README.md

metadata

license: cc-by-3.0
language:
  - en
pretty_name: Character Similarity Dataset
description: >-
  Collection of textual trait descriptions of vertebrates (primarily fish) along
  with the corresponding ontology based similarity measures between trait
  description pairs. The distance is estimated using the Phenoscape
  Knowledgebase as the ontology.
task_categories:
  - feature-extraction
tags:
  - biology
  - organism
  - animals
  - fish
  - traits
  - ontology
  - phenoscape
size_categories: 10K<n<100K
configs:
  - config_name: full_data
    data_files:
      - split: train
        path: all/*_TRAINING.tsv.gz
      - split: test
        path: all/*_ALL_NON_TRAIN.tsv.gz
    default: true
  - config_name: characiformes
    data_files:
      - split: train
        path: characiformes/*_TRAINING.tsv.gz
      - split: test
        path: characiformes/*_ALL_NON_TRAIN.tsv.gz
  - config_name: cypriniformes
    data_files:
      - split: train
        path: cypriniformes/*_TRAINING.tsv.gz
      - split: test
        path: cypriniformes/*_ALL_NON_TRAIN.tsv.gz
  - config_name: gymnotiformes
    data_files:
      - split: train
        path: gymnotiformes/*_TRAINING.tsv.gz
      - split: test
        path: gymnotiformes/*_ALL_NON_TRAIN.tsv.gz
  - config_name: siluriformes
    data_files:
      - split: train
        path: siluriformes/*_TRAINING.tsv.gz
      - split: test
        path: siluriformes/*_ALL_NON_TRAIN.tsv.gz

Dataset Card for Character Similarity Dataset

Dataset Details

The Character Similarity Dataset is a collection of textual trait descriptions along with the corresponding ontology based similarity measures between trait description pairs. The distance is estimated using the Phenoscape Knowledgebase as the ontology. The Knowledgebase is built upon a number of OBO ontologies, most importantly the Uberon anatomy ontology.

Dataset Description

• Curated by: Jim Balhoff, Soumyashree Kar, Juan Garcia, Hilmar Lapp
• Language(s) (NLP): English
• Repository: Imageomics/char-sim
• Paper: Coming soon!

The Character Similarity Dataset is a collection of 19K textual trait descriptions of fish and other vertebrates collected from the Phenoscape Knowledgebase. The dataset also contains the corresponding pairwise similarity measures between trait descriptors (i.e., maxIC, Jaccard, SimGIC). These metrics estimate semantic similarity between the ontological representation of the traits descriptions per the Phenoscape Knowledgebase. The goal is to use this pairwise similarities to inform an embedding space that preserves the structure of the underlying ontology.

Supported Tasks and Leaderboards

Task: Aligned feature extraction. Metric: Spearman's correlation coefficient.

Model	Test set
Trait2Vec	0.7057

Dataset Structure

raw-source/
    phenex-data-merged.ofn.gz
    phenoscape-kb-tbox-classified.ttl.gz
processed-data/
    all/
        data_{percentage}p_TRAINING.tsv.gz
        data_{percentage}p_ALL_NON_TRAIN.tsv.gz
        data_{percentage}p_NON_OVERLAP.tsv.gz
    characiformes/
        data_{percentage}p_TRAINING.tsv.gz
        data_{percentage}p_ALL_NON_TRAIN.tsv.gz
        data_{percentage}p_NON_OVERLAP.tsv.gz
    cypriniformes/
        data_{percentage}p_TRAINING.tsv.gz
        data_{percentage}p_ALL_NON_TRAIN.tsv.gz
        data_{percentage}p_NON_OVERLAP.tsv.gz
    gymnotiformes/
        data_{percentage}p_TRAINING.tsv.gz
        data_{percentage}p_ALL_NON_TRAIN.tsv.gz
        data_{percentage}p_NON_OVERLAP.tsv.gz
    siruliformes/
        data_{percentage}p_TRAINING.tsv.gz
        data_{percentage}p_ALL_NON_TRAIN.tsv.gz
        data_{percentage}p_NON_OVERLAP.tsv.gz

phenex-data-merged.ofn.gz and phenoscape-kb-tbox-classified.ttl.gz are raw data files built as part of the Phenoscape Knowledgebase construction pipeline. Running the processing script creates the four subset folders (characiformes/, cypriniformes/, gymnotiformes/, and siruliformes/, each an order of fish), then combines their data into the all/ directory to create the training and test datasets.

Note: percentage is the parameter passed for the percentage of the data to use for training; in this case, percentage = 80.

Data Instances

Percentage is the proportion of data that will be used for training (i.e. data_{percentage}p_TRAINING.tsv.gz). In case the percentage is smaller than 100, the remaining proportion of the dataset is stored in data{percentage}p_ALL_NON_TRAIN.tsv.gz and a subset of this in data_{percentage}p_NON_OVERLAP.tsv.gz. Each of this files corresponds to a

Data Fields

data_{percentage}p_TRAINING.tsv.gz: [ADD SUMMARY HERE + descriptions of columns below]

• id_1:
• id_2:
• maxIC: Ontology based measure
• jaccard: Ontology based measure
• simGIC: Ontology based measure
• order: Pairwise score indices
• character_1:
• desc_1: Textual trait description.
• character_2:
• desc_2: Textual trait description. [More Information Needed]

  Please be sure to also cite the original data source:

  @ARTICLE{Balhoff2016-aw,
    title    = "The Phenoscape Knowledgebase: tools and {APIs} for computing
                across phenotypes from evolutionary diversity and model organisms",
    author   = "Balhoff, James P and {Phenoscape project team}",
    journal  = "bioRxiv",
    pages    =  071951,
    abstract = "The Phenoscape Knowledgebase (KB) is an ontology-driven database
                that combines existing phenotype annotations from model organism
                databases with new phenotype annotations from the evolutionary
                literature. Phenoscape curators have created phenotype annotations
                for more than 5,000 species and higher taxa, by defining
                computable phenotype concepts for more than 20,000 character
                states from over 160 published phylogenetic studies. These
                phenotype concepts are in the form of Entity-Quality (EQ)
                compositions which incorporate terms from the Uberon anatomy
                ontology, the Biospatial Ontology (BSPO), and the Phenotype and
                Trait Ontology (PATO). Taxonomic concepts are drawn from the
                Vertebrate Taxonomy Ontology (VTO). This knowledge of comparative
                biodiversity is linked to potentially relevant developmental
                genetic mechanisms by importing associations of genes to
                phenotypic effects and gene expression locations from zebrafish
                (ZFIN), mouse (MGI), Xenopus (Xenbase), and human (Human Phenotype
                Ontology project). Thus far, the Phenoscape KB has been used to
                identify candidate genes for evolutionary phenotypes, to match
                profiles of ancestral evolutionary variation with gene phenotype
                profiles, and to combine data across many evolutionary studies by
                inferring indirectly asserted values within synthetic
                supermatrices. Here we describe the software architecture of the
                Phenoscape KB, including data ingestion, integration of OWL
                reasoning, web service interface, and application features.",
    month    =  jan,
    year     =  2016,
    url      = "http://biorxiv.org/cgi/content/short/071951",
    doi      = "10.1101/071951",
    language = "en"
  }
  

  Acknowledgements

  This work was supported by the Imageomics Institute, which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under Award #2118240 (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

  Dataset Card Authors

  Juan Garcia, Jim Balhoff, and Elizabeth Campolongo

  Dataset Card Contact

  Please open a Discussion on the Community Tab with any questions on the dataset.