Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Size:
1K - 10K
License:
File size: 3,849 Bytes
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---
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:
1. Single-Cell transciptomics: rich in CellTypes and Tissues
2. ChembL assay desriptions: rich in CellLines
3. 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
|