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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.

HOSO is an ontology of informational entities and processes related to healthcare organizations and services.

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]

As the variable is one of the most relevant entities to enhance data reuse in the Social Sciences, we provide a framework design to better semantics the variables' relations descriptions. These explicit relations between variables enable comparability and facilitate harmonization across waves. We provide a brief textual identification of the relation type, supported by a controlled vocabulary (CV) and an extended description of the relationship. These relations within variables include but are not limited to different versions, derived formats in new waves, new labels and name wording, and alternative response schema through questionnaires and surveys. For instance, a given variable label is changed from one wave to another, even though its concept remains the same. Their values also are subject to change, such as new cardinalities settings, their categorization, or response scheme and scale measurement. They change based on different conditions, e.g., values are updated by any constraints or modified to comply with the study evolution requirements or a new sociological approach. In the Social Sciences, Economics, and Behaviour Sciences, which investigate, for instance, the social structure of the population, political attitudes of voters and candidates, opinions on family, work, religion, politics and society or competencies of adults, those topics are highly subject to change to fit the empirical reality in a constantly changing world. Thus, we propose widening relations descriptions for Social Sciences variables within datasets beginning from the BasedOnObjectType DDI as a first approach.

This CV, developed within the framework of the DP-R|EX joint project, involving the partner institutions DeZIM, Qualiservice and GESIS, maps the dimensions of discrimination and racialised characteristics relevant to the research area. The compilation is based on a systematic evaluation of the relevant national and international empirical research literature. These include self-attributed characteristics as well as anticipated attributions by others. The characteristics do not necessarily correspond to the studied units in the data set, but can also be the subject of studied concepts or theories in the surveys, such as on specific prejudices and attitudes.