metadata

task_categories:
  - image-classification
tags:
  - butterfly
  - heliconius
  - dorsal
  - ventral
  - RGB
  - full body
  - separated wings
  - mimicry
  - CV
pretty_name: Jiggins Heliconius Collection
size_categories:
  - 10K<n<100K
language:
  - en

Dataset Card for Jiggins Heliconius Collection

Dataset Description

Homepage:
Repository:
Paper:
Leaderboard:
Point of Contact:

Dataset Summary

Subset of the collection records from the research group of Chris Jiggins at the University of Cambridge derived from almost 20 years of field studies. This subset contains approximately 34,000 RGB images across all Heliconius. Many records include images as well as locality data. All detached wings were photographed with a DSLR camera with a 100 mm macro-lens in standardized conditions; images and full records with data are stored in the EarthCape database and on Zenodo.

Both dorsal and ventral images available. Contains both whole butterfly, and 4 wings separate. Large variation in image content.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

[More Information Needed]

Dataset Structure

Jiggins_Zenodo_Img_Master.csv: Information for the approximately 34,000 unprocessed image files included in the Jiggins Heliconius Collection.
- To access the images combine columns zenodo_link and Image_name:
```
zenodo_link + '/files/' + Image_name
```

Data Instances

Multiple species of Heliconius including erato and melpomene. Detached wings in four quadrants (generally). Some subspecies may be photographed differently, needs segmentation preprocessing.

Type: JPG/jpg/tif(very few)
Size (x pixels by y pixels): Unknown yet
Background (color or none): multiple (needs to be normalised)
Fit in frame:
Ruler or Scale: Some with Ruler
Color (ColorChecker, white-balance, None): None

Preprocessing steps (to be done):

Hybrid separation - some images labeled as H.erato and H. melpomene without subspecies names are hybrids and need to be determined what subspecies they are.
Label correction - along with step 1.
Removal of subspecies with no mimic pairs.
Segmentation of four wings from images so we can focus on forewings vs hindwings: WingSeg.

Current preprocessing steps: WingSeg on the Meier subset.

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Image subset of the collection records from the research group of Chris Jiggins at the University of Cambridge derived from almost 20 years of field studies.

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

None

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

Christopher Lawrence (Princeton University) - ORCID: 0000-0002-3846-5968
Chris Jiggins (University of Cambridge) - ORCID: 0000-0002-7809-062X
Butterfly Genetics Group (University of Cambridge)

Licensing Information

[More Information Needed]

Citation Information

Christopher Lawrence, Chris Jiggins, Butterfly Genetics Group (University of Cambridge). (2023). Jiggins Heliconius Collection. Hugging Face. https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection.

If you use this dataset, please cite the original dataset (citation below) as well as this curated subset.

Gabriela Montejo-Kovacevich, Eva van der Heijden, Nicola Nadeau, & Chris Jiggins. (2020). Cambridge butterfly wing collection batch 10. Zenodo. https://doi.org/10.5281/zenodo.4289223

Contributions

The Imageomics Institute is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) Institute program under Award #2118240 (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning).