How Our System Parses and Scrapes Data Automatically

Finding useful data in materials science often means opening dozens of papers, checking tables, and fixing formats by hand. We built a system to make this easier.

Step 1: Collecting Data from Trusted Sources

Our system collects documents from well-known research platforms. These include:

PubChem
Google Scholar
Crossref
ScienceDirect
Material Project

It finds and downloads materials-related papers and metadata for further processing.

Step 2: Parsing the Documents

After collecting the documents, the system checks what kind of file it’s dealing with:

PDF
HTML
XML
Table
Scanned image

Each type is handled differently:

OCR tools read scanned images
PDF parsers extract content from digital PDFs
Custom logic handles HTML and table structures

This step turns messy files into readable, structured content.

How Our System Parses and Scrapes Data Automatically

Step 3: Extracting and Cleaning the Data

While parsing, the system:

Finds important values (dopant, host, formula, etc.)
Removes duplicate or broken data
Organizes everything into a consistent format

You get structured, clean material data—without doing the cleanup yourself.

Let the System Do the Hard Work

With this setup, you don’t need to dig through papers or reformat anything. Just search, filter, and use the data directly.

Less time on prep. More time on research.

How Our System Parses and Scrapes Data Automatically