diff --git a/docs/source/_static/custom.css b/docs/source/_static/custom.css index 2a71f26e..500327b9 100644 --- a/docs/source/_static/custom.css +++ b/docs/source/_static/custom.css @@ -34,6 +34,14 @@ dd > dl { padding-top: 0px; } +.backrefs { + font-size: 0; +} + +.backrefs:after { + font-size: initial; + content: "\00a0"; +} .components { display: flex; flex-flow: row wrap; diff --git a/docs/source/benchmarking/agriculture/agro.rst b/docs/source/benchmarking/agriculture/agro.rst index 41be71f0..6f6710e8 100644 --- a/docs/source/benchmarking/agriculture/agro.rst +++ b/docs/source/benchmarking/agriculture/agro.rst @@ -25,7 +25,29 @@ Agronomy Ontology (AgrO) ======================================================================================================== -An ontology is a formal representation of a disciplinary domain, representing a semantic standard that can be employed to annotate data where key concepts are defined, as well as the relationships that exist between those concepts (Gruber, 2009). Ontologies provide a common language for different kinds of data to be easily interpretable and interoperable, allowing for easier aggregation and analysis. The Agronomy Ontology (AgrO) provides terms from the agronomy domain that are semantically organized and can facilitate the collection, storage, and use of agronomic data, enabling easy interpretation and reuse of the data by humans and machines alike. To fully understand the implications of varying practices within cropping systems and derive insights, it is often necessary to pull together information from data in different disciplinary domains. For example, data on field management, soil, weather, and crop phenotypes may need to be aggregated to assess performance of a particular crop under different management interventions. However, agronomic data are often collected, described, and stored in inconsistent ways, impeding data comparison, mining, interpretation, and reuse. The use of standards for metadata and data annotation plays a key role in addressing these challenges. While the CG Core Metadata Schema provides a metadata standard to describe agricultural datasets, the Agronomy Ontology enables the description of agronomic data variables using standard terms. +The Agronomy Ontology (AgrO) provides terms from the agronomy domain +that are semantically organized and facilitate the collection, storage, +and use of agronomic data, enabling easier interpretation and reuse by +both humans and machines [#cgiar]_ [#obo]_. To analyze the effects of +varying practices within cropping systems, it is often necessary to +integrate data from multiple disciplinary domains. For example, data on +field management, soil, weather, and crop phenotypes may need to be +combined to assess crop performance under different management +interventions. However, agronomic data are often collected, described, +and stored in inconsistent ways, which impedes data comparison, mining, +interpretation, and reuse [#cgiar]_. The use of standards for metadata +and data annotation plays a key role in addressing these challenges. +While the CG Core Metadata Schema provides a metadata standard to +describe agricultural datasets, the Agronomy Ontology enables the +description of agronomic variables using standardized and semantically +defined terms [#cgiar]_ [#agrofims]_. AgrO specifically covers agronomic +practices, techniques, and variables used in agronomic experiments and +reuses terms from other ontologies to support interoperability [#obo]_. + +**Example Usage**: Annotate agronomic field experiment data with AgrO terms for management +practices, treatments, and measured variables to enable standardized +description, interoperable storage, and cross-study comparison of +agricultural data [#obo]_ [#agrofims]_. Metrics & Statistics -------------------------- @@ -134,3 +156,20 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#cgiar] CGIAR. n.d. "Agronomy Ontology." + Available at: https://bigdata.cgiar.org/resources/agronomy-ontology/ + +.. [#obo] OBO Foundry. n.d. "Agronomy Ontology (AGRO)." + Available at: https://obofoundry.org/ontology/agro.html + +.. [#agrofims] Devare, M., Aubert, C., Benites Alfaro, O. E., + Perez Masias, I. O., and Laporte, M.-A. 2021. + "AgroFIMS: A Tool to Enable Digital Collection of + Standards-Compliant FAIR Data." + *Frontiers in Sustainable Food Systems* 5:726646. + Available at: + https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2021.726646/full diff --git a/docs/source/benchmarking/agriculture/agrovoc.rst b/docs/source/benchmarking/agriculture/agrovoc.rst index 14c252ca..ec4e6e12 100644 --- a/docs/source/benchmarking/agriculture/agrovoc.rst +++ b/docs/source/benchmarking/agriculture/agrovoc.rst @@ -23,9 +23,28 @@ AGROVOC Multilingual Thesaurus (AGROVOC) ======================================================================================================== -AGROVOC is a comprehensive Linked Open Data resource developed and maintained by the Food and Agriculture Organization (FAO) of the United Nations. It provides a structured collection of agricultural concepts, terms, definitions, and relationships that enable unambiguous identification of resources and standardized indexing. As a multilingual thesaurus, AGROVOC supports multiple languages, facilitating access and visibility of agricultural data across domains and languages. The ontology covers diverse agricultural domains including crops, livestock, farm management practices, soil science, and food production. AGROVOC's hierarchical structure enables both broader and narrower term relationships, supporting semantic interoperability and making searches more efficient. The resource is widely used by research institutions, government agencies, and international organizations for data annotation, knowledge organization, and information retrieval. With millions of concept nodes and sophisticated relationship mappings, AGROVOC serves as a critical backbone for agricultural knowledge representation and data integration in the global agricultural community. - -**Example Usage**: Annotate a multilingual agricultural dataset with AGROVOC terms for crops, soil types, and farming practices to enable standardized indexing and cross-language search in international agricultural databases. +AGROVOC is a multilingual thesaurus and Linked Open Data resource +developed and maintained by the Food and Agriculture Organization (FAO) +of the United Nations [#fao-home]_ [#agrovoc-paper]_. It provides a +structured collection of agricultural concepts, terms, definitions, and +relationships that support unambiguous resource identification, +standardized indexing, and more efficient search [#fao-home]_. As a +multilingual knowledge organization system, AGROVOC facilitates access +to agricultural information across domains and languages [#fao-home]_ +[#agrovoc-paper]_. It covers concepts relevant to food, agriculture, +fisheries, forestry, environment, and related domains, and supports +semantic interoperability through hierarchical and associative +relationships as well as links to other vocabularies and datasets +[#fao-home]_ [#linked-dataset]_. With over 41,000 concepts and extensive +multilingual term coverage, AGROVOC is widely used for data annotation, +knowledge organization, and information retrieval in agricultural and +food-related information systems [#fao-dpg]_ [#agrovoc-paper]_. + +**Example Usage**: Annotate a multilingual agricultural dataset with AGROVOC concepts for +crops, soil types, pests, livestock, and farming practices to enable +standardized indexing, semantic interoperability, and cross-language +search across international agricultural databases and repositories +[#fao-home]_ [#agrovoc-paper]_. Metrics & Statistics -------------------------- @@ -134,3 +153,27 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#fao-home] Food and Agriculture Organization of the United Nations + (FAO). n.d. "AGROVOC." Available at: + https://www.fao.org/agrovoc/ + +.. [#agrovoc-paper] Subirats-Coll, I., Kolshus, K., Turbati, A., + Stellato, A., Mietzsch, E., Martini, D., and Zeng, M. 2022. + "AGROVOC: The linked data concept hub for food and agriculture." + *Computers and Electronics in Agriculture* 196:105965. + doi:10.1016/j.compag.2020.105965 + +.. [#linked-dataset] Caracciolo, C., Stellato, A., Morshed, A., + Johannsen, G., Rajbhandari, S., Jaques, Y., and Keizer, J. 2013. + "The AGROVOC Linked Dataset." *Semantic Web* 4(3):341-348. + Available at: + https://www.fao.org/agrovoc/publications/agrovoc-linked-dataset + +.. [#fao-dpg] Food and Agriculture Organization of the United Nations + (FAO). 2024. "AGROVOC is now a certified Digital Public Good!" + Available at: + https://www.fao.org/agora/news/agrovoc-now-certified-digital-public-good diff --git a/docs/source/benchmarking/agriculture/atol.rst b/docs/source/benchmarking/agriculture/atol.rst index 40263d23..360d42ce 100644 --- a/docs/source/benchmarking/agriculture/atol.rst +++ b/docs/source/benchmarking/agriculture/atol.rst @@ -23,14 +23,29 @@ Animal Trait Ontology for Livestock (ATOL) ======================================================================================================== -ATOL (Animal Trait Ontology for Livestock) is an ontology of characteristics defining phenotypes of livestock in their environment. ATOL aims to: provide a reference ontology of phenotypic traits of farm animals for the international scientific and educational communities, farmers, etc.; deliver this reference ontology in a language which can be used by computers in order to support database management, semantic analysis and modeling; represent traits as generic as possible for livestock vertebrates; make the ATOL ontology as operational as possible and closely related to measurement techniques; and structure the ontology in relation to animal production. - -The ontology employs a class-based modeling approach, defining classes for different types of phenotypic traits, measurement techniques, and related data, along with properties to describe their characteristics and interactions. Hierarchies are used to organize classes into categories, enabling efficient data retrieval and analysis. ATOL supports the integration of data from various sources, promoting interoperability and data-driven research in animal science. - -Typical applications of ATOL include the development of new phenotypic trait analysis methods, the optimization of livestock management practices, and the integration of diverse datasets to support advanced analytics and knowledge discovery. By providing a standardized vocabulary and framework, ATOL enhances collaboration and innovation in the field of animal science. - -**Example Usage**: -Annotate a livestock dataset with ATOL terms to specify phenotypic traits, measurement techniques, and related data, enabling semantic search and integration with animal science research platforms. +ATOL (Animal Trait Ontology for Livestock) is an ontology of +characteristics defining phenotypes of livestock in their environment +[#inra]_ [#atol-paper]_. ATOL aims to provide a reference ontology of +phenotypic traits for farm animals for the international scientific and +educational communities and other stakeholders, and to deliver this +reference ontology in a form that can be used by computers to support +database management, semantic analysis, and modeling [#inra]_. It is +designed to represent traits as generically as possible for livestock +vertebrates, to remain closely related to measurement techniques, and to +structure the ontology in relation to animal production [#inra]_. The +multi-species ATOL model was developed as a reference source for +indexing phenotype databases and scientific papers, and it covers major +livestock production topics including growth and meat quality, animal +nutrition, milk production, reproduction, and welfare [#atol-paper]_. +By providing a standardized vocabulary and semantic framework, ATOL +supports consistent annotation, interoperability, and integration of +livestock phenotype data across animal science resources [#inra]_ +[#agroportal]_. + +**Example Usage**: Annotate a livestock dataset with ATOL terms to specify phenotypic +traits, measurement techniques, and related data, enabling semantic +search and integration with animal science research platforms +[#inra]_ [#atol-paper]_. Metrics & Statistics -------------------------- @@ -139,3 +154,19 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#inra] INRAE Open Data. n.d. "Animal Trait Ontology for Livestock." + Available at: https://opendata.inra.fr/ATOL/page/ + +.. [#agroportal] AgroPortal. n.d. "ATOL | Summary." + Available at: https://agroportal.lirmm.fr/ontologies/ATOL + +.. [#atol-paper] Golik, W., Dameron, O., Bugeon, J., Fatet, A., Hue, I., + Hurtaud, C., Reichstadt, M., Salaün, M.