A structured OSINT guide for researching publicly available information through a methodical, privacy-conscious workflow.
Data Harvester is an open-source guide that organizes the process of researching publicly available information into a clear, repeatable workflow.
Instead of relying on scattered tools, forum posts, and disconnected techniques, the project brings common OSINT concepts together into one structured reference.
Information about people, organizations, websites, and online services is often publicly available but spread across many different sources.
I wanted to turn that scattered process into something easier to understand and follow - starting with a known piece of information and progressively researching related public data points.
The guide follows a step-by-step research methodology rather than depending on a single tool or service.
OSINT has legitimate applications across journalism, security research, digital-footprint auditing, and general online investigation.
The goal of Data Harvester is to make the research process more organized and educational while encouraging responsible use of publicly available information.
The project is intentionally limited to passive research of information that is already publicly accessible.
It does not involve unauthorized access, credential theft, bypassing authentication, accessing private accounts, or circumventing platform security controls.
The project gave me a better understanding of how seemingly unrelated pieces of public information can be connected through careful research and verification.
More importantly, it reinforced the importance of documenting sources, validating findings, and understanding the boundaries between legitimate research and invasive behavior.
The complete guide, methodology, and project structure are available on GitHub.
