Winners of the Nobel Prize in Physics 2018
The Nobel Prize in Physics is widely regarded as the most prestigious award in Physics. It has been awarded to 207 Nobel Laureates between 1901 and 2017. John Bardeen is the only double Nobel Laureate meaning that 206 physicists have actually won the prize. The will of Alfred Nobel states that that the prize should be awarded to the "person who shall have made the most important discovery or invention within the field of physics". In fact, the prize can actually be awarded to a maximum of 3 people in any year and can be split for a maximum of 2 inventions or discoveries. The prize is not awarded posthumously; however, if a person is awarded a prize and dies before receiving it, the prize may still be presented.
The Nobel Prize in Physics is awarded by The Royal Swedish Academy of Sciences, Stockholm, Sweden. The nomination and selection process is a lengthy and complex process taking just over a year. Three of the key stages are:
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September - Nomination forms are sent out. The Nobel Committee sends out confidential forms to around 3,000 people - selected professors at universities around the world, Nobel Laureates in Physics and Chemistry, and members of the Royal Swedish Academy of Sciences, among others.
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March-May - Consultation with experts. The Nobel Committee sends the names of the preliminary candidates to specially appointed experts for their assessment of the candidates' work.
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October - Nobel Laureates are chosen. In early October, the Academy selects the Nobel Laureates in Physics through a majority vote. The decision is final and without appeal. The names of the Nobel Laureates are then announced.
Furthermore, details of the nominations are not made public until 50 years after. The nature of the selection process has led to claims that the selection process is dominated more by the demographics of the nominee and the nominators than by the quality of the nominee's work. For some more details, see this excellent five part series from Physics Today that examines the data and dives into the history of physicists nominated for the Nobel Prize. This PBS article also describes 8 ways to win the Nobel Prize in Physics of which 5 refer to demographics. Some of the nominee demographics mentioned in both articles include:
- Gender
- Age / years lived
- Nationality
- Institutions studied at and affiliated with
- Connected to past winners of the Nobel Prize in Physics or Chemistry through progeny or academics
- Theorist or experimentalist
- Astronomer or physicist
The Physics Today article claims that "We'll probably never know for sure why some physicists win Nobel glory and others come up short; the Nobel committee is notoriously secretive about their deliberations." However, the data in the article suggests that there may exist underlying patterns that in general enhance a physicist's chance of winning a Nobel prize.
The goals of the project are to answer the following questions:
- Do demographics play a major role in selecting the winner of the Nobel Prize in Physics?
- Which demographic factors have the biggest influence on the outcome?
- Who are the most likely winners of The Nobel Prize in Physics 2018?
The questions will be answered by building a machine learning model, based on demographic data alone, that predicts whether a physicist has won or will win a Nobel Prize. The Nobel Committee has acknowledged the gender bias towards women across all of the Nobel Prizes and is actively looking to address the situation. It seems that a predictive model such as this could provide insight into biases present in the selection process. The Nobel Committee could utilize such a model to make informed decisions that help to permanently erradicate such biases.
A list of physicists notable for their achievements will be created by scraping the following Wikipedia articles:
Lists of Nobel Prize Winners in both Physics and Chemistry from 1901-2017 will be obtained by scraping the following Wikipedia articles:
These lists will be used to obtain demographic data in JSON format for the physicists by sending HTTP requests to DBpedia. DBpedia is a crowd-sourced community effort to extract structured content from the information created in various Wikimedia projects. In this case, the JSON data is similar to the structured data in an Infobox on the top right side of the Wikipedia article for each physicist. The following are examples of data that is available for the physicists:
An environment for computational reproducibility of this project can be setup by following these simple steps:
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Download and install
python 3.6.5 (64-bit)(any 3.6.x version should be ok) for your operating system from python.org or anaconda. Make sure to check the option "Add python 3.6 to PATH" during installation. -
Download and install the latest version (any version should be ok) of git-scm for your operating system.
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Clone the github repository:
git clone https://github.com/covuworie/nobel-physics-prizes.git
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Create a .env file at the root where you cloned the repo. See .env-example for an example.
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Use pipenv to spawn a shell with the virtualenv activated (this will also load the .env environment variables):
pipenv shell
- Install all packages from the Pipfile (both develop and default packages):
pipenv install --dev
- Launch the JupyterLab application in your default browser:
jupyter lab
Notebooks are located under the notebooks directory. The individual notebooks of the projects can be run interactively in JupyterLab. Or if you prefer, there is the run-all notebook, which allows one to run all the notebooks sequentially in a non-interactive manner. This is useful for reproducing the output results of the entire study without having to interact with the individual notebooks.
The outputs of the individual notebooks are located in HTML files under the notebooks/html_output directory and can be viewed in a web browser. They are produced after a notebook has been run by issuing the following command in a terminal from the notebooks directory:
jupyter nbconvert --to html --output-dir=html mynotebook.ipynb
The actual notebooks only contain source code and markdown narrative as the output is cleaned after running them by issuing the following commands in a terminal from the notebooks directory:
jupyter nbconvert --to notebook --ClearOutputPreprocessor.enabled=True mynotebook.ipynb
mv mynotebook.nbconvert.ipynb nbconvert.ipynb
Cleaning the output allows for better source control of notebooks as the diff outputs only contain code and markdown narrative changes. If output diffs are desired then the diffs between the versions of html files can be examined.
Tests are located under the tests directory. There are two sets of tests, tests for the notebooks located at tests/notebooks and tests for the scripts located at tests/src.
Notebook tests use ipytest. The functions in the notebook they are testing need to loaded into the same IPython interactive namespace. There are a few different ways of doing this. However, the simplest way to do this is to use JupyterLab to connect both notebooks to the same kernel. This can be achieved through the Kernel > Change Kernel option in the JupyterLab user interface. Please see the JupyterLab documentation for more information on managing kernels.
Script tests use pytest and can be run from within the virtualenv with the command:
pytest
A website describing the findings of this project is available under the website directory and can be viewed using any web browser. Once you have cloned the repository, just open the index.html file to view the contents of the website offline.