task_categories:token-classification task_ids:named-entity-recognition annotations_creators:expert-generated language_creators:expert-generated multilinguality:monolingual size_categories:1K<n<10K source_datasets:original language:en license:unknown region:us
We have developed an efficient algorithm and implementation of a dictionary-based approach to named entity recognition,
which we here use to identifynames of species and other taxa in text. The tool, SPECIES, is more than an order of
magnitude faster and as accurate as existing tools. The precision and recall was assessed both on an existing gold-standard
corpus and on a new corpus of 800 abstracts, which were manually annotated after the development of the tool. The corpus
comprises abstracts from journals selected to represent many taxonomic groups, which gives insights into which types of
organism names are hard to detect and which are easy. Finally, we have tagged organism names in the entire Medline database
and developed a web resource, ORGANISMS, that makes the results accessible to the broad community of biologists.
@article{pafilis2013species,
         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},
         volume={8},
         number={6},
         pages={e65390},
         year={2013},
         publisher={Public Library of Science}
}
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: species800
dataset_info:
  features:
  - name: id
    dtype: string
  - name: tokens
    sequence: string
  - name: ner_tags
    sequence:
      class_label:
        names:
          '0': O
          '1': B
          '2': I
  config_name: species_800
  splits:
  - name: train
    num_bytes: 2579096
    num_examples: 5734
  - name: validation
    num_bytes: 385756
    num_examples: 831
  - name: test
    num_bytes: 737760
    num_examples: 1631
  download_size: 18204624
  dataset_size: 3702612
---

# Dataset Card for [Dataset Name]

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [SPECIES](https://species.jensenlab.org/)
- **Repository:**
- **Paper:** https://doi.org/10.1371/journal.pone.0065390
- **Leaderboard:**
- **Point of Contact:** [Lars Juhl Jensen](mailto:lars.juhl.jensen@cpr.ku.dk)

### 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 (https://github.com/spyysalo/s800).

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

English (`en`).

## Dataset Structure

### Data Instances

```
{'id': '0',
 'tokens': ['Methanoregula',
  'formicica',
  'sp',
  '.',
  'nov',
  '.',
  ',',
  'a',
  'methane',
  '-',
  'producing',
  'archaeon',
  'isolated',
  'from',
  'methanogenic',
  'sludge',
  '.'],
 '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

[More Information Needed]

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

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

### Citation Information

Original data:
```
@article{pafilis2013species,
         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},
         volume={8},
         number={6},
         pages={e65390},
         year={2013},
         publisher={Public Library of Science}
}
```

Source data of this dataset:
```
@article{10.1093/bioinformatics/btz682,
    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 = {https://doi.org/10.1093/bioinformatics/btz682},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/36/4/1234/48983216/bioinformatics\_36\_4\_1234.pdf},
}
```
and
```
https://github.com/spyysalo/s800
```

### Contributions

Thanks to [@edugp](https://github.com/edugp) for adding this dataset.