Data From “A Biocodicological Analysis of the Medieval Library and Archive From Orval Abbey, Belgium”
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. <em>Royal Society Open Science, 8</em>(6), p.210210.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.008 | 0.033 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.032 | 0.000 |
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 it