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Name TDescription# Ann.AuthorMaintainerUpdated_atStatus

241-260 / 593 show all
guideline annotations 5 guideline annotations with custom vocab0Tiffany Leung2015-11-07Developing
hahm_test hahm_test0hahmkaist_nlp2019-04-02Testing
hydroxychloroquine 2.59 KJin-Dong Kim2023-11-29Developing
HZAU_wangshuguang_Just-for-fun 4wangshuguangwangshuguang2023-11-29Testing
ICD10 Annotation for disease names as defined in ICD101.6 KDBCLSJin-Dong Kim2023-11-29Developing
ichiharatest_150825 test0ichihara_hisakoHisako Ichihara2023-11-29Testing
ichiharatest_150825_2 test0ichihara_hisakoHisako Ichihara2015-09-11Testing
ichiharatest_150825_3 test0ichihara_hisakoHisako Ichihara2023-11-26Testing
ichiharatest_150830_1 test99Hisako Ichihara2023-11-29Testing
ICU_characters 286ming-qi-wang2023-11-27
ID_800 0Suexuan2024-08-25
IMDB-NLP Annotations for chunking and semantic role labeling based on in-memory databases.02016-05-06Uploading
Inflammaging Inflammation axis23.4 Malo332023-11-24Released
infoMED_PsA testing45Timmtimmo2023-11-30Testing
JF-test A test corpus for exploring this service9Johan Fridjohanf2023-12-03Testing
JF-test2 0johanf2020-03-26Testing
jnlpba-st-training The training data used in the task came from the GENIA version 3.02 corpus, This was formed from a controlled search on MEDLINE using the MeSH terms "human", "blood cells" and "transcription factors". From this search, 1,999 abstracts were selected and hand annotated according to a small taxonomy of 48 classes based on a chemical classification. Among the classes, 36 terminal classes were used to annotate the GENIA corpus. For the shared task only the classes protein, DNA, RNA, cell line and cell type were used. The first three incorporate several subclasses from the original taxonomy while the last two are interesting in order to make the task realistic for post-processing by a potential template filling application. The publication year of the training set ranges over 1990~1999.51.1 KGENIAYue Wang2023-11-26Released
JournalClub 170AikoHIRAKI2023-11-29Developing
KAIST_NLP_Annotation10 4.76 Kkaist_nlp2023-11-29Developing
KAIST_NLP_Annotation11 4.88 Kkaist_nlp2023-11-26Developing
Name T# Ann.AuthorMaintainerUpdated_atStatus

241-260 / 593 show all
guideline annotations 0Tiffany Leung2015-11-07Developing
hahm_test 0hahmkaist_nlp2019-04-02Testing
hydroxychloroquine 2.59 KJin-Dong Kim2023-11-29Developing
HZAU_wangshuguang_Just-for-fun 4wangshuguangwangshuguang2023-11-29Testing
ICD10 1.6 KDBCLSJin-Dong Kim2023-11-29Developing
ichiharatest_150825 0ichihara_hisakoHisako Ichihara2023-11-29Testing
ichiharatest_150825_2 0ichihara_hisakoHisako Ichihara2015-09-11Testing
ichiharatest_150825_3 0ichihara_hisakoHisako Ichihara2023-11-26Testing
ichiharatest_150830_1 99Hisako Ichihara2023-11-29Testing
ICU_characters 286ming-qi-wang2023-11-27
ID_800 0Suexuan2024-08-25
IMDB-NLP 02016-05-06Uploading
Inflammaging 23.4 Malo332023-11-24Released
infoMED_PsA 45Timmtimmo2023-11-30Testing
JF-test 9Johan Fridjohanf2023-12-03Testing
JF-test2 0johanf2020-03-26Testing
jnlpba-st-training 51.1 KGENIAYue Wang2023-11-26Released
JournalClub 170AikoHIRAKI2023-11-29Developing
KAIST_NLP_Annotation10 4.76 Kkaist_nlp2023-11-29Developing
KAIST_NLP_Annotation11 4.88 Kkaist_nlp2023-11-26Developing