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.

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. The data management tool DataLad is used to version control the data sets. Commands to run the scripts are executed via the pydoit package.

Installation

• Install datalad according to the DataLad handbook. It is recommended to install globally.
• DataLad is based on Git. Git needs to be installed to run DataLad.
• Install Python
• pydoit

Getting Started

1. Clone the repository

Download the repository using the following command.

datalad clone

Do not use git clone to download the repository! This way DataLad will not have the necessary information to run the program.

2. Easy Access

Users who simply want to download the dataset have the option to access both the original and extracted files with the following command.

dataland get <filename>

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 

3. Executing the program

3.1 Set up the virtual environment with doit

doit setup_env

3.2 Download the version from the command line.

This will download all files from Zenodo as they are.

doit download_version --version <YYMMDD>

3.3 Convert the data sets into CSV and NetCDF files.

doit read_version --version <YYMMDD>

How to add a new version

1. To add a new version go to versions.py in the src directory and create a new value in the 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. python 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, }, } 2. Then run the two commands as described in [3.2] and [3.3]. ## 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> ## For developers ### 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 connect with your 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> ### instructions for merge requests # About this dataset ## General information This is a DataLad dataset (id: 24f90b12-e4a9-4e2c-995d-a54ed4cd49). ## 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. More information on how to install DataLad and how to install it can be found in the DataLad Handbook. ### Get the dataset A DataLad dataset can be cloned by running datalad clone <url> 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. ### Retrieve dataset content After cloning a dataset, you can retrieve file contents by running datalad get <path/to/directory/or/file> This command will trigger a download of the files, directories, or subdatasets you have specified. DataLad datasets can contain other datasets, so called subdatasets. If you clone the top-level dataset, subdatasets do not yet contain metadata and information on the identity of files, but appear to be empty directories. In order to retrieve file availability metadata in subdatasets, run datalad get -n <path/to/subdataset> Afterwards, you can browse the retrieved metadata to find out about subdataset contents, and retrieve individual files with datalad get. If you use datalad get <path/to/subdataset>, all contents of the subdataset will be downloaded at once. ### 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.