Conversational narrative: a meta-analysis of narrative analysis

textThis dissertation is a meta-analysis of the narrative analysis methodologies of Labov and Waletzky (1967), Labov (1972, 1997, 2001, 2002), Polanyi (1985) and Ochs and Capps (2001) using data from the Minnesota Corpus (Barnes, 1984) to test the usefulness of these methodologies. Conversational narrative was first a subject of analysis in the late 60's when Labov and Waletzky, working under the influence of structural linguistics, decided that in order to better understand narrative, one must understand its most basic form, which they felt resided in oral versions of personal experience. Since their groundbreaking 1967 study, the field of conversational narrative analysis has been dominated by structural approaches to narrative that seek to define the structural components of a narrative and formulate an analysis based on these components. Only recently with the introduction of Ochs and Capps' methodology in 2001 has an alternative which values both the context and the interactive nature of narrative and seeks to describe the co-participant's influences on narrative been put forth. This meta-analysis suggests that there are positive and negative qualities to each of the methodologies at issue and that different methodologies are more or less appropriate for different types of data. While the structural approaches to conversational narrative suggested by Labov and Polanyi do not provide an adequate means to analyze interactive narratives, Ochs and Capps' methodology requires more extensive ethnographic information than what were available from the Minnesota corpus data. While the Ochs and Capps' approach seems overall to be the best suited for the type of data at issue in the Minnesota corpus, there are also clear benefits to be derived from applying a more structural approach. Specifically, an analysis of a narrative's Non-Storyworld clauses (as defined by Polanyi) seems to provide important insights. Moreover, these clauses can help the analyst address how interlocutors make sense of the relevance of narrative in coversational discourse, something hinted at by both Labov and Polanyi. I suggest that a combination of elements from both structural and ethnographic approaches provides a more complete methodology with which to analyze interactive narrative data.French and Italia

The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

The Human Phenotype Ontology in 2024: phenotypes around the world

Funder: French Ministry of HealthFunder: Angela Wright Bennett Foundation; DOI: https://doi.org/10.13039/501100020544Funder: McCusker Charitable Foundation; DOI: https://doi.org/10.13039/100014834Funder: Channel 7 Telethon TrustsFunder: the Stan Perron Charitable Foundation and Mineral ResourcesFunder: Prechter Bipolar Research ProgramThe Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

The Human Phenotype Ontology in 2024: phenotypes around the world

Funder: French Ministry of HealthFunder: Angela Wright Bennett Foundation; DOI: https://doi.org/10.13039/501100020544Funder: McCusker Charitable Foundation; DOI: https://doi.org/10.13039/100014834Funder: Channel 7 Telethon TrustsFunder: the Stan Perron Charitable Foundation and Mineral ResourcesFunder: Prechter Bipolar Research ProgramThe Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

The Human Phenotype Ontology in 2024: phenotypes around the world

Funder: French Ministry of HealthFunder: Angela Wright Bennett Foundation; DOI: https://doi.org/10.13039/501100020544Funder: McCusker Charitable Foundation; DOI: https://doi.org/10.13039/100014834Funder: Channel 7 Telethon TrustsFunder: the Stan Perron Charitable Foundation and Mineral ResourcesFunder: Prechter Bipolar Research ProgramThe Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

The Human Phenotype Ontology in 2024: phenotypes around the world

Funder: French Ministry of HealthFunder: Angela Wright Bennett Foundation; DOI: https://doi.org/10.13039/501100020544Funder: McCusker Charitable Foundation; DOI: https://doi.org/10.13039/100014834Funder: Channel 7 Telethon TrustsFunder: the Stan Perron Charitable Foundation and Mineral ResourcesFunder: Prechter Bipolar Research ProgramThe Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

The Human Phenotype Ontology in 2024: phenotypes around the world

Funder: French Ministry of HealthFunder: Angela Wright Bennett Foundation; DOI: https://doi.org/10.13039/501100020544Funder: McCusker Charitable Foundation; DOI: https://doi.org/10.13039/100014834Funder: Channel 7 Telethon TrustsFunder: the Stan Perron Charitable Foundation and Mineral ResourcesFunder: Prechter Bipolar Research ProgramThe Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs