sci-vis What the research was showing, and why you could not see it

Gallery

Every form the figure library can draw. The point is the shape, not the finding, so almost everything below plots the same 342 penguins: hold the data still and the differences between the forms are the only thing left moving.

Each caption says what the form is good at and what it hides, because those are the same sentence read from two directions.

The data. palmerpenguins, Horst AM, Hill AP and Gorman KB, 2020, 10.5281/zenodo.3960218, CC0, retrieved 1 September 2026. Collected by Dr Kristen Gorman and the Palmer Station Long Term Ecological Research Program. Two of the 344 records have no flipper measurement and were dropped; every column was kept and renamed for readability. Figures using bill, mass or sex are drawn from the 333 records where all of those are present. Nothing was averaged, rescaled, trimmed or excluded as an outlier.

Relationships

Scatter

Two measurements against each other, one point per bird. Nothing is aggregated, so it is the honest default whenever the sample is small enough to draw. It stops working when points overlap faster than you can count them.

Source Horst AM, Hill AP, Gorman KB, Zenodo, 2020. CC0 1.0. CC0 1.0

Data zenodo.3960218

Scatter with fits, per group and overall

Simpson's paradox in real measurements. Bill length and depth fall together across the whole sample, shown by the dashed line, and rise together inside every single species. Fitting once through everything reports the opposite of what is happening.

Source Horst AM, Hill AP, Gorman KB, Zenodo, 2020. CC0 1.0. CC0 1.0

Data zenodo.3960218

Hexbin

For when there are more points than the eye can separate. Reads density honestly at any sample size, and loses every individual, including the outlier that might have been the finding.

Birds per bin

Source Horst AM, Hill AP, Gorman KB, Zenodo, 2020. CC0 1.0. CC0 1.0

Data zenodo.3960218

Density contours over points

The shape of each group with the observations still visible underneath. Contours state where the mass is; the points keep you honest about how little of it there may be.

Source Horst AM, Hill AP, Gorman KB, Zenodo, 2020. CC0 1.0. CC0 1.0

Data zenodo.3960218

One variable

Histogram

Counts per bin. Simple and familiar, and entirely at the mercy of where the bin edges land: the same data with different breaks can look unimodal or bimodal.

Source Horst AM, Hill AP, Gorman KB, Zenodo, 2020. CC0 1.0. CC0 1.0

Data zenodo.3960218

Strip plot, dodged

Every measurement placed, with overlapping points nudged sideways rather than hidden. The right default up to a few hundred observations, and this site's house form.

Source Horst AM, Hill AP, Gorman KB, Zenodo, 2020. CC0 1.0. CC0 1.0

Data zenodo.3960218

Box plot

Quartiles, whiskers and outliers in very little space, which is what makes it useful for twenty groups at once. It will show two identical boxes for a distribution with one peak and a distribution with two.

Source Horst AM, Hill AP, Gorman KB, Zenodo, 2020. CC0 1.0. CC0 1.0

Data zenodo.3960218

Ridgeline

Distributions stacked so their shapes can be compared directly. Better than a box plot wherever the shape matters, and it needs vertical room that a panel of twelve will not have.

Source Horst AM, Hill AP, Gorman KB, Zenodo, 2020. CC0 1.0. CC0 1.0

Data zenodo.3960218

Empirical cumulative distribution

Every observation on one line, with no binning decision to argue about. It answers what proportion lies below any value directly. Fewer readers can read one at a glance, which is a real cost.

Source Horst AM, Hill AP, Gorman KB, Zenodo, 2020. CC0 1.0. CC0 1.0

Data zenodo.3960218

Comparisons

Dot and error, with the observations behind

A group mean, a standard deviation, and every bird that produced them. The summary and the spread in one figure, which is what a bar chart with an error bar is usually trying and failing to be.

Source Horst AM, Hill AP, Gorman KB, Zenodo, 2020. CC0 1.0. CC0 1.0

Data zenodo.3960218

Slope chart

Two conditions per group, joined, so the direction and steepness of the change is what you read rather than something you compute from two bar heights.

Source Horst AM, Hill AP, Gorman KB, Zenodo, 2020. CC0 1.0. CC0 1.0

Data zenodo.3960218

Dumbbell

The gap between two values per category, when the gap is the finding. Reads the difference directly, and gives up the ability to show a distribution.

