| traitCurationTest_ichihara | | testProject150806 | 4 | ichihara_hisako | Hisako Ichihara | 2023-11-29 | Testing | |
| ichiharatest_150825_2 | | test | 0 | ichihara_hisako | Hisako Ichihara | 2015-09-11 | Testing | |
| PGDBj_disease_curation1 | | disease curation test | 348 | ichihara_hisako | ichihara_hisako | 2023-12-03 | Testing | |
| Test_Project | | | 0 | Ingenerf | ingenerf | 2023-11-29 | Testing | |
| bionlp-st-bb3-2016-training | | Entity (bacteria, habitats and geographical places) annotation to the training dataset of the BioNLP-ST 2016 BB task.
For more information, please refer to bionlp-st-bb3-2016-development and bionlp-st-bb3-2016-test.
Bacteria
Bacteria entities are annotated as contiguous spans of text that contains a full unambiguous prokaryote taxon name, the type label is Bacteria. The Bacteria type is a taxon, at any taxonomic level from phylum (Eubacteria) to strain. The category that the text entities have to be assigned to is the most specific and unique category of the NCBI taxonomy resource. In case a given strain, or a group of strains is not referenced by NCBI, it is assigned with the closest taxid in the taxonomy.
Habitat
Habitat entities are annotated as spans of text that contains a complete mention of a potential habitat for bacteria, the type label is Habitat. Habitat entities are assigned one or several concepts from the habitat subpart of the OntoBiotope ontology. The assigned concepts are as specific as possible. OntoBiotope defines most relevant microorganism habitats from all areas considered by microbial ecology (hosts, natural environment, anthropized environments, food, medical, etc.). Habitat entities are rarely referential entities, they are usually noun phrases including properties and modifiers. There are rare cases of habitats referred with adjectives or verbs. The spans are generally contiguous but some of them are discontinuous in order to cope with conjunctions.
Geographical
Geographical entities are geographical and organization places denoted by official names. | 1.28 K | INRA | Yue Wang | 2023-11-29 | Released | |
| glycoprotein | | glycoprotein annotation | 54 | issaku yamada | ISSAKU YAMADA | 2023-11-29 | Testing | |
| uniprot-human | | Uniprot proteins for human | 21.8 K | Jin-Dong Kim | Jin-Dong Kim | 2023-11-29 | Testing | |
| JF-test | | A test corpus for exploring this service | 9 | Johan Frid | johanf | 2023-12-03 | Testing | |
| bionlp-st-gro-2013-development | | The development data set of the BioNLP-ST 2013 GRO task, including 50 MEDLINE abstracts that are annotated with concepts and relations of the Gene Regulation Ontology (GRO; http://www.ebi.ac.uk/Rebholz-srv/GRO/GRO.html) | 2.66 K | Jung-jae Kim | Jung-jae Kim | 2023-11-29 | Testing | |
| bionlp-st-gro-2013-training | | The training data set of the BioNLP-ST 2013 GRO task, including 150 MEDLINE abstracts that are annotated with concepts and relations of the Gene Regulation Ontology (GRO; http://www.ebi.ac.uk/Rebholz-srv/GRO/GRO.html) | 8.02 K | Jung-jae Kim | Jung-jae Kim | 2023-11-29 | Testing | |
| CyanoBase | | Cyanobacteria are prokaryotic organisms that have served as important model organisms for studying oxygenic photosynthesis and have played a significant role in the Earthfs history as primary producers of atmospheric oxygen.
Publication: http://www.aclweb.org/anthology/W12-2430 | 1.1 K | Kazusa DNA Research Institute and Database Center for Life Science (DBCLS) | Yue Wang | 2023-11-26 | Released | |
| parkinson | | parkinson's disease | 1.55 K | Kyungeun | Kyungeun | 2023-11-29 | Testing | |
| CoMAGC | | In order to access the large amount of information in biomedical literature about genes implicated in various cancers both efficiently and accurately, the aid of text mining (TM) systems is invaluable. Current TM systems do target either gene-cancer relations or biological processes involving genes and cancers, but the former type produces information not comprehensive enough to explain how a gene affects a cancer, and the latter does not provide a concise summary of gene-cancer relations. In order to support the development of TM systems that are specifically targeting gene-cancer relations but are still able to capture complex information in biomedical sentences, we publish CoMAGC, a corpus with multi- faceted annotations of gene-cancer relations. In CoMAGC, a piece of annotation is composed of four semantically orthogonal concepts that together express 1) how a gene changes, 2) how a cancer changes and 3) the causality between the gene and the cancer. The multi-faceted annotations are shown to have high inter-annotator agreement. In addition, the annotations in CoMAGC allow us to infer the prospective roles of genes in cancers and to classify the genes into three classes according to the inferred roles. We encode the mapping between multi-faceted annotations and gene classes into 10 inference rules. The inference rules produce results with high accuracy as measured against human annotations. CoMAGC consists of 821 sentences on prostate, breast and ovarian cancers. Currently, the corpus deals with changes in gene expression levels among other types of gene changes. | 1.53 K | Lee et al | Hee-Jin Lee | 2023-11-24 | Released | |
| CellFinder | | CellFinder corpus | 4.75 K | Mariana Neves, Alexander Damaschun, Andreas Kurtz, Ulf Leser | Mariana Neves | 2023-11-27 | Released | |
| CHEMDNER-training-test | | The training subset of the CHEMDNER corpus | 29.4 K | Martin Krallinger et al. | Jin-Dong Kim | 2023-11-27 | Testing | |
| genia-medco-coref | | Coreference annotation made to the Genia corpus, following the MUC annotation scheme. It is a product of the collaboration between the Genia and the MedCo projects. | 45.9 K | MedCo project & Genia project | Jin-Dong Kim | 2023-11-24 | Developing | |
| LODQA | | | 0 | Michel Dumontier | micheldumontier | 2015-02-20 | Testing | |
| ICU_characters | | | 286 | ming-qi-wang | | 2023-11-27 | | |
| bionlp-st-cg-2013-training | | The training dataset from the cancer genetics task in the BioNLP Shared Task 2013.
Composed of anatomical and molecular entities. | 10.9 K | NaCTeM | Yue Wang | 2023-11-28 | Released | |
| AnEM_abstracts | | 250 documents selected randomly from citation abstracts
Entity types: organism subdivision, anatomical system, organ, multi-tissue structure, tissue, cell, developing anatomical structure, cellular component, organism substance, immaterial anatomical entity and pathological formation
Together with AnEM_full-texts, it is probably the largest manually annotated corpus on anatomical entities. | 1.91 K | NaCTeM | Yue Wang | 2023-11-29 | Released | |
