> ホーム
ニュース

ドキュメント

PubMed 11,947,753
CORD-19 29,315
PMC 14,226
FirstAuthors 1,276
GrayAnatomy 66

プロジェクト (359)

手動 自動 Sum
公開中 45 22 67
ベータ 4 7 11
開発中 67 26 93
テスト中 85 47 134
N/S 45 4 49
Sum 249 108 359

グループ (25)

名前 説明 proj.数 最終更新日
CORD-19 CORD-19 (COVID-19 Open Research Dataset) is a free, open resource for the global research community provided by the Allen Institute for AI: https://pages.semanticscholar.org/coronavirus-research. As of 2020-03-20, it contains over 29,000 full text articles. This CORD-19 collection at PubAnnotation is prepared for the purpose of collecting annotations to the texts, so that they can be easily accessed and utilized. If you want to contribute with your annotation, take the documents in the CORD-19_All_docs project, produce your annotation to the texts using your annotation system, and contribute the annotation back to PubAnnotation (HowTo). All the contributed annotations will become publicly available. Please note that, during uploading your annotation data, you do not need to be worried about slight changes in the text: PubAnnotation will automatically catch them and adjust the positions appropriately. Once you have uploaded your annotation, please notify it to admin@pubannotation.org admin@pubannotation.org, so that it can be included in this collection, which will make your annotation much easily findable. Note that as the CORD-19 dataset grows, the documents in this collection also will be updated. IMPORTANT: CORD-19 License agreement requires that the dataset must be used for text and data mining only. 9 2020-03-23
LitCovid Thanks to NCBI, a comprehensive literature resource on the subject of Covid-19 is being collected: https://www.ncbi.nlm.nih.gov/research/coronavirus/ This collection is to collect annotations to the documents of the dataset. If you want to contribute, take the documents in the LitCovid-docs project produce annotation to the texts based on your resource, and contribute the annotation back to PubAnnotation (HowTo). All the contributed annotations will become publicly available. Please note that, during uploading your annotation data, you do not need to be worried about slight changes in the text: PubAnnotation will automatically catch them and adjust the positions appropriately. Once you have uploaded your annotation, please notify it to admin@pubannotation.org admin@pubannotation.org, so that it can be included in this collection, which will make your annotation much easily findable. 7 2020-03-18
Annotation of Human Phenotype-Gene Relations - Identification of Negative, False, and Unknown Relations Accessible negative results are relevant for researchers and clinicians not only to limit their search space but also to prevent the costly re-exploration of the hypothesis. However, most biomedical relation extraction data sets do not seek to distinguish between a false and a negative relation. A false relation should express a context where the entities are not related. In contrast, a negative relation should express a context where there is an affirmation of no association between the two entities. Furthermore, when we are dealing with data sets created using distant supervision techniques, we also have some false negative relations that constitute undocumented/unknown relations. Unknown relations are good examples to further exploration by researchers and clinicians. We propose to improve the distinction between these two concepts, by revising the false relations of the PGR corpus with regular expressions. 3 2020-02-21
ngly1-deficiency 11 2020-02-06
med-device-indications PMA approval statements describing indications of class III devices 3 2020-02-05
Glycan Abbreviation Glycan-Abbreviation in GlycoNAVI 1 2019-07-17
Test_Collection naja 0 2019-07-16
GlyCosmos600 A random collection of 600 PubMed abstracts from 6 glycobiology-related journals: Glycobiology, Glycoconjugate journal, The Journal of biological chemistry, Journal of proteome research, Journal of proteomics, and Carbohydrate research. The whole PMIDs were collected on June 11, 2019. From each journal, 100 PMIDs were randomly sampled. 17 2019-06-11
AnEM the largest manually annotated corpus on anatomical entities 2 2019-04-03
PIR Protein Information Resource (PIR) 2 2019-03-12

Shared Tasks (11)

名前 説明 最終更新日
bionlp-ost-19-SeeDev-binary SeeDev-binary subtask of the SeeDev task proposed at BioNLP-OST 2019. SeeDev-binary is a binary relation extraction task. Homepage of SeeDev: https://sites.google.com/view/seedev2019/home 2020-02-07
bionlp-ost-19-BB-kb-ner BB-kb+ner subtask of the Bacteria Biotope task proposed at BioNLP-OST 2019. BB-kb+ner is an entity recognition, normalization and relation extraction task. Homepage of Bacteria Biotope: https://sites.google.com/view/bb-2019/home 2020-02-07
bionlp-ost-19-BB-kb BB-kb subtask of the Bacteria Biotope task proposed at BioNLP-OST 2019. BB-kb is an entity normalization and relation extraction task. Homepage of Bacteria Biotope: https://sites.google.com/view/bb-2019/home 2020-02-07
bionlp-ost-19-BB-rel-ner BB-rel+ner subtask of the Bacteria Biotope task proposed at BioNLP-OST 2019. BB-rel+ner is an entity recognition and relation extraction task. Homepage of Bacteria Biotope: https://sites.google.com/view/bb-2019/home 2020-02-07
bionlp-ost-19-BB-rel BB-rel subtask of the Bacteria Biotope task proposed at BioNLP-OST 2019. BB-rel is a relation extraction task. Homepage of Bacteria Biotope: https://sites.google.com/view/bb-2019/home 2020-02-07
bionlp-ost-19-BB-norm-ner BB-norm+ner subtask of the Bacteria Biotope task proposed at BioNLP-OST 2019. BB-norm+ner is an entity recognition and normalization task. 2020-02-07
bionlp-ost-19-BB-norm BB-norm subtask of the Bacteria Biotope task proposed at BioNLP-OST 2019. BB-norm is an entity normalization task. 2020-02-07
190926 2019-09-26
RDoCTask 2019-03-16
AGAC This is the collection for AGAC track data, a subtask in BioNLP OST 2019, Hong Kong. 2019-03-13