Over 92% of scientific publications include graphs.¹ They serve as the primary method for communicating complex scientific data and findings in publications, grants, and regulatory filings. Yet most graphs and experimental data are still tethered to legacy graphing programs developed over three decades ago. Making a graph still takes hours. Scientists copy-paste and transpose spreadsheets manually, second-guess outlier exclusions, and rebuild graphs from scratch every time the numbers change. Collaboration adds another tax: files bounce between teammates, copies multiply, the latest version of any given graph often sits on a single desktop.

We founded BioRender to accelerate how the world learns, discovers, and communicates science. For most of that time, we've focused on the illustrated half of scientific figures, and today scientists have used BioRender to create more than 30.5 million figures and one in five Nature articles includes a BioRender figure.² But the data half of scientific figures has lived somewhere else, creating two different stands for collaboration.

After years in the making, today we're launching BioRender Graphing, which transforms raw data into publication-quality graphs in minutes. This is a science-first tool, not an AI-first one: where we use automation to speed up the most tedious parts of graphing and analysis. Now, anyone in a lab can run a rigorous analysis in a few clicks, without an expert statistician.

Here's what that looks like in practice:

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1. Smart Data Import: from raw file to graph-ready in seconds

No more copying, pasting, or transposing to prep your data. Graphing handles the formatting automatically the moment you drop the file in, so from a single dataset you can pull a line plot of your time course and a boxplot of the final timepoint side by side, without re-importing.‍

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2. Reliable, one-click analysis guidance

You can see the impact of each outlier and exclude it in one click. Graphing then runs pre-tests for normality and variance, recommends a statistical method, and explains in plain language why that test fits your data, so you can follow the recommendation or pick a different test yourself. Anyone in your lab can run a rigorous analysis in a few clicks without waiting on a stats expert, and every result is reproducible by anyone who wants to check it.

"BioRender Graphing gives us the powerful statistical guidance we didn't know we were missing."
— Thomas Prevot, Scientist and Assistant Professor, CAMH and University of Toronto

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3. Easy-to-customize, publication-quality graphs

Every graph is publication-quality from the start, with the same look and feel as BioRender figures and icons. Customization happens live as you work, so adjusting colors, labels, and axes updates the graph instantly. Style Match keeps a set of graphs visually consistent in a few clicks, and graphs link directly to PowerPoint or Google Slides, so any change to the data syncs across every slide that uses it.‍

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4. Real-time collaboration

Teammates can co-edit a graph live or build new graphs off the same parent dataset without installing anything, and changes save automatically every few minutes. If someone makes a mistake, or you just want to see how your analysis evolved, you can open a previous version and copy what you need without overwriting your current work.

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Research workflows don't work the way it did 30 years ago, and BioRender Graphing is built for how modern labs work. It's is available today in every BioRender account, with a free version to start. Drop your next dataset in and try it out!

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1. Tashiro, M. (2022). Change in the graphics of journal articles in the life sciences field: Analysis of figures and tables in the journal Cell. History and Philosophy of the Life Sciences, 44(3), 33. https://doi.org/10.1007/s40656-022-00516-9
2. Fast Company. Design: The Most Innovative Companies of 2026. Fast Company (2026). https://www.fastcompany.com/91497109/design-most-innovative-companies-2026