This interactive animation shows how the structure of the global import trade network changed from 1993 through 2025. Countries are grouped into subnetworks using the Leiden community-detection algorithm applied to directed, weighted bilateral import data from the IMF. Each color represents a detected trade subnet for a given year, and the timeline allows viewers to follow changes in countries’ subnet memberships over time. Subnet numbers are algorithmic labels and should not be interpreted as fixed groups across all years.

`R` is a programming language and free software environment for statistical computing and graphics. It's not only a powerful statistical programming language but also the go-to data analysis tool for many computational genomics experts. We will explore how high-dimensional genomics datasets can be analyzed with core R packages and functions.

Large biological datasets are often too big to understand by reading numbers in a table. In DREAM-High, we will eventually use heatmaps to look for patterns in breast cancer gene expression data from patients in The Cancer Genome Atlas. A heatmap can help us ask questions such as: - Which samples look similar to each other? - Which genes behave similarly across patients? - Can visual patterns help us discover tumor subtypes? Today we will learn the same basic idea using a small practice dataset that comes with R.

The rise of AI (especially generative AI) is creating a general-purpose technology revolution comparable to past great innovations. Recent studies show vast task and job exposure: for example, ~80% of U.S. workers have at least 10% of their tasks exposed to LLMs, and 19% have half or more tasks exposed. Brookings finds over 30% of workers could see ≥50% of tasks disrupted by generative AI. The economic potential is enormous (McKinsey estimates ≈$4.4 trillion in productivity gains globally), but gains may be delayed. In manufacturing, AI adoption often induces a short-term productivity dip (a “J-curve”) before stronger growth in output and revenue. Moreover, AI’s effects vary by sector: McKinsey reports that healthcare, tech, media/telecom, and even agriculture are investing heavily now, whereas finance, consumer retail, energy, and logistics are (so far) slower to invest. These differences reflect that AI’s impact comes through multiple mechanisms – from automation of routine tasks to augmentation of knowledge work, the creation of entirely new products and services, platformization of industries, and strong data/network effects that favor large incumbents. Emerging AI-native sectors (e.g. synthetic biology, AI-powered healthcare diagnostics, automated creative services) are already taking shape.