-C., Vernet, J., Joret, L., + Papazian, F., Nédellec, C., and Le Bail, P.-Y. 2012. + "ATOL: The Multi-species Livestock Trait Ontology." + In *Metadata and Semantics Research*, CCIS 343, 289-300. + doi:10.1007/978-3-642-35233-1_28 diff --git a/docs/source/benchmarking/agriculture/foodon.rst b/docs/source/benchmarking/agriculture/foodon.rst index 683fe0c5..4b71360e 100644 --- a/docs/source/benchmarking/agriculture/foodon.rst +++ b/docs/source/benchmarking/agriculture/foodon.rst @@ -25,7 +25,31 @@ Food Ontology (FoodON) ======================================================================================================== -FoodOn, the Food Ontology, provides comprehensive vocabulary for naming and classifying food materials throughout the entire food supply chain. It encompasses raw harvested foods and their botanical/zoological origins as well as processed food products designed for both human consumption and animal feed. FoodOn integrates anatomical and taxonomic knowledge, enabling precise semantic representation of food items and their components. The ontology is designed as a neutral, ontology-driven standard that bridges the interests of government agencies, industry stakeholders, nonprofits, and consumers. Its hierarchical structure captures relationships between ingredient components, processing methods, and final food products. FoodOn facilitates standardized naming conventions and interoperability across diverse food-related databases and systems. The ontology supports critical applications including food safety traceability, nutritional analysis, dietary research, and supply chain transparency. By providing unambiguous semantic definitions, FoodOn enables automated systems to track food products, ingredients, and allergens, enhancing data integration across the food industry and supporting evidence-based policy decisions in food security and nutrition. +FoodOn is a farm-to-fork food ontology that provides a comprehensive +vocabulary for naming and classifying food materials across the food +supply chain [#foodon-home]_ [#foodon-paper]_. It covers raw harvested +foods, their botanical and zoological origins, and processed food +products intended for both human consumption and animal feed +[#foodon-home]_ [#foodon-paper]_. FoodOn integrates anatomical, +taxonomic, and other reusable ontology terms to support precise semantic +representation of food items, their components, and related food +processes [#foodon-home]_ [#foodon-paper]_. It is designed as an open, +ontology-driven standard that supports consistent food description and +interoperability across government, industry, research, and other +food-related systems [#foodon-paper]_ [#foodon-home]_. Its hierarchical +structure supports relationships among source organisms, anatomical +parts, processing methods, preservation methods, packaging, and final +food products [#foodon-home]_ [#foodon-paper]_. By providing +unambiguous semantic definitions, FoodOn supports standardized naming, +food safety traceability, quality control, nutrition and dietary +research, and data integration across food-related databases and +applications [#foodon-paper]_ [#foodon-home]_. + +**Example Usage**: Annotate a food product dataset with FoodOn terms for source organisms, +anatomical parts, processing methods, packaging, and final food products +to enable standardized description, interoperable data exchange, and +traceability across food safety, nutrition, and supply chain systems +[#foodon-home]_ [#foodon-paper]_. Metrics & Statistics -------------------------- @@ -134,3 +158,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#foodon-home] FoodOn. n.d. "FoodOn: A farm to fork ontology." + Available at: https://foodon.org/ + +.. [#foodon-paper] Dooley, D. M., Griffiths, E. J., Gosal, G. S., + Buttigieg, P. L., Hoehndorf, R., Lange, M. C., Schriml, L. M., + Brinkman, F. S. L., and Hsiao, W. W. L. 2018. + "FoodOn: a harmonized food ontology to increase global food + traceability, quality control and data integration." + *npj Science of Food* 2:23. + doi:10.1038/s41538-018-0032-6 diff --git a/docs/source/benchmarking/agriculture/po.rst b/docs/source/benchmarking/agriculture/po.rst index d42d6e22..621c770d 100644 --- a/docs/source/benchmarking/agriculture/po.rst +++ b/docs/source/benchmarking/agriculture/po.rst @@ -25,7 +25,30 @@ Plant Ontology (PO) ======================================================================================================== -The Plant Ontology (PO) is a structured vocabulary and database resource that links plant anatomy, morphology and growth and development to plant genomics data. Developed collaboratively by plant biologists and ontology experts, PO provides a comprehensive framework for describing plant structures and developmental stages. The ontology integrates anatomical terms that can be associated with plant genes, enabling researchers to annotate phenotypic data and support comparative genomics across plant species. PO is designed to facilitate seamless data integration and interoperability in plant science research, allowing scientists to discover relationships between genes and plant structures. With its hierarchical organization of plant parts from whole organism level down to cellular structures, PO supports diverse applications including literature curation, genome annotation, and systems biology studies. The ontology is actively maintained by the Planteome project and integrated with other biological ontologies to ensure compatibility and comprehensive semantic representation in the plant science community. +The Plant Ontology (PO) is a structured vocabulary and ontology +resource that links plant anatomy, morphology, growth, and development +to plant genomics and phenomics data [#obo]_ [#po-paper]_. Developed as +a community resource, PO provides a framework for describing plant +structures and developmental stages across plant species [#obo]_ +[#po-dev-paper]_. The ontology integrates anatomical and developmental +terms that can be associated with plant genes and phenotypes, enabling +researchers to annotate data and support comparative genomics and +comparative plant biology [#po-paper]_ [#po-dev-paper]_. PO is designed +to facilitate data integration and interoperability in plant science +research [#obo]_ [#po-paper]_. With its hierarchical organization of +plant structures and developmental stages, including whole plants, +organs, tissues, and cell types, PO supports applications such as +literature curation, genome annotation, and phenotypic data annotation +[#po-paper]_. The ontology is under active development and is integrated +with the Planteome project and other biological ontologies to support +semantic compatibility in the plant science community [#obo]_ +[#planteome]_. + +**Example Usage**: Annotate a plant genomics or phenomics dataset with PO terms for plant +structures and developmental stages, such as leaf, root, flower, seed, +or senescent stage, to enable standardized annotation, cross-species +comparison, and integration with plant science databases and analysis +platforms [#obo]_ [#po-paper]_. Metrics & Statistics -------------------------- @@ -134,3 +157,32 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#obo] OBO Foundry. n.d. "Plant Ontology (PO)." + Available at: `https://obofoundry.org/ontology/po.html `_ + +.. [#po-paper] Cooper, L., Walls, R. L., Elser, J., Gandolfo, M. A., + Stevenson, D. W., Smith, B., Preece, J., Athreya, B., Mungall, C. J., + and Rensing, S. A. 2013. "The Plant Ontology as a Tool for Comparative + Plant Anatomy and Genomic Analyses." + *Plant and Cell Physiology* 54(2): e1. + doi:10.1093/pcp/pcs163 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC3583023/ `_ + +.. [#po-dev-paper] Walls, R. L., Cooper, L., Elser, J., Gandolfo, M. A., + Mungall, C. J., Smith, B., Stevenson, D. W., and Jaiswal, P. 2019. + "The Plant Ontology Facilitates Comparisons of Plant Development + Stages Across Species." + *Frontiers in Plant Science* 10:631. + doi:10.3389/fpls.2019.00631 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC6558174/ `_ + +.. [#planteome] Cooper, L., Elser, J., Laporte, M.-A., Arnaud, E., + and Jaiswal, P. 2024. "Planteome 2024 Update: Reference Ontologies + and Knowledgebase for Plant Biology." + *Nucleic Acids Research* 52(D1): D1548-D1555. + doi:10.1093/nar/gkad1028 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC10767901/ `_ diff --git a/docs/source/benchmarking/arts_and_humanities/chordontology.rst b/docs/source/benchmarking/arts_and_humanities/chordontology.rst index a5e0c7f8..1ad1bb18 100644 --- a/docs/source/benchmarking/arts_and_humanities/chordontology.rst +++ b/docs/source/benchmarking/arts_and_humanities/chordontology.rst @@ -25,7 +25,28 @@ Chord Ontology (ChordOntology) ======================================================================================================== -The Chord Ontology is a formal representation for describing and classifying chords in musical compositions. It provides a structured vocabulary for representing harmonic concepts and chord structures, enabling precise annotation and analysis of music at the semantic level. The ontology captures essential chord properties including chord type (major, minor, diminished, augmented), root note, and constituent pitch classes. It facilitates semantic annotation of audio files, musical scores, and music information retrieval systems, allowing researchers and musicians to query and discover musical pieces based on harmonic content. The ontology integrates with broader music theory frameworks and supports interoperability with other music-related ontologies. By formalizing chord relationships and structures, the Chord Ontology enables computational music analysis, music recommendation systems, and digital musicology applications. It provides a common framework for music annotation across diverse platforms and datasets, supporting music education, composition analysis, and music information systems development. +The Chord Ontology is a formal representation for describing and classifying +chords and chord sequences in musical resources. It provides a structured +vocabulary for representing harmonic concepts and chord structures, enabling +semantic annotation and analysis of music data. The ontology captures core +chord properties including chord type (for example major, minor, diminished, +and augmented), root note, constituent intervals, and bass note. It supports +the annotation of audio files, musical scores, and symbolic music files by +linking chord events to temporal structures and music resources. The ontology +was developed within the OMRAS2 project and is designed to interoperate with +related Semantic Web resources such as the Music Ontology, Timeline Ontology, +and Event Ontology. By formalizing chord relationships and structures, the +Chord Ontology supports computational music analysis, harmonic annotation, +music information retrieval, and digital musicology applications. It provides +a common framework for music annotation across datasets and tools, supporting +harmonic analysis, corpus annotation, and music information systems +development [#chord-spec]_ [#omras2]_ [#music-ontology]_. + +**Example Usage**: Annotate the harmonic timeline of an audio recording, +musical score, or symbolic music file with Chord Ontology terms for chord +events, root notes, intervals, and bass notes to enable semantic search, +computational harmonic analysis, and integration with music information +retrieval datasets and tools [#chord-spec]_ [#omras2]_. Metrics & Statistics -------------------------- @@ -134,3 +155,24 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#chord-spec] Sutton, C., Raimond, Y., and Mauch, M. 2007. + "Chord Ontology Specification." + OMRAS2 Project, Centre for Digital Music, Queen Mary University of London. + Available at: http://purl.org/ontology/chord/ + Also available at: https://motools.sourceforge.net/chord_draft_1/chord.html + +.. [#omras2] Fazekas, G., Raimond, Y., Jacobson, K., and Sandler, M. 2010. + "An Overview of Semantic Web Activities in the OMRAS2 Project." + *Journal