This repository contains notebooks and code for analysing data published to the Beneficial Ownership Data Standard (BODS).
Initial work on qbods.py and the Latvia notebook was carried out as part of the Opening Extractives programme implemented jointly between the Extractive Industries Transparency Initiative International Secretariat and Open Ownership.
The main components are:
-
A Python module
qbods.py, which contains a set of functions for reading, summarising and analysing BODS 0.2 data. This code is under development and will likely contain bugs. -
An iPython notebook
latvia_demo.ipynb, which contains code to run a subset of the functions on an initial dataset released by the Register of Enterprises of the Republic of Latvia, with accompanying text. -
An iPython notebook
Insights_UK_PSC_BODS-02.ipynbwhich contains code to explore ownership structures in the UK’s People with Significant Control (PSC) register. It supports the analysis presented in the research report 'Insights from the UK PSC Register' and includes functions to process UK PSC data in BODS 0.2 format, identify patterns, and flag anomalies.
Additional notebooks may be added to the repository in future.
Clone the repository, open the notebook in a suitable program (e.g. VS code), and follow setup instructions within the notebook.
To run on Deepnote, clone this repository, then create a new project and upload the notebook, alongside qbods.py and requirements.txt as files. Then open the notebook and follow setup instructions.
To run on Google Colab, clone this repository, then click File > Upload notebook, and upload the notebook. Then in the left hand menu, click on icons for 'Files' then 'Upload Files', then upload the files qbods.py and requirements.txt. Then open the notebook and follow setup instructions.
Suggestions for new queries and contributions are welcomed via issues and pull requests, respectively