CatColab is a collaborative environment for formal, interoperable, conceptual modeling. It supports modeling using ologs, categorical databases, causal loop diagrams, stock and flow diagrams, and other languages. While designed around mathematical ideas from category theory, CatColab is designed to be broadly usable.
Software projects
Active
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CatColab
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Catlab.jl & AlgebraicJulia
Learn more View codeCatlab.jl is a programming framework for applied and computational category theory, written in the Julia language. Building on Catlab, the AlgebraicJulia ecosystem provides compositional domain-specific languages for scientific and technical computing, including categorical data structures, algebraic specification, Petri nets, stock-flow diagrams, PDE simulators, and more.
Archived
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Flow graphs for data science
Learn more View codeThis project creates language-agnostic semantic models of data science code, with the aim to promote knowledge sharing, automation, and intelligent tooling in the data science community. Elements of the project include:
- program analysis tools for Python and R, generating raw flow graphs
- a Julia package for semantic enrichment, generating semantic flow graphs
- the Data Science Ontology
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Data Science Ontology
Learn more View codeThe Data Science Ontology is a knowledge base about data science with a focus on computer programming. The concepts of the ontology are drawn from statistics, machine learning, and the practice of software engineering for data science. Besides cataloging and organizing data science concepts, the ontology provides semantic annotations of commonly used Python and R packages, such as pandas, scikit-learn, and R stats.