---
license: cc0-1.0
language:
- en
pretty_name: TreeOfLife-10M Vector database
task_categories: [image-feature-extraction] 
tags:
- biology
- image
- animals
- CV
size_categories: 1M<n<10M 
---

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# Dataset Card for TreeOfLife-10M Vector database

Persistent files for vector Database created with [chromadb](https://docs.trychroma.com/docs/overview/introduction) 
containing the embeddings for all images in the [imageomics/TreeOfLife-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M) dataset.

## Dataset Details



### Dataset Description

- **Curated by:** list curators (authors for _data_ citation, moved up)
- **Language(s) (NLP):** [More Information Needed]
<!-- Provide the basic links for the dataset. These will show up on the sidebar to the right of your dataset card ("Curated by" too). -->
- **Homepage:** https://imageomics.github.io/
- **Repository:** [imageonomics/bioclip-vector-db](https://github.com/Imageomics/bioclip-vector-db)
- **Paper:** 


This dataset contains the generated vector database built using ChromaDb as the backend vector database solution for the entire TreeOfLife-10M dataset.

The rationale behind creating a vector database was to enable blazingly fast nearest neighbor search. 
The vector database is loaded into memory and for a given query embedding in the same dimension as the vectors in the database, we calculate the inner product (ip) to calculate distances.
In this case all vectors are of the same dimension i.e. 512 dims.

The files in this dataset are binaries and cannot be read in raw format. The files in this dataset are intended to be used with chromadb.

Below is an example usage:
```py
import chromadb

client = chromadb.PersistentClient(path=self._collection_dir)

collection = client.get_or_create_collection(
    name="imageomics/TreeOfLife-10M",
    metadata={"hnsw:space": "ip", "hnsw:search_ef": 10},
)
# returns ID of 10 nearest neighbor. If an exact match exists,
# that will be the first returned result with a distance of 0.
collection.query(query_embeddings=[query_embedding], n_results=10)
```

## Dataset Structure
```
tol_vector_db/
├── 97283350-138c-49b4-a4d4-9b93a33960da/
│   ├── header.bin
│   ├── index_metadata.pickle
│   ├── data_level0.bin
│   ├── link_lists.bin
│   └── length.bin
└── chroma.sqlite3
```

## Dataset Creation


### Source Data

[imageomics/TreeOfLife-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M) dataset was used as the source to generate all the embeddings in the vector database. 


## Citation

**BibTeX:**
**Data**
```
@misc{<ref_code>,
  author = {Sreejith Menon},
  title = {Tree Of Life 10M Vector Database},
  year = {2025},
  url = {https://huggingface.co/datasets/imageomics/tree-of-life-vector-db},
}
```


## Acknowledgements

This work was supported by the [Imageomics Institute](https://imageomics.org), which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Speical thanks to:
* [Matt Thompson](https://github.com/thompsonmj)


## Dataset Card Authors 

[Sreejith Menon](https://github.com/smenon8)

## Dataset Card Contact

[Sreejith Menon](https://github.com/smenon8)