| LitCovid-ArguminSci | | Discourse elements for the documents in the LitCovid dataset.
Annotations were automatically predicted by the ArguminSci tool (https://github.com/anlausch/ArguminSci) | 4.9 K | | zebet | 2023-11-27 | Released | |
| bionlp-st-ge-2016-coref | | Coreference annotation to the benchmark data set (reference and test) of BioNLP-ST 2016 GE task.
For detailed information, please refer to the benchmark reference data set (bionlp-st-ge-2016-reference) and benchmark test data set (bionlp-st-ge-2016-test). | 853 | DBCLS | Jin-Dong Kim | 2023-11-28 | Released | |
| bionlp-st-2016-SeeDev-training | | Entities and event annotations from the training set of the BioNLP-ST 2016 SeeDev task.
SeeDev task focuses on seed storage and reserve accumulation on the model organism, Arabidopsis thaliana. The SeeDev task is based on the knowledge model Gene Regulation Network for Arabidopsis (GRNA) that meets the needs of text-mining (i.e. manual annotation of texts and automatic information extraction), experimental data indexing and retrieval and reuse in other plant systems. It is also expected to meet the requirements of the integration of the text knowledge with knowledge derived from experimental data in view of modeling in systems biology.
GRNA model defines 16 different types of entities, and 22 types of event (in five sets of event types) that may be combined in complex events.
For more information, please refer to the task website
All annotations :
Train set
Development set
Test set (without events)
| 35 | | EstelleChaix | 2023-11-28 | Released | |
| pubmed-sentences-benchmark | | A benchmark data for text segmentation into sentences.
The source of annotation is the GENIA treebank v1.0.
Following is the process taken.
began with the GENIA treebank v1.0.
sentence annotations were extracted and converted to PubAnnotation JSON.
uploaded. 12 abstracts met alignment failure.
among the 12 failure cases, 4 had a dot('.') character where there should be colon (':'). They were manually fixed then successfully uploaded: 7903907, 8053950, 8508358, 9415639.
among the 12 failed abstracts, 8 were "250 word truncation" cases. They were manually fixed and successfully uploaded. During the fixing, manual annotations were added for the missing pieces of text.
30 abstracts had extra text in the end, indicating copyright statement, e.g., "Copyright 1998 Academic Press." They were annotated as a sentence in GTB. However, the text did not exist anymore in PubMed. Therefore, the extra texts were removed, together with the sentence annotation to them.
| 18.4 K | GENIA project | Jin-Dong Kim | 2023-11-28 | Released | |
| bionlp-st-id-2011-training | | The training dataset from the infectious diseases (ID) task in the BioNLP Shared Task 2011.
Entity types: - Genes and gene products: gene, RNA, and protein name mentions. - Two-component systems: mentions of the names of two-component regulatory systems, frequently embedding the names of the two Proteins forming the system.- Chemicals: mentions of chemical compounds such as "NaCL".- Organisms: mentions of organism names or organism specification through specific properties (e.g. "graRS mutant").- Regulons/Operons: mentions of names of specific regulons and operons. | 5.61 K | University of Tokyo Tsujii Laboratory, NaCTeM and Biocomplexity Institute of Virginia Tech | Yue Wang | 2023-11-28 | Released | |
| bionlp-st-gro-2013-training | | The training data set of the BioNLP-ST 2013 GRO task, including 150 MEDLINE abstracts that are annotated with concepts and relations of the Gene Regulation Ontology (GRO; http://www.ebi.ac.uk/Rebholz-srv/GRO/GRO.html) | 8.02 K | Jung-jae Kim | Jung-jae Kim | 2023-11-29 | Testing | |
| bionlp-st-bb3-2016-training | | Entity (bacteria, habitats and geographical places) annotation to the training dataset of the BioNLP-ST 2016 BB task.
For more information, please refer to bionlp-st-bb3-2016-development and bionlp-st-bb3-2016-test.
Bacteria
Bacteria entities are annotated as contiguous spans of text that contains a full unambiguous prokaryote taxon name, the type label is Bacteria. The Bacteria type is a taxon, at any taxonomic level from phylum (Eubacteria) to strain. The category that the text entities have to be assigned to is the most specific and unique category of the NCBI taxonomy resource. In case a given strain, or a group of strains is not referenced by NCBI, it is assigned with the closest taxid in the taxonomy.
