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ontology.json
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[ {
"name" : "Informed Consent Ontology",
"id" : "https://data.bioontology.org/ontologies/ICO",
"acronym" : "ICO",
"description" : "Informed Consent Ontology (ICO) is a community-based ontology in the domain of informed consent. It is an OBO library ontology and developed by following the OBO Foundry principles. ",
"categories" : [ "Health" ],
"veiws" : 872,
"projects" : 0,
"objPropertyNo" : 103,
"classNo" : 963,
"classes" : null
}, {
"name" : "Dementia-Related Psychotic Symptoms Non-Pharmacological Treatment Ontology",
"id" : "https://data.bioontology.org/ontologies/DRPSNPTO",
"acronym" : "DRPSNPTO",
"description" : "The DRPSNPTO ontology represents the domain knowledge specific to non-pharmacological treatment of psychotic symptoms in dementia in the long-term care setting.",
"categories" : [ "Taxonomic_Classification", "Health", "Vocabularies" ],
"veiws" : 394,
"projects" : 0,
"objPropertyNo" : 60,
"classNo" : 610,
"classes" : null
}, {
"name" : "GeoSpecies Ontology",
"id" : "https://data.bioontology.org/ontologies/GEOSPECIES",
"acronym" : "GEOSPECIES",
"description" : "This ontology was designed to help integrate species concepts with species occurrences, gene sequences, images, references and geographical information. See also Taxonconcept.org",
"categories" : [ "Other" ],
"veiws" : 1089,
"projects" : 0,
"objPropertyNo" : 116,
"classNo" : 86,
"classes" : null
}, {
"name" : "Time Event Ontology",
"id" : "https://data.bioontology.org/ontologies/TEO",
"acronym" : "TEO",
"description" : "The Time Event Ontology (TEO) is an ontology for representing events, time, and their relationships.",
"categories" : [ "Other" ],
"veiws" : 1979,
"projects" : 0,
"objPropertyNo" : 91,
"classNo" : 994,
"classes" : null
}, {
"name" : "Ontology Metadata Vocabulary",
"id" : "https://data.bioontology.org/ontologies/OMV",
"acronym" : "OMV",
"description" : "A standard for ontology metadata; a vocabulary of terms and definitions describing ontologies which specifies reusability-enhancing ontology features for human and machine processing purposes.",
"categories" : [ "Vocabularies" ],
"veiws" : 675,
"projects" : 0,
"objPropertyNo" : 62,
"classNo" : 16,
"classes" : null
}, {
"name" : "Translational Medicine Ontology",
"id" : "https://data.bioontology.org/ontologies/TMO",
"acronym" : "TMO",
"description" : "This project focuses on the development of a high level patient-centric ontology for the pharmaceutical industry. The ontology should enable silos in discovery research, hypothesis management, experimental studies, compounds, formulation, drug development, market size, competitive data, population data, etc. to be brought together. This would enable scientists to answer new questions, and to answer existing scientific questions more quickly. This will help pharmaceutical companies to model patient-centric information, which is essential for the tailoring of drugs, and for early detection of compounds that may have sub-optimal safety profiles. The ontology should link to existing publicly available domain ontologies.",
"categories" : [ "All_Organisms", "Health" ],
"veiws" : 2016,
"projects" : 0,
"objPropertyNo" : 22,
"classNo" : 225,
"classes" : null
}, {
"name" : "Ontology of Precision Medicine and Investigation",
"id" : "https://data.bioontology.org/ontologies/OPMI",
"acronym" : "OPMI",
"description" : "The Ontology of Precision Medicine and Investigation (OPMI) is an application ontology to support precision medicine and its related investigations. OPMI is community-driven and developed by following the OBO Foundry ontology development principles. OPMI reuses, aligns, and integrates related terms from existing OBO ontologies. ",
"categories" : [ "Health" ],
"veiws" : 278,
"projects" : 0,
"objPropertyNo" : 156,
"classNo" : 3034,
"classes" : null
}, {
"name" : "Open Predictive Microbiology Ontology",
"id" : "https://data.bioontology.org/ontologies/OFSMR",
"acronym" : "OFSMR",
"description" : "Food-matrix ontology for Open Food Safety Model Repository",
"categories" : [ "Biomedical_Resources", "Vocabularies" ],
"veiws" : 2166,
"projects" : 0,
"objPropertyNo" : 2,
"classNo" : 159,
"classes" : null
}, {
"name" : "Molgula occulta Anatomy and Development Ontology",
"id" : "https://data.bioontology.org/ontologies/MOOCCUADO",
"acronym" : "MOOCCUADO",
"description" : "The first ontology describing the anatomy and the development of Molgula occulta. ",
"categories" : [ "Anatomy", "Animal_Development" ],
"veiws" : 139,
"projects" : 1,
"objPropertyNo" : 7,
"classNo" : 856,
"classes" : null
}, {
"name" : "Arctic Data Center Academic Disciplines Ontology",
"id" : "https://data.bioontology.org/ontologies/ADCAD",
"acronym" : "ADCAD",
"description" : "Ontology to support disciplinary annotation of datasets housed at the Arctic Data Center (https://arcticdata.io)",
"categories" : [ ],
"veiws" : 0,
"projects" : 0,
"objPropertyNo" : 0,
"classNo" : 67,
"classes" : null
}, {
"name" : "Life Ontology",
"id" : "https://data.bioontology.org/ontologies/LIFO",
"acronym" : "LIFO",
"description" : "The Life Ontology (LifO) is an ontology of the life of organism. LifO represents the life processes of organisms and related entities and relations. LifO is a general purpose ontology that covers the common features associated with different organisms such as unicellular prokaryotes (e.g., E. coli) and multicellular organisms (e.g., human).",
"categories" : [ "All_Organisms" ],
"veiws" : 378,
"projects" : 0,
"objPropertyNo" : 98,
"classNo" : 215,
"classes" : null
}, {
"name" : "Material Compound",
"id" : "https://data.bioontology.org/ontologies/MATRCOMPOUND",
"acronym" : "MATRCOMPOUND",
"description" : "Material Compound details from SWEET ontology",
"categories" : [ "Upper_Level_Ontology" ],
"veiws" : 278,
"projects" : 0,
"objPropertyNo" : 1,
"classNo" : 8,
"classes" : null
}, {
"name" : "AGRonomy Ontology",
"id" : "https://data.bioontology.org/ontologies/AGRO",
"acronym" : "AGRO",
"description" : "AGRO, the AGRonomy Ontology, describes agronomic practices, agronomic techniques, and agronomic variables used in agronomic experiments. AGRO is being built using traits identified by agronomists, the ICASA variables, and other existing ontologies such as ENVO, UO, and PATO, IAO, and CHEBI. Further, AGRO will power an Agronomy Management System and fieldbook modeled on a CGIAR Breeding Management System to capture agronomic data.",
"categories" : [ ],
"veiws" : 634,
"projects" : 0,
"objPropertyNo" : 205,
"classNo" : 3738,
"classes" : null
}, {
"name" : "Cancer Staging Terms",
"id" : "https://data.bioontology.org/ontologies/CST",
"acronym" : "CST",
"description" : "This ontology consists of cancer staging terms that specify higher-level terms that are useful in classifying cancer, specifically breast cancer, according to the AJCC 7th and 8th editions.",
"categories" : [ ],
"veiws" : 533,
"projects" : 0,
"objPropertyNo" : 0,
"classNo" : 0,
"classes" : null
}, {
"name" : "Regional Healthcare System Interoperability and Information Exchange Measurement Ontology",
