task_categories:
- text-classification
- token-classification
language:
- en
tags:
- biology
- nlp
- ner
pretty_name: CellFinder BRAT
size_categories:
- 1K<n<10K
Dataset Card for CellFinder BRAT
This data is a direct download of a Humboldt university of Berlin resource.
Dataset Details
Dataset Description
From data source https://www.informatik.hu-berlin.de/de/forschung/gebiete/wbi/resources/cellfinder/, this data is described as follows:
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The first version of our corpus is composed of 10 full text documents containing more than 2,100 sentences, 65,000 tokens and 5,200 annotations for entities. The corpus has been annotated with six types of entities (anatomical parts, cell components, cell lines, cell types, genes/protein and species) with an overall inter-annotator agreement around 80%.
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- Curated by: [More Information Needed]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
Dataset Sources
- Link: https://www.informatik.hu-berlin.de/de/forschung/gebiete/wbi/resources/cellfinder/
- Paper: Mariana Neves, Alexander Damaschun, Andreas Kurtz, Ulf Leser. Annotating and evaluating text for stem cell research. Third Workshop on Building and Evaluation Resources for Biomedical Text Mining (BioTxtM 2012) at Language Resources and Evaluation (LREC) 2012.
Uses
We utilised this dataset to train a NER model to annotate the entity types detailed by the authors of this dataset.
In turn this data became part of a larger dataset for our group.
We copied this format of the data to our huggingface project for easier ingestion into our NER-model production pipeline.
Dataset Structure
This data is stored in the BRAT format, with each article, identified through it's PMCID, annotated via a .txt and a .ann file.
Annotations within the .ann file appear as shown in the following example:
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T312 CellType 13239 13246 T cells
T313 GeneProtein 13235 13238 CD4
T314 CellType 13185 13196 macrophages
T315 Species 16981 16986 HIV-1
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With columns corresponding to: ID Entity type Start End Phrase
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Citation
Mariana Neves, Alexander Damaschun, Andreas Kurtz, Ulf Leser. Annotating and evaluating text for stem cell research. Third Workshop on Building and Evaluation Resources for Biomedical Text Mining (BioTxtM 2012) at Language Resources and Evaluation (LREC) 2012.
Dataset Card Authors
Christine Withers - https://huggingface.co/christine-withers
Dataset Card Contact
Christine Withers - https://huggingface.co/christine-withers