BrentLab/harbison_2004

Harbison 2004

This Dataset is a parsed version of the data provided by Richard A. Young's lab. To cite this data, please use:

Harbison CT, Gordon DB, Lee TI, Rinaldi NJ, Macisaac KD, Danford TW, Hannett NM, Tagne JB, Reynolds DB, Yoo J, et al. 2004. Transcriptional regulatory code of a eukaryotic genome. Nature 431: 99–104.doi:10.1038/nature02800

This repo provides 1 dataset:

• harbison_2004: ChIP-chip transcription factor binding data with environmental conditions.

tfbpapi

After installing tfbpapi, you can adapt this tutorial in order to explore the contents of this repository.

huggingface_cli/duckdb

You can retrieves and displays the file paths for each configuration of the "BrentLab/harbison_2004" dataset from Hugging Face Hub.

from huggingface_hub import ModelCard
from pprint import pprint

card = ModelCard.load("BrentLab/harbison_2004", repo_type="dataset")

# cast to dict
card_dict = card.data.to_dict()

# Get partition information
dataset_paths_dict = {d.get("config_name"): d.get("data_files")[0].get("path") for d in card_dict.get("configs")}

pprint(dataset_paths_dict)

If you wish to pull the entire repo, due to its size you may need to use an authentication token. If you do not have one, try omitting the token related code below and see if it works. Else, create a token and provide it like so:

from huggingface_hub import snapshot_download
import os

repo_id = "BrentLab/harbison_2004"

hf_token = os.getenv("HF_TOKEN")

# Download entire repo to local directory
repo_path = snapshot_download(
    repo_id=repo_id,
    repo_type="dataset",
    token=hf_token
)

print(f"\n✓ Repository downloaded to: {repo_path}")

# Construct path to the rossi_annotated_features parquet file
parquet_path = os.path.join(repo_path, "harbison_2004.parquet")
print(f"✓ Parquet file at: {parquet_path}")
➜  ~ cat /home/chase/Downloads/Harbison.2004.md
# Harbison 2004

This Dataset is a parsed version of the data provided by Richard A. Young's lab. To cite
this data, please use:  

[Harbison CT, Gordon DB, Lee TI, Rinaldi NJ, Macisaac KD, Danford TW, Hannett NM, Tagne
JB, Reynolds DB, Yoo J, et al. 2004. Transcriptional regulatory code of a eukaryotic
genome. Nature 431:
99–104.doi:10.1038/nature02800](https://www.nature.com/articles/nature02800)

This repo provides 1 dataset:

- **harbison_2004**: ChIP-chip transcription factor binding data with environmental
  conditions.

### `tfbpapi`

After [installing
tfbpapi](https://github.com/BrentLab/tfbpapi/?tab=readme-ov-file#installation), you can
adapt this [tutorial](https://brentlab.github.io/tfbpapi/tutorials/hfqueryapi_tutorial/)
in order to explore the contents of this repository.

### huggingface_cli/duckdb

You can retrieves and displays the file paths for each configuration of
the "BrentLab/harbison_2004" dataset from Hugging Face Hub.

```python
from huggingface_hub import ModelCard
from pprint import pprint

card = ModelCard.load("BrentLab/harbison_2004", repo_type="dataset")

# cast to dict
card_dict = card.data.to_dict()

# Get partition information
dataset_paths_dict = {d.get("config_name"): d.get("data_files")[0].get("path") for d in card_dict.get("configs")}

pprint(dataset_paths_dict)

If you wish to pull the entire repo, due to its size you may need to use an authentication token. If you do not have one, try omitting the token related code below and see if it works. Else, create a token and provide it like so:

from huggingface_hub import snapshot_download
import os

repo_id = "BrentLab/harbison_2004"

hf_token = os.getenv("HF_TOKEN")

# Download entire repo to local directory
repo_path = snapshot_download(
    repo_id=repo_id,
    repo_type="dataset",
    token=hf_token
)

print(f"\n✓ Repository downloaded to: {repo_path}")

# Construct path to the rossi_annotated_features parquet file
parquet_path = os.path.join(repo_path, "harbison_2004.parquet")
print(f"✓ Parquet file at: {parquet_path}")