"id" : "https://data.bioontology.org/ontologies/HEIO",
"acronym" : "HEIO",
"description" : "The purpose of this ontology is to develop a model for regional healthcare system interoperability and information exchange quantification (measured by the electronic health information exchange (eHIE) indicator). The eHIE is hypothesized as a leading measure of regional healthcare system integration.\r\nA publication associated with this ontology is currently under review. ",
"categories" : [ "Other" ],
"veiws" : 816,
"projects" : 0,
"objPropertyNo" : 47,
"classNo" : 125,
"classes" : null
}, {
"name" : "Radiology Gamuts Ontology",
"id" : "https://data.bioontology.org/ontologies/GAMUTS",
"acronym" : "GAMUTS",
"description" : "In radiology, a \"gamut\" is the set of conditions that can cause a specified imaging finding. The Radiology Gamuts Ontology (RGO) links more than 16,000 diseases and imaging findings. The causal relation (may_cause; inverse, may_be_caused_by) connects findings and possible causes.\r\n\r\nThis work is made available under provisions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.",
"categories" : [ "Imaging", "Health", "Human" ],
"veiws" : 1894,
"projects" : 0,
"objPropertyNo" : 0,
"classNo" : 18001,
"classes" : null
}, {
"name" : "Epigenome Ontology",
"id" : "https://data.bioontology.org/ontologies/EGO",
"acronym" : "EGO",
"description" : "The Epigenome Ontology (EGO) a biomedical ontology for integrative epigenome knowledge representation and data analysis.",
"categories" : [ "Molecule", "Health", "Genomic_and_Proteomic" ],
"veiws" : 377,
"projects" : 0,
"objPropertyNo" : 111,
"classNo" : 2952,
"classes" : null
}, {
"name" : "ISO 19115 Role Codes",
"id" : "https://data.bioontology.org/ontologies/ISO19115ROLES",
"acronym" : "ISO19115ROLES",
"description" : "Role Codes for use in ISO 19115-1:2014 metadata",
"categories" : [ ],
"veiws" : 199,
"projects" : 0,
"objPropertyNo" : 0,
"classNo" : 2,
"classes" : null
}, {
"name" : "Infectious Disease Ontology",
"id" : "https://data.bioontology.org/ontologies/IDO",
"acronym" : "IDO",
"description" : "The IDO ontologies are designed as a set of interoperable ontologies that will together provide coverage of the infectious disease domain. At the core of the set is a general Infectious Disease Ontology (IDO-Core) of entities relevant to both biomedical and clinical aspects of most infectious diseases. Sub-domain specific extensions of IDO-Core complete the set providing ontology coverage of entities relevant to specific pathogens or diseases. \r\n\r\n<br><br>\r\nTo import,<br>\r\nLatest version: <a href=\"http://purl.obolibrary.org/obo/ido.owl\">http://purl.obolibrary.org/obo/ido.owl</a><br>\r\nThis version (2010-12-02): <a href=\"http://purl.obolibrary.org/obo/ido/2010-12-02/ido.owl\">http://purl.obolibrary.org/obo/ido/2010-12-02/ido.owl</a><br>\r\nPrevious versions:<br>\r\n2010-05-26 <a href=\"http://purl.obolibrary.org/obo/ido/2010-05-26/ido.owl\">http://purl.obolibrary.org/obo/ido/2010-05-26/ido.owl</a><br>\r\n2009-08-14 <a href=\"http://purl.obolibrary.org/obo/ido/2009-08-14/ido.owl\">http://purl.obolibrary.org/obo/ido/2009-08-14/ido.owl</a><br>\r\n<br>\r\n<br>\r\nLatest release notes at <a href=\"http://infectiousdiseaseontology.org/page/Download\">http://infectiousdiseaseontology.org/page/Download</a><br>\r\n\r\nPlease note: The ontology metrics displayed by BioPortal do not distinguish IDO-developed terms from terms imported from other ontologies.",
"categories" : [ "All_Organisms", "Health" ],
"veiws" : 5892,
"projects" : 5,
"objPropertyNo" : 43,
"classNo" : 362,
"classes" : null
}, {
"name" : "MARC Code List for Relators",
"id" : "https://data.bioontology.org/ontologies/MARC-RELATORS",
"acronym" : "MARC-RELATORS",
"description" : "Relator terms and their associated codes designate the relationship between a name and a bibliographic resource. The relator codes are three-character lowercase alphabetic strings that serve as identifiers. Either the term or the code may be used as controlled values.",
"categories" : [ ],
"veiws" : 59,
"projects" : 0,
"objPropertyNo" : 0,
"classNo" : 2,
"classes" : null
}, {
"name" : "VODANA-MIGRANTS-INTERVIEWS",
"id" : "https://data.bioontology.org/ontologies/VODANA-MI",
"acronym" : "VODANA-MI",
"description" : "Migrants Interviews Ontologies\r\n",
"categories" : [ "Health", "Vocabularies" ],
"veiws" : 79,
"projects" : 0,
"objPropertyNo" : 5,
"classNo" : 2,
"classes" : null
}, {
"name" : "Chronic Disease Patient Education Ontology",
"id" : "https://data.bioontology.org/ontologies/CDPEO",
"acronym" : "CDPEO",
"description" : "This ontology is used to recommend personalized education materials to chronic disease patients. The constructed ontology mainly consists of two levels. Level 1 includes 5 terms: demographic, disease, vital sign, lifestyle and medication, which describe the characteristics contained in the patient data, as well as the topics of education materials. Leve 2 contains the detailed elements for each Level 1 class. The patient profile class is used to generate the instance of patient data, using object properties to connect to the specific characteristic class. The patient data will be mapped to a vector space generated from the Level 2 classes. SWRL rules are added to implement the semantic logic of mapping from patient original data to the ontology vector space. The pulblication of the ontology is in preperation.",
"categories" : [ "Health" ],
"veiws" : 178,
"projects" : 0,
"objPropertyNo" : 98,
"classNo" : 40,
"classes" : null
}, {
"name" : "FlyBase Controlled Vocabulary",
"id" : "https://data.bioontology.org/ontologies/FB-CV",
"acronym" : "FB-CV",
"description" : "A structured controlled vocabulary used for various aspects of annotation by FlyBase. It includes the Drosophila Phenotype Ontology (DPO) which is also released separately. This ontology is maintained by FlyBase for various aspects of annotation not covered, or not yet covered, by other OBO ontologies. If and when community ontologies are available for the domains here covered FlyBase will use them.",
"categories" : [ "Vocabularies" ],
"veiws" : 337,
"projects" : 1,
"objPropertyNo" : 127,
"classNo" : 2241,
"classes" : null
}, {
"name" : "Illness and Injury",
"id" : "https://data.bioontology.org/ontologies/ILLNESSINJURY",
"acronym" : "ILLNESSINJURY",
