Datasets:

OTAR3088
/

AL_Test_data

license: mit
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
dataset_info:
  features:
    - name: sentence
      dtype: string
    - name: entities
      list:
        - name: end
          dtype: int64
        - name: label
          dtype: string
        - name: start
          dtype: int64
        - name: text
          dtype: string
    - name: data_source
      dtype: string
  splits:
    - name: train
      num_bytes: 859293
      num_examples: 3361
    - name: validation
      num_bytes: 447206
      num_examples: 1819
  download_size: 564719
  dataset_size: 1306499
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*

Proposed Active Learning Data split

Overview

This dataset release represents a proposed and experimental data split designed specifically to support and validate planned active learning (AL) cycles for biomedical Named Entity Recognition (NER).

The current version is not a final benchmark split. Instead, it serves as an initial, controlled setup for testing active learning strategies, model uncertainty sampling, and iterative annotation workflows prior to large-scale development.

Both splits (train and validation) have been carefully curated to ensure coverage of all three target entity types:

CellLine
CellType
Tissue

This balanced representation is critical for meaningful evaluation of active learning behavior across heterogeneous biomedical entities.

Dataset Description

Each of the split contain the following features:

sentence: list of sentences
entities: list of dict of entities found in sentences
data_source: the source of the article where the sentence orginates

The dataset has been curated from three complementary biomedical domains, each contributing distinct entity distributions:

Single-Cell transciptomics: rich in CellTypes and Tissues
ChembL assay desriptions: rich in CellLines
Stem-Cell research: contains all 3 entities

The stem cell–related articles were collected from the CellFinder data repository. The creation and annotation methodology of the original CellFinder dataset are described in the following reference:

Mariana Neves, Alexander Damaschun, Andreas Kurtz, Ulf Leser (2012) Annotating and evaluating text for stem cell research. In Proceedings Third Workshop on Building and Evaluation Resources for Biomedical Text Mining (BioTxtM 2012), Language Resources and Evaluation (LREC) 2012.

Article PMCIDs and source

Train Set

PMCID	In-Split	Source
PMC12435838	train	Single-Cell
PMC11578878	train	Single-Cell
PMC12396968	train	Single-Cell
PMC11116453	train	Single-Cell
PMC12408821	train	Single-Cell
PMC10968586	train	CheMBL-V1
PMC10761218	train	CheMBL-V1
PMC7642379	train	CheMBL-V1
PMC10674574	train	CheMBL-V1
PMC1315352	train	CellFinder
PMC2041973	train	CellFinder
PMC2238795	train	CellFinder

Validation Set

PMCID	In-Split	Source
PMC12256823	val	Single-Cell
PMC12116388	val	Single-Cell
PMC10287567	val	Single-Cell
PMC12133578	val	Single-Cell
PMC8658661	val	CheMBL-V1
PMC11350568	val	CheMBL-V1
PMC12072392	val	CheMBL-V2/CeLLaTe-V2
PMC12115102	val	CheMBL-V2/CeLLaTe-V2
PMC2063610	val	CellFinder
PMC1462997	val	CellFinder

Intended Use

Primary Use

Supervised NER training for biomedical NLP tasks

Not Intended For

Clinical or patient-level decision making

Notes and Limitations

This is an experimental split, subject to change.
Entity distributions may not reflect real-world prevalence.
Annotation density varies across domains by design