| tagtog-demo | | demo documents produced by tagtog annotations | 262 | | tagtog | 2023-11-27 | Developing | |
| bionlp-st-ge-2016-spacy-parsed | | Dependency parses produced by spaCy parser, and part-of-speech tags produced by Stanford tagger (with the wsj-0-18-left3words-nodistsim model). The exact procedure is described here. Data set contains the 34 full paper articles used in the BioNLP 2016 GE task.
| 225 K | Nico Colic | Nico Colic | 2023-11-29 | Released | |
| craft-ca-core-ex-dev | | Development data for CRAFT CA shared task, core concepts + EXTENSIONS. This project contains the development (training) annotations for the Concept Annotation task of the CRAFT Shared Task 2019. This particular set of concept annotations is the "core+extensions" set. See the task description for details, but this set contains annotations to concepts that appear in the original 10 Open Biomedical Ontologies used for annotation PLUS annotations to extension classes created using the core concepts. | 90.2 K | University of Colorado Anschutz Medical Campus | craft-st | 2023-11-29 | Released | |
| craft-ca-core-dev | | Development data for CRAFT CA shared task, core concepts only. This project contains the development (training) annotations for the Concept Annotation task of the CRAFT Shared Task 2019. This particular set of concept annotations is the "core" set. See the task description for details, but this set contains only annotations to concepts that appear in the original 10 Open Biomedical Ontologies used for annotation. (That is to say, it does not contain any annotations to extension classes). | 59.8 K | University of Colorado Anschutz Medical Campus | craft-st | 2023-11-29 | Released | |
| craft-sa-dev | | Development data for CRAFT SA shared task. This project contains the development (training) annotations for the Structural Annotation task of the CRAFT Shared Task 2019. This particular set contains token and sentence annotations with tokens linked via dependency relations. These dependency relations were automatically generated using the manually curated CRAFT constituency treebank files as input. | 490 K | University of Colorado Anschutz Medical Campus | craft-st | 2023-11-27 | Released | |
| blah6 | | device Annotator | 374 | | slee7268 | 2023-11-28 | Testing | |
| LitCovid-ArguminSci | | Discourse elements for the documents in the LitCovid dataset.
Annotations were automatically predicted by the ArguminSci tool (https://github.com/anlausch/ArguminSci) | 4.9 K | | zebet | 2023-11-27 | Released | |
| LitCoin-MONDO_bioort2019 | | DiseaeseOrPhenotypicFeature | 3.72 K | | Eisuke Dohi | 2023-11-29 | Testing | |
| PGDBj_disease_curation1 | | disease curation test | 348 | ichihara_hisako | ichihara_hisako | 2023-12-03 | Testing | |
| Nanbyo-330-20171127 | | Disease descriptions extracted from MHLW | 19.8 K | | Toyofumi Fujiwara | 2023-11-26 | Testing | |
| DisGeNET | | Disease-Gene association annotation. | 3.12 M | Nuria Queralt | Jin-Dong Kim | 2023-11-24 | Beta | |
| DLUT931 | | DLUT NLP Lab.Test our event extration result for 16 GE task. | 4.57 K | | DLUT931 | 2023-11-30 | Testing | |
| PMC-KEGG | | Documents from PMC including the word KEGG, with names of software tools and databases marked. | 27 | | yucca | 2023-11-28 | Developing | |
| c_corpus | | Documents included in the c_corpus: https://github.com/SMAFIRA/c_corpus/blob/master/SMAFIRAc_0.4_Annotations.csv | 107 K | | | 2023-11-29 | Released | |
| RDoCTask1SampleData | | Each annotation file contains an annotated abstract with an RDoC category. Each title span in these sample data is annotated with the corresponding related RDoC construct, although the RDoC category would apply for the entire abstract. The annotation data are formatted as json files. Please refer to the following page for a more detailed description of the json format http://www.pubannotation.org/docs/annotation-format/. | 20 | | mmanani1s | 2023-11-29 | Released | |
| RDoCTask2SampleData | | Each annotation file contains an annotated abstract with the most relevant sentence. The relevant sentence is annotated with the RDoC category name. The annotation data are formatted as json files. Please refer to the following page for a more detailed description of the json format http://www.pubannotation.org/docs/annotation-format/.
| 10 | | mmanani1s | 2023-11-29 | Released | |
| Grays_part1 | | Embryology | 1.44 K | | okubo | 2023-11-30 | Testing | |
| Gene_Chemical | | EMU abstract annotation | 0 | | zhoukaiyin | 2023-11-29 | Developing | |
| bionlp-st-2016-SeeDev-dev | | Entities and event annotations from the development set of the BioNLP-ST 2016 SeeDev task.
SeeDev task focuses on seed storage and reserve accumulation on the model organism, Arabidopsis thaliana. The SeeDev task is based on the knowledge model Gene Regulation Network for Arabidopsis (GRNA) that meets the needs of text-mining (i.e. manual annotation of texts and automatic information extraction), experimental data indexing and retrieval and reuse in other plant systems. It is also expected to meet the requirements of the integration of the text knowledge with knowledge derived from experimental data in view of modeling in systems biology.
GRNA model defines 16 different types of entities, and 22 types of event (in five sets of event types) that may be combined in complex events.
For more information, please refer to the task website
All annotations :
Train set
Development set
Test set (without events)
| 61 | | EstelleChaix | 2023-11-29 | Released | |
| bionlp-st-2016-SeeDev-training | | Entities and event annotations from the training set of the BioNLP-ST 2016 SeeDev task.
SeeDev task focuses on seed storage and reserve accumulation on the model organism, Arabidopsis thaliana. The SeeDev task is based on the knowledge model Gene Regulation Network for Arabidopsis (GRNA) that meets the needs of text-mining (i.e. manual annotation of texts and automatic information extraction), experimental data indexing and retrieval and reuse in other plant systems. It is also expected to meet the requirements of the integration of the text knowledge with knowledge derived from experimental data in view of modeling in systems biology.
GRNA model defines 16 different types of entities, and 22 types of event (in five sets of event types) that may be combined in complex events.
For more information, please refer to the task website
All annotations :
Train set
Development set
Test set (without events)
| 35 | | EstelleChaix | 2023-11-28 | Released | |
