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Update benchmarking docs and add references (#312) #314

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8 changes: 8 additions & 0 deletions docs/source/_static/custom.css
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41 changes: 40 additions & 1 deletion docs/source/benchmarking/agriculture/agro.rst
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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
--------------------------
Expand Down Expand Up @@ -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
49 changes: 46 additions & 3 deletions docs/source/benchmarking/agriculture/agrovoc.rst
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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
--------------------------
Expand Down Expand Up @@ -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
47 changes: 39 additions & 8 deletions docs/source/benchmarking/agriculture/atol.rst
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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
--------------------------
Expand Down Expand Up @@ -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
40 changes: 39 additions & 1 deletion docs/source/benchmarking/agriculture/foodon.rst
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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
--------------------------
Expand Down Expand Up @@ -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
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