> top > users > Jin-Dong Kim
Jin-Dong Kim
User info
Collections
NameDescriptionUpdated at
11-12 / 12 show all
CORD-19CORD-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.2020-04-14
Glycosmos6This 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 2023-11-16
Projects
NameTDescription# Ann.Updated at Status
61-70 / 160 show all
Glycosmos6-GlycoEpitopeAutomatic annotation by PD-GlycoEpitope.19.9 K2023-11-28Developing
GlyCosmos600-CLO1.73 K2023-11-28Testing
pubmed-2016abstracts published in 201602023-11-28
twitter-test132023-11-28Developing
LitCovid-sample-Pubtator3.86 K2023-11-28Uploading
uniprot-mouseProtein annotation based on UniProt11.5 K2023-11-28Developing
pubmed-sentences-benchmarkA benchmark data for text segmentation into sentences. The source of annotation is the GENIA treebank v1.0. Following is the process taken. began with the GENIA treebank v1.0. sentence annotations were extracted and converted to PubAnnotation JSON. uploaded. 12 abstracts met alignment failure. among the 12 failure cases, 4 had a dot('.') character where there should be colon (':'). They were manually fixed then successfully uploaded: 7903907, 8053950, 8508358, 9415639. among the 12 failed abstracts, 8 were "250 word truncation" cases. They were manually fixed and successfully uploaded. During the fixing, manual annotations were added for the missing pieces of text. 30 abstracts had extra text in the end, indicating copyright statement, e.g., "Copyright 1998 Academic Press." They were annotated as a sentence in GTB. However, the text did not exist anymore in PubMed. Therefore, the extra texts were removed, together with the sentence annotation to them. 18.4 K2023-11-28Released
LitCovid-PD-FMA-UBERON1.3 M2023-11-28Developing
testtesttest5.2 K2023-11-29Developing
bionlp-st-ge-2016-uniprotUniProt protein annotation to the benchmark data set of BioNLP-ST 2016 GE task: reference data set (bionlp-st-ge-2016-reference) and test data set (bionlp-st-ge-2016-test). The annotations are produced based on a dictionary which is semi-automatically compiled for the 34 full paper articles included in the benchmark data set (20 in the reference data set + 14 in the test data set). For detailed information about BioNLP-ST GE 2016 task data sets, please refer to the benchmark reference data set (bionlp-st-ge-2016-reference) and benchmark test data set (bionlp-st-ge-2016-test). 16.2 K2023-11-29Beta
Automatic annotators
Editors
NameDescription
1-2 / 2
TextAE-oldTextAE version 4, which was the latest stable version until Apr. 19, 2020.
TextAETextAE version 5, which enables edition of attributes of denotations.