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Project definitions for the openENTRANCE project

Copyright 2020-2023 openENTRANCE consortium

This repository is licensed under the Apache License, Version 2.0 (the "License"); see the LICENSE for details.

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Aim and scope of this repository

openENTRANCE logo

The Horizon 2020 openENTRANCE project aims at developing, using and disseminating an open, transparent and integrated modelling platform for assessing low-carbon transition pathways in Europe. A key requirement for an effective linking of models and consistent analysis is a common "nomenclature", i.e., shared lists of variables, regions and units used across the entire project.

This repository makes available the nomenclature used within the consortium and serves as a discussion platform for extending the lists of terms.

We invite other modelling teams to contribute and join the discussion, hoping to facilitate increased cooperation across research projects on (European) energy and climate policy!

How to work with this repository

There are several ways to interact with the nomenclature and definitions provided in this repository. The simplest approach is to just read the yaml files on GitHub - see the links below.

The repository is structured so that it can be parsed by the Python package nomenclature for scenario ensemble validation and processing. Read more on GitHub!

An installable Python package

Python logo

To facilitate using the definitions in data processing workflows and scripts, there is an installable Python package with several utility functions and dictionaries. More information

Data format structure

The openENTRANCE project uses a common data format based on a template developed by the Integrated Assessment Modeling Consortium (IAMC) and already in use in many model comparison projects at the global and national level. While the IAMC comprises (mostly) integrated-assessment teams, the data format is generic and can be used for a wide range of applications, including energy-systems analysis or modelling of specific sectors like transport, industry or the building stock.

Timeseries data dimensions

In the data format, every timeseries is described by six dimensions (codes):

  1. Model - more information
  2. Scenario - more information
  3. Region - more information
  4. Variable - more information
  5. Unit - see the section on variables for details
  6. Subannual (optional, default 'Year')[1] - more information

In addition to these six dimensions, every timeseries is described by a set of year-value pairs.

The resulting table can be either shown as

  • wide format (see example below, with years as columns), or
  • long format (two columns year and value).
model scenario region variable unit subannual 2015 2020 2025
GENeSYS-MOD Societal Commitment Europe Primary Energy EJ/y Year 69.9 65.7 ...
... ... ... ... ... ... ... ... ...

Data via the IAMC 1.5°C scenario explorer, showing a scenario from the CD-LINKS project.

[1] The index 'Subannual' is an extension of the original format introduced by the openENTRANCE project to accomodate data at a subannual temporal resolution.

Recommended usage of this data format

pyam logo

The Python package pyam was developed to facilitate working with timeseries data conforming to this structure. Features include validation of values, aggregation and downscaling of data, and import/export with various file formats (xlsx, csv, ...) and table layouts (wide vs. long data).

Read the docs for more information!

Funding acknowledgement

EU logo

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