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Related Concept Videos

Mass Spectrometry: Molecular Fragmentation Overview01:20

Mass Spectrometry: Molecular Fragmentation Overview

The ionization of a molecule into a molecular ion inside the mass spectrometer causes instability in the molecule's structure due to the loss of an electron. This eventually leads to the fragmentation or breaking of some bonds in the molecule. The fragmentation occurs predominantly at specific bonds to yield relatively stable fragments.
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Updated: Jun 18, 2026

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
08:35

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Published on: May 29, 2021

Fragment-level FAIRness: annotating scientific data and its provenance using data fragment selectors.

Heinrich Lukas Weil1, Kevin Schneider1, Dominik Brilhaus2

  • 1RPTU Kaiserslautern-Landau, Faculty of Biology, Computational Systems Biology Department, D-67663 Kaiserslautern, Germany.

Journal of Integrative Bioinformatics
|June 17, 2026
PubMed
Summary
This summary is machine-generated.

We introduce Data Fragment Selectors (DFS) for fragment-level data annotation, enabling findable, accessible, interoperable, and reusable (FAIR) data. This format-agnostic method links data fragments to explicit semantics, improving data interpretation and automation.

Keywords:
ARCISARO-Cratedatamapfragment selectorsresearch data management

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Last Updated: Jun 18, 2026

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Area of Science:

  • Data Science
  • Bioinformatics
  • Scientific Data Management

Background:

  • Scientific communication relies on FAIR data, but current data sharing practices face challenges with cross-domain interpretation due to heterogeneous formats and implicit semantics.
  • Existing methods often tie annotations to specific file formats, hindering interoperability and reusability.

Purpose of the Study:

  • To present a format-agnostic method for fragment-level data annotation using Data Fragment Selectors (DFS).
  • To enable precise, machine-actionable links between metadata and specific data portions for enhanced FAIRness.
  • To integrate DFS with existing metadata models like ISA for robust provenance tracking.

Main Methods:

  • Developed Data Fragment Selectors (DFS), URI-addressable pointers to sub-file regions with explicit semantic definitions.
  • Created Datamaps to aggregate DFS, binding data fragments to ontology-backed semantics, including units and object types.
  • Integrated DFS with the Investigation-Study-Assay (ISA) model for sub-file granularity in provenance annotation.
  • Implemented the model in ISA-XLSX and RO-Crate serializations for spreadsheet and web-native data exchange.

Main Results:

  • Demonstrated fragment-level FAIRness through precise, machine-actionable links between metadata and data fragments.
  • Introduced a Spreadsheet Fragment Identification specification for annotating supplementary data in publications.
  • Showcased practical applicability via programmatic exploration of a proteomics dataset.

Conclusions:

  • The DFS method provides a format-agnostic approach to fragment-level annotation, enhancing data interpretability and automation.
  • This approach facilitates reusable schemas and robust data provenance, crucial for advancing scientific communication.
  • The developed specifications enable precise linking of metadata to data portions, improving overall FAIR data practices.