"description" : "This list of Illness and Injury (Morbidity and Mortality) classifications is an abbreviated version of the World Health Organization's (WHO) Startup Mortality List (ICD-10-SMoL). ICD-10-SMoL is the WHO's application of ICD-10 for low-resource settings initial cause of death collection.\r\n\r\nICD-10-SMoL was chosen because it is a simplified version of the ICD-10.\r\nICD (International Classification of Diseases) \"is the foundation for the identification of health trends and statistics globally, and the international standard for reporting diseases and health conditions. It is the diagnostic classification standard for all clinical and research purposes. ICD defines the universe of diseases, disorders, injuries and other related health conditions\" [http://www.who.int/classifications/icd/en/].\r\n\r\nVersion 1.0 of this ontology was compiled for the Canadian Writing Research Collaboratory (CWRC) ontology project and reflects the need to have causes of death and health issues organized in a way to allow for analysis of cultural data. A simplified list was deemed sufficient since the domain under study (cultural documents) will not have detailed medical information with regards to cause of death or health issues. But instead of accepting a list of general usage terms for causes of death or health issues that appear in some sources (e.g. Wikipedia, Wikidatak, etc.), a standardized, medical classification was selected so that it could be of use for future analysis regarding questions involving the intersection of health and culture.\r\n\r\nICD-10-SMoL was chosen since it was the latest version of this list (June, 2018). A stable version of ICD-11 was to be available in June 2018 but there was no ICD-11-SMoL available at this time.\r\n\r\nTo obtain the ICD10-SMoL:\r\nhttp://www.who.int/healthinfo/civil_registration/ICD_10_SMoL.pdf?ua=1",
"categories" : [ "Taxonomic_Classification", "Health", "Human" ],
"veiws" : 259,
"projects" : 0,
"objPropertyNo" : 3,
"classNo" : 21,
"classes" : null
}, {
"name" : "Neuroscience Information Framework (NIF) Dysfunction Ontlogy",
"id" : "https://data.bioontology.org/ontologies/NIFDYS",
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"description" : "This ontology contains the former BIRNLex-Disease, version 1.3.2. -- The BIRN Project lexicon will provide entities for data and database annotation for the BIRN project, covering anatomy, disease, data collection, project management and experimental design. It is built using the organizational framework provided by the foundational Basic Formal Ontology (BFO). It uses an abstract biomedical layer on top of that - OBO-UBO which has been constructed as a proposal to the OBO Foundry. This is meant to support creating a sharable view of core biomedical objects such as biomaterial_entity, and organismal_entity that all biomedical ontologies are likely to need and want to use with the same intended meaning. The BIRNLex biomaterial entities have already been factored to separately maintained ontology - BIRNLexBiomaterialEntity.owl which this BIRNLex-Main.owl file imports. The Ontology of Biomedical Investigation (OBI) is also imported and forms the foundation for the formal description of all experiment-related artifacts. The BIRNLex will serve as the basis for construction of a formal ontology for the multiscale investigation of neurological disease.",
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}, {
"name" : "Read Clinical Terminology Version 2",
"id" : "https://data.bioontology.org/ontologies/RCTV2",
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"description" : "NHS UK Read Codes Version 2",
"categories" : [ "Health" ],
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}, {
"name" : "Mouse gross anatomy and development, timed",
"id" : "https://data.bioontology.org/ontologies/EMAPA",
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"description" : "Abstract (i.e. non-stage specific) stage-specific anatomical structures of the mouse.",
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}, {
"name" : "Bilingual Ontology of Alzheimer's Disease and Related Diseases",
"id" : "https://data.bioontology.org/ontologies/ONTOAD",
"acronym" : "ONTOAD",
"description" : "OntoAD is a bilingual (English-French) domain ontology for modeling knowledge about Alzheimer's Disease and Related Syndromes.",
"categories" : [ "Health", "Dysfunction", "Neurologic_Disease", "Neurological_Disorder", "Human" ],
"veiws" : 3149,
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}, {
"name" : "Tissue Microarray Ontology",
"id" : "https://data.bioontology.org/ontologies/TMA",
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"description" : "Tissue microarrays (TMA) are enormously useful tools for translational research, but incompatibilities in database systems between various researchers and institutions prevent the efficient sharing of data that could help realize their full potential. The Resource Description Framework (RDF) provides a flexible method to represent knowledge in triples, which take the form Subject-Predicate-Object. All data resources are described using Uniform Resource Identifiers (URI), which are global in scope. We present an OWL (Web Ontology Language) schema that expands upon the TMA data exchange specification to address this issue and assist in data sharing and integration.",
"categories" : [ "Experimental_Conditions", "Biomedical_Resources", "Other" ],
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}, {
"name" : "HIVMutation",
"id" : "https://data.bioontology.org/ontologies/HIVMT",
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"description" : "This is an ontology about the mutations Of HIV1 and the relations between them and the clinical practices. And it has imported the main ontology: https://bioportal.bioontology.org/ontologies/HIVO004. ",
"categories" : [ "Biomedical_Resources" ],
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}, {
"name" : "HIVOntologymain.owl",
"id" : "https://data.bioontology.org/ontologies/HIVO004",
"acronym" : "HIVO004",
"description" : "A Project Of Biomedical Ontology for Systems Biomedicine\r\n\r\nThe Project includes so far a main ontology (HIVOnt0**.owl) which is imported by several sub ontologies AIDSClinic0**.owl, HumanGeneCodon0**.owl, HIVCompd*****.owl, HIVGenetics***.owl.\r\n\r\nThe goals of the Project of HIV Ontology(PHIVO) for Knowledge Integrations include \r\n \r\n1. Providing AIDS clinics and HIV virology and the future biomedicines, including the Systems Biomedicine(SBM), the Precision Biomedicine(PBM) and the Knowledge-Integrated Biomedicine(KIMB), with the knowledge resources expressed in the way of ontology, which is able to be operated on engineering ground. \r\n2. Carrying out the methodology researches for the goals above. ",
"categories" : [ "Immunology", "Taxonomic_Classification", "Molecule", "Health", "Dysfunction", "Biomedical_Resources", "Biological_Process", "Subcellular", "Protein" ],
"veiws" : 8648,
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}, {
"name" : "Semanticscience Integrated Ontology",
"id" : "https://data.bioontology.org/ontologies/SIO",
"acronym" : "SIO",
"description" : "The semanticscience integrated ontology (SIO) provides a simple, integrated upper level ontology (types, relations) for consistent knowledge representation across physical, processual and informational entities. It provides vocabulary for the Bio2RDF (http://bio2rdf.org) and SADI (http://sadiframework.org) projects.",
"categories" : [ "Yeast", "All_Organisms", "Cellular_anatomy_", "Taxonomic_Classification", "Molecule", "Biomedical_Resources", "Genomic_and_Proteomic", "Biological_Process", "Subcellular", "Phenotype", "Subcellular_anatomy", "Chemical", "Cell", "Protein", "Gene_Product", "Vocabularies", "Physicochemical" ],
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}, {
