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User 'Jin-Dong Kim'

Collections

NameDescriptionUpdated at
1-7 / 7
GlycoBiologyAnnotations made to the titles and abstracts of the journal 'GlycoBiology'2019-03-10
PreeclampsiaPreeclampsia-related annotations for text mining2019-03-10
bionlp-st-ge-2016The 2016 edition of the Genia event extraction (GE) task organized within BioNLP-ST 20162019-03-11
GlyCosmos600A 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.2019-06-11
LitCovidLitCovid 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. 2020-04-14
CORD-19-sample-annotation2020-04-21
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

Projects

NameTDescription # Ann.Updated atStatus
81-90 / 91 show all
CORD-19_Custom_license_subsetThe Custom license subset of the CORD-19 dataset. The documents in this project will be updated as the CORD-19 dataset grows. See the COVID DATASET LICENSE AGREEMENT.5.08 M2020-04-10Released
CORD-19_Non-commercial_use_subsetThe Non commercial use subset of the CORD-19 dataset. The documents in this project will be updated as the CORD-19 dataset grows. See the COVID DATASET LICENSE AGREEMENT.02020-03-23Released
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 K2016-05-22Beta
metamap-sampleSample annotation of MetaMep, produced by Aronson, et al. An overview of MetaMap: historical perspective and recent advances, JAMIA 201010.9 K2016-12-19Testing
pubtator-sampleSample annotation of PubTator produced by Zhiyong Lu et al.282016-01-19Testing
semrep-sampleSample annotation of SemRep, produced by Rindflesch, et al. Rindflesch, T.C. and Fiszman, M. (2003). The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. Journal of Biomedical Informatics, 36(6):462-477.11.1 K2017-07-15Testing
sentencesSentence segmentation annotation1.27 M2019-07-11Testing
LitCovid-sentencesSentence 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 K2020-04-14Released
GlycoConjugate-collectionThe PubMed entries (titles and abstracts) from the journal of GlycoConjugate02018-02-09Developing
CHEMDNER-training-testThe training subset of the CHEMDNER corpus29.4 K2015-03-23Testing

Automatic annotators

NameDescription
1-10 / 27 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-FMA-PAEPhysical Anatomical Entities from FMA

Editors

NameDescription
1-3 / 3
TextAETextAE version 5, which enables edition of attributes of denotations.
TextAE-oldTextAE version 4, which was the latest stable version until Apr. 19, 2020.
TextAE-DevIt is a development version of TextAE. While this version has richer features, there is a chance of some bugs.