Software, research code and data-science methods.
An ontology representing the model card structure, he aim of this work is to describe machine learning models to communicate information about specific details about the model (trade offs, intended users, licensing, etc.).
OBCS stands for the Ontology of Biological and Clinical Statistics. OBCS is an ontology in the domain of biological and clinical statistics. It is aligned with the Basic Formal Ontology (BFO) and the Ontology for Biomedical Investigations (OBI)
The Kinetic Simulation Algorithm Ontology (KiSAO) is an ontology that describes simulation algorithms and methods used for biological kinetic models, and the relationships between them. This provides a means to unambiguously refer to simulation algorithms when describing a simulation experiment.
The Software Ontology (SWO) is a resource for describing software tools, their types, tasks, versions, provenance and associated data. It contains detailed information on licensing and formats as well as software applications themselves, mainly (but not limited) to the bioinformatics community.
The Ontology for Avida (OntoAvida) project aims to develop an integrated vocabulary for the description of the most widely used computational approach for performing experimental evolution using digital organisms (i.e., self-replicating computer programs that evolve within a user-defined computational environment). The lack of a clearly defined vocabulary makes biologists feel reluctant to embrace the field of digital evolution. This unique ontology has the potential to change this picture overnight.
A defined way or layout of representing and structuring data in a computer file, blob, string, message, or elsewhere. The main focus in EDAM lies on formats as means of structuring data exchanged between different tools or resources. The serialisation, compression, or encoding of concrete data formats/models is not in scope of EDAM. Format 'is format of' Data.
The Bibframe vocabulary consists of RDF classes and properties used for the description of items cataloged principally by libraries, but may also be used to describe items cataloged by museums and archives. Classes include the three core classes - Work, Instance, and Item - in addition to many more classes to support description. Properties describe characteristics of the resource being described as well as relationships among resources. For example: one Work might be a "translation of" another Work;
Klassifikation für interdisziplinäre Forschungsfelder im Rahmen des Kerndatensatz Forschung
This ontology describes the RDF representation of the contents of the dblp computer science bibliography.
OntoApplication is an ontology for representing software applications and their services.
The Digital Assets Ontology (DAOnt) addresses the digital assets domain, that is, any hardware, software, data, or service that exists in or interacts with a digital ecosystem and provides functional or informational value.
This is the formal vocabulary (ontology) describing the FAIR principles.
A Schema.org style Schema definition of WRROC terms. Minimal OWL typing for compatiblity with optional owl:imports https://schema.org/docs/schemaorg.owl
Ontology to describe general clinical studies.
The Mathematical Modelling Ontology (MAMO) is a classification of the types of mathematical models used mostly in the life sciences, their variables, relationships and other relevant features.
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 encoding the core metadata based on the Intelligent Information Request and Delivery (iiRDS) standard
An ontology encoding machinery based on the Intelligent Information Request and Delivery (iiRDS) standard
An ontology encoding software based on the Intelligent Information Request and Delivery (iiRDS) standard
Fachsystematik LinSearch: Fächer für die LinSearch-Fachzuordnung. A vocabulary listed in Bioregistry, TIB Terminology Service.
The Geomatics API ontology (GEOMAPI) is an ontology defining the core concepts of close-range sensing observations. It manage point clouds, imagery, polygonal meshes, BIM and CAD information in relation with the geomapi api (https://github.com/KU-Leuven-Geomatics/geomapi).
The Role Ontology (RoleO) is a ontology in the domain of role classification. RoleO aims to standardize role classification and support computer-assisted reasoning. RoleO is a community-based ontology, and its development follows the OBO Foundry principles.
An ontology for describing machine learning datasets, tasks, pipelines, experiments, software and publications. The ontology extends ML-Schema, DCAT, FaBiO and SDO.
A vocabulary to define FAIR test results, metrics, benchmarks and scoring algorithms in a transparent manner.
BIBFRAME vocabulary with LC extensions, see also https://github.com/lcnetdev/bflc-ontology/: BIBFRAME vocabulary with LC extensions. A ontology listed in Discovered via prefix.cc, TIB Terminology Service.
