Art-historical Dataset with Structured Metadata and Iconographic Triplets from Wikidata
Bibliographic record
Abstract
This dataset contains metadata for 36,043 artworks extracted from Wikidata. Using the Wikidata SPARQL endpoint, we retrieved all objects that are instances of either “visual artwork” (Wikidata item Q4502142) or “artwork series” (Q15709879). Each item in the dataset meets two conditions: It includes at least one two-dimensional digital image (P18), and It includes at least one Iconclass notation (P1257). For every object, we collected the following Wikidata properties: image (P18) title (P1476) creator (P170) inception (P571) instance of (P31) width (P2049) height (P2048) made from material (P186) movement (P135) genre (P136) depicts (P180) main subject (P921) depicts Iconclass notation (P1257) collection (P195) location (P276) country (P17) country of origin (P495) In addition to the Wikidata metadata, the dataset provides automatically generated concepts, tuples, and triplets derived from the Iconclass notations. These were produced using the Qwen3 large language model with the prompting template provided in the file prompt.txt. File structure The images are stored in a ZIP file structured into directories named by the first two characters of each image's hash_id. Within these directories, subfolders named after the next two characters of the hash_id contain the image files, which are named using their full hash_id with a .jpg extension. The annotation data is provided in a JSONL file, where each line encodes metadata for a single image.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.033 |
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".