Our World in Data, But For Biotech

I’m begging you to build this.

In 2011, while working in Brazil, Max Roser began formulating the idea for Our World in Data. He initially planned to publish “data and research on global change,” possibly as a book. Before long, that modest blueprint morphed into something far more ambitious.

Our World in Data went live in May 2014 and, according to Roser, attracted an average of 20,000 visitors per month in its first six months. Today, the website has a worldwide audience. It’s difficult to get exact metrics, but they have more than 300,000 followers on Twitter alone. I’d argue that their true value, though, is not in their “audience reach,” but rather in their global impact.

By publishing numbers and charts about global change on the internet, Our World in Data plays a key role in finding aspects of global development — like malaria cases over time, for example — that are particularly stubborn and, therefore, ripe for philanthropic or government interventions. In essence, they have shown how numbers, displayed in accessible forms, can illuminate which issues deserve urgent attention and where efforts can accelerate progress.

We should build a similar initiative for biotechnology. The Schmidt Foundation has forecasted that the bioeconomy (encompassing everything from medicines to microbe-made materials) “could be a $30 trillion global industry.” If we intend to realize that potential, we first need to benchmark where biotechnology has been, assess where it stands now, and identify the most pressing challenges ahead. 

“If I’m just watching the news, I’m going to find it very difficult to get an all-things-considered sense of how humanity is doing,” researcher Fin Moorhouse has written. “I’d love to be able to visit a single site which shows me — in as close as possible to a single glance — some key overall indicators of how the world’s holding up.” Biotechnology deserves precisely this kind of concentrated, data-driven resource.

More specifically, I’m imagining a website that aggregates information on everything from the computational costs of protein design to the efficiency of gene-editing tools across cell lines. Such a resource would help researchers, investors, and policymakers figure out which areas demand attention and which breakthroughs are worth scaling, all while helping prevent misuse. 

Pieces of this puzzle already exist, but it seems only in scattered or ad-hoc formats. Rob Carlson, managing director of Planetary Technologies, has famously published data on DNA sequencing and synthesis costs. His charts became so popular that people eventually dubbed them “Carlson Curves.” Meanwhile, Epoch AI, a research institute that monitors the computational demands and scaling of AI models, is building the benchmarks and datasets needed to track the AI field’s progress. They could serve as a model for this biotechnology effort.

A dedicated nonprofit research institute for “Biotech Data” could systematically track metrics such as:

• Cloning times over the last several decades. How long does it take to synthesize DNA, stitch it together, and make sure everything works as intended? Bottlenecks in cloning slow scientific progress as a whole; the speed of experiments is a key driver of scientific speed overall.

• CRISPR off-target scores over time. How frequently do gene-editing tools make unintended cuts in the genome, and how can we standardize measurements across studies? We’ll need to make some benchmarks.

• Resolution and speed of cryo-EM. How rapidly have improvements in cryo-electron microscopy accelerated, both in terms of resolution and throughput?

• Antibody manufacturing titers over time. Using a single antibody as reference, what titers are companies achieving in CHO (in g/L) or other cell types over time?

• Bioscience PhDs awarded per year. How many new doctorates emerge from academia, and where do they end up across industry, startups, and research labs?

Note that these datasets span both technical and societal issues. This is deliberate; to scale biotechnology, we have to understand both scientific breakthroughs and the workforce dynamics behind them. Tools are useless without a workforce to wield them. Many of these numbers already exist on the internet, but are buried in unwieldy government PDFs or tucked away in a patchwork of scientific articles. Others may require painstaking curation by combing through decades of research articles.

Starting this nonprofit wouldn’t be too difficult. You could begin by collecting one dataset, transforming it into a chart, and posting it online. People on Twitter and LinkedIn seem to really love data visualizations, so you could probably grow an audience quickly. Over time, you might build automated scraping tools for government websites, create reusable templates to make charts quickly, and even publish short blog posts about various charts (like why, exactly, cryo-EM resolution got so good; what were the key innovations?)

If this vision appeals to you, send me an email (niko@asimov.com), and I’ll help you get started. We briefly considered launching this venture at Asimov Press, but we only have two full-time employees and so don’t have the bandwidth. We might be keen to fund this project.

Until next time,

— Niko McCarty