| PubMed-German-test | | A collection of PubMed abstracts which are written in German | 0 | | Jin-Dong Kim | 2023-11-24 | Developing | |
| PubMed-2017 | | abstracts published in 2017. | 0 | | Jin-Dong Kim | 2023-11-24 | Developing | |
| speech-test | | | 6 | | Jin-Dong Kim | 2023-11-26 | Testing | |
| CORD-19-SciBite-sentences | | | 11.2 K | | Jin-Dong Kim | 2023-11-26 | Testing | |
| LitCovid-PD-FMA-UBERON-v1 | | PubDictionaries annotation for anatomy terms - updated at 2020-04-20
Disease term annotation based on FMA and Uberon. Version 2020-04-20.
The terms in FMA and Uberon are loaded in PubDictionaries
(FMA and
Uberon), with which the annotations in this project are produced.
The parameter configuration used for this project is
here for FMA and
there for Uberon.
Note that it is an automatically generated dictionary-based annotation. It will be updated periodically, as the documents are increased, and the dictionary is improved. | 4.3 K | | Jin-Dong Kim | 2023-11-27 | Released | |
| glycosmos-test-structure-v1 | | | 471 | | ISSAKU YAMADA | 2023-11-27 | Testing | |
| LitCovid-PubTatorCentral | | Named-entities for the documents in the LitCovid dataset. Annotations were automatically predicted by the PubTatorCentral tool (https://www.ncbi.nlm.nih.gov/research/pubtator/) | 4.64 K | | zebet | 2023-11-27 | Released | |
| GlyCosmos600-GlycoProteins | | GlycoProtein annotations were made using the glycoprotein-name dictionary on PubDictionaries:
http://pubannotation.org/projects/GlyCosmos600-docs
The documents were imported from the GlyCosmos600-docs project:
http://pubannotation.org/projects/GlyCosmos600-docs | 3.68 K | | Jin-Dong Kim | 2023-11-27 | Testing | |
| disease_gene_microbe_small | | Small version (48 abstract that mention both Crohns and S. aureus) for development purposes
Abbreviation: dgm Content: annotated abstracts on Crohn’s disease or on on Staphylococcus aureus (according to the jensenlab.org indexing resources) Entity types: (three for a start, organisms (NCBI Taxonomy taxa), disease (Disease Ontology terms), human genes (ENSEMBL proteins) Aim: Explore indirect associations of diseases to microbial species in this corpus via gene co-mentions | 536 | | evangelos | 2023-11-27 | Testing | |
| consensus_PMA_Age_Indications | | | 1.7 K | | laurenc | 2023-11-28 | Beta | |
| PMID_GLOBAL | | Global sentencer tagging of public PMID abstracts.
Open and publicly available to the global community. | 2.24 M | | alo33 | 2023-11-24 | Developing | |
| 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-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 | |
| LitCovid-docs | | Updated at 2021-01-12
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
| 18 | | Jin-Dong Kim | 2023-11-28 | Testing | |
| 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 | |
| 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 | |
| LitCovid-sample-PD-UBERON | | PubDictionaries annotation for UBERON terms - updated at 2020-04-30
It is annotation for anatomical entities based on Uberon.
The terms in Uberon are uploaded in PubDictionaries
(Uberon),
with which the annotations in this project are produced.
The parameter configuration used for this project is
here.
Note that it is an automatically generated dictionary-based annotation.
It will be updated periodically, as the documents are increased, and the dictionary is improved.
| 310 | | Jin-Dong Kim | 2023-11-28 | Beta | |
| 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 | |
| Zierdiyeerkenaili_800_3 | | | 4.12 K | | Zierdiyeerkenaili | 2024-09-27 | | |
| OryzaGP_2021_v2 | | OryzaGP_2021_v2 will use a second annotator | 208 K | | larmande | 2023-11-29 | Developing | |