of New Music Research* 39(4): 295-311. + doi:10.1080/09298215.2010.536555 + +.. [#music-ontology] Raimond, Y., Abdallah, S. A., Sandler, M. B., + and Giasson, F. 2007. + "The Music Ontology." + In *Proceedings of the 8th International Conference on Music Information + Retrieval (ISMIR 2007)*, Vienna, Austria, pp. 417-422. + Available at: https://ismir2007.ismir.net/proceedings/ISMIR2007_p417_raimond.pdf diff --git a/docs/source/benchmarking/arts_and_humanities/icon.rst b/docs/source/benchmarking/arts_and_humanities/icon.rst index 96b82c9b..fcdb9b9b 100644 --- a/docs/source/benchmarking/arts_and_humanities/icon.rst +++ b/docs/source/benchmarking/arts_and_humanities/icon.rst @@ -25,7 +25,33 @@ Icon Ontology (ICON) ======================================================================================================== -The ICON ontology provides a formal framework for high-granularity art interpretation and analysis. It was developed by conceptualizing Panofsky's theory of levels of interpretation, enabling artworks to be systematically described according to three complementary analytical perspectives: Pre-iconographical (visual elements and their formal properties), Iconographical (symbolic meaning and subject matter), and Iconological (deeper cultural and philosophical context). This three-level approach enables comprehensive semantic annotation of artworks, capturing both surface-level visual descriptions and deeper interpretive insights. The ontology supports structured knowledge representation of artistic elements, iconographic themes, cultural references, and symbolic meanings. It facilitates semantic interoperability in digital art collections, museum databases, and art history research platforms. The ICON ontology enables advanced search and discovery capabilities based on artistic interpretation levels, supporting art historians, curators, and researchers in analyzing and understanding artworks. It also supports linked data integration with other cultural heritage ontologies and knowledge bases, enabling rich cross-domain art historical research and interpretation analysis. +The ICON ontology provides a formal framework for high-granularity art +interpretation and analysis. It was developed by conceptualizing Panofsky’s +theory of levels of interpretation, enabling artworks to be systematically +described according to three complementary analytical perspectives: +Pre-iconographical (visual recognition and formal elements), Iconographical +(subject matter and represented themes), and Iconological (deeper symbolic, +cultural, and philosophical meanings) [#icon-paper]_. This three-level +approach enables comprehensive semantic annotation of artworks, capturing +both surface-level visual descriptions and deeper interpretive insights +[#icon-paper]_ [#icon-extension]_. The ontology supports structured +knowledge representation of artistic elements, iconographic themes, +cultural references, and symbolic meanings [#icon-paper]_. It facilitates +semantic interoperability in linked cultural heritage data by aligning with +other ontologies and knowledge bases, including CIDOC-CRM and VIR +[#icon-paper]_ [#icon-doc]_. The ICON ontology enables detailed modelling +of artistic interpretation for art history research, corpus annotation, +and linked data applications, supporting researchers and curators in +analyzing and comparing artworks across interpretation levels +[#icon-paper]_ [#icon-extension]_. + +**Example Usage**: Annotate an artwork or museum object with ICON ontology +terms describing pre-iconographical elements, iconographical subjects, +and iconological meanings for example, identifying depicted figures, +their symbolic attributes, and the broader cultural or philosophical +interpretation of the scene to support semantic search, comparative art +historical analysis, and linked data integration across cultural heritage +datasets [#icon-paper]_ [#icon-extension]_. Metrics & Statistics -------------------------- @@ -134,3 +160,24 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#icon-paper] Sartini, B., Baroncini, S., van Erp, M., Tomasi, F., + and Gangemi, A. 2023. "ICON: An Ontology for Comprehensive Artistic + Interpretations." + *ACM Journal on Computing and Cultural Heritage* 16(3), Article 59: 1-38. + doi:10.1145/3594724 + +.. [#icon-extension] Sartini, B. 2023. + "A Comparative Study of Simple and Complex Art Interpretations in + Linked Open Data Using ICON Ontology." + In *Proceedings of the Semantic Web and Ontology Design for Cultural + Heritage Workshop (SWODCH 2023)*. + CEUR Workshop Proceedings 3540. + Available at: `https://ceur-ws.org/Vol-3540/paper4.pdf `_ + +.. [#icon-doc] ICON Ontology Documentation. n.d. + "ICON Ontology Documentation 2.0." + Available at: `https://br0ast.github.io/ICON/ICONOntologyDocumentation2.0/index-en.html `_ diff --git a/docs/source/benchmarking/arts_and_humanities/musicontology.rst b/docs/source/benchmarking/arts_and_humanities/musicontology.rst index 587e007d..788c7432 100644 --- a/docs/source/benchmarking/arts_and_humanities/musicontology.rst +++ b/docs/source/benchmarking/arts_and_humanities/musicontology.rst @@ -26,6 +26,32 @@ Music Ontology (MusicOntology) ======================================================================================================== The Music Ontology Specification provides a comprehensive framework for describing music and related entities on the Semantic Web. It defines core concepts and properties for representing artists, albums, tracks, performances, and musical relationships. The ontology enables standardized music metadata annotation, facilitating interoperability across music information systems, streaming platforms, and digital libraries. It supports rich description of musical works including production details, distribution information, and artistic collaborations. The Music Ontology integrates with other semantic web vocabularies and allows linking of music resources with external datasets and knowledge bases. It enables music recommendation systems, search engines, and music discovery applications to leverage structured semantic data. The ontology supports various music-related use cases including discography management, performance tracking, playlist creation, and music history documentation. By providing a common framework for music representation, the Music Ontology facilitates semantic data integration across the music industry and enables advanced music information retrieval and analysis capabilities. +The Music Ontology Specification provides a comprehensive framework for +describing music and related entities on the Semantic Web. It defines +core concepts and properties for representing artists, albums, tracks, +performances, recordings, and musical relationships [#mo-spec]_ +[#mo-paper]_. The ontology enables standardized music metadata +annotation, facilitating interoperability across music information +systems and digital music libraries [#mo-spec]_ [#mo-paper]_. It +supports rich description of musical works, performances, recordings, +signals, and associated editorial, cultural, and acoustic information +[#mo-paper]_ [#mo-spec]_. The Music Ontology integrates with other +Semantic Web vocabularies and allows linking music resources with +external datasets and knowledge bases [#mo-paper]_. It provides a common +framework for publishing and integrating structured music-related data, +supporting applications such as discography representation, performance +description, playlist and collection modelling, and music information +retrieval [#mo-spec]_ [#mo-paper]_. By enabling interoperable semantic +descriptions of music resources, the Music Ontology supports data +integration and analysis across diverse music datasets and tools +[#mo-paper]_. + +**Example Usage**: Annotate a music dataset, recording collection, or +linked music catalog with Music Ontology terms for artists, albums, +tracks, performances, recordings, and release relationships to enable +semantic search, metadata integration, and interoperability with music +information retrieval and linked data applications [#mo-spec]_ +[#mo-paper]_. Metrics & Statistics -------------------------- @@ -134,3 +160,17 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#mo-spec] Raimond, Y., Abdallah, S. A., Sandler, M. B., + and Giasson, F. n.d. "Music Ontology Specification." + Available at: `https://motools.sourceforge.net/doc/musicontology.html `_ + +.. [#mo-paper] Raimond, Y., Abdallah, S. A., Sandler, M. B., + and Giasson, F. 2007. "The Music Ontology." + In *Proceedings of the 8th International Conference on Music + Information Retrieval (ISMIR 2007)*, Vienna, Austria, + pp. 417-422. + Available at: `https://ismir2007.ismir.net/proceedings/ISMIR2007_p417_raimond.pdf `_ diff --git a/docs/source/benchmarking/arts_and_humanities/nomisma.rst b/docs/source/benchmarking/arts_and_humanities/nomisma.rst index 0e99de67..4b94637f 100644 --- a/docs/source/benchmarking/arts_and_humanities/nomisma.rst +++ b/docs/source/benchmarking/arts_and_humanities/nomisma.rst @@ -25,7 +25,31 @@ Nomisma Ontology (Nomisma) ======================================================================================================== -The Nomisma Ontology is a collaborative project that provides stable, standardized digital representations of numismatic concepts following the principles of Linked Open Data. It offers HTTP URIs that provide persistent access to information about numismatic entities in multiple formats, enabling seamless integration with other linked open data resources. Developed collaboratively by the American Numismatic Society and the Institute for the Study of the Ancient World at New York University, Nomisma represents a comprehensive framework for describing coins, denominations, mints, rulers, and monetary systems across different periods and cultures. The ontology facilitates semantic annotation of numismatic data, supporting interoperability across digital coin collections, archaeological databases, and historical research platforms. It enables advanced querying and analysis capabilities for numismatists, archaeologists, and historians seeking to understand monetary systems and economic aspects of historical societies. The ontology integrates with broader cultural heritage and historical linked data ecosystems, supporting cross-domain research on ancient economies, trade networks, and political history. By providing standardized semantic representations, Nomisma enhances discoverability and reusability of numismatic data in the global digital humanities and cultural heritage communities. +The Nomisma Ontology is a collaborative framework that provides stable, +standardized digital representations of numismatic concepts according to +the principles of Linked Open Data. It offers HTTP URIs that provide +persistent access to reusable information about numismatic entities and +related concepts, enabling integration with other linked data resources +[#nomisma-project]_ [#numismatics-lod]_. The Nomisma community maintains +a formalized RDF ontology and a data model for encoding concepts, coins, +typologies, hoards, and other kinds of numismatic objects as linked open +data [#nomisma-project]_. The ontology and controlled vocabulary support +the description of entities such as coins, denominations, mints, rulers, +regions, and related numismatic concepts across different periods and +cultures [#nomisma-project]_ [#diginuma]_. It facilitates semantic +annotation of numismatic data and supports interoperability across +digital coin collections, archaeological datasets, and historical +research resources [#numismatics-lod]_ [#diginuma]_. By providing +standardized semantic representations, Nomisma enables querying, +integration, and comparative analysis of monetary and material culture +data within broader cultural heritage and digital humanities ecosystems +[#numismatics-lod]_ [#semantic-solutions]_. + +**Example Usage**: Annotate a digital coin collection, hoard dataset, or +archaeological numismatic catalog with Nomisma terms for coin types, +denominations, mints, rulers, regions, and materials to enable semantic +search, linked data integration, and comparative analysis across +numismatic and historical datasets [#nomisma-project]_ [#numismatics-lod]_. Metrics & Statistics -------------------------- @@ -134,3 +158,26 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#nomisma-project] Deutsches Archäologisches Institut. n.d. + "Nomisma.org - a linked open data approach to numismatics." + Available at: `https://www.dainst.org/en/research/projects/nomismaorg-a-linked-data-approach-to-numismatics/2098 `_ + +.. [#numismatics-lod] Gruber, E. 2021. "Numismatics and Linked Open Data." + *ISAW Papers* 20(6). + Available at: `https://dlib.nyu.edu/awdl/isaw/isaw-papers/20-6/ `_ + +.. [#diginuma] Rantala, H., Oksanen, E., Ehrnsten, F., and Hyvönen, E. 2022. + "Harmonizing and Using Numismatic Linked Data in Digital Humanities + Research and Application Development: Case DigiNUMA." + In *The Semantic Web: ESWC 2022 Satellite Events*, pp. 141-146. + Available at: `https://2022.eswc-conferences.org/wp-content/uploads/2022/05/pd_Rantala_et_al_paper_238.pdf `_ + +.. [#semantic-solutions] Hyvönen, E., Rantala, H., Oksanen, E., and + Ehrnsten, F. 2023. "Semantic Solutions for Democratizing + Archaeological and Numismatic Data." + *Journal on Computing and Cultural Heritage* 16(4). + doi:10.1145/3625302 diff --git a/docs/source/benchmarking/arts_and_humanities/timelineontology.rst b/docs/source/benchmarking/arts_and_humanities/timelineontology.rst index 61f4378b..3af397a7 100644 --- a/docs/source/benchmarking/arts_and_humanities/timelineontology.rst +++ b/docs/source/benchmarking/arts_and_humanities/timelineontology.rst @@ -25,7 +25,33 @@ Timeline Ontology (TimelineOntology) ======================================================================================================== -The Timeline Ontology provides a formal framework for representing and managing temporal information in multimedia and music contexts. It centers around the notion of timelines as temporal backbones that can support various types of media including signals, videos, musical scores, and musical works. The ontology enables precise temporal annotation and synchronization of multimedia elements, allowing for structured representation of time-based relationships between different media components. It supports rich temporal modeling including durations, intervals, and temporal landmarks within multimedia documents. The Timeline Ontology facilitates content synchronization across different representations, such as aligning audio signals with musical notation or video with accompanying metadata. It is particularly valuable for music information retrieval systems, multimedia annotation tools, and digital humanities research. The ontology enables advanced applications including temporal querying of multimedia content, cross-media alignment, and time-aware metadata management. By providing a common temporal framework, the Timeline Ontology supports interoperability in music and media analysis systems, enabling researchers and practitioners to work with complex temporal structures in a standardized, machine-readable format. +The Timeline Ontology provides a formal framework for representing and +managing temporal information in multimedia and music contexts. It is +centered around the notion of timelines as temporal backbones that can +support various types of media and temporal objects, including signals, +videos, performances, scores, and musical works [#timeline-spec]_. The +ontology enables precise temporal annotation by allowing instants and +intervals to be defined on a timeline, supporting structured +representation of time-based relationships between different media +components [#timeline-spec]_. It supports temporal modelling of +durations, intervals, and temporal positions within multimedia and music +resources [#timeline-spec]_ [#mo-paper]_. The Timeline Ontology +facilitates synchronization across different representations, such as +aligning audio signals with musical notation or linking performances and +recordings to temporal metadata [#timeline-spec]_ [#mo-paper]_. It is +particularly useful in music information retrieval, multimedia +annotation, and Semantic Web applications that require machine-readable +temporal descriptions [#timeline-spec]_ [#omras2]_. By providing a +common temporal framework, the Timeline Ontology supports interoperability +across music and media analysis systems and enables temporal querying and +integration of complex time-based data [#timeline-spec]_ [#omras2]_. + +**Example Usage**: Annotate an audio recording, video, or symbolic music +file with Timeline Ontology terms for timelines, instants, and +intervals in order to align chord events, note events, subtitles, or +performance segments with precise temporal positions, enabling temporal +querying, cross-media synchronization, and interoperable multimedia +annotation [#timeline-spec]_ [#omras2]_. Metrics & Statistics -------------------------- @@ -134,3 +160,23 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#timeline-spec] Raimond, Y., and Abdallah, S. A. 2006. + "The Timeline Ontology." + OWL ontology specification. + Available at: `https://motools.sourceforge.net/timeline/timeline.html `_ + +.. [#mo-paper] Raimond, Y., Abdallah, S. A., Sandler, M. B., + and Giasson, F. 2007. "The Music Ontology." + In *Proceedings of the 8th International Conference on Music + Information Retrieval (ISMIR 2007)*, Vienna, Austria, + pp. 417-422. + Available at: `https://ismir2007.ismir.net/proceedings/ISMIR2007_p417_raimond.pdf `_ + +.. [#omras2] Fazekas, G., Raimond, Y., Jacobson, K., and Sandler, M. 2010. + "An Overview of Semantic Web Activities in the OMRAS2 Project." + *Journal of New Music Research* 39(4): 295-311. + doi:10.1080/09298215.2010.536555 diff --git a/docs/source/benchmarking/biology_and_life_sciences/biopax.rst b/docs/source/benchmarking/biology_and_life_sciences/biopax.rst index 4aac998b..a19032e0 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/biopax.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/biopax.rst @@ -25,9 +25,31 @@ Biological Pathways Exchange (BioPAX) ======================================================================================================== -BioPAX (Biological Pathways Exchange) is a standard RDF/OWL-based language and ontology for exchanging, integrating, and analyzing biological pathway data. It enables comprehensive representation of molecular interaction networks, including biochemical reactions, gene regulatory pathways, signaling cascades, and transport processes. BioPAX models core pathway concepts such as Interactions (reactions, complexes), Participants (proteins, small molecules), Pathways (sequences of interactions), and their physical and functional properties. The ontology is designed to reduce complexity in data interchange by providing a unified format that bridges disparate pathway databases, modeling tools, and computational analysis platforms. It supports interoperability across systems like Reactome, KEGG, and other pathway databases, facilitating systems biology analysis and network visualization. - -**Example Usage**: Represent a phosphorylation reaction as a BioPAX BiochemicalReaction with specific proteins as catalysts and substrates, linked to cellular locations and regulatory conditions. +BioPAX (Biological Pathway Exchange) is a standard RDF/OWL-based +language and ontology for exchanging, integrating, and analyzing +biological pathway data [#biopax-paper]_ [#biopax-spec]_. It enables the +representation of molecular interaction networks, including metabolic and +signaling pathways, molecular and genetic interactions, and gene +regulation processes [#biopax-paper]_ [#biopax-spec]_. BioPAX models +core pathway concepts such as interactions, physical entities +(for example proteins, DNA, RNA, complexes, and small molecules), +pathways, and their associated biological and cellular properties +[#biopax-paper]_ [#biopax-spec]_. The ontology is designed to reduce +complexity in data interchange by providing a unified format that +supports integration across pathway databases, visualization tools, and +computational analysis platforms [#biopax-paper]_. BioPAX is widely used +in pathway informatics and has been adopted by major resources and tools +for pathway data sharing and integration [#biopax-paper]_ [#pathway-commons]_. +By providing a common semantic framework for pathway representation, +BioPAX supports systems biology analysis, pathway visualization, and +interoperable exchange of biological knowledge across diverse resources +[#biopax-paper]_. + +**Example Usage**: Represent a phosphorylation event as a BioPAX +BiochemicalReaction in which a protein substrate is converted into its +phosphorylated form, linked to the relevant catalyst or controller, +cellular location, and pathway context to enable pathway exchange, +visualization, and computational analysis [#biopax-spec]_ [#biopax-paper]_. Metrics & Statistics -------------------------- @@ -136,3 +158,42 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#biopax-paper] Demir, E., Cary, M. P., Paley, S., Fukuda, K., + Lemer, C., Vastrik, I., Wu, G., D'Eustachio, P., Schaefer, C., + Luciano, J., Schacherer, F., Martinez-Flores, I., Hu, Z., + Jimenez-Jacinto, V., Joshi-Tope, G., Kandasamy, K., Lopez-Fuentes, + A. C., Mi, H., Pichler, E., Rodchenkov, I., Splendiani, A., + Tkachev, S., Zucker, J., Gopinath, G., Rajasimha, H., Ramakrishnan, + R., Shah, I., Syed, M., Anwar, N., Babur, O., Blinov, M., Brauner, + E., Corwin, D., Donaldson, S., Gibbons, F., Goldberg, R., + Hornbeck, P., Luna, A., Murray-Rust, P., Neumann, E., Reubenacker, + O., Samwald, M., van Iersel, M., Wimalaratne, S., Allen, K., + Braun, B., Whirl-Carrillo, M., Cheung, K.-H., Dahlquist, K., + Finney, A., Gillespie, M., Glass, E., Gong, L., Haw, R., + Honig, M., Hubaut, O., Kane, D., Krupa, S., Kutmon, M., + Leonard, J., Marks, D., Merberg, D., Petri, V., Pico, A., + Ravenscroft, D., Ren, L., Shah, N., Sunshine, M., Tang, R., + Whaley, R., Letovksy, S., Buetow, K. H., Rzhetsky, A., + Schriml, L. M., Shah, N. H., Wilkinson, M. D., Kelder, T., + Collado-Vides, J., Goto, S., Hofestädt, R., Hermjakob, H., + and Bader, G. D. 2010. "The BioPAX Community Standard for Pathway + Data Sharing." + *Nature Biotechnology* 28(9): 935-942. + doi:10.1038/nbt.1666 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC3001121/ `_ + +.. [#biopax-spec] BioPAX Editorial Board. n.d. + "BioPAX Level 3 Documentation." + Available at: `https://biopax.github.io/Paxtools/ `_ + +.. [#pathway-commons] Cerami, E. G., Gross, B. E., Demir, E., + Rodchenkov, I., Babur, O., Anwar, N., Schultz, N., Bader, G. D., + and Sander, C. 2011. "Pathway Commons, a Web Resource for Biological + Pathway Data." + *Nucleic Acids Research* 39(Database issue): D685-D690. + doi:10.1093/nar/gkq1039 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC3013641/ `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/efo.rst b/docs/source/benchmarking/biology_and_life_sciences/efo.rst index 80d3f206..87ceb984 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/efo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/efo.rst @@ -23,10 +23,31 @@ Experimental Factor Ontology (EFO) ======================================================================================================== -The Experimental Factor Ontology (EFO) is a comprehensive ontology developed to provide systematic, standardized descriptions of experimental variables and factors in biological and biomedical research. EFO integrates terms from multiple biological ontologies, including UBERON (anatomy), ChEBI (chemical compounds), and Cell Ontology, to support the annotation, analysis, and visualization of experimental data. It is widely used for annotating datasets in EBI databases, the GWAS catalog, and as the core ontology for Open Targets. EFO enables semantic interoperability, data integration, and advanced queries across diverse experimental datasets, facilitating reproducibility and meta-analysis. The ontology is actively maintained by the EMBL-EBI Samples, Phenotypes and Ontologies Team (SPOT) and is continuously updated to reflect new experimental techniques and research needs. By providing a unified framework for describing experimental factors, EFO supports data sharing, discovery, and knowledge integration in genomics, transcriptomics, and other life sciences domains. - -**Example Usage**: -Annotate a gene expression dataset with EFO terms to specify experimental variables such as tissue type, disease state, treatment, and assay platform, enabling cross-study comparison and meta-analysis. +The Experimental Factor Ontology (EFO) is a comprehensive ontology +developed to provide systematic, standardized descriptions of +experimental variables and factors in