vision of the European Open Science Cloud.

This report considers the potential for data management beyond the management of raw data to record, link, combine and publish information about other data, digital objects, actors and processes involved in the whole facilities science lifecycle – broadly covered by the term provenance of information. In particular, the report will consider: The data continuum involved in the lifecycle of facilities science, considering the stages undertaken in the lifecycle, the actors and computing systems typically involved at each stage, and the metadata required to capture the information at each step. Consider a specific but representative example of a scientific lifecycle within facilities science and discuss its consequences for practical data management including provenance in facilities Consider a number of other specific examples where parts of the scientific lifecycle can be given additional support to derive additional benefit for facilities infrastructure staff and facilities users., PaN-data Open Data Infrastructure: Deliverable D6.1