Global_CO2_from_cement_production

Global CO2 from Cement Production Dataset

This repository downloads the Andrew dataset on global CO2 emissions from cement production from Zenodo. The dataset is converted to the PRIMAP2 format and provided in the csv based interchange format and the netCDF based native primap2 format. Several version of the dataset are available.

Description

This repository downloads data on global CO2 emissions from cement production from Zenodo. The downloaded dataset can then be converted into CSV (.csv file extension) or NetCDF (.nc file extension) format. Converted data are available for the following versions:

| v231016 |Zenodo | | v230913 |Zenodo | | v230428 |Zenodo | | v220915 |Zenodo | | v220516 |Zenodo | The data management tool DataLad is used to version control the data sets. Commands to run the scripts are executed via the pydoit package.

DataLad datasets and how to use them

This repository is a DataLad dataset. It provides
fine-grained data access down to the level of individual files, and allows for
tracking future updates. In order to use this repository for data retrieval,
DataLad is required. It is a free and open source
command line tool, available for all major operating systems, and builds up on
Git and git-annex to allow sharing,
synchronizing, and version controlling collections of large files.

Installation

• Install datalad according to the DataLad handbook. It is recommended to install globally.
• Install Python
• pydoit

Note that for simply downloading the dataset, Python and pydoit are not required.

Getting Started

Clone the repository

A DataLad dataset can be cloned by running

datalad clone

Do not use git clone to download the repository! This way DataLad will not have the necessary information to run the program. Once a dataset is cloned, it is a light-weight directory on your local machine.
At this point, it contains only small metadata and information on the identity
of the files in the dataset, but not actual content of the (sometimes large)
data files.

Easy access

Users who simply want to retrieve the dataset have the option to access both the original and extracted files with

dataland get <filename>

This command will trigger a download of the files, directories, or subdatasets
you have specified.

For example, the CSV file for the 2023/09/13 release can be downloaded with

datalad get extracted_data/v230913/Robbie_Andrew_Cement_Production_CO2_230913.csv 

Stay up-to-date

DataLad datasets can be updated. The command datalad update will fetch
updates and store them on a different branch (by default
remotes/origin/master). Running

datalad update --merge  

will pull available updates and integrate them in one go.

Find out what has been done

DataLad datasets contain their history in the git log. By running git log (or a tool that displays Git history) in the dataset or on specific
files, you can find out what has been done to the dataset or to individual
files by whom, and when.

Contributing

For those who wish to contribute to the repository, below we go through the key commands you will need to use.

Set up the virtual environment with doit

doit setup_env

Download the version from the command line.

This will download all files from Zenodo as they are for a specific version (note this version must already be in versions.py, if you want to add a new version, see the section on adding a new version below).

doit download_version --version <YYMMDD>

Convert the data sets into CSV and NetCDF files.

doit read_version --version <YYMMDD>

How to add a new version

To add a new version go to versions.py in the src directory and create a new value in the versions dictionary. Fill all the required information similar to the previous entries. For example, the value v230913 in the versions dictionary describes the 13-Sep-2023 release.

versions = {
    "v230913": {
        'date': '13-Sep-2023',
        'ver_str_long': 'version 230913',
        'ver_str_short': '230913',
        "folder": "v230913",
        "transpose": False,
        "filename": "0. GCP-CEM.csv",
        'ref': '10.5281/zenodo.8339353',
        'ref2': '10.5194/essd-11-1675-2019',
        'title': 'Global CO2 emissions from cement production',
        'institution': "CICERO - Center for International Climate Research",
        'filter_keep': {},
        'filter_remove': {},
        'contact': "johannes.guetschow@climate-resource.com",
        'comment': ("Published by Robbie Andrew, converted to PRIMAP2 format by "
                    "Johannes Gütschow"),
        'unit': 'kt * CO2 / year',
        'country_code': True,
    },
}

Then run the two commands read_version and download_version as described in Contributing.

Help

Show all doit commands

doit help

See a list with possible doit commands specific to this repository

doit list

Get help on a specific command

doit help <command>

Contributing

Repository structure

• .datalad/ contains config file for datalad
• downloaded_data/ contains original data from Zenodo.
• extracted_data/ contains data in .csv and .nc format
• literature/ contains link to publication by Robbie M. Andrew. Can be downloaded with datalad get command
• src/
  • download_version.py downloads files from zenodo for a given version. The version to read will be taken from the command line using argparse.
  • download_version_datalad.py calls datalad to run the data reading function.
  • helper_functions.py contains a function to map country codes.
  • read_version.py reads the data for a given version and saves to PRIMAP2 native and interchange format.
  • read_version_datalad.py calls datalad to run the data reading function.
  • version.py is a dictionary that contains metadata for each release. This file should be updated when adding a new version
• dodo.py defines pydoit commands.
• pyproject.toml configuration file
• requirements.txt requirements
• requirements_dev.txt development requirements
• setup.cfg requirements
• setup.py installs python packages

Make sure to correctly set up the DataLad siblings

Git repositories can configure clones of a dataset as remotes in order to fetch, pull, or push from and to them. A datalad sibling is the equivalent of a git clone that is configured as a remote.

Query information about about all known siblings with

datalad siblings

Add a sibling to allow pushing to github

datalad siblings add --dataset . --name <name> --url git@github.com:JGuetschow/Global_CO2_from_cement_production.git

SSH-access is needed to run this command. Note that name can be freely chosen.

Push to the github repository

datalad push --to <name>

Issues

There always issues open regarding coding, some of them easy to resolve, some harder.

Your ideas

Contributing is ouf course not limited to the categories above. I you have ideas for improvements just open an issue or a discussion page to discuss you idea with the community.

Technical HowTo for contributors

As we have a datalad repository using github and gin the process of contributing code and data is a bit different from pure git repositories. As the data is only stored on gin, the gin repository is the source to start from. As gin currently has a problem with forks (the annexed data is not forked) we have to use branches for development and, thus, to contribute you first need to contact the maintainers to get write access to the gin repository. You have to clone the repository using ssh to be able to push to it. For that you first need to store your public ssh key on the gin server (settings -> SSH Keys).

Instructions for merge requests

Once you have everything set up you can create a new branch branch and work there. When you're done create a pull request to integrate your work into the main branch. This should be done first on github to allow for discussions and review (gin servers don't have the same review features). Afterwards the changes can be actually merged on gin (so that the annex is merged properly too).