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README.md

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 license: cc-by-4.0
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-# CHR Tree Detection in Paintings
+# Motif Segmentation in Paintings
 
-Segmentation masks and metadata for trees detected in a corpus of paintings, produced with SAM3 as part of research at the Center for Humanities Computing Aarhus (chcaa).
+Segmentation masks and metadata for trees detected in a corpus of paintings, produced with SAM3 as part of research in the golden matrix project at the Center for Humanities Computing Aarhus (chcaa).
 
 ## Dataset Description
 
-This dataset supports a study of tree motifs in 19th-century Northern European landscape painting, examining how trees are used to naturalize ideas of nation and homeland. It covers 4,727 paintings drawn from museum and Wikidata sources, with tree instances detected using [SAM3](https://ai.meta.com/sam2/) via a "tree" text prompt.
+This dataset supports a studies of landscape and tree motifs in 19th-century Northern European paintings. It covers 4,727 paintings drawn from museum and Wikidata sources, with tree instances detected using [SAM3]
 
-For each detected tree, the dataset records a bounding box, a confidence score, and a segmentation mask, alongside painting-level metadata (artist, nationality, date, collection, image rights). This lets downstream users compute things like how often trees appear across a corpus, how much canvas area they occupy, where they tend to be positioned, and how their color/rendering varies by period or region — without having to re-run detection themselves.
+For each detected tree, the dataset records a bounding box, a confidence score, and a segmentation mask, alongside painting-level metadata (artist, nationality, date, collection)
 
 ## Contents
 
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 ## License
 
-This dataset (masks, metadata, embeddings, and annotations produced by the chcaa team) is released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). Please credit chcaa when reusing this data.
+This dataset (masks, metadata, embeddings, and annotations produced by the chcaa team (Rie Schmidt Eriksen, Louise Brix Hansen & Marta Kipke)) is released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). Please credit chcaa when reusing this data.