"name" : "Protein Modification Ontology",
"id" : "https://data.bioontology.org/ontologies/PSIMOD",
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"description" : "PSI-MOD is an ontology consisting of terms that describe protein chemical modifications, logically linked by an is_a relationship in such a way as to form a direct acyclic graph (DAG). The PSI-MOD ontology has more than 45 top-level nodes, and provides alternative hierarchical paths for classifying protein modifications either by the molecular structure of the modification, or by the amino acid residue that is modified.",
"categories" : [ ],
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}, {
"name" : "Resource of Asian Primary Immunodeficiency Diseases (RAPID) Phenotype Ontology",
"id" : "https://data.bioontology.org/ontologies/RPO",
"acronym" : "RPO",
"description" : "RAPID phenotype ontology presents controlled vocabulary of ontology class structures and entities of observed phenotypic terms for primary immunodeficiency diseases (PIDs) that facilitate global sharing and free exchange of PID data with users’ communities",
"categories" : [ "Immunology", "Phenotype", "Human" ],
"veiws" : 399,
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}, {
"name" : "Biomedical Informatics Research Network Project Lexicon",
"id" : "https://data.bioontology.org/ontologies/BIRNLEX",
"acronym" : "BIRNLEX",
"description" : "The BIRN Project lexicon will provide entities for data and database annotation for the BIRN project, covering anatomy, disease, data collection, project management and experimental design.",
"categories" : [ "Imaging", "Anatomy" ],
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}, {
"name" : "Children's Health Exposure Analysis Resource",
"id" : "https://data.bioontology.org/ontologies/CHEAR",
"acronym" : "CHEAR",
"description" : "Children's health and wellbeing are influenced by interactions between environmental and genetic factors. NIEHS is establishing an infrastructure, the Children's Health Exposure Analysis Resource (CHEAR), to provide the extramural research community access to laboratory and data analyses that add or expand the inclusion of environmental exposures in children's health research. The goal of CHEAR is to provide tools so researchers can assess the full range of environmental exposures which may affect children's health. We anticipate that CHEAR will be used by children's health researchers conducting epidemiological or clinical studies that currently have limited consideration of environmental exposures, or those who have collected exposure data but seek more extensive analyses.",
"categories" : [ "Health", "Human" ],
"veiws" : 1464,
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}, {
"name" : "Pathway Ontology",
"id" : "https://data.bioontology.org/ontologies/PW",
"acronym" : "PW",
"description" : "The goal of the Pathway Ontology is to cover all types of biological pathways, including altered and disease pathways, and to capture the relationships between them within the hierarchical structure of a Directed Acyclic Graph (DAG). The five nodes of the ontology are: classic metabolic, regulatory, signaling, drug and disease pathways. An extensive survey of the review literature along with searches of existing pathway databases have been used to choose terms and their position within the tree. The ontology is continually expanding along with the development of Pathway and Disease Portals at RGD. Mapping of pathways in other databases to terms in PW as synonyms further increased the content of the ontology. It also provided the means to bring in annotations made by these databases and link to their sites via automatic pipelines built at RGD. The ontology allows for the standardized annotation of rat as well as human and mouse genes to pathway terms. It also serves as a vehicle to connect between genes and ontology reports, between reports and interactive pathway diagrams, between pathways that directly connect to one another within a diagram or between pathways that in some fashion are globally related in pathway suites and suite networks. Metabolic, regulatory or signaling pathway terms have associated terms for the altered version of the pathway and the level at which the alteration(s) may occur. Proteins with mutations known to affect a particular pathway can be annotated to the normal and altered version(s) of the pathway and to a disease pathway, if known. Both the provision of terms for the altered versions of pathways and the representation of disease pathway diagrams as a collection of altered pathways are unique to the PW ontology and RGD's pathway data.\r\nThe Pathway ontology is distributed under the terms of the Creative Commons Attribution license version 3.0 Unported (CC BY 3.0), see\r\nhttp://creativecommons.org/licenses/by/3.0 for details. ",
"categories" : [ "Biological_Process" ],
"veiws" : 25945,
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}, {
"name" : "Data Collection Ontology",
"id" : "https://data.bioontology.org/ontologies/GDCO",
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"description" : "The Data Collection Ontology (DCO) is an ontology designed for data collection providing classes and relations on top of the Basic Formal Ontology (BFO) to facilitate data collection processes, subjects, and data anaylsis through the use of classifiers.\r\n\r\nDCO is based on the notion of reasoning to validate incoming data through their definitions based on classifiers.",
"categories" : [ "Development" ],
"veiws" : 258,
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}, {
"name" : "HPO - ORDO Ontological Module",
"id" : "https://data.bioontology.org/ontologies/HOOM",
"acronym" : "HOOM",
"description" : "Orphanet provides phenotypic annotations of the rare diseases in the Orphanet nomenclature using the Human Phenotype Ontology (HPO). HOOM is a module that qualifies the annotation between a clinical entity and phenotypic abnormalities according to a frequency and by integrating the notion of diagnostic criterion. In ORDO a clinical entity is either a group of rare disorders, a rare disorder or a subtype of disorder. The phenomes branch of ORDO has been refactored as a logical import of HPO, and the HPO-ORDO phenotype disease-annotations have been provided in a series of triples in OBAN format in which associations, frequency and provenance are modeled.\r\n\r\nHOOM is provided as an OWL (Ontologies Web Languages) file, using OBAN, the Orphanet Rare Disease Ontology (ORDO), and HPO ontological models.\r\n\r\nHOOM provides extra possibilities for researchers, pharmaceutical companies and others wishing to co-analyse rare and common disease phenotype associations, or re-use the integrated ontologies in genomic variants repositories or match-making tools. ",
"categories" : [ "Health", "Biomedical_Resources", "Phenotype", "Human" ],
"veiws" : 1266,
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}, {
"name" : "Bacterial interlocked Process ONtology",
"id" : "https://data.bioontology.org/ontologies/BIPON",
"acronym" : "BIPON",
