I am preparing a quantitative study on “Inequality in participation versus visibility in online communities” . The raw material will come from one or more publicly-available Kaggle datasets; the challenge is to turn those data into a coherent, publication-level research project. Here is what I need from you: • Help me locate or combine the right Kaggle datasets, then document the download and preprocessing steps (Kaggle API, Python pandas, or R tidyverse are fine). • Define robust operational metrics for participation (e.g., post frequency, comment depth) and visibility (e.g., up-votes, follower counts, ranking on leaderboards). • Build the analysis pipeline—cleaning scripts, exploratory statistics, and the main inferential models (regression, GEE, or other techniques that withstand peer review). • Supply well-commented code in Jupyter Notebooks (or an RMarkdown file) plus a short methods memo that explains variable construction, analytic decisions, and reproducibility details. • Deliver publication-ready tables, figures, and any supplementary material required by typical communication or media-studies journals. The end goal is a turnkey quantitative study: data, code, and narrative all lined up so I can focus on writing the introduction, literature review, and discussion. If you have prior authorship or co-authorship experience, please mention it alongside relevant GitHub links or DOIs.