biological and biomedical research +[#efo-site]_ [#efo-faq]_. EFO integrates terms from multiple biological +ontologies, including UBERON for anatomy, ChEBI for chemical compounds, +and the Cell Ontology, in order to support the annotation, analysis, +and visualization of experimental data [#efo-site]_ [#efo-faq]_. It is +widely used for annotating datasets in EMBL-EBI resources and external +projects such as the NHGRI-EBI GWAS Catalog, and it is also used as the +core ontology for Open Targets [#efo-site]_ [#gwas-2023]_. EFO enables +semantic interoperability, data integration, and ontology-based querying +across diverse datasets, facilitating cross-study comparison and data +reuse [#efo-site]_ [#gwas-2018]_. The ontology is actively maintained at +EMBL-EBI and continues to evolve in response to new data types and +research needs [#efo-team]_ [#efo-site]_. By providing a unified +framework for describing experimental factors, EFO supports data +sharing, discovery, and knowledge integration in genomics, +transcriptomics, and related life science domains [#efo-site]_ +[#gwas-2023]_. + +**Example Usage**: Annotate a gene expression or association dataset with +EFO terms to specify experimental variables such as tissue type, +disease or phenotype, treatment, and assay-related factors, enabling +semantic search, cross-study comparison, and meta-analysis across +biological datasets [#efo-site]_ [#gwas-2018]_. Metrics & Statistics -------------------------- @@ -135,3 +156,37 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#efo-site] EMBL-EBI. n.d. "The Experimental Factor Ontology." + Available at: `https://www.ebi.ac.uk/efo/ `_ + +.. [#efo-faq] EMBL-EBI. n.d. "FAQ EFO." + Available at: `https://www.ebi.ac.uk/efo/faq.html `_ + +.. [#efo-team] EMBL-EBI. n.d. "Samples, Phenotypes and Ontologies." + Available at: `https://www.ebi.ac.uk/about/teams/samples-phenotypes-ontologies/ `_ + +.. [#gwas-2018] Buniello, A., MacArthur, J. A. L., Cerezo, M., + Harris, L. W., Hayhurst, J., Malangone, C., McMahon, A., + Morales, J., Mountjoy, E., Sollis, E., Suveges, D., Vrousgou, O., + Whetzel, P. L., Amode, R., Guillen, J. A., Riat, H. S., + Trevanion, S. J., Hall, P., Junkins, H., Flicek, P., + Burdett, T., Hindorff, L. A., Cunningham, F., and Parkinson, H. + 2019. "The NHGRI-EBI GWAS Catalog of Published Genome-Wide + Association Studies, Targeted Arrays and Summary Statistics 2019." + *Nucleic Acids Research* 47(D1): D1005-D1012. + doi:10.1093/nar/gky1120 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC6323933/ `_ + +.. [#gwas-2023] Sollis, E., Mosaku, A., Abid, A., Buniello, A., + Cerezo, M., Gil, L., Groza, T., Güneş, O., Hall, P., + Hayhurst, J. D., McMahon, A., Mountjoy, E., Parton, A., + Paschall, J., Lopes, E. N., Sanseau, P., Shamout, S., + Sheth, T., Riat, H. S., et al. 2023. "NHGRI-EBI GWAS Catalog: + Knowledgebase and Deposition Resource." + *Nucleic Acids Research* 51(D1): D977-D985. + doi:10.1093/nar/gkac1010 + Available at: `https://academic.oup.com/nar/article/51/D1/D977/6814460 `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/go.rst b/docs/source/benchmarking/biology_and_life_sciences/go.rst index bfdf1929..79e31d71 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/go.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/go.rst @@ -25,9 +25,31 @@ Gene Ontology (GO) ======================================================================================================== -The Gene Ontology (GO) is a comprehensive resource that provides structured controlled vocabularies for the annotation of gene products with respect to their molecular function, cellular component, and biological process role. Developed collaboratively by a consortium of model organism databases and bioinformatics resources, GO enables consistent annotation of genes and proteins across diverse species and databases. The ontology is organized into three hierarchical namespaces: Biological Process (GO:BP) describing what the gene product does in biological context, Molecular Function (GO:MF) characterizing its biochemical activity, and Cellular Component (GO:CC) indicating where it functions. GO supports sophisticated biological data analysis, enabling researchers to discover functional similarities between genes, identify enriched biological processes in genomics datasets, and understand relationships between genes in biological systems. - -**Example Usage**: Annotate a protein like "TP53" with GO terms such as "GO:0006294 (nucleotide-excision repair)" for process, "GO:0003677 (DNA binding)" for molecular function, and "GO:0005634 (nucleus)" for cellular component. +The Gene Ontology (GO) is a comprehensive resource that provides +structured controlled vocabularies for the annotation of gene products +with respect to their molecular function, cellular component, and +biological process roles [#go-site]_ [#go-paper]_. Developed +collaboratively by the Gene Ontology Consortium, GO enables consistent +annotation of genes and proteins across diverse species and databases +[#go-paper]_ [#go-2026]_. The ontology is organized into three +hierarchical namespaces: Biological Process (BP), describing the larger +biological objectives to which a gene product contributes; Molecular +Function (MF), characterizing its molecular activity; and Cellular +Component (CC), indicating where that activity occurs [#go-overview]_ +[#go-annotations]_. GO supports biological data analysis by enabling +researchers to compare gene functions, identify enriched biological +processes or functions in genomics datasets, and understand +relationships among genes and gene products in biological systems +[#go-paper]_ [#go-2026]_. By providing a shared semantic framework for +functional annotation, GO facilitates data integration, comparative +genomics, and computational analysis across the life sciences +[#go-paper]_ [#go-2026]_. + +**Example Usage**: Annotate a protein such as TP53 with GO terms for +biological process, molecular function, and cellular component. For +example, terms related to apoptotic process, DNA binding, and nucleus to +enable standardized functional annotation, enrichment analysis, and +cross-database comparison [#go-annotations]_ [#go-site]_. Metrics & Statistics -------------------------- @@ -136,3 +158,28 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#go-site] Gene Ontology Consortium. n.d. "The Gene Ontology Resource." + Available at: `https://geneontology.org/ `_ + +.. [#go-overview] Gene Ontology Consortium. n.d. "Gene Ontology Overview." + Available at: `https://geneontology.org/docs/ontology-documentation/ `_ + +.. [#go-annotations] Gene Ontology Consortium. n.d. + "Introduction to GO Annotations." + Available at: `https://geneontology.org/docs/go-annotations/ `_ + +.. [#go-paper] The Gene Ontology Consortium. 2021. + "The Gene Ontology Resource: Enriching a GOld Mine." + *Nucleic Acids Research* 49(D1): D325-D334. + doi:10.1093/nar/gkaa1113 + Available at: `https://pubmed.ncbi.nlm.nih.gov/33290552/ `_ + +.. [#go-2026] The Gene Ontology Consortium. 2026. + "The Gene Ontology Knowledgebase in 2026." + *Nucleic Acids Research* 54(D1): D1779-D1790. + doi:10.1093/nar/gkaf1292 + Available at: `https://academic.oup.com/nar/article/54/D1/D1779/8383826 `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/lifo.rst b/docs/source/benchmarking/biology_and_life_sciences/lifo.rst index cd2efb66..3a4654c1 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/lifo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/lifo.rst @@ -23,10 +23,29 @@ Life Ontology (LifO) ======================================================================================================== -The Life Ontology (LifO) is a general-purpose ontology designed to represent the life processes of organisms and their associated entities and relationships. It provides a structured framework for describing common biological features across diverse organisms, including unicellular prokaryotes like E. coli and multicellular organisms such as humans. LifO captures essential biological concepts such as growth, reproduction, metabolism, and adaptation, enabling researchers to model and analyze life processes in a standardized manner. The ontology supports interoperability between biological databases and facilitates the integration of diverse datasets for comparative studies. LifO is particularly useful in systems biology, evolutionary research, and bioinformatics applications, where a unified representation of life processes is essential for data analysis and hypothesis generation. By providing a common vocabulary for life sciences, LifO enhances data sharing, reproducibility, and collaborative research. - -**Example Usage**: -Use LifO to annotate a dataset describing the metabolic pathways of E. coli, linking each pathway to its corresponding life process and associated biological entities, enabling cross-species comparisons and functional analyses. +The Life Ontology (LifO) is a general-purpose ontology designed to +represent the life processes of organisms and their associated entities +and relationships. It provides a structured framework for describing +common biological features across diverse organisms, including +unicellular prokaryotes such as *E. coli* and multicellular organisms +such as humans [#lifo-github]_ [#lifo-bioportal]_. LifO represents life +processes of organisms together with related entities and relations, +providing a common vocabulary for modelling biological phenomena in a +standardized way [#lifo-github]_ [#lifo-bioportal]_. The ontology is +intended as a broad life-science resource that can support interoperable +description of organism-level biological knowledge across different +systems and datasets [#lifo-github]_ [#lifo-bioportal]_. By providing a +shared framework for representing biological processes and related +entities, LifO can support comparative studies, knowledge organization, +and bioinformatics applications that benefit from a common semantic +structure [#lifo-github]_. + +**Example Usage**: Use LifO to annotate a dataset describing organismal +life processes or related biological entities. For example, linking +metabolic or reproductive processes in *E. coli* or human-related +datasets to standardized ontology terms to support consistent +description, comparison, and integration across biological datasets +[#lifo-github]_ [#lifo-bioportal]_. Metrics & Statistics -------------------------- @@ -135,3 +154,13 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#lifo-github] He, Y. 2018. "LifO: An Ontology of the Life of Organism." + GitHub repository. + Available at: `https://github.com/lifeontology/lifo `_ + +.. [#lifo-bioportal] NCBO BioPortal. n.d. "Life Ontology (LIFO)." + Available at: `https://bioportal.bioontology.org/ontologies/LIFO `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/marinetlo.rst b/docs/source/benchmarking/biology_and_life_sciences/marinetlo.rst index 7248a0c6..cb705af8 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/marinetlo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/marinetlo.rst @@ -25,7 +25,29 @@ Marine Taxonomy and Life Ontology (MarineTLO) ======================================================================================================== -MarineTLO is a top level ontology, generic enough to provide consistent abstractions or specifications of concepts included in all data models or ontologies of marine data sources and provide the necessary properties to make this distributed knowledge base a coherent source of facts relating observational data with the respective spatiotemporal context and categorical (systematic) domain knowledge. It can be used as the core schema for publishing Linked Data, as well as for setting up integration systems for the marine domain. It can be extended to any level of detail on demand, while preserving monotonicity. For its development and evolution we have adopted an iterative and incremental methodology where a new version is released every two months. For the implementation we use OWL 2, and to evaluate it we use a set of competency queries, formulating the domain requirements provided by the related communities. +MarineTLO is a top-level ontology for the marine domain, designed to +provide consistent abstractions for concepts appearing across marine +data models and ontologies. It provides the properties needed to make a +distributed marine knowledge base a coherent source of facts, relating +observational data to spatiotemporal context and categorical +(systematic) domain knowledge [#marinetlo-site]_ [#marinetlo-paper]_. +It can be used as a core schema for publishing linked data and for +building integration systems for the marine domain [#marinetlo-site]_ +[#marinetlo-paper]_. MarineTLO is generic enough to be extended to +different levels of detail while preserving monotonicity, and it has +been implemented in OWL 2 and evaluated through competency queries that +capture domain requirements provided by related communities +[#marinetlo-site]_ [#marinetlo-doc]_. By providing a shared top-level +semantic framework, MarineTLO supports semantic interoperability and the +integration of heterogeneous marine biodiversity and observation data +across distributed sources [#marinetlo-paper]_ [#marinetlo-site]_. + +**Example Usage**: Use MarineTLO as a core schema to integrate marine +species, observations, habitats, and sampling-event data from multiple +sources, linking each observation to its taxonomic, spatial, and +temporal context to enable semantic querying and interoperable analysis +across marine biodiversity datasets [#marinetlo-paper]_ +[#marinetlo-site]_. Metrics & Statistics -------------------------- @@ -134,3 +156,24 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#marinetlo-site] Institute of Computer Science, FORTH. 