"description" : "BiPON is an ontology permitting a multi-scale systemic representation of bacterial cellular\r\nprocesses and the coupling to their mathematical models. BiPON is further composed of two sub-\r\nontologies, bioBiPON and modelBiPON. bioBiPON aims at organizing the systemic description of\r\nbiological information while modelBiPON aims at describing the mathematical models (including\r\nparameters) associated to each biological process.",
"categories" : [ "Biological_Process" ],
"veiws" : 356,
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}, {
"name" : "Molecular Interactions",
"id" : "https://data.bioontology.org/ontologies/MI",
"acronym" : "MI",
"description" : "A structured controlled vocabulary for the annotation of experiments concerned with protein-protein interactions. Developed by the HUPO Proteomics Standards Initiative.",
"categories" : [ ],
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}, {
"name" : "Ctenophore Ontology",
"id" : "https://data.bioontology.org/ontologies/CTENO",
"acronym" : "CTENO",
"description" : "An anatomical and developmental ontology for ctenophores (Comb Jellies)",
"categories" : [ "Anatomy" ],
"veiws" : 139,
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}, {
"name" : "Glioblastoma",
"id" : "https://data.bioontology.org/ontologies/GBM",
"acronym" : "GBM",
"description" : "Ontology of Glioblastoma",
"categories" : [ "Health", "Anatomy", "Human" ],
"veiws" : 434,
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}, {
"name" : "Ontology for Genetic Susceptibility Factor",
"id" : "https://data.bioontology.org/ontologies/OGSF",
"acronym" : "OGSF",
"description" : "Ontology for Genetic Susceptibility Factor (OGSF) is an application ontology to model/represent the notion of genetic susceptibility to a specific disease or an adverse event or a pathological biological process. It is developed using BFO2.0's framwork. The ontology is under the domain of genetic epidemiology.",
"categories" : [ "Health", "Genomic_and_Proteomic", "Human" ],
"veiws" : 237,
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}, {
"name" : "Relations Ontology",
"id" : "https://data.bioontology.org/ontologies/OBOREL",
"acronym" : "OBOREL",
"description" : "A collection of relations intended primarily for standardization across ontologies in the OBO Foundry and wider OBO library. It incorporates ROCore upper-level relations such as part of as well as biology-specific relationship types such as develops from.",
"categories" : [ ],
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}, {
"name" : "Radiomics Ontology",
"id" : "https://data.bioontology.org/ontologies/RO",
"acronym" : "RO",
"description" : "The Radiomics Ontology aims to cover the radiomics feature domain with a strong focus on first order, shape, textural radiomics features. In addition, in the original version. it includes classes about segmentation algorithms and imaging filters.\r\nDue to a recent collaboration with the IBSI (International Biomarkers Standardization Initiative), the ontology has been expanded (v 1.6) and it includes all the entities presented in the IBSI document. Therefore, a broad coverage of not only radiomics features, but also every entity (e.g. software properties, filter properties, features extraction parameters) involved into radiomics computation has been added. In the latest version (v2.0), the ontology URIs have been updated to reflect the codes avaialble in the IBSI latest manual",
"categories" : [ "Imaging", "Biomedical_Resources" ],
"veiws" : 3138,
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}, {
"name" : "Non-coding RNA Ontology",
"id" : "https://data.bioontology.org/ontologies/NCRO",
"acronym" : "NCRO",
"description" : "The NCRO is a reference ontology in the non-coding RNA (ncRNA) field, aiming to provide a common set of terms and relations that will facilitate the curation, analysis, exchange, sharing, and management of ncRNA structural, functional, and sequence data.",
"categories" : [ ],
"veiws" : 535,
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"classNo" : 11883,
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}, {
"name" : "Molgula occidentalis Anatomy and Development Ontology",
"id" : "https://data.bioontology.org/ontologies/MOOCCIADO",
"acronym" : "MOOCCIADO",
"description" : " The first ontology describing the anatomy and the development of Molgula occidentalis. ",
"categories" : [ "Anatomy", "Animal_Development" ],
"veiws" : 119,
"projects" : 1,
"objPropertyNo" : 7,
"classNo" : 856,
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}, {
"name" : "PhenomeBLAST Ontology",
"id" : "https://data.bioontology.org/ontologies/PHENOMEBLAST",
"acronym" : "PHENOMEBLAST",
"description" : null,
"categories" : [ "Mouse_Anatomy", "All_Organisms", "Fish_Anatomy", "Gross_Anatomy", "Dysfunction", "Biological_Process", "Other", "Phenotype", "Anatomy", "Human" ],
"veiws" : 217,
"projects" : 1,
"objPropertyNo" : 0,
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}, {
"name" : "Ontology for Parasite LifeCycle",
"id" : "https://data.bioontology.org/ontologies/OPL",
"acronym" : "OPL",
"description" : "The Ontology for Parasite LifeCycle (OPL) models the life cycle stage details of various parasites, including Trypanosoma sp., Leishmania major, and Plasmodium sp., etc. In addition to life cycle stages, the ontology also models necessary contextual details, such as host information, vector information, and anatomical location. OPL is based on the Basic Formal Ontology (BFO) and follows the rules set by the OBO Foundry consortium.",
"categories" : [ "Phenotype", "Development", "Vocabularies" ],
"veiws" : 515,
"projects" : 0,
"objPropertyNo" : 35,
"classNo" : 446,
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}, {
"name" : "BioPortal Metadata Ontology",
"id" : "https://data.bioontology.org/ontologies/BP-METADATA",
"acronym" : "BP-METADATA",
"description" : "This ontology represents the structure that BioPortal uses to represent all of its metadata (ontology details, mappings, notes, reviews, views)",
"categories" : [ "Other" ],
"veiws" : 1222,
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}, {
"name" : "CrossRef Funder Registry",
"id" : "https://data.bioontology.org/ontologies/XREF-FUNDER-REG",
"acronym" : "XREF-FUNDER-REG",
"description" : "The Funder Registry provides a common taxonomy of international funding body names that funding data participants should use to normalize funder names and IDs for deposit with Crossref.\r\nThe list can be used to present authors with a pre-populated \"Funder Name\" option at the time of submission, and can also be used to match funder names extracted from papers.\r\nThe list is available to download as an RDF file, and is freely available under a CC0 license waiver.\r\nThis information was obtained from a GitLab repository created by Bryan Vickery (see Homepage). The first version downloaded was 1.34.",
"categories" : [ "Other" ],
"veiws" : 0,
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"classNo" : 5,
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}, {
"name" : "Biofilm Ontology",
"id" : "https://data.bioontology.org/ontologies/BIFO",
