Translating and Formalizing the MIRAGE Guidelines to a Prototype MIRAGE Ontology and DCAT3 Extension Vocabulary for Glycomics Data Management
Bibliographic record
Abstract
The Minimum Information Required for A Glycomics Experiment (MIRAGE) guidelines haveestablished comprehensive reporting standards for glycomics research, yet their implementationin semantic web technologies remains limited. We present the first comprehensive semanticformalization of MIRAGE guidelines through an integrated RDF ontology framework comprising the MIRAGE Ontology and MIRAGE-DCAT3 vocabulary. The MIRAGE Ontologymodels glycan structures, biological specimens, analytical instruments, and experimental processes with formal OWL semantics and SHACL validation constraints. The complementaryMIRAGE-DCAT3 vocabulary extends W3C DCAT3 with glycomics-specific metadata propertiesfor dataset cataloging and discovery. Our implementation addresses critical challenges inglycomics data interoperability through comprehensive mappings to established ontologiesincluding GlycoRDF, PSI-MS, and DCTERMS. This semantic framework enables automatedquality assessment, federated data querying, and enhanced reproducibility in glycomics research, supporting broader adoption of FAIR principles in the glycobiology community. Theframework demonstrates comprehensive coverage of MIRAGE reporting requirements acrossmultiple analytical platforms including mass spectrometry, liquid chromatography, capillaryelectrophoresis, NMR spectroscopy, and lectin microarray analysis
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".