2020. + "MarineTLO | A Top Level Ontology for the Marine/Biodiversity Domain." + Available at: `https://projects.ics.forth.gr/isl/MarineTLO/ `_ + +.. [#marinetlo-doc] Tzitzikas, Y., and collaborators. n.d. + "MarineTLO: A Top Level Ontology for the Marine Domain." + Documentation. + Available at: `https://projects.ics.forth.gr/isl/MarineTLO/files/MarineTLO.pdf `_ + +.. [#marinetlo-paper] Tzitzikas, Y., Allocca, C., Bekiari, C., + Marketakis, Y., Fafalios, P., Doerr, M., Minadakis, N., Patkos, T., + and Candela, L. 2016. "Unifying Heterogeneous and Distributed + Information about Marine Species through the Top Level Ontology + MarineTLO." + *Program* 50(1): 16-40. + doi:10.1108/PROG-10-2014-0072 + Available at: `https://www.vliz.be/imisdocs/publications/283055.pdf `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/mged.rst b/docs/source/benchmarking/biology_and_life_sciences/mged.rst index 3854f28a..aec9885b 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/mged.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/mged.rst @@ -23,10 +23,31 @@ MGED Ontology (MGED) ======================================================================================================== -The MGED Ontology (MGED) is a domain-specific ontology designed to standardize the description of microarray experiments. It provides a structured vocabulary for representing experimental designs, protocols, and data in the context of microarray gene expression studies. The ontology is divided into two components: the MGED Core Ontology, which aligns with the Microarray Gene Expression (MAGE) standard, and the MGED Extended Ontology, which introduces additional classes and associations beyond the MAGE specification. MGED facilitates interoperability between microarray data repositories, enabling researchers to share, compare, and analyze experimental data effectively. By providing a common framework for describing experimental metadata, MGED supports reproducibility, data integration, and meta-analysis in functional genomics research. The ontology is widely used in bioinformatics tools and databases to annotate experimental datasets and ensure compliance with community standards. - -**Example Usage**: -Annotate a microarray experiment with MGED terms to describe the experimental design, sample preparation protocols, and data processing steps, ensuring that the dataset is interoperable with other repositories and tools. +The MGED Ontology (MGED) is a domain-specific ontology developed to +standardize the description of microarray experiments. It provides a +structured vocabulary and semantic framework for representing +experimental designs, protocols, biomaterials, array platforms, and +data-related aspects of microarray gene expression studies +[#mged-paper]_ [#mged-bioportal]_. MGED was developed by the microarray +community to support consistent annotation of experiments and to align +with broader microarray data standards such as MIAME and MAGE +[#mged-paper]_ [#mged-standards]_. The ontology has been described as +including a more stable core aligned with MAGE and an extended part that +adds further terms and associations for richer experimental description +[#mged-fairsharing]_ [#mged-scicrunch]_. MGED facilitates +interoperability between microarray data repositories and tools, +supporting the sharing, comparison, and analysis of experimental data +[#mged-paper]_ [#mged-standards]_. By providing a common framework for +experimental metadata, MGED supports reproducibility, data integration, +and meta-analysis in functional genomics and microarray informatics +[#mged-paper]_ [#mged-standards]_. + +**Example Usage**: Annotate a microarray experiment with MGED terms to +describe the experimental design, sample and biomaterial +characteristics, hybridization and sample-preparation protocols, array +platform, and data-processing steps, so that the dataset can be shared, +interpreted, and compared consistently across repositories and analysis +tools [#mged-paper]_ [#mged-bioportal]_. Metrics & Statistics -------------------------- @@ -135,3 +156,33 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#mged-paper] Whetzel, P. L., Parkinson, H., Causton, H. C., + Fan, L., Fostel, J., Fragoso, G., Game, L., Heiskanen, M., + Morrison, N., Rocca-Serra, P., Sansone, S.-A., and Stoeckert, C. J. Jr. + 2006. "The MGED Ontology: a resource for semantics-based description + of microarray experiments." + *Bioinformatics* 22(7): 866-873. + doi:10.1093/bioinformatics/btl091 + +.. [#mged-standards] Ball, C. A., Brazma, A., Causton, H., + Chervitz, S., Edgar, R., Hingamp, P., Hermjakob, H., Ikeo, K., + Quackenbush, J., Sherlock, G., Spellman, P., Stoekert, C., + Tateno, Y., and Sarkans, U. 2006. "MGED standards: work in progress." + *OMICS* 10(2): 138-144. + Available at: `https://pubmed.ncbi.nlm.nih.gov/16901218/ `_ + +.. [#mged-bioportal] NCBO BioPortal. n.d. + "Microarray and Gene Expression Data Ontology." + Available at: `https://bioportal.bioontology.org/ontologies/MO `_ + +.. [#mged-fairsharing] FAIRsharing. n.d. + "Microarray and Gene Expression Data Ontology." + Available at: `https://fairsharing.org/1193 `_ + +.. [#mged-scicrunch] SciCrunch. n.d. + "MGED Ontology." + Available at: `https://scicrunch.org/resolver/SCR_004484 `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/mo.rst b/docs/source/benchmarking/biology_and_life_sciences/mo.rst index 693e00b1..873fc148 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/mo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/mo.rst @@ -23,10 +23,28 @@ Microscopy Ontology (MO) ======================================================================================================== -The Microscopy Ontology (MO) is a domain ontology designed to provide a structured framework for describing microscopy and microanalysis experiments, data, and equipment. MO extends the PMDco ontological framework and enables semantic integration and interoperability of diverse data sources in microscopy research. The ontology covers key concepts such as imaging modalities, sample preparation methods, instrument components, acquisition parameters, and data analysis techniques. MO supports the annotation of experimental workflows, facilitating data sharing, reproducibility, and advanced analysis across various microscopy studies. By providing a standardized vocabulary, MO enables the development of adaptable data applications and cross-experiment comparisons in materials science, biology, and medical research. The ontology is actively maintained and extended to incorporate new microscopy techniques and research requirements. - -**Example Usage**: -Annotate a microscopy dataset with MO terms to specify the imaging modality (e.g., "scanning electron microscopy"), sample preparation method, instrument configuration, and analysis workflow, enabling semantic search and integration with other microscopy data sources. +The Microscopy Ontology (MO) is a domain ontology developed to provide a +structured framework for describing microscopy and microanalysis +experiments, data, and equipment. It extends the PMD Core Ontology +(PMDco) and was developed within the Platform MaterialDigital ecosystem +to support semantic integration and interoperability of microscopy data +[#mo-repo]_ [#mo-paper]_. The ontology covers microscopy-specific +concepts and relationships needed to describe processes, equipment, and +parameters in microscopy and microanalysis workflows [#mo-paper]_ +[#mo-overview]_. MO is intended to improve the semantic representation +of microscopy knowledge and support better query results and logical +linking among related terms and data objects [#mo-paper]_ [#mo-repo]_. +By providing a standardized vocabulary grounded in PMDco, the ontology +supports interoperable data description and integration across +materials-science microscopy datasets and related digital research +infrastructures [#mo-overview]_ [#pmdco-paper]_. + +**Example Usage**: Annotate a microscopy dataset with MO terms to specify +the imaging modality (for example scanning electron microscopy or +transmission electron microscopy), relevant equipment and parameters, +sample-related descriptors, and analysis-related concepts, enabling +semantic search, interoperable data integration, and improved querying +across microscopy data sources [#mo-paper]_ [#mo-repo]_. Metrics & Statistics -------------------------- @@ -135,3 +153,27 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#mo-paper] Bayerlein, B., Schilling, M., Curran, M., and Lau, J. W. + 2024. "Natural Language Processing-Driven Microscopy Ontology + Development." + *Integrating Materials and Manufacturing Innovation*. + doi:10.1007/s40192-024-00378-y + +.. [#mo-repo] materialdigital. n.d. "Microscopy Ontology (MO)." + GitHub repository. + Available at: `https://github.com/materialdigital/microscopy-ontology `_ + +.. [#mo-overview] Bayerlein, B., Schilling, M., Bruns, S., and others. + 2024. "Concepts for a Semantically Accessible Materials Data Space: + Overview over Specific Implementations in Materials Science." + *Advanced Engineering Materials*. + Available at: `https://advanced.onlinelibrary.wiley.com/doi/10.1002/adem.202401092 `_ + +.. [#pmdco-paper] Schilling, M., Bayerlein, B., Birkholz, H., and others. + 2024. "PMD Core Ontology: Achieving Semantic Interoperability in + Materials Science." + *Materials & Design* 237: 112563. diff --git a/docs/source/benchmarking/biology_and_life_sciences/npo.rst b/docs/source/benchmarking/biology_and_life_sciences/npo.rst index 40a52b98..41826a15 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/npo.