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NameTDescription# Ann.AuthorMaintainerUpdated_atStatus

41-60 / 381 show all
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 Kzebet2020-04-01Released
LitCovid-OGER Using OGER (http://www.ontogene.org/resources/oger) to detect entities from 10 different vocabularies9.31 KFabio RinaldiNico Colic2020-04-02Released
LitCovid-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 0Jin-Dong Kim2020-04-08Released
CORD-19_Custom_license_subset The Custom license subset of the CORD-19 dataset. The documents in this project will be updated as the CORD-19 dataset grows. See the COVID DATASET LICENSE AGREEMENT.5.08 MJin-Dong Kim2020-04-10Released
LitCovid-sentences Sentence segmentation of all the texts in the LitCovid literature. The segmentation is automatically obtained using the TextSentencer annotation service developed and maintained by DBCLS.16.5 KJin-Dong Kim2020-04-14Released
LitCovid-PubTator 22.6 KJin-Dong Kim2020-04-21Released
CORD-19-PD-MONDO PubDictionaries annotation for MONDO terms - updated at 2020-04-30 It is disease term annotation based on MONDO. Version 2020-04-20. The terms in MONDO are loaded in PubDictionaries, 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.6.32 MJin-Dong Kim2020-04-30Released
CORD-19-PD-UBERON PubDictionaries annotation for UBERON terms - updated at 2020-04-30 It is disease term annotation 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.1.42 MJin-Dong Kim2020-04-30Released
LitCovid-PD-FMA-UBERON 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 KJin-Dong Kim2020-05-10Released
LitCovid-PD-MONDO PubDictionaries annotation for disease terms - updated at 2020-04-20 It is based on MONDO Version 2020-04-20. The terms in MONDO are loaded in PubDictionaries, 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.13.4 KJin-Dong Kim2020-05-10Released
CORD-19-PD-HP PubDictionaries annotation for HP terms - updated at 2020-04-30 Disease term annotation based on HP. Version 2020-04-20. The terms in HP are loaded in PubDictionaries, 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.1.15 MJin-Dong Kim2020-05-12Released
LitCovid-PD-HP PubDictionaries annotation for human phenotype terms - updated at 2020-04-20 Disease term annotation based on HP. Version 2020-04-20. The terms in HP are loaded in PubDictionaries, 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.3.03 KJin-Dong Kim2020-05-25Released
LitCovid-OGER-BB Using OGER (www.ontogene.com) and Biobert to obtain annotations for 10 different vocabularies.308 KFabio RinaldiNico Colic2020-06-04Released
SMAFIRA_Feedback_Research_Goal 15zebet2020-09-09Released
bionlp-st-ge-2016-reference It is the benchmark reference data set of the BioNLP-ST 2016 GE task. It includes Genia-style event annotations to 20 full paper articles which are about NFκB proteins. The task is to develop an automatic annotation system which can produce annotation similar to the annotation in this data set as much as possible. For evaluation of the performance of a participating system, the system needs to produce annotations to the documents in the benchmark test data set (bionlp-st-ge-2016-test). GE 2016 benchmark data set is provided as multi-layer annotations which include: bionlp-st-ge-2016-reference: benchmark reference data set (this project) bionlp-st-ge-2016-test: benchmark test data set (annotations are blined) bionlp-st-ge-2016-test-proteins: protein annotation to the benchmark test data set Following is supporting resources: bionlp-st-ge-2016-coref: coreference annotation bionlp-st-ge-2016-uniprot: Protein annotation with UniProt IDs. pmc-enju-pas: dependency parsing result produced by Enju UBERON-AE: annotation for anatomical entities as defined in UBERON ICD10: annotation for disease names as defined in ICD10 GO-BP: annotation for biological process names as defined in GO GO-CC: annotation for cellular component names as defined in GO A SPARQL-driven search interface is provided at http://bionlp.dbcls.jp/sparql.14.4 KDBCLSJin-Dong Kim2020-09-13Released
bionlp-st-ge-2016-reference-tees NER and event extraction produced by TEES (with the default GE11 model) for the 20 full papers used in the BioNLP 2016 GE task reference corpus.14.6 KNico Colic Nico Colic2020-09-13Released
CellFinder CellFinder corpus4.75 KMariana Neves, Alexander Damaschun, Andreas Kurtz, Ulf LeserMariana Neves2020-09-15Released
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) 35EstelleChaix2020-09-15Released
bionlp-st-2016-SeeDev-test Entities annotations from the test 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) 184EstelleChaix2020-09-15Released
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) 61EstelleChaix2020-09-15Released
NameT# Ann.AuthorMaintainerUpdated_atStatus

41-60 / 381 show all
LitCovid-PubTatorCentral 4.64 Kzebet2020-04-01Released
LitCovid-OGER 9.31 KFabio RinaldiNico Colic2020-04-02Released
LitCovid-docs 0Jin-Dong Kim2020-04-08Released
CORD-19_Custom_license_subset 5.08 MJin-Dong Kim2020-04-10Released
LitCovid-sentences 16.5 KJin-Dong Kim2020-04-14Released
LitCovid-PubTator 22.6 KJin-Dong Kim2020-04-21Released
CORD-19-PD-MONDO 6.32 MJin-Dong Kim2020-04-30Released
CORD-19-PD-UBERON 1.42 MJin-Dong Kim2020-04-30Released
LitCovid-PD-FMA-UBERON 4.3 KJin-Dong Kim2020-05-10Released
LitCovid-PD-MONDO 13.4 KJin-Dong Kim2020-05-10Released
CORD-19-PD-HP 1.15 MJin-Dong Kim2020-05-12Released
LitCovid-PD-HP 3.03 KJin-Dong Kim2020-05-25Released
LitCovid-OGER-BB 308 KFabio RinaldiNico Colic2020-06-04Released
SMAFIRA_Feedback_Research_Goal 15zebet2020-09-09Released
bionlp-st-ge-2016-reference 14.4 KDBCLSJin-Dong Kim2020-09-13Released
bionlp-st-ge-2016-reference-tees 14.6 KNico Colic Nico Colic2020-09-13Released
CellFinder 4.75 KMariana Neves, Alexander Damaschun, Andreas Kurtz, Ulf LeserMariana Neves2020-09-15Released
bionlp-st-2016-SeeDev-training 35EstelleChaix2020-09-15Released
bionlp-st-2016-SeeDev-test 184EstelleChaix2020-09-15Released
bionlp-st-2016-SeeDev-dev 61EstelleChaix2020-09-15Released