An OWL ontology capturing the concepts needed to describe machine learning interatomic potentials: algorithm families, hyperparameters, training datasets with DFT provenance, and published benchmarks.
moody (Multi-Objective Optimization ontologY) is a OWL2 ontology aiming to provide the ontology model to allow the semantic consolidation of the data related to multi-objective optimization experiments, including algorithms, problems and quality indicators.
This ontology defines classes and properties for describing participants, infrastructure, data and services of the International Data Spaces (formerly known as Industrial Data Space).
This ontology covers concepts of technical drawings from the perspective of computer vision-based drawing analysis to be used in collaborative settings or for post-processing. The preferred namespace prefix is `dano` for the namespace IRI [https://w3id.org/dano](https://w3id.org/dano).
This ontology contains all the classes and properties related to data. Data is devoted to annotating all the data flowing throughout the analytic workflow. It is declared as EquivalentTo IO-Class of DMOP. This aligning enables datatypes defined by third parties’ ontologies to be contextualised in the analysis. BIGOWLData contains the main data properties defined for this class, namely: path, to annotate the origin of data; and hasDataType, which establishes the relationship with class DataType. This last is used to define the data type, i.e.
An ontology for the description of information objects.
This is a vocabulary collection utilized by XHTML Family modules and document types using XHTML Modularization, including XHTML Role and XHTML + RDFa as defined in rdfa-syntax. The XHTML specifications are developed by the W3C XHTML 2 Working Group as part of the W3C HTML Activity. [from homepage]
To ensure comparability between schemas from different data models, the Description of a Data Source (DSD) vocabulary has been developed.
PRISSMA (Presentation of Resources for Interoperable Semantic and Shareable Mobile Adaptability) is a domain-independent vocabulary for displaying Web of Data resources in mobile environments.
A lightweight vocabulary to create fine-grained access control policies for Linked Data.
The SQL AST Vocabulary allows SQL code abstract syntax trees to be published in RDF.
An ontology to model accountability of generic systems.
The Basic Access Control ontology describes how access rights (access control) are defined on the web.
A set of best practices and simple approach for a read-write Linked Data architecture, based on HTTP access to web resources that describe their state using the RDF data model.
A vocabulary for embedding provenance metadata into webpages via RDFa
The Protocol for Web Description Resources (POWDER) allows metadata to be associated with groups of resources such as those found on a Web site. Its main 'unit of information' is the Description Resource (DR), one or more of which are contained in a POWDER document. Processing such a document yields RDF triples describing the resources that are within the scope of the DRs.
This ontology is for use in describing Workspaces. Workspaces are places where data is stored and associated policies of privacy. A given application typically stores information in several different workspaces, some being user private, some shared, and some public. it is crucial that the user has easy control over the destiny of information.
RDF-STaX is an OWL 2 DL ontology that enables describing the types of RDF streams and defines relations between them.
The NORIA-O project is a data model for IT networks, events and operations information. The ontology is developed using web technologies (e.g. RDF, OWL, SKOS) and is intended as a structure for realizing an IT Service Management (ITSM) Knowledge Graph (KG) for Anomaly Detection (AD) and Risk Management applications. The model has been developed in collaboration with operational teams, and in connection with third parties linked vocabularies. Alignment with third parties vocabularies is implemented on a per class or per property basis when relevant (e.g.
WSMO-Lite is a lightweight approach to the semantic annotation of Web service descriptions, defined by the STI2 working group Conceptual Models for Services
The AtomOWL ontology is inspired from the work done by the atom working group. This ontology is working off the rfc 4287 published among othe places at http://www.atompub.org/rfc4287.html . The AtomOWL ontology uses as much as possible the same terms as the format there to make the relation easy to understand. The AtomOWL name space is slightly different from the atom namespace [see post http://www.imc.org/atom-syntax/mail-archive/msg16476.html]. But this is a good thing as it helps distinguish the ontology from the rfc 4287 serialisation.
An ontology to represent the content management systems and their interaction with the triplestore.
This is an OWL formalization of the statements defined in the Experience API (xAPI) specification. It is a module of the LUDO ontology.
Browse all 88 in the catalog →
STATO is the statistical methods ontology. It contains concepts and properties related to statistical methods, probability distributions and other concepts related to statistical analysis, including relationships to study designs and plots.
