shedding-hub 0.1.1

Data and statistical models for biomarker shedding.

Shedding Hub

The Shedding Hub collates data and statistical models for biomarker shedding (such as viral RNA or drug metabolites) in different human specimen (such as stool or sputum samples). Developing wastewater-based epidemiology into a quantitative, reliable epidemiological monitoring tool motivates the project.

Datasets are extracted from appendices, figures, and supplementary materials of peer-reviewed studies. Each dataset is stored as a .yaml file and validated against our data schema to verify its integrity.

📊 Getting the Data

You can obtain the data by downloading it from GitHub. We also provide a convenient Python package so you can download the most recent data directly in your code or obtain a specific version of the data for reproducible analysis. Install the package by running pip install shedding-hub from the command line. The example below downloads the data from Wölfel et al. (2020) as of the commit 259ca0d.

>>> import shedding_hub as sh

>>> sh.load_dataset('woelfel2020virological', ref='259ca0d')
{'title': 'Virological assessment of hospitalized patients with COVID-2019',
 'doi': '10.1038/s41586-020-2196-x',
 ...}

🤝 Contributing

Thank you for contributing your data to the Shedding Hub and supporting wastewater-based epidemiology! If you hit a bump along the road, create a new issue and we'll sort it out together.

We use pull requests to add and update data, allowing for review and quality assurance. Learn more about the general workflow here. To contribute your data, follow these easy steps (if you're already familiar with pull requests, steps 2 and 3 are for you):

1. Create a fork of the Shedding Hub repository by clicking here and clone the fork to your computer. You only have to do this once.
2. Create a new my_cool_study/my_cool_study.yaml file in the data directory and populate it with your data. See here for a comprehensive example from Wölfel et al. (2020). A minimal example for studies with a single analyte (e.g., SARS-CoV-2 RNA concentration in stool samples) is available here, and a minimal example for studies with multiple analytes (e.g., crAssphage RNA concentration in stool samples and caffeine metabolites in urine) is available here.
3. Optionally, if you have a recent version of Python installed, you can validate your data to ensure it has the right structure before contributing it to the Shedding Hub.
   • Run pip install -r requirements.txt from the command line to install all the Python packages you need.
   • Run pytest from the command line to validate all datasets, including the one you just created.
4. Create a new branch by running git checkout -b my_cool_study. Branches let you isolate changes you are making to the data, e.g., if you're simultaneously working on adding multiple studies–much appreciated! You should create a new branch from the main branch for each dataset you contribute; see here for more information.
5. Add your changes by running git add data/my_cool_study/my_cool_study.yaml and commit them by running git commit -m "Add data from Someone et al. (20xx).". Feel free to pick another commit message if you prefer.
6. Push the dataset to your fork by running git push origin my_cool_study. This will send the data to GitHub, and the output of the command will include a line Create a pull reuqest for 'my_cool_study' on GitHub by visiting: https://github.com/[your-username]/shedding-hub/pull/new/my_cool_study. Click on the link and follow the next steps to create a new pull request.

Congratulations, you've just created your first pull request to contribute a new dataset! We'll now review the changes you've made to make sure everything looks good. Once any questions have been resolved, we'll merge your changes into the repository. You've just contributed your first dataset to help make wastewater-based epidemiology a more quantitative public health monitoring tool–thank you!