> For the complete documentation index, see [llms.txt](https://user-manual.imagetwin.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://user-manual.imagetwin.ai/detailed-image-analysis/detailed-image-analysis-1/xrd-plot-analysis.md). # XRD Plot Analysis X-ray diffraction plots were considered nearly impossible to screen for duplication, until recently. In one of the recent webinar, Imagetwin co-founder and CTO Marcus Zlabinger sat down with Mu Yang, PhD, a behavioral neuroscientist at Columbia University Medical Center who has become one of the most active research integrity sleuths working in XRD, glass, and spectra images. She walked through how she spots duplicated traces by eye, and Marcus shared what Imagetwin’s new automated XRD detection feature found when it was pointed at thousands of recent papers. ### What is So Difficult about XRD Plots XRD is used to confirm that a material’s crystal structure matches what a paper claims, and the peaks, widths, and baseline noise in a trace all carry real information. That also makes XRD data relatively context-poor compared to, say, a microscopy image, which is exactly why it took so long for any automated tool to reliably flag duplication in it. Peaks and baseline noise are the two things Mu Yang sees edited most often. ### Four Things a Clean Trace Should Never Do Before getting into specific cases, Mu Yang laid out the basics she checks on every plot: * [ ] Traces should never travel backward * [ ] Baseline noise is stochastic and should never repeat within or across traces * [ ] Every point should have one x and one y value * [ ] Unexplained line breaks are a red flag. ### Common Issues and Red Flags The three issues she flags most often are: * [ ] Reused traces (usually the noise sections get recycled) * [ ] “Patchwork” (chopping up traces and reassembling them to look new) * [ ] Hand-drawn traces, which she says are the hardest to catch because they don’t always break a specific rule, they just look wrong. On top of that, three visual cues tend to give away an edited plot: * [ ] Unusually thick or chunky lines (easier to hide a seam in) * [ ] Mislabeled axes (wavenumbers on an axis that should read 2-theta) * [ ] Fuzzy axes, which often mean two images were stacked to fake a new trace. ### How to Spot Issues in XRD in Practice The core technique: anchor your eyes on one small section of the plot, say around 2-theta = 40 degrees, and run them straight down across all traces rather than scanning the whole figure at once.

Five XRD traces for different Sb-Ge-Se thin film compositions. Anchoring on the boxed section and scanning straight down shows the same trace repeating across all five, supposedly independent, samples.

The trickier lesson: traces that look completely different can still share the same underlying data, just with color, intensity, or waveform shape altered.

Two XRD figures from two different papers that look unrelated at first glance, until the same section is isolated and compared side by side: the baseline noise is nearly identical.

In one case study, she found just three recurring trace “motifs” reused across 72 different papers, once she built a visual key to spot them.

Motifs A, B, and C, each one reused across dozens of papers with only the color, intensity, or waveform shape changed to disguise it.

Patchwork is easiest to catch once you zoom into the image file itself, not the PDF preview.

The boxed sections show the same baseline noise recycled across panels within a single figure.

Two more visual tells: pay attention to where traces end, since editors tend to focus effort on the middle of a figure and get sloppy at the edges, and treat unusually thick traces with suspicion, since a thick line makes it easier to hide a seam.

The 10-degree and 80-degree ends of this trace carry matching noise, a spot editors often overlook.

And then there’s the hand-drawn category, images that clearly didn’t come from an instrument but don’t technically violate any named rule, until they do, like a trace that abruptly reverses direction.

A hand-drawn trace pasted into an FTIR plot, from a paper that has since been retracted. Zoomed in, it backtracks on itself, something no real instrument trace can do.

### Mu Yang's Field Tips * [ ] Anchor your eyes on a small section and remember it before scanning vertically across traces. * [ ] Review the actual image file, not the version embedded in a PDF. * [ ] Watch for chunky traces and check the ends of every plot. * [ ] Binge a suspicious author’s papers in one sitting. Working memory fades fast, so cluster the review. * [ ] Use the figure preview tools some publishers already offer (e.g. ScienceDirect). * [ ] Zoom in. Always zoom in.