Even though they’re the , whales remain difficult to track. So experts often turn to historical whaling data to inform current research. A dataset maintained by the (IWC) contains detailed information on commercial whale catches — more than 2.1 million records, predominantly from 1880 until the IWC banned whaling in 1986. Yet for researchers, distilling that data can prove its own challenge.

A team at the 91̽�� has created an online interactive map called WhaleVis, which lets whale researchers visualize the IWC’s data on global whale catches and whaling routes. From this, researchers can estimate the animals’ spatial distribution and the effort whalers put into hunts.

By comparing historical data and its trends with current information, scientists can better understand how populations of whales have changed over time, where they’ve been, and how to better protect those still living.

The 91̽��team presented Oct. 25 at the in Melbourne (Narrm), Australia. The tool is online, but users must have permission from the IWC to access it.

“Scientific data is a really important aspect of big data, but scientists all over the world have access to completely different hardware and software. Maybe they can’t use big servers to process huge data sets quickly,” said senior author , a 91̽��assistant professor in the Paul G. Allen School of Computer Science & Engineering. “So when creating WhaleVis we had to ask: How do we design a tool that can visualize millions of data points, but that doesn’t rely on super beefy servers?”

The team approached this in a couple of ways. First, instead of trying to render more than 2 million points on a global map at once, taxing the computer processor and creating a “hairball visualization” — an illegible mess of lines and dots — the researchers aggregated whale catches in clusters. One large blue dot at South Georgia island in the South Atlantic Ocean, for example, signifies 130,611 whale catches, most of them fin whales. As researchers continue to develop the tool, they’ll allow users to zoom in on parts of the map to access greater detail.

Second, they built the tool for web browsers, instead of as a standalone app, to make it function on different computing platforms.

“It was important to make this data accessible so it can easily be used to generate actionable insights,” said lead author , a 91̽��doctoral student in the Allen School. “Tools like this make information more tangible and comprehensible.”

WhaleVis came about through the UW’s , an initiative bringing together computer and climate scientists to collaborate. , a co-author on the paper and 91̽��professor in the School of Aquatic and Fishery Sciences, had been working with the IWC data set and wanted help visualizing it, especially in such a way that would estimate how much effort had gone into each whale catch. Battle and Patil were seeking a project that combined environmental science with data visualization, and the IWC data set fit the bill.

“Being able to visualize the data like this helps us answer a huge number of questions,” Branch said. “For example, it is difficult to separate two of the subspecies of blue whales — massive Antarctic blue whales and pygmy blue whales that are about 20 feet shorter. Visualizing the expeditions that caught big whales versus pygmies lets us clearly and quickly see the boundary between those two subspecies.”

In its current iteration, WhaleVis uses the density of expeditions in certain areas to let scientists approximate the effort whalers put into each hunt. If researchers can quantify this effort — that is, the time and distance between catches on these expeditions — it gives a better sense of size, density and location of historical whale populations.

In the future, the team plans to refine the methods of estimating whalers’ efforts, normalizing for factors such as time between catches on each expedition. The researchers also intend to add interactive prediction modeling for different scenarios and to apply the methods used on WhaleVis to other animal populations.

“From a researcher point of view, what’s already online is very, very cool,” Branch said, “and way past anything that’s been available up to now. Only when you start playing with the data in a nice visualization do you discover some of the anomalies and surprises in it.”

, a doctoral student in quantitative ecology and resource management, was a co-author on this paper. This research was funded by the 91̽�� Computing for the Environment Initiative and the National Science Foundation.

For more information, contact Patil at ameyap2@cs.washington.edu, Battle at leibatt@cs.washington.edu, and Branch at tbranch@uw.edu.

Two 91̽�� faculty members have been awarded early-career fellowships from the Alfred P. Sloan Foundation. The new Sloan Fellows, announced Feb. 15, are , an assistant professor in the Paul G. Allen School of Computer Science & Engineering, and , an assistant professor in the Department of Mathematics.

Open to scholars in eight scientific and technical fields — chemistry, computer science, Earth system science, economics, mathematics, neuroscience and physics — the fellowships honor those early-career researchers whose achievements mark them among the next generation of scientific leaders.

The 126 were selected in coordination with the research community. Candidates are nominated by their peers, and fellows are selected by independent panels of senior scholars based on research accomplishments, creativity and potential to become a leader in their field. Each fellow will receive $75,000 to apply toward research endeavors.

This year’s fellows come from 54 institutions across the United States and Canada, spanning fields from evolutionary biology to data science.

Battle is an assistant professor of computer science and co-leads the . Her research investigates the interactive visual exploration of massive datasets and stands at the intersection of several academic disciplines, including healthcare, business and climate science.

“What piqued my interest in data science was the juxtaposition of the incredible power of existing tools and their underutilization by the vast majority of data analysts in the world,” Battle said. “Why are we not making better use of these tools? This sparked a multi-year journey to better understand why people use or don’t use various data science tools and how those tools could be made accessible to and effective for a wider range of users.”

Zhu is an assistant professor in mathematics. His research explores the theory and geometry of surfaces governed by their curvature, and how these surfaces interact with the space around them. Mathematical notions of curvature form the language by which many natural phenomena are described, cell membranes, soap films, and the structure of spacetime via general relativity.

“Mathematics, and especially geometry, is all around us,” Zhu said. “By carefully studying seemingly everyday phenomena — such as soap bubbles — we are able to develop profound mathematical theories, which have applications to phenomena that are otherwise impossibly out of reach, such as black holes. In doing so, we create completely new concepts by which to understand the world we live in.”

For more information, contact Battle at leibatt@cs.washington.edu or Zhu at jonozhu@uw.edu.