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README.md

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 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: source
-    dtype: string
-  - name: title
-    dtype: string
-  - name: text
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 436243353
-    num_examples: 27431
-  - name: validation
-    num_bytes: 8575570
-    num_examples: 559
-  download_size: 220685021
-  dataset_size: 444818923
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
+pretty_name: PKPD Dataset
+language:
+  - en
+task_categories:
+  - text-generation
+size_categories:
+  - 10K<n<100K
+source_datasets:
+  - extended
+annotations_creators:
+  - machine-generated
 ---
+
+# PKPD Dataset
+
+## Dataset summary
+
+This dataset is a pharmacokinetics/pharmacodynamics (PK/PD) and pharmacometrics corpus built for domain-adaptive pretraining (DAPT).
+
+It was created from automatically downloadable biomedical literature using:
+
+- PubMed search via NCBI E-utilities
+- PMID to PMCID mapping via the official PMC id conversion service
+- Europe PMC / PMC open-access full-text XML retrieval
+- JATS/XML parsing and heuristic filtering
+
+The current released corpus contains **27,990 documents** and approximately **109.4 million estimated tokens**.
+
+This release contains only the **core PubMed/PMC open-access article corpus**. Optional FDA guidance and open-source repository documentation were implemented in the pipeline but are **not included** in the current dataset export.
+
+## Scope
+
+The search strategy targets:
+
+- pharmacokinetics
+- pharmacodynamics
+- PK/PD modeling
+- population PK/PD
+- nonlinear mixed effects modeling
+- NONMEM / Monolix / SAEM / FOCE / NLME
+- PBPK
+- exposure-response
+- dose selection
+- model-informed drug development
+- clinical pharmacology
+- covariate modeling
+- Bayesian PKPD
+
+The corpus is intended for:
+
+- domain-adaptive pretraining
+- continued pretraining of biomedical or general LLMs
+- information retrieval / RAG experiments
+- corpus analysis for pharmacometrics language
+
+## Source data and collection pipeline
+
+### Source systems
+
+Primary source systems used for this release:
+
+1. **PubMed / NCBI E-utilities**
+2. **PMC ID conversion API**
+3. **Europe PMC fullTextXML endpoint**
+
+Excluded from this release:
+
+- paywalled journal scraping
+- copyrighted textbook scraping
+- FDA guidance pages
+- open-source repository docs
+
+### Date range
+
+- **Search period:** 2010-01-01 to 2026-03-12
+
+### Query families
+
+The PubMed search used five overlapping query families:
+
+1. `pkpd_core`
+2. `population_pkpd`
+3. `nlme_platforms`
+4. `pbpk`
+5. `exposure_response_midd`
+
+Per-query unique PMID counts before cross-query deduplication:
+
+| Query family | Unique PMIDs |
+|---|---:|
+| `pkpd_core` | 145,533 |
+| `population_pkpd` | 6,847 |
+| `nlme_platforms` | 3,932 |
+| `pbpk` | 4,951 |
+| `exposure_response_midd` | 12,331 |
+
+After deduplication across query families, the search yielded:
+
+- **156,274 unique PMIDs**
+
+### Retrieval and filtering stages
+
+Pipeline totals:
+
+1. PubMed search: **156,274 unique PMIDs**
+2. PMID to PMCID mapping: **66,948 PMCIDs**
+3. Europe PMC / PMC XML retrieved: **49,097 XML articles**
+4. Parsed JATS records: **49,097**
+5. Final kept DAPT documents: **27,990**
+
+Retrieval outcomes:
+
+- PMCIDs with XML successfully materialized locally: **49,097**
+- PMCIDs mapped but not available through Europe PMC fullTextXML: **18,215**
+- Fetch failures: **0** at the end of the completed run
+
+Filtering outcomes:
+
+- Parsed input docs: **49,097**
+- Final kept docs: **27,990**
+- Rejected for low relevance: **19,052**
+- Rejected for too short length: **2,054**
+- Rejected as duplicates: **1**
+
+## Data fields
+
+Each record in the final JSONL contains:
+
+- `id`: document identifier, usually PMCID-based
+- `source`: source group
+- `title`: article title
+- `text`: cleaned training text
+
+Example schema:
+
+```json
+{
+  "id": "PMC10010492",
+  "source": "core_pubmed_pmc",
+  "title": "Integrative population pharmacokinetic/pharmacodynamic analysis of nemonoxacin capsule in Chinese patients with community-acquired pneumonia",
+  "text": "..."
