> 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 atStatus
111-120 / 159 show all
LitCoin-PubTator-for-TuningA set of randomly selected PubMed articles with PubTator annotation. The labels of PubTator annotations are converted to corresponding labels for LitCoin as follows: 'Gene' -> 'GeneOrGeneProduct', 'Disease' -> 'DiseaseOrPhenotypicFeature', 'Chemical' -> 'ChemicalEntity' 'Species' -> 'OrganismTaxon' 'Mutation' -> 'SequenceVariant' 'CellLine' -> 'CellLine'14.2 K2023-11-29
bionlp-st-ge-2016-referenceIt is the benchmark reference data set of the BioNLP-ST 2016 GE task. It includes Genia-style event annotations to 20 full paper articles which are about NFκB proteins. The task is to develop an automatic annotation system which can produce annotation similar to the annotation in this data set as much as possible. For evaluation of the performance of a participating system, the system needs to produce annotations to the documents in the benchmark test data set (bionlp-st-ge-2016-test). GE 2016 benchmark data set is provided as multi-layer annotations which include: bionlp-st-ge-2016-reference: benchmark reference data set (this project) bionlp-st-ge-2016-test: benchmark test data set (annotations are blined) bionlp-st-ge-2016-test-proteins: protein annotation to the benchmark test data set Following is supporting resources: bionlp-st-ge-2016-coref: coreference annotation bionlp-st-ge-2016-uniprot: Protein annotation with UniProt IDs. pmc-enju-pas: dependency parsing result produced by Enju UBERON-AE: annotation for anatomical entities as defined in UBERON ICD10: annotation for disease names as defined in ICD10 GO-BP: annotation for biological process names as defined in GO GO-CC: annotation for cellular component names as defined in GO A SPARQL-driven search interface is provided at http://bionlp.dbcls.jp/sparql.14.4 K2023-11-29Released
LitCoin-training-merged14.8 K2023-11-24
LitCoin-GeneOrGeneProduct-v0https://pubdictionaries.org/text_annotation.json?dictionary=NCBIGene-NER&threshold=0.85&abbreviation=true15.8 K2023-11-29
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
LitCovid-sentences-v1Sentence segmentation of all the texts in the LitCovid literature. The segmentation is automatically obtained using the TextSentencer annotation service developed and maintained by DBCLS.16.5 K2023-11-27Released
GO-CCAnnotation for cellular components as defined in the "Cellular Component" subtree of Gene Ontology17.6 K2023-11-30Developing
test-21061417.8 K2024-01-05Testing
preeclampsia_genes17.8 K2023-11-29Developing
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
Automatic annotators
NameDescription
1-10 / 38 show all
PubTator-ChemicalTo pull the pre-computed chemical annotation from PubTator.
PubTator-GeneTo pull the pre-computed gene annotation from PubTator.
PubTator-SpeciesTo pull the pre-computed Species annotation from PubTator.
TextSentencersentence segmentation
PubTator-DiseaseTo pull the pre-computed disease annotation from PubTator.
PubTator-MutationTo pull the pre-computed mutation annotation from PubTator.
discourse-simplifierA discourse analyzer developed by Univ. Manchester.
PD-NGLY1-deficiency-BA batch annotator for NGLY1 deficiency
PD-UBERON-AEIt annotates for anatomical entities, based on the UBERON-AE dictionary on PubDictionaries. Threshold is set to 0.85.
PD-MONDOPubDictionaries annotation with the MONDO dictionary.
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.