Update README.md

README.md

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 # 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)
-
-
-## Dataset Details
-
-The data was extract from The [Young Lab's website](http://younglab.wi.mit.edu/regulatory_code/GWLD.html).
-
-I pulled the 'Excel' versions of both the P values (the downloaded file is called files_for_paper_abbr.zip)
-and Binding ratios (Ratio_forpaper_abbr.zip).  
-
-The following is copied from the [Young's lab explanation of the analysis](http://younglab.wi.mit.edu/regulatory_code/Analysis.html):  
-
-> The microarrays were scanned using an Axon200B scanner, and the images were analyzed with Genepix 5.0. Columns corresponding to the background
-> subtracted intensities and standard deviation of the background were extracted for further analysis. The intensities for the two channels, representing
-> the immunoprecipitated (test) and unenriched (control) samples, were normalized by using the median of each channel to calculate a normalization factor,
-> normalizing all datasets to a single median intensity. The log ratio of the intensity in the test channel to the control channel was calculated.
-> To account for biases in the immunoprecipitation reaction, these log ratios were normalized for each spot by subtracting the average log ratio of each
-> spot across all arrays. The intensities in the test channel were then adjusted to yield this normalized ratio. Finally, an error model was used to
-> calculate significance of enrichment on each chip and to combine data for replicates to obtain a final average ratio and significance of enrichment
-> for each intergenic region. Each intergenic region was assigned to the genes it is most likely to regulate.
-> We have included new refinements in our analysis relative to that used in Lee et al. Notably, we have excluded artefactual spots from analysis,
-> selected more reliable probes for normalization and assigned quality metrics to individual arrays to identify low quality experiments.
-
-The script used to parse this data from the data provided by the Young lab into the parquet dataset presented here is included in `scripts/`
-
-## Dataset Structure
-
-
-| Field                 | Description                                                                                                                                                                                      |
-|-----------------------|-------------------------------------------------------------------------------------------------------------------------|
-| `condition`           | See below for a definition of each level                                                                                |
-| `regulator_locus_tag` | The systematic ID of the ChIP'ed regulator.                                                                             |
-| `regulator_symbol`    | The common name of the ChIP'ed regulator.                                                                               |
-| `target_locus_tag`    | the systematic ID of the feature to which the Young lab assigned the effect/pvalue                                      |
-| `target_symbol`       | the common name of the feature to which the Young lab assigned the effect/pvalue                                        |
-| `effect`              | chip based binding ratio                                                                                                |
-| `pvalue`              | pvalue of the effect                                                                                                    |
-
-- **YPD**: Rich media - Cells were grown in YPD (1% yeast extract/2% peptone/2% glucose) to an OD600 of ~0.8
-- **SM**: Amino acid starvation - Cells were grown to an OD600 of ~0.6 in synthetic complete medium followed by treatment with the inhibitor of amino acid biosynthesis sulfometuron methyl (0.2 mg/ml final) for two hours
-- **RAPA**: Nutrient deprived - Cells were grown in YPD to an OD600 of ~0.8 followed by treatment with rapamycin (100 nM final) for 20 minutes
-- **H2O2Hi**: Highly hyperoxic - Cells were grown in YPD to an OD600 of ~0.5 followed by treatment with hydrogen peroxide (4 mM final) for 30 minutes
-- **H2O2Lo**: Moderately hyperoxic - Cells were grown in YPD to an OD600 of ~0.5 followed by treatment with hydrogen peroxide (0.4 mM final) for 20 minutes
-- **Acid**: Acidic medium - Cells were grown in YPD to an OD600 of ~0.5 followed by treatment for 30 minutes with succinic acid (0.05 M final) to reach a pH of 4.0
-- **Alpha**: Mating inducing - Cells were grown in YPD to an OD600 of ~0.8 followed by treatment with the alpha factor pheromone (5 mg/ml) for 30 minutes
-- **BUT14**: Filamentation inducing (14 hours) - Cells were grown in YPD containing 1% butanol for 14 hours (corresponding to an OD600 of ~0.8)
-- **BUT90**: Filamentation inducing (90 minutes) - Cells were grown in YPD containing 1% butanol for 90 minutes (corresponding to an OD600 of ~0.8)
-- **Thi-**: Vitamin deprived medium - Cells were grown in synthetic complete medium lacking thiamin to a final OD of ~0.8
-- **GAL**: Galactose medium - Cells were grown in YEP medium supplemented with galactose (2%) to an OD600 of ~0.8
-- **HEAT**: Elevated temperature - Cells were grown in YPD at 30°C to an OD600 of ~0.5 followed by a temperature shift to 37°C for 45 minutes
-- **Pi-**: Phosphate deprived medium - Cells were grown in synthetic complete medium lacking phosphate to a final OD of ~0.8
-- **RAFF**: Raffinose medium - Cells were grown in YEP medium supplemented with raffinose (2%) to an OD600 of ~0.8
-
-**Dataset Author and Contact**: Chase Mateusiak [@cmatKhan](https://github.com/cmatkhan/)
+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](https://huggingface.co/docs/hub/en/security-tokens).
+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:
+
+```python
+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](https://huggingface.co/docs/hub/en/security-tokens).
+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:
+
+```python
+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}")
+```