nomenclature: Working with IAMC-format project definitions

Release v0.8.

license doi python black pytest rtd

Overview

The nomenclature package facilitates validation and processing of scenario data. It allows to manage definitions of data structures for model comparison projects and scenario analysis studies using the data format developed by the Integrated Assessment Modeling Consortium (IAMC).

A data structure definition consists of one or several “codelists”. A codelist is a list of allowed values (or “codes”) for dimensions of IAMC-format data, typically regions and variables. Each code can have additional attributes: for example, a “variable” has to have an expected unit and usually has a description. Read the SDMX Guidelines for more information on the concept of codelists.

The nomenclature package supports three main use cases:

  • Management of codelists and mappings for model comparison projects

  • Validation of scenario data against the codelists of a specific project

  • Region-processing (aggregation and renaming) from “native regions” of a model to “common regions” (i.e., regions that are used for scenario comparison in a project).

The codelists and mappings are stored as yaml files. Refer to the User Guide for more information.

Table of Contents

Integration with the pyam package

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The nomenclature package is designed to complement the Python package pyam, an open-source community toolbox for analysis & visualization of scenario data. The pyam package was developed to facilitate working with timeseries scenario data conforming to the format developed by the IAMC. It is used in ongoing assessments by the IPCC and in many model comparison projects at the global and national level, including several Horizon 2020 & Horizon Europe projects.

The validation and processing features of the nomenclature package work with scenario data as a pyam.IamDataFrame object.

Read the Docs for more information!

Acknowledgement

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This package is based on the work initially done in the Horizon 2020 project openENTRANCE, which aims to develop, use and disseminate an open, transparent and integrated modelling platform for assessing low-carbon transition pathways in Europe.

Refer to the openENTRANCE/openentrance repository on GitHub for more information.

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 835896.