Duplications in Spectrum Plots

One of my recent investigations led me to expand my set of figure types to look at. For our 2016 mBio study, in which I scanned >20,000 papers for image duplication, I focused on real photos of Western blots, agarose gels, tissue sections, etc.

Some examples of photos from biomedical papers. Top left: Western blot. Top right: agarose gel. Bottom left: petridish with bacteria. Bottom right: immunostaining of tissue sections. None of these photos have image duplications. Source: Wikimedia Commons.

My scans also included flow cytometry images, since these consists of thousands of individual dots that will land at a slightly different and unique place, even if the experiment gets repeated. So if two flow cytometry images, or parts of those images, are identical, that is unexpected, and I might flag these as image duplications.

Flow cytometry image, consisting of 1000s of dots in a unique pattern. Source: Wikimedia Commons.

In my searches I had not really looked at other figures in biomedical papers, such as line graphs. It is hard for me to compare line or bar graphs since they all look very similar. And if they are smoothened, it is very hard to tell if two graphs within the same paper or between two papers are the same. In addition, line graphs are easy to manipulate behind your computer without the reader being able to tell.

Some examples of line graphs. Source: Wikimedia Commons.

But recently I noticed that some line graphs might be easier to tell apart – and easier to tell if they are unexpectedly similar. As I was looking at a set of papers in the field of nanoparticles, I noticed that these papers often contained certain types of line graphs with a lot of detail. These plots look a bit like earthquake measurements, with a lot of tremors, and every plot looked unique. That makes them easier to compare to each other, and to tell them apart or not.

One type of graph that had that amount of detail that you can tell them apart are XRD plots. XRD stands for “X-ray diffraction”, and it is a technique to analyze the structure of a crystal and its arrangement of atoms. The details of this method are obviously way outside of my scope of knowledge, but Barbara L Dutrow from Louisiana State University and Christine M. Clark from Eastern Michigan University wrote an explanation here.

In short, the crystal is bombarded with X-rays in different angles in a rotating machine, and the intensity of the reflected rays gets continuously measured. At certain angles that are specific for certain crystal structures, the rays get reflected. The results of these measurements are displayed as a spectrum that plots the angle of the X-rays (X-axis) vs. the intensity of the reflected signal (Y-axis). The position of the peaks in this spectrum will tell the user the structure and dimensions of a crystal (I hope I got this right hahaha).

Examples of XRD spectra showing plots that contain peaks specific for certain atoms, as well as background “noise”. Source: ResearchGate.

Another type of spectra that research papers on nanoparticles often contain is XPS plots. XPS stands for “X-ray photoelectron spectroscopy”. Here, the chemical composition of the surface of a material can be analyzed. XPS plots show peaks that are characteristic for certain elements, and it will tell the user which elements are present in the surface layer of an unknown material.

XPS plots are somewhat similar to XRD plots in that they show characteristic peaks separated by “noise” measurements.

Obviously, if two materials are very similar in structure and composition, the peaks in their XRD or XPS plots will fall at the same position, and two plots might look very similar at first glance. However, the noise in between the peaks should look different.

Here is a graph taken from a PLOS ONE article with four XRD plots, each from a nanoparticle baked at a different temperature, to illustrate this. The structure and composition of these nanoparticles are expected to be very similar.

XRD plot of nanoparticles baked at different temperatures. Source: https://doi.org/10.1371/journal.pone.0154704.g001 (edited by me).

I have marked the peaks on the top graph in blue. All four spectra look very similar to each other if you just look at the peaks. That is expected because they have the same composition.

But in between the peaks there are regions where no reflection was measured. Here you will see what is called “noise”, just the tremble of the background measurement. I have marked some of those regions in red. The noise parts of the 4 graphs all are different. So, even though the four graphs look similar to each other at first glance, each graph is unique in the noise parts.

However, I am not an expert in crystallography, XRD, or XPS. Maybe I am wrong here. So my question is: would one expect the noise parts of these types of plots to look exactly the same, even if the same crystal was measured twice? If you are an expert, please let me know in the comments!

For now, based on the plot above, and many others that I found online (see here, here, and here) I will assume that the noise parts of these plots should never look the same.

