| --- |
| license: mit |
| task_categories: |
| - token-classification |
| 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: 1699146 |
| num_examples: 6956 |
| download_size: 754166 |
| dataset_size: 1699146 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| language: |
| - en |
| pretty_name: CeLLaTe_all |
| --- |
| |
|
|
| # A resolved representation of the CeLLaTe Dataset |
| ## Overview |
| This dataset represents an un-edited, whole version of CeLLaTe. |
| There are no splits, and no entity tags outside of those from the base dataset schema: |
| - **CellType** |
| - **CellLine** |
| - **Tissue** |
|
|
| This version of CeLLaTe allows for a view which is not manipulated or obscured by any downstream tasks carried out in efforts to train transformer models. |
|
|
| ### On 'vague' terms |
| Two versions of this dataset have been created: one retaining _'vague'_ entities and one excluding them (this dataset). |
| _'Vague'_ terms were removed through the use of a vague terms dictionary, seen at: _https://github.com/EuropePMC/OTAR3088/blob/main/docs/vague_entity_examples.tsv_ |
|
|
| The filtering of _'vague'_ entities applies exclusively to the subsects of CeLLaTe which were manually curated. |
| These are the splits which contain articles of 'single-cell transcriptomics' and 'pharmacology' themes. |
|
|
| We define vague entities as terms that do not strictly satisfy the criteria of a well-defined |
| named entity, but may exhibit entity-like characteristics depending on contextual usage. |
|
|
|
|
| ## Dataset Schema |
|
|
| Columns are as follows: |
| - sentence: a single sentence extracted from a biomedical article |
| - entities: a list of entity annotations associated with the sentence |
| - data_source: the originating corpus or article collection from which the sentence was derived |
| |
| Annotations are provided at the sentence level to facilitate downstream NER training, evaluation, and AL-driven re-annotation workflows |
| |
| |
| ## Data Sources and Domain Composition |
| |
| The dataset integrates articles from three complementary biomedical domains, each contributing distinct entity distributions: |
| |
| ### 1. Single cell transcriptomics Literature: |
| |
| - High prevalence of CellType and Tissue entities |
| - Rich terminology diversity |
| - Manually curated |
| |
| ### 2. Pharmacology literature |
| - Enriched in CellLine mentions |
| - MedChem articles were retrieved here from the ChEMBL 34 database |
| - SQLite queries linked the ASSAY DESCRIPTIONS and DOCS tables, collecting supporting literature for assay descriptions mentioning cell lines |
| - Manually curated |
| |
| ### 3. Stem Cell Research (CellFinder) |
| - Contains all three entity types |
| - Particularly rich in CellType mentions |
| - Historically curated dataset with expert annotations (Dated more than 10 years ago) |
| |
| |
| This multi-domain composition allows the evaluation of: |
| |
| - Cross-domain robustness |
| - Entity distribution shifts |
| - Label imbalance behaviour |
| - Domain adaptation strategies |
| |
| - - - - - - - - |
| ## Stem Cell Article Source (CellFinder) |
| |
| Stem cell–related articles were obtained from the CellFinder repository.The original dataset and annotation methodology are described in: |
| |
| >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. |
| |
| The CellFinder corpus provides historically curated annotations across multiple stem-cell–related entity types. |
| |
