Update README.md

README.md

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----
-license: mit
----
+---
+license: pddl
+task_categories:
+- text-classification
+- question-answering
+- text2text-generation
+language:
+- en
+tags:
+- stxbp1
+- clinvar
+- genomics
+- biomedical
+- variant
+- rare-disease
+- neurology
+- epilepsy
+- nlp
+- llm
+- question-answering
+- text-classification
+- bioinformatics
+- snare
+- gene-editing
+- crispr
+- cas9
+- open-data
+- instruction-tuning
+pretty_name: STXBP1 ClinVar Pathogenic Variants (Curated)
+size_categories:
+- 100K<n<1M
+---
+
+# stxbp1_clinvar_curated_pathogenic
+
+_Curated set of **307,587 pathogenic and likely pathogenic** STXBP1 and related variants from ClinVar, ready for LLM, variant curation, and biomedical NLP applications._
+
+**License:**  
+This dataset is licensed under the ODC Public Domain Dedication and License (PDDL).  
+To the extent possible under law, the author(s) have dedicated this data to the public domain worldwide by waiving all rights to the work under copyright law, including all related and neighboring rights, to the extent allowed by law.  
+NO WARRANTY is provided.  
+See [ODC-PDDL](https://opendatacommons.org/licenses/pddl/1-0/) for full legal text.
+
+---
+
+## Dataset Overview
+
+A **hand-curated, LLM-friendly** dataset of STXBP1 and family variants from ClinVar, filtered for clinical significance (`Pathogenic`, `Likely_pathogenic`).  
+Ideal for medical language modeling, rare disease NLP, AI-powered variant curation, and biomedical Q&A.
+
+**Formats included:**  
+- Structured JSONL (`.jsonl`, main split)
+- Q/A pairs (`.txt`, for demo/fine-tuning)
+- Parquet conversion recommended for large-scale use
+
+---
+
+## Curation Criteria
+
+Variants included here are:
+- Annotated as **Pathogenic** or **Likely_pathogenic** in ClinVar
+- Matching gene family:  
+  - STXBP1, MUNC18, STXBP2, STXBP3, STXBP4, STXBP5, STXBP6
+  - Related SNARE-complex/CRISPR/neurological disorder keywords
+
+---
+
+## Features
+
+- **Natural language clinical summaries for each variant**
+- **Structured JSONL** (parquet-compatible) for data science and NLP
+- **Q/A pairs** for LLM training and evaluation
+- Full coverage: variant, gene, disease, clinical significance, HGVS, database links, review status, and more
+
+---
+
+## Dataset Statistics
+
+| Format    | Size (bytes) | Number of Examples/Lines |
+|-----------|-------------:|------------------------:|
+| QA (.txt) |     159,728  |    615,174              |
+| JSONL     |     205,212  |    307,587              |
+
+_Main split for Hugging Face: JSONL format (see above for statistics)._
+
+---
+
+## Schema
+
+| Field                  | Description                                                    |
+|------------------------|----------------------------------------------------------------|
+| ID                     | ClinVar Variation ID                                           |
+| chrom                  | Chromosome                                                    |
+| pos                    | Genomic position (GRCh38)                                     |
+| ref                    | Reference allele                                              |
+| alt                    | Alternate allele                                              |
+| gene                   | Gene symbol                                                   |
+| disease                | Disease/phenotype name                                        |
+| significance           | Clinical significance (e.g., Pathogenic, Likely_pathogenic)   |
+| hgvs                   | HGVS variant description                                      |
+| review                 | ClinVar review status                                         |
+| molecular_consequence  | Sequence Ontology + effect                                    |
+| variant_type           | SNV, Insertion, Deletion, etc.                                |
+| clndisdb               | Disease database links (OMIM, MedGen, etc.)                   |
+| clndnincl              | Included variant disease name                                 |
+| clndisdbincl           | Included variant disease database links                       |
+| onc_fields             | Dict of oncogenicity fields                                   |
+| sci_fields             | Dict of somatic clinical impact fields                        |
+| incl_fields            | Dict of included fields (INCL)                                |
+
+---
+
+## Data Example
+
+**JSON record:**
+```json
+{
+  "ID": "3385321",
+  "chrom": "1",
+  "pos": "66926",
+  "ref": "AG",
+  "alt": "A",
+  "gene": "STXBP1",
+  "disease": "Developmental and epileptic encephalopathy, 4",
+  "significance": "Pathogenic",
+  "hgvs": "NC_000001.11:g.66927del",
+  "review": "criteria_provided, single_submitter",
+  "molecular_consequence": "SO:0001627: intron_variant",
+  "variant_type": "Deletion",
+  "clndisdb": "Human_Phenotype_Ontology:HP:0000547,MONDO:MONDO:0019200,MeSH:D012174,MedGen:C0035334,OMIM:268000",
+  "clndnincl": null,
+  "clndisdbincl": null,
+  "onc_fields": {},
+  "sci_fields": {},
+  "incl_fields": {}
+}
+```
+
+
+===================================================================================================================
+## You can easily load this dataset using the 🤗 Datasets library.
+
+The Hugging Face infrastructure will automatically use the efficient Parquet files by default, but you can also specify the JSONL if you prefer.
+
+### Install dependencies (if needed):
+
+```bash
+pip install datasets
+```
+
+## Load the full dataset (JSONL, recommended)
+
+```from datasets import load_dataset
+
+ds = load_dataset("SkyWhal3/ClinVar-STXBP1-NLP-Dataset", data_files="ClinVar-STXBP1-NLP-Dataset.jsonl", split="train")
+print(ds[0])
+```
+
+## Parquet conversion (for large scale)
+
+```import pandas as pd
+
+df = pd.read_json("ClinVar-STXBP1-NLP-Dataset.jsonl", lines=True)
+df.to_parquet("ClinVar-STXBP1-NLP-Dataset.parquet")
+```
+
+## Other ways to use the data
+   Load all Parquet shards with pandas
+
+```import pandas as pd
+import glob
+
+# Load all Parquet shards in the train directory
+parquet_files = glob.glob("default/train/*.parquet")
+df = pd.concat([pd.read_parquet(pq) for pq in parquet_files], ignore_index=True)
+print(df.shape)
+print(df.head())
+```
+
+## Filter for a gene (e.g., STXBP1)
+
+```df = pd.read_parquet("default/train/0000.parquet")
+stxbp1_df = df[df["gene"] == "STXBP1"]
+print(stxbp1_df.head())
+```
+
+## Randomly sample a subset
+
+```sample = df.sample(n=5, random_state=42)
+print(sample)
+```
+
+## Load with Polars (for high performance)
+
+```import polars as pl
+
+df = pl.read_parquet("default/train/0000.parquet")
+print(df.head())
+```
+
+## Query with DuckDB (SQL-style)
+
+```import duckdb
+
+con = duckdb.connect()
+df = con.execute("SELECT * FROM 'default/train/0000.parquet' WHERE gene='STXBP1' LIMIT 5").df()
+print(df)
+```
+
+## Streaming mode with 🤗 Datasets
+
+```ds = load_dataset("YOURPATH/ClinVar-STXBP1-NLP-Dataset", split="train", streaming=True)
+for record in ds.take(5):
+    print(record)
+```
+
+
+Created by Adam Freygang, A.K.A. SkyWhal3