Datasets:
linnaeus

Task Categories: token-classification
Languages: en
Multilinguality: monolingual
Size Categories: 10K<n<100K
Licenses: unknown
Language Creators: expert-generated
Annotations Creators: expert-generated
Source Datasets: original
Dataset Preview Go to dataset viewer
id (string)tokens (json)ner_tags (json)
0
[ "Scp160p", ",", "a", "multiple", "KH", "-", "domain", "protein", ",", "is", "a", "component", "of", "mRNP", "complexes", "in", "yeast" ]
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[ "Abstract" ]
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[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
93
[ "Western", "blot", "analysis" ]
[ 0, 0, 0 ]
94
[ "Western", "blot", "analysis", "was", "performed", "essentially", "as", "described", "previously", "(", "19", ")", "." ]
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
95
[ "Briefly", ",", "samples", "to", "be", "analyzed", "were", "mixed", "with", "sample", "buffer", ",", "boiled", ",", "electrophoresed", "through", "a", "10", "%", "SDS", "-", "polyacrylamide", "gel", ",", "and", "electro", "-", "blotted", "onto", "nitrocellulose", "(", "Bio", "-", "Rad", ")", "." ]
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
96
[ "FLAG", "-", "Scp160p", "fusion", "protein", "was", "detected", "by", "incubation", "of", "the", "filter", "with", "mouse", "M2", "anti", "-", "FLAG", "monoclonal", "antibody", "(", "10", "micro", "g", "/", "ml", "final", "concentration", ")", ",", "followed", "by", "HRP", "-", "conjugated", "sheep", "anti", "-", "mouse", "secondary", "antibody", "(", "Amersham", ")", ",", "diluted", "1", ":", "5000", "as", "per", "the", "manufacturer", "'", "s", "instructions", ",", "and", "ECL", "reagent", "(", "Amersham", ")", ",", "followed", "by", "exposure", "to", "X", "-", "ray", "film", "." ]
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
97
[ "HA", "-", "tagged", "Scp160p", "and", "HA", "-", "tagged", "Bfr1p", "were", "detected", "using", "the", "12CA5", "mAb", "(", "Boehringer", "Mannheim", ")", "at", "a", "final", "concentration", "of", "0", ".", "8", "micro", "g", "/", "ml", ";", "Pab1p", "and", "Pub1p", "were", "detected", "using", "1G1", "mAb", "at", "1", ":", "5000", ",", "and", "4C3", "mAb", "at", "1", ":", "1000", ",", "respectively", ",", "both", "generous", "gifts", "from", "Dr", "Maurice", "Swanson", "(", "22", ",", "23", ")", "." ]
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
98
[ "Where", "appropriate", ",", "films", "were", "analyzed", "by", "scanning", "densitometry", "(", "Molecular", "Dynamics", ")", ",", "and", "quantitated", "using", "ImageQuant", "software", "(", "Molecular", "Dynamics", ")", "." ]
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
99
[ "Colloidal", "G250", "coomassie", "staining" ]
[ 0, 0, 0, 0 ]
End of preview (truncated to 100 rows)

Dataset Card for [Dataset Name]

Dataset Summary

LINNAEUS is a general-purpose dictionary matching software, capable of processing multiple types of document formats in the biomedical domain (MEDLINE, PMC, BMC, OTMI, text, etc.). It can produce multiple types of output (XML, HTML, tab-separated-value file, or save to a database). It also contains methods for acting as a server (including load balancing across several servers), allowing clients to request matching over a network. A package with files for recognizing and identifying species names is available for LINNAEUS, showing 94% recall and 97% precision compared to LINNAEUS-species-corpus.

Supported Tasks and Leaderboards

This dataset is used for species Named Entity Recognition.

Languages

The dataset is in English.

Dataset Structure

Data Instances

An example from the dataset is:

{'id': '2',
'tokens': ['Scp160p', 'is', 'a', '160', 'kDa', 'protein', 'in', 'the', 'yeast', 'Saccharomyces', 'cerevisiae', 'that', 'contains', '14', 'repeats', 'of', 'the', 'hnRNP', 'K', '-', 'homology', '(', 'KH', ')', 'domain', ',', 'and', 'demonstrates', 'significant', 'sequence', 'homology', 'to', 'a', 'family', 'of', 'proteins', 'collectively', 'known', 'as', 'vigilins', '.'],
'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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

name train validation test
linnaeus 11936 4079 7143

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

[More Information Needed]

Citation Information

@article{Gerner2010,
abstract = {The task of recognizing and identifying species names in biomedical literature has recently been regarded as critical for a number of applications in text and data mining, including gene name recognition, species-specific document retrieval, and semantic enrichment of biomedical articles.},
author = {Gerner, Martin and Nenadic, Goran and Bergman, Casey M},
doi = {10.1186/1471-2105-11-85},
issn = {1471-2105},
journal = {BMC Bioinformatics},
number = {1},
pages = {85},
title = {{LINNAEUS: A species name identification system for biomedical literature}},
url = {https://doi.org/10.1186/1471-2105-11-85},
volume = {11},
year = {2010}
}

Contributions

Thanks to @edugp for adding this dataset.

Update on GitHub
Papers with Code