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/npo.rst @@ -23,10 +23,30 @@ NanoParticle Ontology (NPO) ======================================================================================================== -The NanoParticle Ontology (NPO) is a domain ontology developed within the Basic Formal Ontology (BFO) framework to represent knowledge about the preparation, chemical composition, and characterization of nanomaterials, especially in cancer research and nanomedicine. NPO provides a structured vocabulary for describing nanoparticle types, synthesis methods, physicochemical properties, surface modifications, and biological interactions. The ontology supports semantic annotation of nanomaterial data, enabling interoperability, data integration, and advanced queries across toxicology, biomedical, and materials science databases. NPO is publicly available through BioPortal and is maintained by the National Center for Biomedical Ontology, with ongoing editorial and governance processes for review and growth. By providing a standardized framework, NPO facilitates reproducibility, regulatory compliance, and knowledge sharing in nanotechnology research. The ontology is actively extended to incorporate new nanomaterial types, experimental techniques, and application domains as the field evolves. - -**Example Usage**: -Annotate a nanomedicine study with NPO terms to specify nanoparticle type (e.g., "gold nanoparticle"), synthesis method, surface coating, and biological assay results, enabling cross-study comparison and regulatory reporting. +The NanoParticle Ontology (NPO) is a domain ontology developed within +the Basic Formal Ontology (BFO) framework to represent knowledge about +the preparation, chemical composition, and characterization of +nanomaterials, especially in cancer research and nanomedicine +[#npo-paper]_ [#npo-bioportal]_. NPO provides a structured vocabulary +for describing nanoparticle composition, preparation methods, +physicochemical characteristics, and related entities relevant to +nanotechnology research [#npo-paper]_ [#enanomapper]_. The ontology +supports semantic annotation of nanomaterial data, enabling data +integration, interoperability, and ontology-based querying across +biomedical and nanoinformatics resources [#npo-paper]_ [#enanomapper]_. +NPO is publicly available through NCBO BioPortal and has been used as a +reference ontology in nanomaterial data standardization efforts +[#npo-bioportal]_ [#enanomapper]_. By providing a standardized semantic +framework for nanomaterial representation, NPO supports knowledge +sharing, data reuse, and computational analysis in nanotechnology and +nanomedicine research [#npo-paper]_ [#nanoinformatics]_. + +**Example Usage**: Annotate a nanomedicine study with NPO terms to +specify nanoparticle composition (for example, a gold nanoparticle), +preparation or formulation characteristics, surface functionalization, +and measured physicochemical or biological assay properties, enabling +cross-study comparison, semantic search, and integration across +nanomaterial datasets [#npo-paper]_ [#enanomapper]_. Metrics & Statistics -------------------------- @@ -135,3 +155,30 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#npo-paper] Thomas, D. G., Pappu, R. V., and Baker, N. A. 2011. + "NanoParticle Ontology for Cancer Nanotechnology Research." + *Journal of Biomedical Informatics* 44(1): 59-74. + doi:10.1016/j.jbi.2010.03.001 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC3042056/ `_ + +.. [#npo-bioportal] NCBO BioPortal. n.d. "NanoParticle Ontology (NPO)." + Available at: `https://bioportal.bioontology.org/ontologies/NPO `_ + +.. [#enanomapper] Hastings, J., Jeliazkova, N., Owen, G., Tsiliki, G., + Munteanu, C. R., Steinbeck, C., Willighagen, E., Del Pozo, A., + Džeroski, S., Jeliazkov, V., and others. 2015. + "eNanoMapper: Harnessing Ontologies to Enable Data Integration for + Nanomaterial Risk Assessment." + *Journal of Biomedical Semantics* 6:10. + doi:10.1186/s13326-015-0005-5 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC4374589/ `_ + +.. [#nanoinformatics] Panneerselvam, S., and Choi, S. 2014. + "Nanoinformatics: Emerging Databases and Available Tools." + *International Journal of Molecular Sciences* 15(5): 7158-7182. + doi:10.3390/ijms15057158 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC4057665/ `_ diff --git a/docs/source/benchmarking/biology_and_life_sciences/pato.rst b/docs/source/benchmarking/biology_and_life_sciences/pato.rst index 36d60915..dce64c40 100644 --- a/docs/source/benchmarking/biology_and_life_sciences/pato.rst +++ b/docs/source/benchmarking/biology_and_life_sciences/pato.rst @@ -23,10 +23,26 @@ Phenotype and Trait Ontology (PATO) ======================================================================================================== -The Phenotype and Trait Ontology (PATO) is a structured vocabulary designed to describe phenotypic qualities, attributes, and traits across a wide range of biological organisms. It provides a standardized framework for annotating and analyzing phenotypic data, enabling researchers to compare and integrate data across species and studies. PATO defines qualities such as color, shape, size, and behavior, and links them to specific biological entities and contexts. The ontology is widely used in genetics, developmental biology, and evolutionary studies to describe phenotypic variations and their underlying genetic and environmental factors. By providing a common language for phenotypic traits, PATO facilitates data sharing, integration, and computational analysis in the life sciences. The ontology is actively maintained and updated to incorporate new terms and relationships as research advances. - -**Example Usage**: -Annotate a genetic study with PATO terms to describe phenotypic traits such as "PATO:0000323 (red coloration)" for a flower petal or "PATO:0000383 (increased size)" for a specific organ, enabling cross-study comparisons and meta-analyses. +The Phenotype and Trait Ontology (PATO) is a structured vocabulary for +describing phenotypic qualities, attributes, and traits in a +species-neutral way [#pato-obo]_ [#pato-framework]_. It provides a +standardized framework for annotating and analyzing phenotypic data by +defining qualities such as size, shape, color, morphology, and other +characteristics that can be combined with biological entity ontologies +to describe phenotypes [#pato-framework]_ [#pato-anatomy]_. PATO is +widely used in phenotype annotation and in the logical definition of +phenotype terms across species, supporting data integration and +comparative analysis in genetics, developmental biology, and related +life science domains [#pato-obo]_ [#pato-integration]_. By providing a +common language for phenotypic qualities, PATO facilitates cross-species +interoperability, computational reasoning, and semantic analysis of +phenotype data [#pato-anatomy]_ [#oba-paper]_. + +**Example Usage**: Annotate a genetic or phenotype study with PATO terms +to describe qualities such as red coloration, increased size, abnormal +shape, or altered morphology in association with a specific biological +entity, enabling cross-study comparison, semantic integration, and +computational phenotype analysis [#pato-framework]_ [#pato-integration]_. Metrics & Statistics -------------------------- @@ -135,3 +151,37 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#pato-obo] OBO Foundry. n.d. "Phenotype And Trait Ontology (PATO)." + Available at: `https://obofoundry.org/ontology/pato.html `_ + +.. [#pato-framework] Gkoutos, G. V., Green, E. C. J., Mallon, A.-M., + Hancock, J. M., and Davidson, D. 2005. + "Using Ontologies to Describe Mouse Phenotypes." + *Genome Biology* 6:R8. + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC545487/ `_ + +.. [#pato-integration] Mungall, C. J., Gkoutos, G. V., Smith, C. L., + Haendel, M. A., Lewis, S. E., and Ashburner, M. 2010. + "Integrating Phenotype Ontologies Across Multiple Species." + *Genome Biology* 11:R2. + doi:10.1186/gb-2010-11-1-r2 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC2847714/ `_ + +.. [#pato-anatomy] Gkoutos, G. V., Schofield, P. N., and Hoehndorf, R. + 2018. "The Anatomy of Phenotype Ontologies: Principles, Properties + and Applications." + *Briefings in Bioinformatics* 19(5): 1008-1021. + doi:10.1093/bib/bbx035 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC6169674/ `_ + +.. [#oba-paper] Stefancsik, R., Mungall, C. J., Robinson, P. N., + Smith, C. L., Haendel, M. A., and Gkoutos, G. V. 2023. + "The Ontology of Biological Attributes (OBA)—Computational Traits for + the Life Sciences." + *Database* 2023: baad038. + doi:10.1093/database/baad038 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC9900877/ `_ diff --git a/docs/source/benchmarking/ecology_and_environment/envo.rst b/docs/source/benchmarking/ecology_and_environment/envo.rst index c031c2bb..3a8ba7ab 100644 --- a/docs/source/benchmarking/ecology_and_environment/envo.rst +++ b/docs/source/benchmarking/ecology_and_environment/envo.rst @@ -25,9 +25,31 @@ Environment Ontology (ENVO) ======================================================================================================== -ENVO is a comprehensive, expressive community-driven ontology that enables humans, machines, and semantic web applications to understand environmental entities and concepts across multiple scales, from microscopic organisms to astronomical phenomena. It provides standardized vocabulary for describing environmental features including biomes, ecosystems, habitats, environmental materials (air, water, soil), and environmental conditions (temperature, humidity, pollution levels). ENVO captures relationships between environmental entities and enables precise semantic annotation of environmental datasets, research findings, and monitoring data. As a FAIR-compliant resource, ENVO promotes semantic interoperability in environmental science by providing concise, controlled descriptions of environmental concepts using formal ontological definitions. The ontology supports diverse applications including environmental data management, ecology research, climate science studies, and environmental impact assessment. -**Example Usage**: Annotate an environmental science dataset with ENVO terms such as "tropical rainforest" (biome), "Amazonia" (biogeographic region), and "high soil moisture" (environmental condition) to enable automated discovery of related environmental studies and climate research. +ENVO (Environment Ontology) is a comprehensive, community-driven +ontology for the concise, controlled description of environmental +systems, components, and processes [#envo-obo]_ [#envo-2013]_. It +provides standardized vocabulary for describing environmental features +such as biomes, ecosystems, habitats, environmental materials (for +example air, water, and soil), and environmental conditions [#envo-2016]_ +[#envo-2013]_. ENVO captures semantic relationships between environmental +entities and supports precise annotation of environmental, ecological, +biological, and biomedical datasets [#envo-2013]_ [#envo-2016]_. As an +open, FAIR-enabling ontology resource, ENVO promotes semantic +interoperability by providing formal ontological definitions for +environmental concepts that can be used by humans, machines, and +Semantic Web applications [#envo-obo]_ [#envo-2016]_. The ontology +supports diverse applications including environmental data management, +ecology, biodiversity and microbiome studies, and other research that +requires interoperable environmental descriptions [#envo-2013]_ +[#envo-2016]_. + +**Example Usage**: Annotate an environmental dataset with ENVO terms for +a biome, habitat, environmental material, or environmental condition. For +example, terms describing a tropical rainforest environment, a +biogeographic setting, or elevated soil moisture to enable semantic +search, cross-study integration, and automated discovery of related +environmental and ecological data [#envo-2013]_ [#envo-2016]_. Metrics & Statistics -------------------------- @@ -136,3 +158,24 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#envo-obo] OBO Foundry. n.d. "Environment Ontology (ENVO)." + Available at: `https://obofoundry.org/ontology/envo.html `_ + +.. [#envo-2013] Buttigieg, P. L., Morrison, N., Smith, B., Mungall, C. J., + and Lewis, S. E. 2013. "The Environment Ontology: Contextualising + Biological and Biomedical Entities." + *Journal of Biomedical Semantics* 4:43. + doi:10.1186/2041-1480-4-43 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC3904460/ `_ + +.. [#envo-2016] Buttigieg, P. L., Pafilis, E., Lewis, S. E., + Schildhauer, M. P., Walls, R. L., and Mungall, C. J. 2016. + "The Environment Ontology in 2016: Bridging Domains with Increased + Scope, Semantic Density, and Interoperation." + *Journal of Biomedical Semantics* 7:57. + doi:10.1186/s13326-016-0097-6 + Available at: `https://pmc.ncbi.nlm.nih.gov/articles/PMC5035502/ `_ diff --git a/docs/source/benchmarking/ecology_and_environment/oeo.rst b/docs/source/benchmarking/ecology_and_environment/oeo.rst index a305b5bf..f47db892 100644 --- a/docs/source/benchmarking/ecology_and_environment/oeo.rst +++ b/docs/source/benchmarking/ecology_and_environment/oeo.rst @@ -25,10 +25,31 @@ The Open Energy Ontology (OEO) ======================================================================================================== -The Open Energy Ontology (OEO) is a comprehensive domain ontology specifically designed for the energy system analysis context, covering concepts, relationships, and entities relevant to energy research and planning. Developed as part of the Open Energy Platform ecosystem, OEO provides standardized terminology for representing energy systems including generation, transmission, distribution, and consumption across diverse energy sources and technologies. The ontology is expressed in Manchester OWL Syntax to ensure user-friendliness for editing and version control, facilitating collaborative development