| 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 | |
| FSU-PRGE | | A new broad-coverage corpus composed of 3,306 MEDLINE abstracts dealing with gene and protein mentions.
The annotation process was semi-automatic.
Publication: http://aclweb.org/anthology/W/W10/W10-1838.pdf | 59.5 K | CALBC Project | Yue Wang | 2023-11-26 | Released | |
| TEST0 | | | 3.37 M | | Yue Wang | 2023-11-24 | | |
| 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 | |
| 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 K | GENIA | Yue Wang | 2023-11-26 | Released | |
| funRiceGenes-exact | | | 841 | | Yue Wang | 2023-11-28 | Developing | |
| bionlp-st-pc-2013-training | | The training dataset from the pathway curation (PC) task in the BioNLP Shared Task 2013.
The entity types defined in the PC task are simple chemical, gene or gene product, complex and cellular component. | 7.86 K | NaCTeM and KISTI | Yue Wang | 2023-11-27 | Released | |
| PIR-corpus1 | | The Protein Information Resource (PIR) is not biased towards any particular biomedical domain, and is expected to provide more diverse protein names in a given sample size.
Annotation category: protein, compound-protein, acronym. | 4.44 K | University of Delaware and Georgetown University Medical Center | Yue Wang | 2023-11-27 | Released | |
| 0_colil | | | 781 K | | Yue Wang | 2023-11-24 | | |
| DisGeNET5_gene_disease | | The file contains gene-disease associations obtained by text mining MEDLINE abstracts using the BeFree system including the gene and disease off sets. | 2.04 M | IBI Group | Yue Wang | 2023-11-24 | Released | |
| AIMed | | The AIMed corpus is one of the most widely used corpora for protein-protein interaction extraction. The protein annotations are either parts of the protein interaction annotations, or are uninvolved in any protein interaction annotation.
Publication: http://www.cs.utexas.edu/~ml/papers/bionlp-aimed-04.pdf | 4.04 K | The University of Texas at Austin | Yue Wang | 2023-11-27 | Testing | |
| OryzaGP | | A dataset for Named Entity Recognition for rice gene | 29.1 K | Huy Do and Pierre Larmande | Yue Wang | 2023-11-24 | Uploading | |
| AnEM_full-texts | | 250 documents selected randomly from full-text papers
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_abstracts, it is probably the largest manually annotated corpus on anatomical entities. | 687 | NaCTeM | Yue Wang | 2023-11-29 | Uploading | |
| PubMed_ArguminSci | | Predictions for PubMed automatically extracted with the ArguminSci tool (https://github.com/anlausch/ArguminSci). | 777 K | | zebet | 2023-11-24 | Released | |
| GoldHamster | | | 285 K | | zebet | 2023-11-29 | Beta | |
| 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 | |
| SMAFIRA_Feedback_Labels | | | 0 | | zebet | 2021-01-21 | Developing | |
| SMAFIRA-Case-Studies-19735549 | | From: https://github.com/SMAFIRA/c_corpus/blob/master/SMAFIRAc_0.4_Annotations.csv | 0 | | zebet | 2023-11-30 | Developing | |
| SMAFIRA_OGER_TEXT | | | 116 | | zebet | 2023-11-28 | Developing | |
| LitCovid-PubTatorCentral | | Named-entities for the documents in the LitCovid dataset. Annotations were automatically predicted by the PubTatorCentral tool (https://www.ncbi.nlm.nih.gov/research/pubtator/) | 4.64 K | | zebet | 2023-11-27 | Released | |
