The GST Climate Datathon is a call for open data and tools to support the GST ahead of COP27. Outcomes should be aimed toward making data sets interoperable, providing aggregated insights, support tools, and visualizations. Three winners from each category will have the opportunity to present their findings at the COP27 Presidency’s ‘Science and Information Day’.
There are three thematic areas of the global stocktake: mitigation, adaptation, and means of implementation and support (including finance, and loss and damage). Information is being prepared for the global stocktake between now and June 2023, with technical dialogues also ongoing until June 2023. At COP28 in 2023 the outputs of the global stocktake will be considered and are expected to inform parties in updating their commitments toward the Paris Agreement.
We ask the hackers to embark on an unusual task — help us collectively build the web that expands upon and ties different datasets together. Hackers are to examine publicly available data sets, determine their data taxonomies, make recommendations for improvements, develop tools for interoperability, fill in data gaps with modeling, record metadata, establish clear data provenance for databases, use projection analysis to determine collective action.
All submissions should be made via the GitHub repository. To submit your project, please
- Fork a copy of the datathon repository
- Create a subfolder under the Submissions/ folder with the format “TeamName_PromptBucket”
- Include/Embed any documentation and/or presentations in the README file 4.Include any relevant code snippets and/or datasets
- Submit a pull request to the repository
If you have significant amounts of code (for e.g., if you have built a web tool/app) or if you have created a GitHub page for your submission, you can also make use of GitHub’s Submodule system to include a submodule to your own GitHub repository within your submission.
Join the datathon Discord channel here!
** Crowd-sourced and compiled datasets** Crowd-sourced datasets via our [google form[(https://docs.google.com/forms/d/e/1FAIpQLSfP0migRTasiXnfIiuCd8SZRU0hIthBRExwyfFOTu4JYpixNA/viewform?pli=1) are available on this Google Drive folder
DDL-OEF-CAD 2.0 Data model
The DDL-OEF-CAD 2.0 Data model is an attempt led by DDL and OEF (and supported by the CAD 2.0 community) to create a data model that would allow climate data from disparate sources to be harmonized for use in a Digitally-enabled Independent Global Stocktake.
Participants are encouraged to transform their data into the format of the data model (accessible here) when submitting any datasets, and to leave any comments they have on the data model on this version here.
R
Spatial Data Programming with R
Python
Python for Data Science Cheatsheet
GitHub