Habitat
Habitat entities are annotated as spans of text that contains a complete mention of a potential habitat for bacteria, the type label is Habitat. Habitat entities are assigned one or several concepts from the habitat subpart of the OntoBiotope ontology. The assigned concepts are as specific as possible. OntoBiotope defines most relevant microorganism habitats from all areas considered by microbial ecology (hosts, natural environment, anthropized environments, food, medical, etc.). Habitat entities are rarely referential entities, they are usually noun phrases including properties and modifiers. There are rare cases of habitats referred with adjectives or verbs. The spans are generally contiguous but some of them are discontinuous in order to cope with conjunctions.
Geographical
Geographical entities are geographical and organization places denoted by official names. | 1.28 K | INRA | Yue Wang | 2023-11-29 | Released | |
| bionlp-st-2016-SeeDev-dev | | Entities and event annotations from the development set of the BioNLP-ST 2016 SeeDev task.
SeeDev task focuses on seed storage and reserve accumulation on the model organism, Arabidopsis thaliana. The SeeDev task is based on the knowledge model Gene Regulation Network for Arabidopsis (GRNA) that meets the needs of text-mining (i.e. manual annotation of texts and automatic information extraction), experimental data indexing and retrieval and reuse in other plant systems. It is also expected to meet the requirements of the integration of the text knowledge with knowledge derived from experimental data in view of modeling in systems biology.
GRNA model defines 16 different types of entities, and 22 types of event (in five sets of event types) that may be combined in complex events.
For more information, please refer to the task website
All annotations :
Train set
Development set
Test set (without events)
| 61 | | EstelleChaix | 2023-11-29 | Released | |
| PIR-corpus2 | | The protein tag was used to tag proteins, or protein-associated or -related objects, such as domains, pathways, expression of gene.
Annotation guideline: http://pir.georgetown.edu/pirwww/about/doc/manietal.pdf | 5.52 K | University of Delaware and Georgetown University Medical Center | Yue Wang | 2023-11-29 | Released | |
| RDoCTask1SampleData | | Each annotation file contains an annotated abstract with an RDoC category. Each title span in these sample data is annotated with the corresponding related RDoC construct, although the RDoC category would apply for the entire abstract. The annotation data are formatted as json files. Please refer to the following page for a more detailed description of the json format http://www.pubannotation.org/docs/annotation-format/. | 20 | | mmanani1s | 2023-11-29 | Released | |
| SPECIES800 | | SPECIES 800 (S800): an abstract-based manually annotated corpus. S800 comprises 800 PubMed abstracts in which organism mentions were identified and mapped to the corresponding NCBI Taxonomy identifiers.
Described in:
The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text.
Pafilis E, Frankild SP, Fanini L, Faulwetter S, Pavloudi C, et al. (2013). PLoS ONE, 2013, 8(6): e65390. doi:10.1371/journal.pone.0065390 | 3.71 K | Evangelos Pafilis, Sune P. Frankild, Lucia Fanini, Sarah Faulwetter, Christina Pavloudi, Aikaterini Vasileiadou, Christos Arvanitidis, Lars Juhl Jensen | evangelos | 2023-11-28 | Released | |
| LitCovid-v1-docs | | A comprehensive literature resource on the subject of Covid-19 is collected by NCBI:
https://www.ncbi.nlm.nih.gov/research/coronavirus/
The LitCovid project@PubAnnotation is a collection of the titles and abstracts of the LitCovid dataset, for the people who want to perform text mining analysis. Please note that if you produce some annotation to the documents in this project, and contribute the annotation back to PubAnnotation, it will become publicly available together with contribution from other people.
If you want to contribute your annotation to PubAnnotation, please refer to the documentation page:
http://www.pubannotation.org/docs/submit-annotation/
The list of the PMID is sourced from here
The 6 entries of the following PMIDs could not be included because they were not available from PubMed:32161394,
32104909,
32090470,
32076224,
32161394
32188956,
32238946.
