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PubMed 12,137,340
PMC 134,086
CORD-19 36,823
PubMed-fre 7,510
PubMed-ger 6,732
FirstAuthors 1,276
GrayAnatomy 66

Projects (442)

Manual Automatic Sum
Released 46 25 71
Beta 7 17 24
Developing 85 52 137
Testing 95 49 146
N/S 51 4 55
Sum 290 150 442

Collections (31)

Name Description # proj. Updated at
LitCovid 8 2021-01-18
LitCovid-sample Various annotations to a sample set of LitCovid, to demonstrate potential of harmonized various annotations. 16 2021-01-14
WMT Biomedical Task 0 2020-12-18
LitCovid-v1 This collection includes the result from the Covid-19 Virtual Hackathon. LitCovid is a comprehensive literature resource on the subject of Covid-19 collected by NCBI: https://www.ncbi.nlm.nih.gov/research/coronavirus/ Since the literature dataset was released, several groups are producing annotations to the dataset. To facilitate a venue for aggregating the valuable resources which are highly relevant to each other, and should be much more useful when they can be accessed together, this PubAnnotation collection is set up. It is a part of the Covid19-PubAnnotation project. In this collection, the LitCovid-docs project contains all the documents contained in the LitCovid literature collection, and the other projects are annotation datasets contributed by various groups. It is an open collection, which means anyone who wants to contribute can do so, in the following way: take the documents in the, LitCovid-docs project produce annotation to the texts based on your resource, and contribute the annotation back to this collection: create your own project at PubAnnotaiton, upload your annotation to the project (HowTo), and add the project to this collection. 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. Should you have any question, please feel free to mail to admin@pubannotation.org. 12 2020-11-20
new_collection 1 2020-09-29
Testing 0 2020-05-31
CORD-19-sample-annotation 10 2020-04-21
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. 12 2020-04-14
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

Shared Tasks (11)

Name Description Updated at
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