The application of phenotype and environment ontologies to Natural History Collections Rutger Vos

The NBC natural history collection Naturalis is the keeper of the Dutch national natural history collection, which holds approximately 37 million specimens and thereby places in the global top 5, by size.

Going digital Research activities on natural history collections focus on patterns of biodiversity in space (species distributions) and time (systematics) as generated by evolutionary processes. This now happens with a strong and growing application of digital sensor technologies such as NGS, 3D scanning, MicroCT, GIS/remote sensing, digital photography (and all the supporting computing).

Open source culture We've adopted an open source culture that binds the informatics researchers and the ICT department in one community. Currently 27 github organization members that care for 62 repositories. Next month our first hackathon, on enriching biodiversity data with semantic annotations and links.

Ontologies in our present neighborhood In genomics research we encounter the usual, stable ontologies such as SO and GO. In our data sharing API we adopt the community standards from biodiversity informatics, e.g. DwC. In our data enrichment pipelines we will pragmatically adopt whatever works to normalize locations, publications, environments, traits, etc. (In addition, I have a particular interest in the semantics of phylogenetic inference.)

Phenotype and environment ontologies?

Natural language processing Old editions of several tropical floras have been scanned, OCR-ed and converted into structured formats. Species descriptions in these data sets hold non- normalized, but identifiable, concepts such as taxa, localities, traits and environmental conditions. Linking these to ontology terms is one of the key motivating use cases for the upcoming hackathon.

Automated phenotyping in 2D and 3D A lot of 'traditional' research of morphology, e.g. for systematics and taxonomy, benefits from implicit or explicit phenotype ontology. Newly emerging research on image feature classification using neural networks may also benefit. Likewise will our comparative morphometric analysis of 3D objects.

Phyloclimatic modeling Features of bioclimatic response envelopes obtained by ecological niche modeling could be treated as comparable traits. Their comparative analysis would yield insight into tempo and mode of evolutionary responses to changing environments.

Ontological needs We are going to need identifiable terms for features we extract from images as training data for machine learning. We are going to want to identify landmarks on 3D scans in order to be able to reconstruct partially damaged objects and to perform comparative analysis. We will probably want additional ontologies for concepts encountered in floristic treatments, including environments. We may also want ontologies that describe features of bioclimatic response envelopes for comparative phyloclimatic modeling.

Thanks!