"acronym" : "BIFO",
"description" : "BIFO is intended to provide a biofilm ontology capable of understanding fundamental terminologies the relations between common biofilm terminologies following the guidelines of the Basic Formal Ontology (BFO).",
"categories" : [ "Biomedical_Resources", "Biological_Process" ],
"veiws" : 99,
"projects" : 0,
"objPropertyNo" : 15,
"classNo" : 78,
"classes" : null
}, {
"name" : "EDDA Study Designs Taxonomy",
"id" : "https://data.bioontology.org/ontologies/EDDA",
"acronym" : "EDDA",
"description" : "The EDDA Study Designs Taxonomy (v2.0) was developed by the Evidence in Documents, Discovery, and Analytics (EDDA) Group: Tanja Bekhuis (Principal Scientist); Eugene Tseytlin (Systems Developer); Ashleigh Faith (Taxonomist); Faina Linkov (Epidemiologist). The EDDA Group is a division of TCB Research & Indexing LLC. This work was made possible, in part, by the US National Library of Medicine, National Institutes of Health, grant no. R00LM010943. \r\n\r\nFoundational research is described in Bekhuis T, Demner Fushman D, Crowley RS. Comparative effectiveness research designs: an analysis of terms and coverage in Medical Subject Headings (MeSH) and Emtree. Journal of the Medical Library Association (JMLA). 2013 April;101(2):92-100. PMC3634392. \r\n\r\nCoverage of the terminology appearing in JMLA was extended with terms from MeSH, NCI Thesaurus (NCI), Emtree, the HTA Database Canadian Repository [international repository for health technology assessment], and Robert Sandieson's synonym ring for research synthesis. Collected terms were enriched with terms from the NCI Metathesaurus. Variants include synonyms for preferred terms, singular and plural forms, and American and British spellings. Definitions, if they exist, are mainly from MeSH, NCI, Emtree, and medical dictionaries.\r\n\r\nA PDF of this taxonomy is available in ResearchGate DOI: 10.13140/RG.2.1.3769.2406/1.\r\n\r\nThe EDDA Study Designs Taxonomy by Tanja Bekhuis and Eugene Tseytlin is licensed under a Creative Commons Attribution–NonCommercial–ShareAlike 4.0 International License (CC BY-NC-SA 4.0). For information about this license, see https://creativecommons.org/licenses/by-nc-sa/4.0/.",
"categories" : [ "Experimental_Conditions", "Vocabularies" ],
"veiws" : 3595,
"projects" : 1,
"objPropertyNo" : 2,
"classNo" : 502,
"classes" : null
}, {
"name" : "postpartum depression ontology",
"id" : "https://data.bioontology.org/ontologies/PARTUMDO",
"acronym" : "PARTUMDO",
"description" : "This ontology is for use in extracting postpartum depression condition codes, and also comorbidities and diseases often mistaken for postpartum depression. The ontology describes not only the codes but also the relationships between these various diseases/risk factors for use in automatically extracting relevant patient populations from Electronic Health Records",
"categories" : [ "Phenotype", "Neurologic_Disease", "Neurological_Disorder", "Human", "Vocabularies" ],
"veiws" : 79,
"projects" : 0,
"objPropertyNo" : 13,
"classNo" : 734,
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}, {
"name" : "vfb_drivers",
"id" : "https://data.bioontology.org/ontologies/VFB_DRIVERS",
"acronym" : "VFB_DRIVERS",
"description" : "Ontology of D. melanogaster drivers and expression patterns.",
"categories" : [ ],
"veiws" : 0,
"projects" : 0,
"objPropertyNo" : 89,
"classNo" : 26428,
"classes" : null
}, {
"name" : "KB_Bio_101",
"id" : "https://data.bioontology.org/ontologies/AURA",
"acronym" : "AURA",
"description" : "KB_Bio_101 is the ontology of the AURA + Inquire project @ SRI International, Menlo Park. \r\n\r\nThis work is owned by Vulcan Inc. and is licensed for use under the Creative Commons Attribution-NonCommerical-ShareAlike 3.0 license (http://creativecommons.org/licenses/by-nc-sa/3.0/). Vulcan Inc., June 2013",
"categories" : [ ],
"veiws" : 1453,
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"objPropertyNo" : 358,
"classNo" : 27396,
"classes" : null
}, {
"name" : "General Formal Ontology for Biology",
"id" : "https://data.bioontology.org/ontologies/GFO-BIO",
"acronym" : "GFO-BIO",
"description" : "GFO-Bio is a biological core ontology built on the General Formal Ontology.",
"categories" : [ "Mouse_Anatomy", "All_Organisms", "Cellular_anatomy_", "Molecule", "Gross_Anatomy", "Biological_Process", "Subcellular", "Phenotype", "Human_Developmental_Anatomy", "Plant_Anatomy", "Anatomy", "Plant", "Human", "Subcellular_anatomy", "Chemical", "Cell", "Development", "Protein", "Animal_Gross_Anatomy", "Gene_Product", "Plant_Development", "Animal_Development" ],
"veiws" : 814,
"projects" : 0,
"objPropertyNo" : 73,
"classNo" : 168,
"classes" : null
}, {
"name" : "Family Health History Ontology",
"id" : "https://data.bioontology.org/ontologies/FHHO",
"acronym" : "FHHO",
"description" : "The FHHO facilitates representing the family health histories of persons related by biological and/or social family relationships (e.g. step, adoptive) who share genetic, behavioral, and/or environmental risk factors for disease. SWRL rules are included to compute 3 generations of biological relationships based on parentage and family history findings based on personal health findings.",
"categories" : [ "Health" ],
"veiws" : 1483,
"projects" : 0,
"objPropertyNo" : 433,
"classNo" : 238,
"classes" : null
}, {
"name" : "Ontology of Data Mining Investigations",
"id" : "https://data.bioontology.org/ontologies/ONTODM-KDD",
"acronym" : "ONTODM-KDD",
"description" : "OntoDM-KDD is an ontology for representing data mining investigations. Its goal is to allow the representation of knowledge discovery processes and be general enough to represent the data mining investigations. The ontology is based on the CRISP-DM process methodology.",
"categories" : [ ],
"veiws" : 575,
"projects" : 1,
"objPropertyNo" : 34,
"classNo" : 264,
"classes" : null
}, {
"name" : "Friend of a Friend Vocabulary",
"id" : "https://data.bioontology.org/ontologies/FOAF",
"acronym" : "FOAF",
"description" : "Friend of a Friend Vocabulary",
"categories" : [ "Other" ],
"veiws" : 337,
"projects" : 0,
"objPropertyNo" : 67,
"classNo" : 22,
"classes" : null
}, {
"name" : "Delete test",
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"name" : "Web Annotation Ontology",
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"description" : "The Web Annotation Data Model specification describes a structured model and format to enable annotations to be shared and reused across different hardware and software platforms. Annotations are typically used to convey information about a resource or associations between resources. Simple examples include a comment or tag on a single web page or image, or a blog post about a news article.",
"categories" : [ "Upper_Level_Ontology" ],
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}, {
"name" : "Ontology for General Medical Science",
