update

README.md

@@ -5,14 +5,14 @@ annotations_creators:
 language:
   - en
 
-license: cc-by-4.0 # Or the specific license of the Muscle Aging Atlas data if different
+license: cc-by-4.0
 
 multilinguality: monolingual
 
 pretty_name: Human Skeletal Muscle Aging Atlas (sn/scRNA-seq)
 
 size_categories:
-  - 100K<n<1M # Based on 183,161 cells, adjust if your downloaded file varies
+  - 100K<n<1M
 
 source_datasets:
   - original
@@ -146,7 +146,7 @@ from huggingface_hub import hf_hub_download
 import os
 
 # Define the Hugging Face repository ID and the local directory for downloads
-HF_REPO_ID = "longevity-db/human-muscle-aging-atlas-snRNAseq" # THIS IS YOUR NEW REPO ID
+HF_REPO_ID = "longevity-db/human-muscle-aging-atlas-snRNAseq" # THIS IS YOUR REPO ID
 LOCAL_DATA_DIR = "downloaded_human_muscle_data"
 
 os.makedirs(LOCAL_DATA_DIR, exist_ok=True)
@@ -164,8 +164,8 @@ parquet_files = [
     "gene_statistics.parquet",
     "cell_type_proportions_overall.parquet",
     "donor_metadata.parquet"
-    # Note: cell_type_proportions_by_{grouping_column}.parquet might have a dynamic name,
-    # so users might need to download it separately or infer its name.
+    # Note: If 'cell_type_proportions_by_{grouping_column}.parquet' was generated,
+    # its name will depend on the grouping column found. You might need to add it separately.
 ]
 
 # Download each file
@@ -241,6 +241,16 @@ else:
 # This data can then be split into train/test sets and used to train various ML models.
 ```
 
+### Creating a Model Card
+
+The structured Parquet files in this dataset are perfectly suited for generating comprehensive **Hugging Face Model Cards** for models trained using this data. The various components provide crucial information for different sections of a model card:
+
+  - **`Data Overview`:** Information directly from this README (sections 1 and 2), describing the dataset's origin, scope, and relevance.
+  - **`Usage Examples`:** The provided Python code for loading the data demonstrates how a model might consume `expression.parquet` or `pca_embeddings.parquet` (as input features) and `cell_metadata.parquet` (for labels like 'age' or 'cell\_type').
+  - **`Limitations and Bias`:** `cell_metadata.parquet` can be analyzed to understand the demographics (e.g., age distribution, sex, genotype if available) of the original human donors, helping to identify potential biases or limitations in the dataset's representativeness.
+  - **`Dataset Transformations`:** Details from the "Data Cleaning and Processing" section of this README, explaining how the data was preprocessed before model training.
+  - **`Metrics and Evaluation Data`:** If a model is trained, `pca_embeddings.parquet` and `cell_metadata.parquet` can be used as inputs for evaluation metrics, and their distributions can be visualized as part of the model card's evaluation section.
+  - **`Environmental Impact`:** Details on the computational resources (e.g., CPU/GPU hours) used for data processing or model training, which can be part of a model card.
 
 -----
 
@@ -249,9 +259,7 @@ else:
 Please ensure you cite the original source of the Human Skeletal Muscle Aging Atlas data. Refer to the project's official website for the most up-to-date citation information for the atlas and its associated publications:
 
 **Human Skeletal Muscle Aging Atlas Official Website:**
-[https://www.muscleageingcellatlas.org/](https://www.muscleageingcellatlas.org/)
-
-If you use the `scanpy` library for any further analysis or preprocessing, please also cite Scanpy.
+[https://www.muscleageingcellatlas.org/human-pp/](https://www.muscleageingcellatlas.org/human-pp/)
 
 ## **7. Contributions**