Source Horst AM, Hill AP, Gorman KB, Zenodo, 2020. CC0 1.0. CC0 1.0

Data zenodo.3960218

Small multiples

One panel per group on shared scales. The answer to overplotting that does not throw anything away, and the reason it is not always right is that comparison across panels is harder than comparison within one.

Source Horst AM, Hill AP, Gorman KB, Zenodo, 2020. CC0 1.0. CC0 1.0

Data zenodo.3960218

Grouped bars

Counts across two categories. Legitimate here because a count really does start at zero and really is a length. Grouped rather than stacked, so every bar shares a baseline and can be compared.

Source Horst AM, Hill AP, Gorman KB, Zenodo, 2020. CC0 1.0. CC0 1.0

Data zenodo.3960218

A box plot of body mass per species wipes to reveal every individual measurement on the identical axis, showing that Adelie and Chinstrap overlap almost completely while Gentoo sits above both.

AdelieChinstrapGentoo2,5003,0003,5004,0004,5005,0005,5006,0006,500↑ Body mass (g)
AdelieChinstrapGentoo2,5003,0003,5004,0004,5005,0005,5006,0006,500↑ Body mass (g)

A summary against the observations, on one axis

Both halves share an axis, tick for tick, so only the marks change. The boxes are a fair summary. They would look much the same for a distribution with one peak and a distribution with two, and the points would not.

Source Horst AM, Hill AP, Gorman KB, Zenodo, 2020. CC0 1.0. CC0 1.0

Data zenodo.3960218

Correlation matrix

Every pair of measurements at once, as a way of deciding which relationship deserves a proper figure. A diverging scale with a neutral midpoint, because zero correlation is a meaningful middle and not just a low value.

Pearson r

Source Horst AM, Hill AP, Gorman KB, Zenodo, 2020. CC0 1.0. CC0 1.0

Data zenodo.3960218

Forms that need other data

Nothing below is data. Every value comes from an equation printed with the figure, or from a seeded generator, and exists only so the shape can be shown. No experiment produced any of it, and none of it may be reused in a piece: a piece needs the real deposited data behind the real result.

Dose-response

Curves from the four-parameter logistic, y = bottom + (top - bottom) / (1 + (EC50/x)^hill), with bottom 2, top 98, hill 1.3, and EC50 12 nM for compound A and 140 nM for compound B. Points are three replicates per dose scattered about the curve. The x axis is logarithmic, which is what makes a sigmoid of it.

Source sci-vis.com, 2026. Original work for sci-vis.com.

Kaplan-Meier survival

A step function from an example cohort of forty per arm, event times drawn from an exponential with medians of 22 and 41 days, censored at 60. Steps rather than a smooth line, because nothing is known between one event and the next and drawing a slope there would claim otherwise.

Source sci-vis.com, 2026. Original work for sci-vis.com.

Volcano

Effect size against significance for two thousand generated features, most null and a small tail with real effects. Grey for what does not clear the thresholds, so the eye is not asked to weigh two thousand equally coloured points.

Source sci-vis.com, 2026. Original work for sci-vis.com.

Growth curves

Logistic growth, N(t) = K / (1 + ((K - N0)/N0) e^(-rt)), with K 1, N0 0.02, and three growth rates. Labelled at the end of each line rather than in a legend.

Source sci-vis.com, 2026. Original work for sci-vis.com.

Scroll-driven

One figure in four states, for walking a reader through a result a step at a time. Scroll through it. With JavaScript off, or with reduced motion asked for, the four states stand as a numbered sequence and the steps are numbered to match: nothing has to move for it to be readable.

A line through everything, and a line through each

The same 333 birds four times. A single fit through the whole sample slopes downward. A fit within each species slopes upward, every time. Both lines are correct arithmetic on the same numbers.

Source Horst AM, Hill AP, Gorman KB, Zenodo, 2020. CC0 1.0. CC0 1.0

Data zenodo.3960218

Three dimensions

The test for 3D is whether the third dimension carries information the reader needs. An antibody passes it: the Y is a real shape, the two arms sit at an angle to each other that no side-on projection conveys, and the hinge that lets them move is the first thing a flat figure loses.

The crystal structure of an intact IgG2a monoclonal antibody, 10,434 atoms, shown as a ribbon. Two Fab arms extend from a hinge region at an angle to one another, with the Fc stem below, making the characteristic Y.

The still above shows the same structure. Loading the viewer lets you rotate and zoom it.

An intact IgG2a antibody

The whole molecule, not a fragment. Two Fab arms that bind antigen, one Fc stem that the rest of the immune system reads, and a hinge between them. Most published figures of an antibody are a cartoon of a Y; this is the measured thing, at 2.8 angstrom.

