Document the file size, MD5/SHA-256 hashes, and the tools used to extract it (e.g., 7-Zip, FTK Imager).
A high-level overview of what the archive contains (e.g., "The archive contains memory dumps and network logs related to an unauthorized access event"). Bias.7z
If the data includes NYSE or TORQ database features, note how specific trading procedures (like trade reversals) affect the results. To give you a more precise outline, could you clarify: Document the file size, MD5/SHA-256 hashes, and the
Use visualizations like histograms or heatmaps to show where the "bias" exists in the data. To give you a more precise outline, could
(e.g., a specific university course, a CTF platform like HackTheBox, or a workplace task)?
In some academic contexts, "Bias" refers specifically to errors in trade classification models. If your paper is about market microstructure:
If the file contains datasets (e.g., CSV or JSON files) used to study algorithmic fairness, your paper should focus on the statistical implications:
