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Published on: April 13, 2016
Topic classification of synchrotron experiment proposals using the OpenAlex model: enhancing metadata granularity and
Terence Tan1,2, Oliver J Clark2, Matthew J Derry3
1Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford OX1 3QG, United Kingdom.
Journal of Synchrotron Radiation
|August 3, 2026
Summary
This study introduces a real-time topic classification service for synchrotron experiment proposals using machine learning. The model shows promise for live verification, improving data discoverability and reuse.
Area of Science:
- Scientific data management
- Machine learning applications
- Synchrotron science
Background:
- Experiment proposals lack standardized metadata for classification and reuse.
- Current metadata hinders efficient review and data discovery at synchrotron facilities.
Purpose of the Study:
- To define and test a real-time topic classification service for experiment proposals.
- To evaluate the feasibility of using an open-source machine learning model for this task.
- To improve data infrastructure alignment with the OpenAlex ecosystem.
Main Methods:
- Applied the OpenAlex topic classification model to 5384 experiment proposals.
- Selected 209 proposals for independent evaluation by three domain experts.
- Assessed model performance using Krippendorff's alpha and proposal-based precision.
Main Results:
- Achieved a Krippendorff's alpha of 0.572, indicating reviewer consensus.
- 74.2% of proposals had at least one unanimously relevant topic identified by the model.
- Proposal-based precision score of 56.0% suggests need for human oversight in automated systems.
Conclusions:
- The developed topic classification service demonstrates sufficient performance for live verification with human oversight.
- Aligning facility data infrastructure with OpenAlex can enable comprehensive research activity records.
- Further integration is necessary for a complete record of research activities within OpenAlex.
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