This is the README to accompany and describe the purpose and contents of this github repository.
The purpose of this repository is twofold:
- it presents the method used for the IIED issue paper 'Locally Implemented or Locally Led? Tracking finance for community climate action' (Treichel et al. 2025; hereafter T25) to conduct a keyword search of the database of climate funding that is maintained by the Organisation for Economic Co-operation and Development (OECD) Development Assistance Committee (DAC).
- it provides a pedagogical introduction and example for any researchers new to the use of the python for these kinds of keyword searches. As we describe our approach and methodology below, we will also include links to some useful resources for new practitioners.
The contents of the repository include:
- A Jupyter notebook that includes the code used for the T25 analysis, along with a pedogagical description of the methods (LLA_keyword_tracking.ipynb);
- A simple textfile specifying the keywords searched for in T25 (keywords_list.txt); and
- Plain text (csv) versions of the OECD data sources used for the analyis (CRDF-RP-all years-2000-2023.csv.gz and Table1/2.csv).
You can click here to review the contents of the LLA_keyword_tracking notebook using github's file preview. Alternatively, you can see a pure HTML rendering of the notebook by clicking here.
To load and interact with the LLA_keyword_tracking notebook in a Jupyter environment, you can use google colab. From the 'Open Notebook' window, select 'Gitlab' from the options in the lefthand panel, and then paste 'https://github.com/entaylor/LLAkeywordsearch' (the URL for this repository) into the search box. Alternatively, you may be able to load and interact with the LLA_keyword_tracking notebook via github's development environment by clicking here.
If you want to create your own local working copy of the data and code, then you can download a .zip archive of this repo via the green button that says '<> Code' at the top of this window. Note that if you go this route, you will need your own installations first of python and, then of jupyter notebooks. If you are installing python for the first time, then Spyder is a good place to start. Spyder is an open source Interactive Development Environoment (IDE) that will do a lot of the work of managing your python and jupyter installs for you. If you do use Spyder (or another comparable IDE; common examples include PyCharm or Microsoft's Visual Studio Code, with Zed as a new and fast developing alternative), then you can also use its in-built git features to clone this repo directly within your IDE, instead of a direct download.
If you have any questions about the content of this notebook, you can open an issue here, or you can email the authors here.