ORCID Profile
0000-0002-2908-3327
Current Organisations
University of Colorado Anschutz Medical Campus
,
The Data Detektiv
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Publisher: Oxford University Press (OUP)
Date: 2020
Abstract: People are one of the best known and most stable entities in the bio ersity knowledge graph. The wealth of public information associated with people and the ability to identify them uniquely open up the possibility to make more use of these data in bio ersity science. Person data are almost always associated with entities such as specimens, molecular sequences, taxonomic names, observations, images, traits and publications. For ex le, the digitization and the aggregation of specimen data from museums and herbaria allow us to view a scientist’s specimen collecting in conjunction with the whole corpus of their works. However, the metadata of these entities are also useful in validating data, integrating data across collections and institutional databases and can be the basis of future research into bio ersity and science. In addition, the ability to reliably credit collectors for their work has the potential to change the incentive structure to promote improved curation and maintenance of natural history collections.
Publisher: Cold Spring Harbor Laboratory
Date: 27-01-2023
DOI: 10.1101/2023.01.26.525742
Abstract: Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.
Publisher: Public Library of Science (PLoS)
Date: 30-08-2012
Publisher: Elsevier BV
Date: 12-2012
Publisher: Springer Science and Business Media LLC
Date: 09-11-2015
Location: United States of America
Location: United States of America
No related grants have been discovered for Anne Thessen.