| Virus300 | | 300 abstracts from virology journals annotated with viral proteins and species | 0 | http://aclweb.org/anthology/W/W17/W17-2311.pdf | helencook | 2017-08-07 | Released | |
| tmVarCorpus | | Wei C-H, Harris BR, Kao H-Y, Lu Z (2013) tmVar: A text mining approach for extracting sequence variants in biomedical literature, Bioinformatics, 29(11) 1433-1439, doi:10.1093/bioinformatics/btt156. | 1.43 K | Chih-Hsuan Wei , Bethany R. Harris , Hung-Yu Kao and Zhiyong Lu | Chih-Hsuan Wei | 2023-11-24 | Released | |
| 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 | |
| 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 | |
| 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 | |
| PennBioIE | | The PennBioIE corpus (0.9) covers two domains of biomedical knowledge. One is the inhibition of the cytochrome P450 family of enzymes (CYP450 or CYP for short) , and the other domain is the molecular genetics of dance (oncology or onco for short). | 23.8 K | UPenn Biomedical Information Extraction Project | Yue Wang | 2023-11-26 | Released | |
| 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 | |
| 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 | |
| bionlp-st-ge-2016-test-proteins | | Protein annotations to the benchmark test data set of the BioNLP-ST 2016 GE task.
A participant of the GE task may import the documents and annotations of this project to his/her own project, to begin with producing event annotations.
For more details, please refer to the benchmark test data set (bionlp-st-ge-2016-test).
| 4.34 K | DBCLS | Jin-Dong Kim | 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 | |
| 123123123 | | 123123123 | 150 | | yaoxinzhi | 2023-11-27 | Released | |
| CellFinder | | CellFinder corpus | 4.75 K | Mariana Neves, Alexander Damaschun, Andreas Kurtz, Ulf Leser | Mariana Neves | 2023-11-27 | Released | |
| SCAI-Test | | A small corpus for the evaluation of dictionaries containing chemical entities.
Publication: http://www.scai.fraunhofer.de/fileadmin/images/bio/data_mining/paper/kolarik2008.pdf
Original source: https://www.scai.fraunhofer.de/en/business-research-areas/bioinformatics/downloads/corpora-for-chemical-entity-recognition.html | 1.21 K | CALBC Project | Yue Wang | 2023-11-28 | Released | |
| SMAFIRA_Feedback_Research_Goal | | | 15 | | zebet | 2023-11-28 | Released | |
| 2015-BEL-Sample-2 | | The 295 BEL statements for sample set used for the 2015 BioCreative challenge. | 11.4 K | Fabio Rinaldi | Nico Colic | 2023-11-28 | Released | |
| PubMed_Structured_Abstracts | | Sections (zones) as retrieved from PubMed. | 131 K | | zebet | 2023-11-28 | Released | |
| SPECIES800 | | SPECIES 800 (S800): an abstract-based manually annotated corpus. S800 comprises 800 PubMed abstracts in which organism mentions were identified and mapped to the corresponding NCBI Taxonomy identifiers.
Described in:
The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text.
Pafilis E, Frankild SP, Fanini L, Faulwetter S, Pavloudi C, et al. (2013). PLoS ONE, 2013, 8(6): e65390. doi:10.1371/journal.pone.0065390 | 3.71 K | Evangelos Pafilis, Sune P. Frankild, Lucia Fanini, Sarah Faulwetter, Christina Pavloudi, Aikaterini Vasileiadou, Christos Arvanitidis, Lars Juhl Jensen | evangelos | 2023-11-28 | 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 | |
| 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 | |
| pubmed-sentences-benchmark | | A benchmark data for text segmentation into sentences.
The source of annotation is the GENIA treebank v1.0.
Following is the process taken.
began with the GENIA treebank v1.0.
sentence annotations were extracted and converted to PubAnnotation JSON.
uploaded. 12 abstracts met alignment failure.
among the 12 failure cases, 4 had a dot('.') character where there should be colon (':'). They were manually fixed then successfully uploaded: 7903907, 8053950, 8508358, 9415639.
among the 12 failed abstracts, 8 were "250 word truncation" cases. They were manually fixed and successfully uploaded. During the fixing, manual annotations were added for the missing pieces of text.
30 abstracts had extra text in the end, indicating copyright statement, e.g., "Copyright 1998 Academic Press." They were annotated as a sentence in GTB. However, the text did not exist anymore in PubMed. Therefore, the extra texts were removed, together with the sentence annotation to them.
| 18.4 K | GENIA project | Jin-Dong Kim | 2023-11-28 | Released | |
