Establish a "Data Lake" for AI-driven Content Generation

[langbart]

Objective

Centralize our existing research assets—papers, repositories, media links, and policy briefs—into a single "Data Lake." This repository will serve as the primary source for AI tools (like NotebookLM, ChatGPT, or Gemini) to generate high-quality outreach material.

The Vision

Nowadays AI tools are quite powerful and accurate in generating complementary material from different sources. We have a wealth of intellectual property scattered across different platforms. By gathering this material, we can use AI to:

• Generate explanatory videos and scripts from technical papers.
• Create infographics summarizing complex data briefs.
• Produce summaries for non-expert stakeholders.
• Automate newsletters based on recent repository updates.

Action Items

• Identify the primary storage location (e.g., a specific GitHub repo, SharePoint, or cloud drive).
• Inventory current "high-value" materials (PDFs, URLs, Readmes).
• Define metadata standards (tags) so the AI knows the context of the source files.

[m13v]

the data lake framing assumes the bottleneck is scattered sources, but the AI tools you listed already stitch across PDFs, repos, and links fine. the variable that actually decides whether this outreach material lands isn't centralization, it's matching output format to consumption context. a policy stakeholder skims a one-pager, but a field researcher absorbs the same content on a commute as audio. the 'automate newsletters from repo updates' item especially: a written digest competes with a full inbox, whereas audio rides dead time nobody is using. worth deciding the consumption mode before the storage layout.