trouble_w_triples-master

A zip file containing the scripts used to analyze a large dataset of identifiers from different systems - VertNet, BOLD and Genbank. The identifiers are also provided

A schematic representation showing proportional numbers of Darwin Core Triplets – represented as different sized ellipses - across repositories and the overlap between them.

<p>The inset shows the overlap regions in more detail. The numbers associated with each repository and areas of overlap are for all types of matches, not just triplet-to-triplet matches. The percentages represent the number of matches between two (or three repositories) divided by shared triples in the smallest of the two (or three) polygons.</p

Summary of DwC Triplets per repository.

<p>A canonical DwC Triplet is a triplet that is complete and conforms to standard representation. A “Coerced” DwC Triplet is one that is either missing a part of the triplet (e.g., a collection code) or in a non-standard syntax.</p><p>Summary of DwC Triplets per repository.</p

Data_Sheet_1_The Plant Phenology Ontology: A New Informatics Resource for Large-Scale Integration of Plant Phenology Data.pdf

<p>Plant phenology – the timing of plant life-cycle events, such as flowering or leafing out – plays a fundamental role in the functioning of terrestrial ecosystems, including human agricultural systems. Because plant phenology is often linked with climatic variables, there is widespread interest in developing a deeper understanding of global plant phenology patterns and trends. Although phenology data from around the world are currently available, truly global analyses of plant phenology have so far been difficult because the organizations producing large-scale phenology data are using non-standardized terminologies and metrics during data collection and data processing. To address this problem, we have developed the Plant Phenology Ontology (PPO). The PPO provides the standardized vocabulary and semantic framework that is needed for large-scale integration of heterogeneous plant phenology data. Here, we describe the PPO, and we also report preliminary results of using the PPO and a new data processing pipeline to build a large dataset of phenology information from North America and Europe.</p

The Genomic Observatories Metadatabase (GeOMe) workflow.

<p>Steps in blue are those conducted within the Field Information Management System (FIMS) of GeOMe while those in white are independent of GeOMe.</p

Screen shot of the Genomic Observatories Metadatabase (GeOMe) query system for <i>Acanthaster planci</i>, the crown of thorns sea star.

<p>Each number indicates the number of specimens in the database from that location. When a group of specimens is selected, distinct samples are visible as a spiral radiating from the chosen location, and individual records report summary information about each sample.</p

Acronym definitions.

<p>Acronym definitions.</p

Metrics on current versions of the BCO, ENVO, and PCO.

1<p>. For BCO and PCO, the number of relations includes only relations that point to a BCO or PCO term, to adjust for the large proportion of imported terms.</p>2<p>. 39 imported from Basic Formal Ontology, 13 imported from Information Artifact Ontology, 10 imported from Ontology for Biomedical Investigations, 1 imported from Common Anatomy Reference Ontology.</p>3<p>. 172 imported from Chemical Entities of Biological Interest, 49 from Phenotypic Quality Ontology.</p>4<p>. 39 imported from Basic Formal Ontology, 1269 imported from Gene Ontology, 11 imported from Information Artifact Ontology, 2 imported from Common Anatomy Reference Ontology.</p

Structured sampling schemes.

<p>(<b>A</b>) Biological sampling can be structured in both space and time. Environmental sampling of ocean water often includes sampling along a transect, with samples collected at multiple depths at each location. Additionally, each sample of water collected may be subsampled for metagenomic analysis or measuring chemical content. (<b>B</b>) Sampling schemes in ecological studies are often nested and may include plot; subplot or transect within plot; individual within plot, subplot, or transect; organ (e.g., leaf) within individual; tissue within organ; and DNA or mineral (e.g., C or N) within tissue. DNA extracted from a leaf of a tree that is present in a sub-plot may therefore be characterized by environmental features of the plot.</p

Core terms of the Biological Collections Ontology (BCO) and their relations to upper ontologies.

<p>Core BCO terms (in orange) are subclasses of terms from the Basic Formal Ontology (BFO – in yellow) or the Ontology for Biomedical Investigations (OBI – in blue). For example, BCO:<i>material sample</i> is a subclass of BFO:<i>material entity</i> and has role BFO:<i>material sample role</i> (which is a BFO:<i>role</i>), while BFO:<i>material sampling process</i> is a subclass of OBI:<i>planned process</i>, and has as specified output BCO:<i>material sample</i>.</p