Data From “A Biocodicological Analysis of the Medieval Library and Archive From Orval Abbey, Belgium”
Bibliographic record
Abstract
The dataset contains the first-ever comprehensive biocodicological analysis of medieval library books and charters using Zooarchaeological Mass Spectrometry (ZooMS). Here, we analyze 68 codices and 59 charters (1490+59 samples in total) from one single monastic institution, namely the Cistercian abbey of Orval in present-day Belgium. The data entails both peptide mass fingerprinting (using MALDI-ToF) and peptide sequencing (using LC-MS/MS) analysis of almost the entire library and all the preserved single leaf charters from the monastery. MALDI-ToF data is stored in Zenodo – a multidisciplinary open access repository while the LC-MS/MS data is deposited in ProteomeXchange Consortium via PRIDE – a publicly available repository for MS-based proteomics data. Mass spectrometric data generated from an entire monastic library and archive is of immense value to integrate with multiple case studies aiming at understanding parchment production and use in medieval Europe. Paper linked with data: Ruffini-Ronzani, N., Nieus, J.F., Soncin, S., Hickinbotham, S., Dieu, M., Bouhy, J., Charles, C., Ruzzier, C., Falmagne, T., Hermand, X., Collins, M.J. and Deparis, O 2021. A biocodicological analysis of the medieval library and archive from Orval Abbey, Belgium. Royal Society Open Science, 8(6), p.210210.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.022 |
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".