Aggregated species trait data

aggregatedspeciestraitdata

Providing the framework to capture all available trait information for any species, and interactions between species.

Progress: limited (limited existing standardization; needs further development)

PROJECTS WHICH CONTRIBUTE TO THIS COMPONENT

Scientists need access to analysable data about the characteristics of organisms and the interactions between them. Being able to capture, index, query and curate such data will revolutionize how we understand large-scale biological systems. This will enable us to build more accurate models of how they will behave over time and under changing conditions, and to create a new generation of identification tools.

The community has already developed some major data repositories. For traits these include: TraitNet, TRY, LEDA, MorphoBank, Animal Diversity Web, the Phenoscape KnowledgeBase, species traits in the Encyclopedia of Life and a growing number of model organism and agricultural resource databases. Interactions are recorded in the Interaction Web Database (IWDB), Semantic Web Informatics for Species in Space and Time (SWISST) and the Encyclopedia of Life. Species trait data standards include Structured Descriptive Data (SDD), the Plinian Core, the Phenotypic Quality Ontology, and the Animal Natural History ontology, which also includes species interactions. The next tasks are to standardize across these vocabularies; ensure that the data recorded reflect the complex relationships among traits, their genetic base and the environment; and report traits and interactions at multiple scales.

In the short term, the priority will be to produce summaries of which organisms interact with each other in defined ways such as predator/prey or parasite/host relationships or in more general terms (pollinators, invasive species) and to continue to develop and improve identification tools.

In the medium term, the priorities will be systems to serve rich trait data for the best-understood organisms, and identifying techniques for large-scale trait data capture building on the Data focus area.

In the long term, the priorities will be to capture high-quality trait and interaction information at increasingly large taxonomic and geographic scales using highthroughput descriptions equivalent to NextGen sequencing.