Pharmacokinetic Named Entity Recognition Benchmark

Annotated data for Named Entity Recognition of pharmacokinetic parameters in scientific text. The dataset includes training, development, and test files released in JSONL format by Ferran Gonzalez Hernandez of University College London. Each line contains an annotated sentence with character-level entity boundaries, entity types, and article metadata.

Use Cases

• Training NER models based on annotated pharmacokinetic parameters in sentences.
• Evaluating model performance on a benchmark for scientific text extraction.
• Extracting structured pharmacokinetic data from research articles based on annotated entity spans and types.

Strengths

• Data is structured for NER with explicit character-level boundaries and entity types.
• Includes unique identifiers for sentences and article-level metadata for traceability.
• Provides a standard split into training, development, and test sets for benchmarking.

Limitations

• Row count and total dataset size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

University College London

Collection Method

Annotated sentences extracted from scientific articles.