A community specification for packaging research data with their metadata, using schema.org in JSON-LD, so datasets, software and workflows can be shared with their context.
The SPDX License List is a list of commonly found licenses and exceptions used in free and open source and other collaborative software or documentation. The purpose of the SPDX License List is to enable easy and efficient identification of such licenses and exceptions in an SPDX document, in source files or elsewhere. The SPDX License List includes a standardized short identifier, full name, vetted license text including matching guidelines markup as appropriate, and a canonical permanent URL for each license and exception.
This ontology models classes and relationships describing deep learning networks, their component layers and activation functions, as well as potential biases.
EDAM is an ontology of general bioinformatics concepts, including topics, data types, formats, identifiers and operations. EDAM provides a controlled vocabulary for the description, in semantic terms, of things such as: web services (e.g. WSDL files), applications, tool collections and packages, work-benches and workflow software, databases and ontologies, XSD data schema and data objects, data syntax and file formats, web portals and pages, resource catalogues and documents (such as scientific publications).
DUO is an ontology which represent data use conditions.
A data standard and schema for neurophysiology: extracellular and intracellular electrophysiology, optical physiology, behavior and stimuli, extensible via neurodata extensions (NDX).
The semanticscience integrated ontology (SIO) provides a simple, integrated upper level ontology (types, relations) for consistent knowledge representation across physical, processual and informational entities.
Brick is an open-source effort to standardize semantic descriptions of the physical, logical and virtual assets in buildings and the relationships between them. Brick consists of an extensible dictionary of terms and concepts in and around buildings, a set of relationships for linking and composing concepts together, and a flexible data model permitting seamless integration of Brick with existing tools and databases.
DCAT is an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web
The HL7 standard for exchanging healthcare information electronically, defining resources such as Patient and Observation with JSON, XML and RDF representations and a REST API.
The Intelligence Task Ontology (ITO) provides a comprehensive map of machine intelligence tasks, as well as broader human intelligence or hybrid human/machine intelligence tasks.
An XML-based language (with XSD schema) for describing detailed neuron, synapse and network models in a simulator-independent way.
This document describes a vocabulary for annotating descriptions of vocabularies with examples and usage notes.
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]
The namespace name http://www.w3.org/ns/prov# is intended for use with the PROV family of documents that support the interchange of provenance on the web.
An ontology for describing the steps in the workflow associated with the publication of a document or other publication entity.
The Generative Artificial Intelligence Delegation Taxonomy (GAIDeT) assigns identifiers to contributor roles as an extension to the Contributor Roles Taxonomy (CRediT) to support promoting transparency and accountability in academic publishing when AI contribtors are involved in research. It is operationalized in the [GAIDeT Declaration Generator](https://panbibliotekar.github.io/gaidet-declaration/), an interactive tool for researchers to disclose the delegation of tasks to generative AI (GAI) tools in accordance with the GAIDeT taxonomy.
This document is an up-to-date specification of all metadata terms maintained by the Dublin Core Metadata Initiative, including properties, vocabulary encoding schemes, syntax encoding schemes, and classes.
The ODRL Vocabulary and Expression defines a set of concepts and terms (the vocabulary) and encoding mechanism (the expression) for permissions and obligations statements describing digital content usage based on the ODRL Information Model.
The Smart Applications REFerence ontology (SAREF) is intended to enable interoperability between solutions from different providers and among various activity sectors in the Internet of Things (IoT), thus contributing to the development of the global digital market.
This ontology extends the SAREF ontology for the Smart City domain. This work has been developed in the context of the STF 534 (https://portal.etsi.org/STF/STFs/STFHomePages/STF534.aspx), which was established with the goal to create three SAREF extensions, one of them for the Smart City domain.
The FAIRsharing Subject Ontology (SRAO) is an application ontology for the categorization of research disciplines across all research domains, from the humanities to the natural sciences. It utilizes multiple external vocabularies.
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]
Other categories: Foundational & Upper Ontologies · Metadata, Provenance & Publishing · Life Sciences · Health & Clinical · Neuroscience · Chemistry & Materials · Earth & Environment · Agriculture, Food & Plants · Physical Sciences, Maths & Units · Engineering & Built Environment · Humanities & Culture · Privacy, Security & Law · Society, Government & Business · Multidisciplinary & General
