Copyright 2020 openENTRANCE consortium
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
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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!
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.
To facilitate using the definitions in data processing workflows and scripts, the nomenclature can be installed as a Python package with several utility functions and dictionaries. More information
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.
In the data format, every timeseries is described by six dimensions (codes):
- Model - more information
- Scenario - more information
- Region - more information
- Variable - more information
- Unit - see the section on variables for details
- 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.
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!