Although most XRD and XPS plots in the biochemical literature appear to be unique, some others are remarkably similar. Here are some PubPeer entries questioning the unexpected identity of such plots.

In some cases there is even apparent repetition within the same plot. Here is an example of some unexpected noise stutter, as marked by a pseudonymous PubPeer user.

Stuttering plot, highlighted by user Hoya Camphorifolia on Pubpeer. Source: https://pubpeer.com/publications/B060C4FC51F7918BBA893B2B3780E7#2

Smut Clyde (pseudonym) has found many of those noise stutter examples, where plots appear to be composed of repeating parts. For example, read Smut’s article called “Nanodandruff and synthetic spectroscopy” on Leonid’s blog For Better Science.

Some more examples of unexpected repeats within spectra plots can be found on PubPeer:

Let me know what you think about these unexpected repeats in the comments below. Do you think these repeats can occur by chance?


Falsification: The Andrew Wakefield case

The Andrew Wakefield case

One of the best-known examples of data falsification is a study described in a 1998 Lancet paper with Dr. Andrew Wakefield as the lead author. In this paper, 12 children with autism and chronic enterocolitis were described, and these symptoms started immediately after MMR (Measles / Mumps / Rubella) vaccination in 8 of these children.

However, a 2004 investigation by Sunday Times reporter Brian Deer revealed concerning issues with patient recruitment and undisclosed financial conflicts of interest.

Continue reading “Falsification: The Andrew Wakefield case”

What is Research Misconduct? Part 3: Fabrication

This is Part 3 of a series of 3, which also includes Part 1: Plagiarism, and Part 2: Falsification.

In Part 1 and Part 2 of this series, I showed some examples of plagiarism and falsification in scientific papers, which the Office of Research Integrity (ORI) considers two of the three forms of Research Misconduct. Here, we will look at the third type of misconduct, fabrication. ORI defines fabrication as follows:

Fabrication is making up data or results and recording or reporting them.”

Office of Research Integrity: Definition of Research Misconduct
Continue reading “What is Research Misconduct? Part 3: Fabrication”

What is Research Misconduct? Part 2: Falsification

This is Part 2 of a series of 3, which also includes Part 1: Plagiarism, and Part 3: Fabrication.

In Part 1 of this series, I showed some examples of plagiarism in scientific papers, which the Office of Research Integrity (ORI) considers one of the three forms of Research Misconduct. Here, we will look at the second type of misconduct, falsification. ORI defines falsification as follows:

Falsification is manipulating research materials, equipment, or processes, or changing or omitting data or results such that the research is not accurately represented in the research record.”

Office of Research Integrity: Definition of Research Misconduct
Continue reading “What is Research Misconduct? Part 2: Falsification”

What is Research Misconduct? Part 1: Plagiarism

This is Part 1 of a series of 3, which also includes Part 2: Falsification, and Part 3: Fabrication.

The Office of Research Integrity (ORI), part of the USA Department of Health and Human Services, defines Research Misconduct on their website:

Let’s clarify that a bit more with some examples. In this blog post, I will discuss plagiarism.

Continue reading “What is Research Misconduct? Part 1: Plagiarism”

A new Science Integrity Blog

Thanks for joining me!

I am Elisabeth Bik, microbiome and science integrity consultant, and this blog will be my new place to talk about science integrity. There might be posts about how to report scientific papers of concern, image issues in biomedical papers, plagiarism, data detectives, conflict of interests, “predatory publishers” (not my term), and many other issues.

I got a PhD in Microbiology from the University of Utrecht in The Netherlands, and was a staff scientist at Stanford University, Scientific and Editorial Director at uBiome, and a Director of Science at Astarte Medical, all in California, USA.

In addition, I have been actively searching for image duplications in biomedical science papers, and I wrote several publications on this topic:

My work also has been featured in major science and news outlets:

Also check out my other blog, MicrobiomeDigest.com, which is currently run by a fantastic team of volunteers. Here, you will find (nearly) daily posts with the latests scientific papers on host-associated or environmental microbial communities.

Science builds upon science. Science should be open for self-correction.

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