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"description" : "The Ontology for General Medical Science (OGMS) is based on the papers Toward an Ontological Treatment of Disease and Diagnosis and On Carcinomas and Other Pathological Entities. The ontology attempts to address some of the issues raised at the Workshop on Ontology of Diseases (Dallas, TX) and the Signs, Symptoms, and Findings Workshop(Milan, Italy). OGMS was formerly called the clinical phenotype ontology. Terms from OGMS hang from the Basic Formal Ontology. See http://ontology.buffalo.edu/medo/Disease_and_Diagnosis.pdf",
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}, {
"name" : "Mental Health Management Ontology",
"id" : "https://data.bioontology.org/ontologies/MHMO",
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"description" : "This ontology can be used to assist in the management of mental healthcare network to allow the integration and interoperability between independent databases and to enable the generation of relevant indicators to mental health, to improve decision making, increasing the efficiency and effectiveness of mental health management.\r\nOntology created in the Laboratory of Health Intelligence - Ribeirao Preto Medical School, University of Sao Paulo, Brazil.\r\n",
"categories" : [ "Health" ],
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}, {
"name" : "Alzheimer Disease Relevance Ontology by Process",
"id" : "https://data.bioontology.org/ontologies/AD-DROP",
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"description" : "Alzheimer Disease Relevance Ontology by Process (AD-DROP) aimed at classifying Disease Relevant Process according to their specificity, frequency and pathogenic intensity properties toward AD",
"categories" : [ "Biomedical_Resources" ],
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}, {
"name" : "Biomedical Resource Ontology",
"id" : "https://data.bioontology.org/ontologies/BRO",
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"description" : "A controlled terminology of resources, which is used to improve the sensitivity and specificity of web searches.",
"categories" : [ "Biomedical_Resources" ],
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}, {
"name" : "Neurologic Examination Ontology",
"id" : "https://data.bioontology.org/ontologies/NEO",
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"description" : "An ontology to capture findings from the neurological examination. The ontology is based on concepts from UMLS Metathesaurus and SNOMED CT. Only abnormal findings are included. The purpose of the ontology is to act as a limited vocabulary for coding the neurological examination for big data projects or machine learning.\r\n\r\nHier DB, Brint SU. A Neuro-ontology for the neurological examination. BMC Med Inform Decis Mak. 2020;20(1):47. Published 2020 Mar 4. doi:10.1186/s12911-020-1066-7",
"categories" : [ "Taxonomic_Classification", "Health", "Neurologic_Disease", "Neurological_Disorder" ],
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}, {
"name" : "Psychology Ontology",
"id" : "https://data.bioontology.org/ontologies/APAONTO",
"acronym" : "APAONTO",
"description" : "APA thesaurus . Flat ontology file",
"categories" : [ "Biomedical_Resources" ],
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}, {
"name" : "Hypersensitivity pneumonitis ontology",
"id" : "https://data.bioontology.org/ontologies/HP_O",
"acronym" : "HP_O",
"description" : "Hypersensitivity Pneumonitis Ontology involves the domain of knowledge of 1. diseases caused by organic dusts exposure including hypersensitivity pneumonitis as well as 2. organic dusts.",
"categories" : [ "Other" ],
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}, {
"name" : "ISO 19110 Methodology for Feature Cataloguing",
"id" : "https://data.bioontology.org/ontologies/ISO19110",
"acronym" : "ISO19110",
"description" : "ISO 19110:2005 Methodology for feature cataloguing",
"categories" : [ "Other" ],
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}, {
"name" : "Contributor Role Ontology",
"id" : "https://data.bioontology.org/ontologies/CRO",
"acronym" : "CRO",
"description" : "The Contributor Role Ontology (CRO) is an extension of the CASRAI Contributor Roles Taxonomy (CRediT).",
"categories" : [ "Other" ],
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}, {
"name" : "Social Prescribing Ontology",
"id" : "https://data.bioontology.org/ontologies/SOCPRES",
"acronym" : "SOCPRES",
"description" : "The social prescribing ontology specifies concepts associated to social prescribing practices and the well being of social prescribing recipients. The ontology will be primary used to harmonise data related social prescribing processes across various parts of the health system.",
"categories" : [ "Other" ],
"veiws" : 375,
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}, {
"name" : "Evidence Graph Ontology",
"id" : "https://data.bioontology.org/ontologies/EVI",
"acronym" : "EVI",
"description" : "The Evidence Graph ontology extends core concepts from the W3C Provenance Ontology PROV-O and Bioschemas' Profiles to describe evidence for correctness of findings in biomedical publications. The semantic data model in EVI is expressed using OWL2 Web Ontology Language.",
"categories" : [ "Experimental_Conditions", "Biomedical_Resources", "Biological_Process", "Other" ],
"veiws" : 40,
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}, {
"name" : "Nurse Transitional",
"id" : "https://data.bioontology.org/ontologies/TRANS",
"acronym" : "TRANS",
"description" : "This is an ontology of a transitional care nurse's process domain",
"categories" : [ "Health" ],
"veiws" : 80,
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}, {
"name" : "M4M19 Subjects",
"id" : "https://data.bioontology.org/ontologies/M4M19-SUBS",
"acronym" : "M4M19-SUBS",
"description" : "Controlled vocabularies allow an accurate and controlled approach in describing physical and digital assets (e.g., data). One of such controlled vocabulary is NICEST-2 Subjects. This controlled vocabulary is a result of Metadata 4 Machine (M4M) Workshop 19 (M4M.19 funded by EOSC-Nordic ) provided to the NICEST-2 project.\r\n\r\nsheet2rdf and OntoStack, developed by FAIR Data Collective, are used to build and serve NICEST-2 Subjects, while PURL will be used to persist identifiers for the vocabulary terms and properties:\r\n\r\nhttp://purl.org/m4m19/subjects/",
"categories" : [ ],
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}, {
"name" : "Plant Trait Ontology",
"id" : "https://data.bioontology.org/ontologies/PTO",
"acronym" : "PTO",
"description" : "A controlled vocabulary to describe phenotypic traits in plants. Each trait is a distinguishable feature, characteristic, quality or phenotypic feature of a developing or mature plant, or a plant part.",
"categories" : [ "Phenotype", "Plant" ],
"veiws" : 6803,
"projects" : 4,
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}, {
"name" : "Molecular Process Ontology",
"id" : "https://data.bioontology.org/ontologies/MOP",
"acronym" : "MOP",
"description" : "Processes at the molecular level.",
"categories" : [ ],
"veiws" : 220,
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}, {
"name" : "Presence Ontology",
"id" : "https://data.bioontology.org/ontologies/PREO",
"acronym" : "PREO",