Below is a notice from the original LitCovid dataset:
PUBLIC DOMAIN NOTICE
National Center for Biotechnology Information
This software/database is a "United States Government Work" under the
terms of the United States Copyright Act. It was written as part of
the author's official duties as a United States Government employee and
thus cannot be copyrighted. This software/database is freely available
to the public for use. The National Library of Medicine and the U.S.
Government have not placed any restriction on its use or reproduction.
Although all reasonable efforts have been taken to ensure the accuracy
and reliability of the software and data, the NLM and the U.S.
Government do not and cannot warrant the performance or results that
may be obtained by using this software or data. The NLM and the U.S.
Government disclaim all warranties, express or implied, including
warranties of performance, merchantability or fitness for any particular
purpose.
Please cite the authors in any work or product based on this material :
Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193
| 0 | | Jin-Dong Kim | 2023-11-29 | Released | |
| GENIAcorpus | | multi_cell (1,782)
mono_cell (222)
virus (2,136)
protein_family_or_group (8,002)
protein_complex (2,394)
protein_molecule (21,290)
protein_subunit (942)
protein_substructure (129)
protein_domain_or_region (1,044)
protein_other (97)
peptide (521)
amino_acid_monomer (784)
DNA_family_or_group (332)
DNA_molecule (664)
DNA_substructure (2)
DNA_domain_or_region (39)
DNA_other (16)
RNA_family_or_group (1,545)
RNA_molecule (554)
RNA_substructure (106)
RNA_domain_or_region (8,237)
RNA_other (48)
polynucleotide (259)
nucleotide (243)
lipid (2,375)
carbohydrate (99)
other_organic_compound (4,113)
body_part (461)
tissue (706)
cell_type (7,473)
cell_component (679)
cell_line (4,129)
other_artificial_source (211)
inorganic (258)
atom (342)
other (21,056)
| 78.9 K | GENIA Project | Yue Wang | 2023-11-29 | Released | |
| OryzaGP_2021_v2 | | OryzaGP_2021_v2 will use a second annotator | 208 K | | larmande | 2023-11-29 | Developing | |
| AnEM_abstracts | | 250 documents selected randomly from citation abstracts
Entity types: organism subdivision, anatomical system, organ, multi-tissue structure, tissue, cell, developing anatomical structure, cellular component, organism substance, immaterial anatomical entity and pathological formation
Together with AnEM_full-texts, it is probably the largest manually annotated corpus on anatomical entities. | 1.91 K | NaCTeM | Yue Wang | 2023-11-29 | Released | |
| LitCovid-sample-docs | | A comprehensive literature resource on the subject of Covid-19 is collected by NCBI:
https://www.ncbi.nlm.nih.gov/research/coronavirus/
The LitCovid project@PubAnnotation is a collection of the titles and abstracts of the LitCovid dataset, for the people who want to perform text mining analysis. Please note that if you produce some annotation to the documents in this project, and contribute the annotation back to PubAnnotation, it will become publicly available together with contribution from other people.
If you want to contribute your annotation to PubAnnotation, please refer to the documentation page:
http://www.pubannotation.org/docs/submit-annotation/
The list of the PMID is sourced from here
Below is a notice from the original LitCovid dataset:
PUBLIC DOMAIN NOTICE
National Center for Biotechnology Information
This software/database is a "United States Government Work" under the
terms of the United States Copyright Act. It was written as part of
the author's official duties as a United States Government employee and
thus cannot be copyrighted. This software/database is freely available
to the public for use. The National Library of Medicine and the U.S.
Government have not placed any restriction on its use or reproduction.
Although all reasonable efforts have been taken to ensure the accuracy
and reliability of the software and data, the NLM and the U.S.
Government do not and cannot warrant the performance or results that
may be obtained by using this software or data. The NLM and the U.S.
Government disclaim all warranties, express or implied, including
warranties of performance, merchantability or fitness for any particular
purpose.
Please cite the authors in any work or product based on this material :
Chen Q, Allot A, & Lu Z. (2020) Keep up with the latest coronavirus research, Nature 579:193
| 0 | | Jin-Dong Kim | 2023-11-29 | Uploading | |
