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The aim of DALIA project is to build a platform for learning material for Research Data Management (RDM) and Data Science. The platform is based on a knowledge graph. We introduce MoDalia, the base ontology of DALIA knowledge graph. Modalia inherits some modules from EduCOR. It includes also modelling of micro -credentials and certificates

MIBiG (Minimum Information about a Biosynthetic Gene Cluster) is a data repository and associated data standard designed to describe biosynthetic gene clusters involved in the production of specialized metabolites. It also stores data on measured biological activities and links to other resources such as NCBI, NPAtlas, and ChEBI. MIBiG is used as a reference database, knowledgebase, and training dataset for machine learning.

METPO (Microbial Ecophysiological Trait and Phenotype Ontology) provides standardized terms for describing microbial phenotypes, growth characteristics, and culture conditions. It includes classes for growth media, temperature tolerances, pH tolerances, and relationships like "grows in" and "does not grow in".

The midlevel energy ontology (MENO) is a BFO-based midlevel ontology. It comprises the concepts for energy qualities, energy-based dispositions and energy-driven transformation and transfer processes and their interrelations. It has the goal to provide an upper level structure for these concepts for energy-related domain ontologies.

The mission of MediaDive is to transform poorly structured media recipes into a standardized database. The contents of the database include mined thousands of PDF and HTML documents. To ensure the quality of the media and continous improvement of the database, we developed an internal editor interface. Experts at the DSMZ are creating new media and curating the existing media using this interface. [adapted from https://mediadive.dsmz.de/about]

The mission of MediaDive is to transform poorly structured media recipes into a standardized database. The contents of the database include mined thousands of PDF and HTML documents. To ensure the quality of the media and continous improvement of the database, we developed an internal editor interface. Experts at the DSMZ are creating new media and curating the existing media using this interface. [adapted from https://mediadive.dsmz.de/about]

The mission of MediaDive is to transform poorly structured media recipes into a standardized database. The contents of the database include mined thousands of PDF and HTML documents. To ensure the quality of the media and continous improvement of the database, we developed an internal editor interface. Experts at the DSMZ are creating new media and curating the existing media using this interface. [adapted from https://mediadive.dsmz.de/about]

MathModDB is a database of mathematical models developed by the Mathematical Research Data Initiative (MaRDI). MathModDB defines a data model with classes (Mathematical Model, Mathematical Formulation, Research Field, Research Problem, Quantity [Kind], Computational Task, Publication), object properties/relations, data properties and annotation properties as an ontology. This ontology is populated with individuals/data from various fields of applied mathematics, making it a knowledge graph. [from homepage]

This vocabulary and grammar defines which types of objects are admissible to the MathAlgoDB - the algorithm knowledge graph - and by which properties they can relate. All in all five classes, "problem", "algorithm", "benchmark", "software", "publication", are defined, as well as a minimal but intuitively intelligible number of properties. As opposed to the more liberal WikiData, MathAlgoDB relies on the strict adherence to the ontology to provide a reliable machine-readable database of (numerical) algorithm knowledge. [from homepage]

The ontology Metadata4Ing is developed within the NFDI Consortium NFDI4Ing with the aim of providing a thorough framework for the semantic description of research data, with a particular focus on engineering sciences and neighbouring disciplines. This ontology allows a description of the whole data generation process (experiment, observation, simulation), embracing the object of investigation, all sample and data manipulation procedures, a summary of the data files and the information contained, and all personal and institutional roles. The subordinate classes and relations can be built according to the two principles of inheritance and modularity. "Inheritance" means that a subclass inherits all properties of its superordinate class, possibly adding some new ones. Modularity means that all expansions are independent of each other; this makes possible for instance to generate expanded ontologies for any possible combinations of method × object of research.

A concept scheme that defines the types of relationships between a learning resource and a node in an educational framework.

The Learning Resource Metadata Innovation (LRMI) specification is a collection of classes and properties for markup and description of educational resources. The specification builds on the extensive vocabulary provided by Schema.org and other standards. LRMI terms not included in schema.org may nevertheless be used to augment and enrich Schema.org markup. (from homepage)

The Livestock Product Trait Ontology (LPT) is a controlled vocabulary for the description of traits (measurable or observable characteristics) pertaining to products produced by or obtained from the body of an agricultural animal or bird maintained for use and profit.

The LIDO Terminology is committed to the Linked Open Data paradigm by making each LIDO Term referenceable through a Uniform Resource Identifier (URI). It is recommended best practice to use the URI from the terminology.lido-schema.org/ namespace to indicate the type of a LIDO element or attribute. The primary objective of this practice is to support data providers in adapting or mapping their data structures to LIDO, thus facilitating the processing of LIDO data for service providers, increasing the interoperability of LIDO data, and supporting information retrieval across different collections. [from homepage]

Provides the worldwide dog research community a variety of data services including access to genes, genomes, SNPs, breed/disease Traits, gene expression experiments, dog-guman homology, and literatur. In addition, iDog provides online tools for performing genomic data visualization and analyses.

The HPC Ontology describes software, hardware, and artifacts in the domain of High-Performance Computing. It can be used to annotate training datasets and machine learning models used in HPC software analyses and optimizations. The goal is to make datasets and AI models FAIR.

An ontology of processes triggered by homeostatic imbalance, with a focus on COVID-19 infectious processes.

HEPRO is an ontology of informational entities and processes related to health procedures and health activities.

An _gentle_ implementation of the Unified Foundational Ontology (UFO), which is an upper level ontology like BFO that is concerned with e.g. expressing temporal relationships between events.

Comprehensive reference information for the world's languages, especially the lesser known languages. [from homepage]