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Annotations made to the titles and abstracts of the journal 'GlycoBiology'
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.
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 firstname.lastname@example.org email@example.com, 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.
Preeclampsia-related annotations for text mining
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 firstname.lastname@example.org.
The 2016 edition of the Genia event extraction (GE) task organized within BioNLP-ST 2016
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collection of PubMed articles which appear in the BioASQ sample data set.
The training subset of the CHEMDNER corpus
Sample annotation of PubTator produced by Zhiyong Lu et al.
FMA ontology-based annotation to GlycoBiology abstracts
Sample annotation of MetaMep, produced by Aronson, et al. An overview of MetaMap: historical perspective and recent advances, JAMIA 2010
Protein annotation based on UniProt
annotation for EDAM topics
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To pull the pre-computed chemical annotation from PubTator.
To pull the pre-computed gene annotation from PubTator.
To pull the pre-computed Species annotation from PubTator.
To pull the pre-computed disease annotation from PubTator.
To pull the pre-computed mutation annotation from PubTator.
A discourse analyzer developed by Univ. Manchester.
A batch annotator for NGLY1 deficiency
It annotates for anatomical entities, based on the UBERON-AE dictionary on PubDictionaries. Threshold is set to 0.85.
PubDictionaries annotation with the MONDO dictionary.
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TextAE version 4, which was the latest stable version until Apr. 19, 2020.
TextAE version 5, which enables edition of attributes of denotations.
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