README.md

metadata

annotations_creators:
  - crowd-generated
language:
  - de
  - en
  - fr
license: cc-by-sa-4.0
task_categories:
  - object-detection
  - image-classification
pretty_name: 'ARTigo: Social Image Tagging'
tags:
  - lam
dataset_info:
  features:
    - name: id
      dtype: int64
    - name: hash_id
      dtype: string
    - name: titles
      struct:
        - name: id
          list: int64
        - name: name
          list: string
    - name: creators
      struct:
        - name: id
          list: int64
        - name: name
          list: string
    - name: location
      dtype: string
    - name: institution
      dtype: string
    - name: source
      struct:
        - name: id
          dtype: int64
        - name: name
          dtype: string
        - name: url
          dtype: string
    - name: path
      dtype: string
    - name: tags
      struct:
        - name: id
          list: int64
        - name: name
          list: string
        - name: language
          list: string
        - name: count
          list: int64
        - name: regions
          list:
            - name: x
              list: float64
            - name: 'y'
              list: float64
            - name: width
              list: float64
            - name: height
              list: float64
    - name: image
      dtype: image
  splits:
    - name: train
      num_bytes: 5905082165
      num_examples: 60633
  download_size: 11451421751
  dataset_size: 5905082165
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

ARTigo: Social Image Tagging

60,633 digital reproductions of artworks with crowdsourced tags, from ARTigo — a citizen-science project run since 2010 by the Institute of Art History and the Institute of Informatics at LMU Munich. Players are shown an image and type tags against a clock, scoring when a tag matches one their anonymous opponent gives or one recorded in an earlier session. The aggregate of those game rounds is this dataset. Built from the v1.5 Zenodo deposit (1 August 2023), whose data.jsonl holds 60,981 records; the deposit is refreshed monthly, so current versions on Zenodo are larger than this build.

Measured on that deposit: 270,024 distinct tags, 4.9M tag–image pairs, and just under 10M individual taggings, at a median of 61 distinct tags per image. Images come from nine sources, named in source.name: Artemis (26,402), Koeln (21,055), Rijksmuseum (5,633), Mkg-Hamburg (3,965), Kunsthalle-Karlsruhe (2,145), Amherst-College (1,365), and small contributions from Albertina, Stadt-Land-Bild and Streetart.

Fields

field	notes
id, hash_id	integer id; content hash used as the image filename
titles, creators	lists of {id, name}; 8,769 distinct creator names
location, institution	holding place and institution, where known
source	{id, name, url} — one of the nine collections above
path	URL of the image on the ARTigo API
tags	list of {id, name, language, count, regions}
image	the reproduction itself

Metadata coverage is partial: creators is empty on 12,182 records (20%), institution on 13,687 (22%), location on 17,795 (29%). Tags are unedited — the depositors deliberately left player spelling errors uncorrected rather than risk mangling specialist art-historical vocabulary.

from datasets import load_dataset

ds = load_dataset("biglam/artigo", split="train")
row = ds[0]
row["image"]                 # the artwork
row["tags"]["name"]          # tags are stored column-wise, not as a list of dicts
row["tags"]["language"]      # tag language, mostly de

The tags are overwhelmingly German

By tag–image pair: German 4,378,382 (89%), English 472,028 (9.6%), French 68,557 (1.4%). Anything trained on this without filtering learns a German tag vocabulary.

language is also a weaker signal than it looks. It records which language version of ARTigo the tag was collected on, not the language of the tag: the data paper notes that "English tags created in the German version of ARTigo are labelled as German".

regions is almost always empty

Visual annotation — tagging a rectangle rather than the whole image — only started in 2022. In this deposit 521 records out of 60,981 (0.9%) carry any region at all, across 1,935 tag–image pairs. The object-detection task tag in the metadata is not supported at usable scale; in practice this is an image-tagging dataset. Where regions exist, the data paper describes them as normalised bounding boxes given by the x and y of the bottom-left corner plus width and height.

Use

• Training or evaluating image tagging and text-to-image retrieval on art-historical images, in German.
• count is a per-image agreement signal — head tags were given by many players, the long tail by one. It is the obvious weight or threshold for filtering noise.
• Comparing player vocabulary against curatorial vocabulary, using titles and creators as the institutional description of the same object.

Licence

The Zenodo record is CC BY-SA 4.0. That covers the deposit as a whole, including the aggregated annotations; the record does not break out rights per source institution for the reproductions themselves, which come from nine third-party collections. Check the source before redistributing images, and note that share-alike propagates to derivatives.

Citation

@dataset{bry_et_al_artigo,
  author       = {Bry, François and
                  Kohle, Hubertus and
                  Krefeld, Thomas and
                  Riepl, Christian and
                  Schneider, Stefanie and
                  Schön, Gerhard and
                  Schulz, Klaus},
  title        = {{ARTigo}: Social Image Tagging (Aggregated Data)},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.8202331},
  url          = {https://doi.org/10.5281/zenodo.8202331}}

Data paper: Schneider, Stefanie (2024). "ARTigo: Data from Social Tagging with Art-historical Images", Journal of Open Humanities Data 10, 10.5334/johd.247. Code: arthist-lmu/artigo.