"description" : "The Presence Ontology is a systematic vocabulary of terms with defined relationships that models the encounters taking place every day among providers, patients, and family members or friends in environments such as hospitals and clinics. The Presence Ontology provides a conceptual model for the human experience in medicine. This is a preliminary approach, but further use of methods we have developed here may aid in providing clarity and consensus to topics in healthcare. The ontology is also a model for interdisciplinary collaboration, as it was developed in conjunction with experts from bioinformatics, medicine, anthropology, business, and communication sciences through multiple iterative stages.",
"categories" : [ "Health", "Biomedical_Resources", "Human" ],
"veiws" : 355,
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}, {
"name" : "Bioentities",
"id" : "https://data.bioontology.org/ontologies/BE",
"acronym" : "BE",
"description" : "Bioentities is a collection of resources for grounding biological entities from text and describing their hierarchical relationships, focusing on protein families and complexes. ",
"categories" : [ ],
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}, {
"name" : "Mouse Experimental Design Ontology",
"id" : "https://data.bioontology.org/ontologies/MEDO",
"acronym" : "MEDO",
"description" : "A representation of experimental design for high-throughput mouse analysis pipelines.",
"categories" : [ ],
"veiws" : 753,
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}, {
"name" : "Simple Measurement Ontology",
"id" : "https://data.bioontology.org/ontologies/SIMON",
"acronym" : "SIMON",
"description" : "The SImple Measurement ONtology (SIMON) is a small vocabulary (3 top level classes: PunnedClass, Measurement, and Unit) to represent measurements and units. It uses the pattern to represent N-ary relations and a recursive structure to make the representation of various measurements and units (e.g., \"5 kilograms\", \"4 miles per hour\", \"5 meters per second per second\" straight forward and obvious. It utilizes the Semantic Web Rule Language (SWRL) to automatically order measurements of the same type (e.g., Speed) regardles of units. E.g., that 5 kilometers per hour is greater than 5 meters per hour. It does this by transforming all measures of a specific type (e.g., distance, mass, speed, acceleration) to canonical units. The canonical units can be modified by changing a few rules. I also include on the github page some SPARQL transformations that generate the rdfs:label for each measurement instance so the developer doesn't have to take the effort and potential error prone risk of doing this manually. The version included here is the version after the SWRL rules have run and with the inferences exported and also after the SPARQL transforms have generated all the rdfs:label values. Although there are other vocabularies that address this problem they are either much more complex (addressing many additional issues besides units and measures) and/or do not automatically order measures of different units. ",
"categories" : [ "Vocabularies" ],
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}, {
"name" : "Ontology of Arthropod Circulatory Systems",
"id" : "https://data.bioontology.org/ontologies/OARCS",
"acronym" : "OARCS",
"description" : "OArCS is an ontology describing the Arthropod ciruclatory system.",
"categories" : [ "Anatomy" ],
"veiws" : 139,
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}, {
"name" : "Biological interlocked Process Ontology for metabolism",
"id" : "https://data.bioontology.org/ontologies/BIPOM",
"acronym" : "BIPOM",
"description" : "BiPOm is an ontology based on systemic representation of metabolic processes. BiPOm is an ontological model carrying the main biological processes and molecular roles/functions at a high level of genericity where the usual annotated resources are treated as instances.\r\nBiPOm, 1) contains biological knowledge as instances and 2) uses automatic reasoning through Semantic Web Rule Language (SWRL) in order to automatically infer, formalize and refine properties of molecules.",
"categories" : [ "All_Organisms" ],
"veiws" : 216,
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}, {
"name" : "Pregnancy Information Needs Ontology",
"id" : "https://data.bioontology.org/ontologies/PINO",
"acronym" : "PINO",
"description" : "An ontology about pregnancy information needs across all stages of pregnancy",
"categories" : [ "Health" ],
"veiws" : 0,
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}, {
"name" : "Timebank Ontology",
"id" : "https://data.bioontology.org/ontologies/TIMEBANK",
"acronym" : "TIMEBANK",
"description" : "The Timebank Ontology is used to describe Timebank systems for Peer-to-Peer Service Exchange. A Timebank allows user to store virtual money, often called a Time Dollar, into a bank account. This virtual currency can be earned by helping fellow Timebank users and spent by requesting help from others. The main goal of the ontology is to facilitate the matching between helpers and requesters of help.",
"categories" : [ "Other", "Human" ],
"veiws" : 20,
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}, {
"name" : "dada",
"id" : "https://data.bioontology.org/ontologies/DADA",
"acronym" : "DADA",
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"categories" : [ ],
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}, {
"name" : "Pre-eclampsia Ontology",
"id" : "https://data.bioontology.org/ontologies/PE-O",
"acronym" : "PE-O",
"description" : "The PEO incorporates a wide range of key concepts and terms of PE from clinical and biomedical research in structuring the knowledge base that is specific to PE.",
"categories" : [ "Health", "Human" ],
"veiws" : 338,
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}, {
"name" : "Cognitive Atlas Ontology",
"id" : "https://data.bioontology.org/ontologies/COGAT",
"acronym" : "COGAT",
"description" : "The Cognitive Atlas is a collaborative knowledge building project that aims to develop an ontology that characterizes the state of current thought in cognitive science. It defines a set of mental concepts along with a set of mental tasks, and the measurement relations between those classes.",
"categories" : [ "Phenotype", "Human", "Vocabularies" ],
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}, {
"name" : "Health Level Seven Reference Implementation Model, Version 3",
"id" : "https://data.bioontology.org/ontologies/HL7",
"acronym" : "HL7",
"description" : "This version is the first update to Normative RIM, Release 3. It is based on changes approved in Harmonization in November 2010. This release of the RIM is bound to HL7 Abstract Data Types Release 2.",
"categories" : [ "Health" ],
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}, {
"name" : "Doremus List of Keys",
"id" : "https://data.bioontology.org/ontologies/DOREMUS-KEYS",
"acronym" : "DOREMUS-KEYS",
"description" : "The sum of relations, melodic and harmonic, existing between the tones of a scale or musical system.",
"categories" : [ ],
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