Source Harris, L.J., Larson, S.B., Hasel, K.W., McPherson, A., Biochemistry, 1997. CC0 1.0. CC0 1.0

Data PDB 1IGT · retrieved 2026-09-03 · CC0 1.0
ProcessingNone. The deposited binary CIF is served exactly as downloaded, from this site rather than from RCSB, so the figure does not depend on another service at read time.

Nothing is downloaded until the button is pressed. Until then the page has fetched a still, and the still and the caption carry the point on their own. Auto-rotation is off for anyone who has asked for reduced motion.

Depth

A confocal stack is a sequence of optical sections, and it is almost always published as one flat image: the brightest value along each line of sight, from every section at once. That picture is easy to make and easy to misread, because it puts objects at different depths on top of one another and shows a hollow shell as a filled disc.

A confocal stack of mouse embryo nuclei. In a single optical section about ten nuclei are in focus, each showing LaminB1 as a bright ring around a darker interior. In the maximum-intensity projection roughly twenty-five nuclei appear together and each ring is filled, so depth and hollowness are both lost.

Every optical section in the stack, in order. Move through them one at a time with the slider once it appears.

Nuclei in a mouse embryo, section by section

Move through the 62 sections and watch nuclei come into focus and leave again: at any one depth about a third of them are present. Switch to the projection and all of them appear at once, in the same plane, each envelope filled in rather than hollow.

Source Blin G, Sadurska D, Migueles RP, Chen N, Watson JA, Lowell S, PLoS Biology, 2019. CC BY 4.0. CC BY 4.0

Data IDR idr0062, image 6001240 · retrieved 2026-09-03 · CC BY 4.0
ProcessingRead from the deposited OME-Zarr at pyramid level 0. Sections 30 to 215 of 236 were taken, every 3rd one, giving 62: outside that range the stack is background. Intensity was scaled once across the whole selected substack rather than per section, so a dim section at the edge of a nucleus stays dim; scaling each section on its own would look better and would destroy the depth information this figure is about. The display range is the 0.5 to 99.95 percentile of each channel. Channel colours were changed from the blue and yellow the archive renders to this site's azure and amber.

Blue is LaminB1, at the nuclear envelope. Amber is DAPI, the chromatin inside it. Every section is in one image that loads once, so moving through depth costs nothing after that.

A three-dimensional cloud of 90,167 Gaussians reconstructed from the confocal stack. Nuclei appear as hollow blue shells with amber interiors, distributed through the depth of the volume rather than lying in one plane.

The still above is the same volume, projected flat. Loading the scene lets you turn it.

The same volume as Gaussians

Every voxel above a threshold becomes one 3D Gaussian, 90,167 of them, positioned in micrometres from the microscope's own voxel spacing. Turn it and the nuclei separate in depth: what the projection stacked into one plane is a cloud about 93 micrometres deep.

Source Blin G, Sadurska D, Migueles RP, Chen N, Watson JA, Lowell S, PLoS Biology, 2019. CC BY 4.0. CC BY 4.0

Data IDR idr0062, image 6001240 · retrieved 2026-09-03 · CC BY 4.0
ProcessingRead from the deposited OME-Zarr at pyramid level 1, every plane from 30 to 215. Each voxel whose brighter channel exceeds 22 per cent of the display range becomes one Gaussian: position from the voxel index times the recorded spacing of 0.72 by 0.72 by 0.50 micrometres, size half a voxel in each axis, colour and opacity from the intensity. There is no optimisation and no training: a stack is already a volume, so this is a conversion and nothing in it is inferred.

Splatting suits this because the source is a measured volume rather than geometry. The opposite case, splatting a molecular structure whose coordinates are already known exactly, would be a mistake: a mesh is smaller, sharper and more faithful. What is here is the plain conversion, one Gaussian per voxel. Fitting Gaussians to a volume, which is where the research currently is, is a harder problem and a different figure.

How these are drawn

Every chart on this page is drawn when the site is built, so what arrives is the figure and not the machinery for making one. There is no charting library to download, the figures are there with scripts switched off, and they appear intact in the feed and on paper. The molecular structure and the Gaussian scene are the two exceptions, and neither fetches anything until you ask it to.

Colour comes from one palette file and nothing on the site may name a colour of its own. Those palettes are free to take, as hex lists and as lookup tables you can drop straight into Fiji.