+}
+```
+
+## Split / repartition
+
+Current files on disk:
+
+- `final_merged_dapt.jsonl`: **27,990** records
+- `train.jsonl`: **27,431** records
+- `eval.jsonl`: **559** records
+
+Split proportions:
+
+- **Train:** 27,431 / 27,990 = **98.0%**
+- **Validation:** 559 / 27,990 = **2.0%**
+
+Source repartition in the final release:
+
+| Source | Documents | Share |
+|---|---:|---:|
+| `core_pubmed_pmc` | 27,990 | 100% |
+
+Character / token scale:
+
+- Total characters: **437,602,093**
+- Average characters per kept document: **15,638.38**
+- Rough token estimate: **109,400,619**
+
+## Text extraction details
+
+The XML parser keeps article components most useful for PKPD DAPT:
+
+- title
+- abstract
+- methods
+- modeling
+- statistical analysis
+- results
+- discussion
+- conclusion
+
+The parser drops low-value or non-training sections when possible:
+
+- references
+- acknowledgements
+- funding boilerplate
+- author contributions
+- supplementary boilerplate
+
+Whitespace is normalized, and some inline citation clutter is removed.
+
+## Quality notes
+
+This corpus was built with **high recall rather than high precision**. It is strong for:
+
+- PK/PD language
+- clinical pharmacology
+- PBPK
+- exposure-response
+- dose optimization
+- drug disposition and modeling methods
+
+However, the query strategy is broad, and some retained articles are only **adjacent** to pharmacometrics rather than strictly within it. For example, some documents concern:
+
+- broader translational pharmacology
+- oncology therapeutics
+- drug-protein binding
+- formulation or delivery topics
+
+This makes the dataset suitable for a **prototype DAPT corpus**, but not yet a perfectly clean pharmacometrics-only benchmark.
+
+## Intended uses
+
+Recommended uses:
+
+- domain-adaptive pretraining for LLMs
+- continued pretraining of Qwen/Llama/Mistral-style causal LMs
+- corpus mining and keyword analysis
+- retrieval experiments on PKPD literature
+
+Not recommended as-is for:
+
+- strict pharmacometrics benchmarking without extra curation
+- legal redistribution assumptions without checking article-level terms
+- clinical decision support
+
+## Licensing and redistribution note
+
+This dataset is derived from **PMC / Europe PMC open-access full-text XML** and related PubMed metadata, but the corpus should **not** be interpreted as having a single unified license automatically inherited across all articles.
+
+Important note:
+
+- PMC / Europe PMC accessibility does **not** guarantee identical downstream redistribution terms for every document.
+- Before making the dataset public, article-level licensing and redistribution conditions should be reviewed carefully.
+
+For conservative use, private hosting is recommended until licensing is fully audited.
+
+## Reproducibility
+
+The dataset was generated by the local pipeline in:
+
+- `scripts/01_search_pubmed.py`
+- `scripts/02_map_pmids_to_pmcids.py`
+- `scripts/03_fetch_fulltext_xml.py`
+- `scripts/04_parse_jats_xml.py`
+- `scripts/05_build_dapt_jsonl.py`
+- `scripts/08_merge_and_report.py`
+
+Summary reports used for this card:
+
+- `data/reports/pubmed_search_summary.json`
+- `data/reports/fulltext_retrieval_report.json`
+- `data/reports/parsed_xml_report.json`
+- `data/reports/core_pubmed_build_report.json`
+- `data/reports/corpus_summary.json`
+
+## Loading example
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("Khalilbraham/PKPD-Dataset")
+print(ds)
+print(ds["train"][0].keys())
+```
+
+## Suggested citation
+
+If you use this dataset, cite:
+
+1. PubMed / NCBI E-utilities
+2. PMC / Europe PMC
+3. The dataset repository itself
+
+You may also cite the associated local corpus-building pipeline if released separately.