| yzq800 | | | yaoziqian1 | 2024-08-25 | |
| DPCIRCT | | | Suexuan | 2024-08-20 | |
| Glycosmos6 | | This collection contains annotation projects
which target all the PubMed abstracts (at the time of January 14, 2022) from the 6 glycobiology-related journals:
Glycobiology
Glycoconjugate journal
The Journal of biological chemistry
Journal of proteome research
Journal of proteomics
Carbohydrate research
| Jin-Dong Kim | 2023-11-16 | |
| LitCoin | | | Jin-Dong Kim | 2021-12-14 | |
| 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 | | | Jin-Dong Kim | 2021-10-18 | |
| NeuroBridge Test | | | raywang | 2021-05-13 | |
| LASIGE: Annotating a multilingual COVID-19-related corpus for BLAH7 | | The global motivation is the creation of parallel multilingual datasets for text mining systems in COVID-19-related literature. Tracking the most recent advances in the COVID-19-related research is essential given the novelty of the disease and its impact on society. Still, the pace of publication requires automatic approaches to access and organize the knowledge that keeps being produced every day. It is necessary to develop text mining pipelines to assist in that task, which is only possible with evaluation datasets. However, there is a lack of COVID-19-related datasets, even more, if considering other languages besides English. The expected contribution of the project will be the annotation of a multilingual parallel dataset (EN-PT), providing this resource to the community to improve the text mining research on COVID-19-related literature. | dpavot | 2021-02-17 | |
| SMAFIRA | | Web tool | zebet | 2021-01-27 | |
| 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 | |
| LitCovid-sample | | Various annotations to a sample set of LitCovid, to demonstrate potential of harmonized various annotations. | Jin-Dong Kim | 2021-01-14 | |
| WMT Biomedical Task | | | wmtbio | 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.
| Jin-Dong Kim | 2020-11-20 | |
| new_collection | | | serenity | 2020-09-29 | |
| Testing | | | ewha-bio | 2020-05-31 | |
| CORD-19-sample-annotation | | | Jin-Dong Kim | 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. | Jin-Dong Kim | 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. | dpavot | 2020-02-21 | |
| 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 | ldeleger | 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 | ldeleger | 2020-02-07 | |
