Alphafold Impact

Overview

In recent years, structural biology has undergone seismic shifts, with technologies like DeepMind’s AlphaFold reshaping established research paradigms. While AlphaFold offers substantial scientific opportunities, it also brings challenges and alters dynamics both within the academic community and beyond. The extent and nuances of these changes, particularly regarding dynamics in research methodologies, clinical applications, and career trajectories, remain under-explored. A systematic, iterative approach is vital to understand the broader societal and scientific impacts of such an innovation.

We plan to use new datasets to uncover more aspects of AlphaFold’s impact on society and science. At the same time, our indicators and methods will help us study how far and wide AlphaFold’s effects have spread. This includes the exploration of predicting the translational applicability of academic works that cite AlphaFold, and shifts in the research community as scientists shift their focus following the solution of the single protein prediction challenge.

Take a look at the Kedro documentation to get started.

Rules and guidelines

In order to get the best out of the template:

• Don't remove any lines from the .gitignore file we provide
• Make sure your results can be reproduced by following a data engineering convention
• Don't commit data to your repository
• Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in conf/local/

How to install dependencies

Declare any dependencies in requirements.txt for pip installation.

To install them, run:

pip install -r requirements.txt

How to run your Kedro pipeline

You can run your Kedro project with:

kedro run

How to test your Kedro project

Have a look at the files src/tests/test_run.py and src/tests/pipelines/test_data_science.py for instructions on how to write your tests. Run the tests as follows:

pytest

To configure the coverage threshold, look at the .coveragerc file.

Project dependencies

To see and update the dependency requirements for your project use requirements.txt. Install the project requirements with pip install -r requirements.txt.

Further information about project dependencies

AWS S3 credentials

AWS S3 credentials need to be stored in local/credentials.yml in the format:

s3_credentials:
  key: xxx
  secret: yyy

How to work with Kedro and notebooks

Note: Using kedro jupyter or kedro ipython to run your notebook provides these variables in scope: catalog, context, pipelines and session.

Jupyter, JupyterLab, and IPython are already included in the project requirements by default, so once you have run pip install -r requirements.txt you will not need to take any extra steps before you use them.

Jupyter

To use Jupyter notebooks in your Kedro project, you need to install Jupyter:

pip install jupyter

After installing Jupyter, you can start a local notebook server:

kedro jupyter notebook

JupyterLab

To use JupyterLab, you need to install it:

pip install jupyterlab

You can also start JupyterLab:

kedro jupyter lab

IPython

And if you want to run an IPython session:

kedro ipython

How to ignore notebook output cells in git

To automatically strip out all output cell contents before committing to git, you can use tools like nbstripout. For example, you can add a hook in .git/config with nbstripout --install. This will run nbstripout before anything is committed to git.

Note: Your output cells will be retained locally.

Further information about using notebooks for experiments within Kedro projects.

Package your Kedro project

Further information about building project documentation and packaging your project.