| PMID_PMC | | Updated annotation(s) of PMC data and results | alo33 | 2023-03-20 | |
| Preeclampsia | | Preeclampsia-related annotations for text mining | Jin-Dong Kim | 2019-03-10 | |
| AnEM | | the largest manually annotated corpus on anatomical entities | Yue Wang | 2019-04-03 | |
| Grays | | | okubo | 2019-03-11 | |
| SMAFIRA | | Web tool | zebet | 2021-01-27 | |
| Glycan Abbreviation | | Glycan-Abbreviation in GlycoNAVI | ISSAKU YAMADA | 2019-07-17 | |
| Testing | | | ewha-bio | 2020-05-31 | |
| Test_Collection | | naja | ingenerf | 2019-07-16 | |
| med-device-indications | | PMA approval statements describing indications of class III devices | therightstef | 2020-02-05 | |
| ngly1-deficiency | | | Nuria | 2020-02-06 | |
| 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. | dpavot | 2020-02-21 | |
| new_collection | | | serenity | 2020-09-29 | |
| WMT Biomedical Task | | | wmtbio | 2020-12-18 | |
| LASIGE(old) | | The global motivation is the creation of parallel multilingual datasets for text mining systems in COVID-19-related literature. The expected contribution of the project will be the annotation of a multilingual parallel dataset (EN-ES and EN-PT), providing this resource to the community to improve the text mining research on COVID-19-related literature. | pruas_18 | 2021-01-20 | |
| GlycoBiology | | Annotations made to the titles and abstracts of the journal 'GlycoBiology' | Jin-Dong Kim | 2019-03-10 | |
| 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. | Jin-Dong Kim | 2021-10-22 | |
| 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.
| Jin-Dong Kim | 2020-11-20 | |
| LitCovid-sample | | Various annotations to a sample set of LitCovid, to demonstrate potential of harmonized various annotations. | Jin-Dong Kim | 2021-01-14 | |
| CORD-19-sample-annotation | | | Jin-Dong Kim | 2020-04-21 | |
| LitCovid | | | Jin-Dong Kim | 2021-10-18 | |
