Findable Big Data from Various Material Characterisation Techniques - project image

Science clusters

PANOSC - Photon and Neutron Science
Other
Annotation of data & type of data

Summary

Unlocking the potential of experimental data, the project Findable Big Data from Various Material Characterisation Techniques enhances the findability of experimental datasets across photon and neutron (PaN) facilities and materials science laboratories through a foundational classification of experimental techniques. The integration of the PaNET ontology with the NeXus Ontology enables the establishment of a foundational classification of PaN experiment techniques, fostering semantic coherence across data catalogues and records within major European facilities and world-wide. 

By bridging semantic gaps and establishing coherent classification systems towards ontology harmonisation, the project paves the way for seamless data discovery and analysis, empowering researchers to advance their Open Science initiatives across diverse materials characterization techniques.

Research domains:
Photon/neutron sources-based experimental research, Materials Science, X-ray Spectroscopy, Atom Probe Microscopy, Electron Microscopy, Scanning Probe Microscopy
Partner(s):
FAIR Data Infrastructure for Physics, Chemistry, Materials Science, and Astronomy e.V. (FAIR-DI), Helmholtz-Zentrum Berlin für Materialien und Energie (HZB), European Synchrotron Radiation Facility (ESRF)
Project team member(s):
Claudia Draxl, Sandor Brockhauser (Humboldt-Universität zu Berlin), Heike Görzig (Helmholtz-Zentrum Berlin), Renaud Duyme (European Synchrotron Radiation Facility)

Challenge

Open Science project, Open Science Service, Industry cooperation, Cross-domain/Cross-RI

In the realm of Open Science, the findability of experimental datasets presents significant challenges, particularly at PaN facilities, where data is abundant yet often dispersed across diverse platforms and repositories. The complexity of finding relevant data highlights the need for improved ontology harmonisation to optimise the data discovery process.

Solution

To improve the semantic clarity of dataset discovery, the project aims to extend and integrate the PaNET ontology into the research data management platform NOMAD, and to the metadata ecosystem of institutions, such as ESRF, or HZB. This will enhance FAIRification, by ensuring comprehensive descriptions of experimental endeavours and facilitating precise identification of the techniques utilised in each instance. This integration will improve the discoverability of datasets by providing comprehensive descriptions of experimental techniques, facilitating semantic interoperability, and establishing best practices for data management. 

Our goals will be achieved by:

  • Refining and enriching the PaNET ontology
  • Connecting the NeXus-ET ontology and NeXus Application Classes to PaNET
  • Extending the ESRF-ET ontology and connecting it to PaNET
  • Integration of all this into NOMAD

Scientific Impact

The integration of the enhanced PaNET ontology into EOSC services, such as the PaN data portals and the NOMAD service developed by FAIRmat, represents a significant and exemplary milestone in advancing the accessibility and discoverability of experimental data. It enables researchers to easily find relevant experiments across different platforms and facilities.

The institutions involved in the project, from the German NFDI projects FAIRmat and DAPHNE4NFDI to  the EFRI landmark ESRF, will contribute to the integration of PaNET into their respective search platforms, thus augmenting search capabilities and facilitating streamlined data discovery processes.

 

This enhanced data findability lays the groundwork for comprehensive analysis of experimental big data, also leveraging tools, such as the NOMAD's Artificial Intelligence Toolkit

Results

Ontology service in NOMAD: The demonstration of the power of the integrated ontology ESRFET-NeXus-PaNET is made available via the research data management platform NOMAD. Its pynxtools plugin being responsible for parsing and normalising NeXus data has been extended by an ontology service which allows the injection of all relevant terms of experiment techniques to the metadata of uploaded datasets. The solution allows an efficient search for any datasets belonging to specific ontology terms - https://github.com/FAIRmat-NFDI/pynxtools 

Events

  • 28 November 2024 | Bonn, Germany - PUNCH Annual Meeting
  • 5 February 2025 | online - Infineon Winter School - Talk on "Open and FAIR Experimental Data Management"
  • 2 April 2025 | Berlin, Germany - NeXus workshop on the NeXus data format and its use at HZB
  • 23 May 2025 | Online - PaNOSC-OSCARS projects mentorship meeting
  • 27 May 2025 | Online - DAPHNE Seminar - Talk on ‘NeXus Ontology and its Use for FAIR Experimental Data’
  • 20-26 September 2025 | Chicago, USA - ICALEPCS’25 - Talk on ‘Converting experiment data to NeXus application definitions at BESSY II’
  • 25 February 2026 | Grenoble, France - Ontology Workshop with review of XAS data at ESRF 
  • Weekly meetings: Weekly coordination meetings and workshops with occasional presentations from external experts and collaborators 

Publications


Keywords
big data, material characterisation, spectroscopy, PaNET ontology, data discovery, data findability, data analysis
Project start date:
Project duration:
24 months

Principal investigator

Claudia Draxl - PI Findable Big Data from Various Material Characterisation Techniques project
Claudia Draxl
Institut für Physik - Humboldt-Universität zu Berlin
BIO

Claudia Draxl is Einstein professor at the Humboldt-Universität zu Berlin. Her research interests cover theoretical concepts and methodology to gain insight into a variety of materials and their properties, with a focus on theoretical spectroscopy. In her group, the all-electron full-potential package exciting and the cluster-expansion package CELL are developed. Claudia Draxl is spokesperson of the NFDI consortium FAIRmat, which is developing NOMAD, a research data management service for collecting, organising, sharing, analysing, and publishing FAIR materials-science data.

QUOTE
"Bringing together diverse partners from across the European research landscape, this project will foster a collaborative effort towards more FAIRness."