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Dataset Summary

S800 Corpus: a novel abstract-based manually annotated corpus. S800 comprises 800 PubMed abstracts in which organism mentions were identified and mapped to the corresponding NCBI Taxonomy identifiers.

To increase the corpus taxonomic mention diversity the S800 abstracts were collected by selecting 100 abstracts from the following 8 categories: bacteriology, botany, entomology, medicine, mycology, protistology, virology and zoology. S800 has been annotated with a focus at the species level; however, higher taxa mentions (such as genera, families and orders) have also been considered.

The Species-800 dataset was pre-processed and split based on the dataset of Pyysalo (

Supported Tasks and Leaderboards

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English (en).

Dataset Structure

Data Instances

{'id': '0',
 'tokens': ['Methanoregula',
 'ner_tags': [1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}

Data Fields

  • id: Sentence identifier.
  • tokens: Array of tokens composing a sentence.
  • ner_tags: Array of tags, where 0 indicates no species mentioned, 1 signals the first token of a species and 2 the subsequent tokens of the species.

Data Splits

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Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

The species-level S800 corpus is subject to Medline restrictions.

Citation Information

Original data:

         title={The SPECIES and ORGANISMS resources for fast and accurate identification of taxonomic names in text},
         author={Pafilis, Evangelos and Frankild, Sune P and Fanini, Lucia and Faulwetter, Sarah and Pavloudi, Christina and Vasileiadou, Aikaterini and Arvanitidis, Christos and Jensen, Lars Juhl},
         journal={PloS one},
         publisher={Public Library of Science}

Source data of this dataset:

    author = {Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
    title = "{BioBERT: a pre-trained biomedical language representation model for biomedical text mining}",
    journal = {Bioinformatics},
    volume = {36},
    number = {4},
    pages = {1234-1240},
    year = {2019},
    month = {09},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btz682},
    url = {},
    eprint = {\_36\_4\_1234.pdf},



Thanks to @edugp for adding this dataset.

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