We had a kettle; we let it leak:
Our not repairing made it worse.
We haven`t had any tea for a week...
The bottom is out of the Universe.
― Rudyard Kipling
Not so much a project as a mixed bag of tools, queries, data and code snippets for use with Open Repair Data (ORDS).
For anyone who fancies tinkering with Python, NLP, SQL and lots of messy but interesting Open Data. Many challenges lie ahead!
Some of the things you can do:
- Get familiar with ORDS data.
- Slice the data up in various ways using Python DataFrames and/or MySQL/SQLite.
- Investigate devices against the timeline of consumer electronics.
- Calculate how much waste was prevented from going to landfill.
- Detect languages in the text and translate using the DeepL API.
- Muck about with regular expressions.
- Try out some Natural Language Processing and even a little basic machine learning.
- Generate Open Repair Poetry.
There is also an installer and ORDS configuration for an Apache Solr search platform that demonstrates indexing and querying multi-lingual text with international (ICU) characters.
The dataset contains over 100k records representing over 10 years worth of electronic repairs at events by community repair groups all over the world. See the Setup section below for how to get hold of the data.
It is compiled and published by the Open Repair Alliance (ORA) an organisation founded by The Restart Project, a UK based charity.
- Enabling thousands of UK-based community repair events.
- Formulating the Open Repair Data Standard.
- Compiling and publishing the ORDS data twice yearly.
- Organisers of FixFest, a regular global gathering of repairers and tinkerers, activists, policy-makers, thinkers, educators and companies from all over the world.
- Winners of the European Union Prize for Citizen Science Digital Communities Award for demonstrating excellence in creating and supporting communities, delivering social benefits, and fostering an open and inclusive civil society through the innovative or alternative use of digital technologies.
- Use-cases
- Tests
- Data visualisation
- Document all the things
- Clean up poor quality code
- Sort out requirements
- Refactor pandas to polars for speed and clarity
- Code
- Data
- CC BY-SA 4.0 (Creative Commons Attribution-ShareAlike 4.0 International)
- ORDS Quest Data is covered by the license for ORDS Data
- CC BY-SA 4.0 (Creative Commons Attribution-ShareAlike 4.0 International)
- Python with venv module.
- Optional MySQL 8.x and libmysqlclient-dev.
python3 -m venv ords-tools
cd ords-tools
git init
git remote add origin [email protected]:openrepair/tools.git
git pull origin main
git branch --set-upstream-to=origin/main main
source bin/activate
pip install -r requirements.txt
Grab the Aggregated Open Repair Data files, unzip the files into the dat/ords directory.
Copy .env.example to .env and edit as necessary.
.env is in .gitignore, do not add it to this repo.