Automating Scientific Research with Python: A Comprehensive Guide
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Introduction
In the realm of research, staying current with the latest papers is often a time-consuming task. Many researchers, including myself as a Data Scientist and a Ph.D. candidate in Astrophysics, have contemplated ways to automate tedious tasks, allowing more focus on substantial work.
While AI tools like chatGPT are frequently suggested, I believe they aren't universally applicable. At their core, AI systems primarily perform two functions:
- They convert noise into meaningful signals.
- They replicate existing data based on training sets.
This means they lack the inherent creativity essential for research.
Moreover, employing AI can be computationally intensive. Companies like Google and Microsoft have access to vast computational resources, which isn't the case for most individuals.
Side Note: If you're looking for GPU resources for Deep Learning but are limited by budget, consider using Google Colaboratory (Colab). I have detailed its advantages and how to get started with it in my article:
Free GPU for Deep Learning A step-by-step guide addressing its limitations and solutions. [Read More](https://medium.datadriveninvestor.com)
Returning to our main topic, my goal is to empower individuals to leverage data science and programming in their daily activities. I will provide guidance on how to automate the search for relevant research papers using just Python on your machine.
Data Sources and Accessibility
A fundamental aspect of scientific research is the ability to apply methodologies across various fields. For instance, during the COVID-19 pandemic, I used Python to analyze medical research papers without needing AI, relying solely on basic programming skills.
We will utilize arxiv.org, a well-known repository that hosts both published and preprint papers across diverse disciplines including Astrophysics, Physics, Economics, Computer Science, and Engineering.
Pro Tip: If you lack access to journals like Nature, searching arxiv for the title or authors may yield the paper as open-access. However, published papers generally undergo more rigorous review processes.
Tools Required for Automation
In this tutorial, I will focus on the most current and user-friendly methods for automating research paper searches. The primary libraries we'll utilize are BeautifulSoup and Regular Expressions (re), along with some optional supplementary libraries.
The program flow is as follows:
- Access the arxiv.org website.
- Search for relevant papers based on chosen criteria.
- Retrieve the data.
- (Optional) Save the outputs locally for future reference.
- (Add-on) Deploy to the cloud for complete automation.
How to Begin Harvesting Data
Before extracting data from a website, understanding its structure is crucial. To analyze the arxiv.org webpage:
- Navigate to the site.
- Right-click on the page.
- Select "Inspect" to view the HTML code.
From there, identify the relevant section of the HTML document. For example, in the astro-ph section under recent, the content resides within the <div id="dlpage">, which contains a list of terms paired with descriptions.
Note: A detailed explanation of HTML structure is beyond this tutorial's scope, but I am open to creating more HTML-focused content if there's interest.
With the relevant sections identified, we can proceed to write our program.
Code for Data Harvesting
Accessing the Data
After importing the necessary libraries, we will initiate HTML parsing using Beautiful Soup. The steps include:
- Using the URL “https://arxiv.org/list/astro-ph/new” to request the page.
- Parsing the page into a BeautifulSoup object, commonly called soup.
- Extracting the section of interest identified by id='dlpage'.
Retrieving Relevant Data
Next, we must define the keywords we want to search for. While I will provide general examples, you should use keywords pertinent to your research interests. We will loop through the list of papers and check each title for our keywords. If a match is found, that paper will be appended to our list.
Add-on: You may also check abstracts for better results by adding more keywords.
Remember, we're building a program designed to run autonomously (when deployed on the cloud or a server), so investing time upfront will save you time in the long run.
If no keywords are found, we can include a message to indicate the absence of results. Here’s how to implement this in Python.
As of today (2023.02.04), the results are as follows:
Found [‘Parity’] in The Density Parity Model for the Evolution of the Galaxy Inner Spin Alignments with the Cosmic Web.
Found 1 title you might be interested in: [‘https://arxiv.org/pdf/2302.00679’]
Wonderful! Our code is functioning as intended.
Enhancements and Automation
You can save the script as a .py file and execute it via the terminal, for instance: python Beautiful_Soup_Harvester.py. This will be significantly faster than manually navigating to the arxiv site each morning to find recent articles.
One enhancement could be incorporating the os and datetime libraries to automatically create a local folder for each day’s results. Alternatively, we can deploy it to the cloud for complete automation, which is a more advanced feature I will discuss in future articles.
You may also opt to use a UNIX-based job scheduler called cron to automate script execution. This can be set up on a remote server or cloud environment.
Automating Python Scripts Using Cron A complete guide for automating scripts in UNIX systems (Linux and macOS). [Read More](https://medium.datadriveninvestor.com)
Putting It All Together
Below is the complete code described throughout this tutorial. Simply copy and paste it, set your keywords, and enjoy your newfound free time while the code does the harvesting for you!
In conclusion, this tutorial covers one aspect of automating scientific research. There are multiple pathways to achieve similar results, so feel free to explore the provided GitHub repository for alternative methods, both with and without the BeautifulSoup package.
I hope you found this article insightful! If you have any questions or feedback, please don't hesitate to reach out.
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