I have a collection of scientific articles in text form and I want a clear, reproducible way to uncover the key themes and topics running through them. The core of the task is natural-language processing: ingest the articles, clean and normalize the text, then apply topic-modeling or similar techniques to surface the dominant concepts. My preference is a Python-based workflow—think spaCy, NLTK, gensim, BERTopic, or comparable libraries—but I’m open to whatever stack you feel will give the most coherent results. Visual summaries such as word clouds or interactive topic maps are definitely welcome if they help make the findings intuitive. Deliverables • Well-commented code or notebook that I can run locally without tweaks • A short methodological write-up (steps taken, models used, parameter choices) • The final theme/topic list with brief descriptions and any supporting visualizations • Instructions for adding new articles to the pipeline I only need “Key themes and topics” right now; citation or sentiment analysis is not required, though knowing the approach could be extended later would be a plus. Accuracy, readability, and clear documentation will drive acceptance.