and maintenance. OEO is actively maintained and continuously extended to incorporate emerging energy concepts, technologies, and regulatory frameworks relevant to energy system analysis and planning. The ontology is governed by a Steering Committee (OEO-SC) that ensures quality, alignment with community needs, and integration with ongoing energy research and policy projects. OEO enables standardized data representation, knowledge integration, and automated reasoning about energy systems for research, policy analysis, and strategic planning. - -**Example Usage**: -Annotate an energy system dataset with OEO terms to describe energy sources (solar, wind, biomass), generation capacity, transmission networks, demand patterns, and storage systems to enable automated analysis of energy system configurations and transition scenarios. +The Open Energy Ontology (OEO) is a domain ontology designed for the +energy system analysis context, covering concepts, relationships, and +entities relevant to energy research and planning [#oeo-paper]_ +[#oeo-site]_. Developed as part of the Open Energy Family and used +within the Open Energy Platform ecosystem, OEO provides standardized +terminology for representing energy systems, including generation, +conversion, transmission, distribution, storage, and consumption +concepts across different technologies and sectors [#oeo-paper]_ +[#oeo-github]_. The ontology is represented in Manchester OWL Syntax, +chosen to support user-friendly editing and version control in +collaborative development workflows [#oeo-paper]_ [#oeo-github]_. OEO is +updated regularly through a release cycle and is continuously extended +to incorporate new concepts relevant to energy system modelling and +analysis [#oeo-site]_ [#oeo-github]_. By providing a shared semantic +framework, OEO supports standardized data annotation, knowledge +integration, semantic search, model interfacing, and automated +reasoning for energy system research and related applications +[#oeo-paper]_ [#oeo-site]_. + +**Example Usage**: Annotate an energy system dataset with OEO terms to +describe energy carriers and sources, generation technologies, +transmission or distribution infrastructure, storage systems, demand +concepts, or scenario-study entities, enabling semantic search, +interoperable data integration, and analysis of energy system +configurations and transition scenarios [#oeo-paper]_ [#oeo-site]_. Metrics & Statistics -------------------------- @@ -137,3 +158,23 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#oeo-paper] Booshehri, M., Emele, L., Flügel, S., Förster, H., + Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., + Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., + Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., + Robinius, M., Sehn, V., and Stappel, M. 2021. + "Introducing the Open Energy Ontology: Enhancing Data Interpretation + and Interfacing in Energy Systems Analysis." + *Energy and AI* 5:100074. + doi:10.1016/j.egyai.2021.100074 + Available at: `https://publications.pik-potsdam.de/pubman/item/item_25641_2/component/file_25642/25641oa.pdf `_ + +.. [#oeo-site] Open Energy Platform. n.d. "OEO Ontology." + Available at: `https://openenergyplatform.org/ontology/ `_ + +.. [#oeo-github] OpenEnergyPlatform. n.d. "Repository for the Open Energy Ontology (OEO)." + Available at: `https://github.com/OpenEnergyPlatform/ontology `_ diff --git a/docs/source/benchmarking/ecology_and_environment/sweet.rst b/docs/source/benchmarking/ecology_and_environment/sweet.rst index f07874af..c07cc814 100644 --- a/docs/source/benchmarking/ecology_and_environment/sweet.rst +++ b/docs/source/benchmarking/ecology_and_environment/sweet.rst @@ -26,8 +26,31 @@ Semantic Web for Earth and Environment Technology Ontology (SWEET) ======================================================================================================== SWEET is a comprehensive collection of interconnected ontologies designed to enhance discovery and utilization of Earth science data through semantic understanding of web resources and Earth system science concepts. It conceptualizes a knowledge space for Earth system science including orthogonal (cross-cutting) concepts such as space, time, Earth realms (atmosphere, hydrosphere, lithosphere), physical quantities, and units, alongside integrative science knowledge concepts such as phenomena, events, and processes. SWEET is represented in OWL (Web Ontology Language) to enable automated reasoning and semantic interoperability in Earth science research. The ontology supports integration of heterogeneous Earth science datasets and models by providing shared semantic definitions across atmospheric science, oceanography, geology, and climate science domains. SWEET facilitates Earth science data discovery and knowledge management by enabling semantic search and automated linking of related datasets and research findings. - -**Example Usage**: Annotate a climate dataset with SWEET terms to describe measured phenomena (e.g., "temperature increase"), spatial context (e.g., "atmosphere" realm, "troposphere" layer), temporal extent (e.g., "1980-2020"), and physical quantities (e.g., "degrees Celsius"). +SWEET (Semantic Web for Earth and Environmental Terminology) is a +comprehensive collection of interconnected ontologies designed to +improve discovery and use of Earth science data through semantic +understanding of web resources and Earth system science concepts +[#sweet-paper]_ [#sweet-repo]_. It conceptualizes a knowledge space for +Earth system science that includes cross-cutting concepts such as +space, time, Earth realms, phenomena, physical quantities, and units, +alongside more domain-specific scientific concepts [#sweet-paper]_ +[#sweet-repo]_. SWEET is represented in OWL and organized as a highly +modular ontology suite, enabling semantic interoperability and automated +reasoning in Earth and environmental science applications +[#sweet-paper]_ [#sweet-repo]_. The ontology supports integration of +heterogeneous Earth science datasets and models by providing shared +semantic definitions across domains such as atmospheric science, +oceanography, geology, and climate science [#sweet-paper]_ +[#sweet-search]_. By providing a shared semantic framework, SWEET +supports Earth science data discovery, semantic search, and knowledge +management across distributed datasets and services [#sweet-paper]_ +[#sweet-search]_. + +**Example Usage**: Annotate a climate or Earth observation dataset with +SWEET terms to describe observed phenomena, Earth realm or layer, +spatial and temporal context, and relevant physical quantities and +units, enabling semantic search, automated linking of related datasets, +and interoperable Earth science analysis [#sweet-paper]_ [#sweet-repo]_. Metrics & Statistics -------------------------- @@ -136,3 +159,22 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#sweet-paper] Raskin, R. G., and Pan, M. J. 2005. + "Knowledge Representation in the Semantic Web for Earth and + Environmental Terminology (SWEET)." + *Computers & Geosciences* 31(9): 1119-1125. + Available at: `https://www.sciencedirect.com/science/article/pii/S0098300405001020 `_ + +.. [#sweet-repo] ESIP Federation. n.d. + "SWEET: Official repository for Semantic Web for Earth and + Environmental Terminology Ontologies." + Available at: `https://github.com/ESIPFed/sweet `_ + +.. [#sweet-search] Pouchard, L. C., Huhns, M. N., and McGuinness, D. L. + 2013. "Linking Earth and Climate Science to Support Semantic Search." + *Semantic Web*. + Available at: `https://semantic-web-journal.net/content/linking-earth-and-climate-science-semantic-search-supporting-investigation-climate-change `_ diff --git a/docs/source/benchmarking/finance/goodrelations.rst b/docs/source/benchmarking/finance/goodrelations.rst index 699ce0c3..396a6e41 100644 --- a/docs/source/benchmarking/finance/goodrelations.rst +++ b/docs/source/benchmarking/finance/goodrelations.rst @@ -25,9 +25,32 @@ Good Relations Language Reference (GoodRelations) ======================================================================================================== -GoodRelations is a widely-used ontology for describing products, services, offers, and commercial entities on the Web. It provides a rich vocabulary for modeling commercial information such as Products, Offers, Stores, Sellers, Payment and Delivery options, Price specifications, and Availability. GoodRelations emphasizes machine-actionable, fine-grained descriptions that support e-commerce discovery, comparison shopping, and automated processing by search engines, marketplaces, and recommendation systems. Key characteristics include a modular class structure (distinguishing Products/Services from Offers and Sellers), detailed modeling of price specifications (including currency, unit price, and price components), temporal validity of offers, and explicit representation of delivery and payment methods. The ontology is designed for interoperability: it can be embedded in HTML pages (microdata/RDFa/JSON-LD), linked with vocabularies like schema.org and FOAF, and exported in RDF/OWL for semantic-web use. GoodRelations supports provenance and business metadata, enabling trust and auditing use cases in marketplaces. Typical applications include SEO and product rich snippets, integration of catalog data across vendors, automated price aggregation, and semantic matching in recommender systems. - -**Example usage**: describe a product offering as an gr:Offering that links to a gr:Product (with identifiers and brand), includes a gr:UnitPriceSpecification (with priceCurrency, price, and validFrom/validThrough), and connects to a gr:BusinessEntity representing the seller with contact details and opening hours. +GoodRelations is a widely used ontology for describing products, +services, offers, and commercial entities on the Web [#gr-paper]_ +[#gr-wiki]_. It provides a rich vocabulary for modeling commercial +information such as offers, business entities, price specifications, +availability, payment options, and delivery methods [#gr-paper]_ +[#gr-ref]_. GoodRelations emphasizes machine-processable, fine-grained +descriptions of e-commerce information that support product discovery, +comparison, and automated processing on the Web [#gr-paper]_ [#gr-wiki]_. +A key design principle is the distinction between products or services, +the offers made for them, and the legal entities that provide them, +together with detailed modeling of prices and commercial conditions +[#gr-paper]_ [#gr-ref]_. The ontology is designed for interoperability +and can be used in RDF/OWL as well as embedded in Web pages; it also +influenced and was integrated into the schema.org e-commerce model +[#gr-wiki]_ [#schema-releases]_. By providing a shared semantic +framework for commercial data, GoodRelations supports e-commerce SEO, +catalog integration, offer aggregation, and other Semantic Web and Web +data applications [#gr-paper]_ [#gr-wiki]_. + +**Example Usage**: Describe a product offering as a +``gr:Offering`` that links to a product or service, includes a +``gr:UnitPriceSpecification`` with currency and price information, and +connects to a ``gr:BusinessEntity`` representing the seller together +with relevant payment, delivery, and offer-validity information, so +that the offering can be processed consistently by search engines, +marketplaces, and other web applications [#gr-ref]_ [#gr-paper]_. Metrics & Statistics -------------------------- @@ -136,3 +159,25 @@ Use the following code to import this ontology programmatically: term_types = data.term_typings taxonomic_relations = data.type_taxonomies non_taxonomic_relations = data.type_non_taxonomic_relations + +References +---------- + +.. [#gr-paper] Hepp, M. 2008. + "GoodRelations: An Ontology for Describing Products and Services + Offers on the Web." + In *Knowledge Engineering: Practice and Patterns*, Lecture Notes in + Computer Science 5268, pp. 329-346. + Available at: `https://www.heppnetz.de/files/GoodRelationsEKAW2008-crc-final.pdf `_ + +.. [#gr-ref] Hepp, M. 2010. + "GoodRelations Language Reference." + Available at: `https://www.heppnetz.de/ontologies/goodrelations/20100412/v1.html `_ + +.. [#gr-wiki] GoodRelations Wiki. n.d. + "Documentation/Intro." + Available at: `https://wiki.goodrelations-vocabulary.org/Documentation/Intro `_ + +.. [#schema-releases] Schema.org. 2026. + "Schema.org Releases." + Available at: `https://schema.org/docs/releases.html `_