CASRAI Dictionary

Tag: fabrication research misconduct

  • How Research Misconduct in Engineering Differs

    Research misconduct in engineering is fabrication, falsification, or plagiarism involving structural test data, materials properties, or simulation results — but unlike biomedical misconduct, it is judged against physical, re-testable evidence and can trigger professional-licence discipline, not just retraction. Where biomedical fraud is typically uncovered through statistical forensics on data that cannot be re-run on the same human subjects, engineering fraud is frequently caught — and proven — by re-testing the actual material, structure, or component in question.

    Most misconduct coverage focuses on biomedicine and psychology, where retractions and clinical-trial scandals dominate. Engineering misconduct is less visible but carries a distinct evidentiary logic, a distinct enforcement path through licensing bodies, and a distinct risk profile: infrastructure and physical safety rather than patient health. This analysis sets out what makes engineering cases different.

    What Is Research Misconduct in Engineering?

    Research misconduct is fabrication, falsification, or plagiarism (FFP) in proposing, performing, or reviewing research, or in reporting results, as defined by the US Office of Research Integrity (ORI), the federal body overseeing Public Health Service-funded research. This definition applies without modification to engineering research; what changes is the object being faked.

    In engineering, misconduct usually touches quantifiable physical properties: yield strength, fatigue-cycle counts, thermal tolerance, load-bearing capacity, corrosion resistance, or finite-element simulation outputs. A fabricated result in this domain is not an abstract statistical artefact — it is a claim about whether a material or structure will hold under real load. That distinction shapes every part of how allegations are investigated and resolved.

    The Committee on Publication Ethics (COPE) supplies the procedural backbone most engineering journals use to handle allegations, via its published core practices and case-specific flowcharts covering fabricated data, image manipulation, plagiarism, authorship disputes, and undisclosed conflicts of interest.

    Why Engineering’s Evidentiary Standards Differ From Biomedicine’s

    Engineering misconduct investigations lean on re-testable physical evidence far more than biomedical ones do. A disputed tensile-strength figure can, in principle, be checked by re-machining a sample and re-running the test rig; a disputed clinical-trial outcome in a now-treated or deceased patient population usually cannot be re-run at all. This single fact reshapes the entire evidentiary standard.

    Three structural differences follow from it:

    • Physical re-testability. Engineering claims about materials, structures, and components can often be independently re-verified against the original artefact, lab notebook, or calibration log — a forensic route rarely available in human-subjects research.
    • Professional licensure exposure. Many engineering academics also hold a Professional Engineer (PE) or Chartered Engineer licence. A misconduct finding can trigger parallel discipline from a state or national licensing board — a structural check with no direct academic equivalent for most non-clinician biomedical researchers.
    • Public-safety framing. The National Society of Professional Engineers’ Code of Ethics states that engineers “shall hold paramount the safety, health, and welfare of the public” as its first fundamental canon. Biomedical research ethics is instead anchored in the Belmont Report’s principles of respect for persons, beneficence, and justice — a subject-protection frame rather than an infrastructure-safety frame.

    Prevalence data reflects the same underlying pattern. A widely cited 2009 meta-analysis (Fanelli, published via PLOS ONE) found that close to 2% of scientists admitted to fabricating or falsifying data at least once, with up to a third admitting other questionable research practices — figures drawn predominantly from biomedical and life-science samples. A 2021 systematic review and meta-analysis published in Science and Engineering Ethics (Xie et al.) updated pooled prevalence estimates for both research misconduct and questionable research practices, underlining that engineering-specific base rates remain comparatively under-studied against biomedicine’s much larger evidence base.

    Types of Misconduct Most Relevant to Engineering Research

    The core FFP taxonomy applies across disciplines, but its practical expression in engineering research differs from its expression in biomedicine.

    Misconduct type Typical biomedical expression Typical engineering expression
    Fabrication Invented clinical outcomes, patient counts, or assay readings Invented structural-test results, fatigue-cycle counts, or simulation outputs
    Falsification Selective omission of adverse trial data; altered statistical models Altered materials-strength certificates; suppressed failed load tests
    Image manipulation Reused or altered western blots, microscopy, or gel images Altered micrographs, stress-map renders, or non-destructive-test scans
    Plagiarism Copied text, methods, or literature review sections Copied methodology or design specifications without attribution

    Image manipulation as research misconduct deserves particular attention: COPE guidance treats any enhancing, obscuring, moving, or adding of image features as misconduct, while proportionate brightness/contrast adjustments applied equally across an image (and its controls) remain acceptable. In engineering, the equivalent images are typically micrographs, non-destructive-testing scans, or stress-distribution renders — evidence that, unlike a clinical image, can sometimes be regenerated from the original physical specimen if it still exists.

    Structural-Testing and Materials-Data Fraud: Four Real Cases

    Research misconduct case studies in engineering rarely make front-page news the way clinical-trial scandals do, but several documented cases illustrate the pattern.

    • Kobe Steel (2017). The Japanese manufacturer admitted to falsifying quality-inspection data on the strength and durability of aluminium, copper, and steel products, which had been supplied into automotive, rail, and aerospace supply chains — a case that, while industrial rather than academic, shows how falsified materials data propagates once it enters downstream engineering use.
    • Ranga Dias superconductivity claims. A University of Rochester investigation concluded that physicist Ranga Dias had committed research misconduct, including data fabrication and falsification, in connection with room-temperature superconductivity claims published in Nature. Multiple papers were retracted and Dias was dismissed — a rare case where fabricated materials-property data was caught partly through failed independent replication attempts by other labs.
    • Falsified precast-concrete inspection records. A case reported through American Society of Civil Engineers (ASCE) channels involved a materials-testing firm supplying falsified paperwork claiming that required inspections of precast concrete units had been carried out when they had not — misconduct that, had it gone undetected, would have compromised a live construction project rather than merely a journal record.
    • Forged structural engineering seals. Separately reported cases have involved individuals using stolen or copied software to forge a licensed engineer’s professional seal on structural plans, bypassing the licensure check that engineering — uniquely among the research disciplines discussed here — relies on as a second line of defence beyond peer review.

    The common thread: in three of the four cases, the fraud was exposed through re-inspection of a physical artefact — steel stock, a concrete unit, a stamped drawing — rather than statistical anomaly detection alone.

    Common Questions and What Comes Next

    What are the types of research misconduct?

    The three recognised types are fabrication (inventing data or results), falsification (manipulating data, equipment, or processes to misrepresent findings), and plagiarism (using others’ work without attribution). Related but distinct issues — undisclosed conflicts of interest, authorship disputes, and citation manipulation — are handled under separate publication-ethics procedures rather than the core misconduct definition.

    What is the difference between fabrication and falsification?

    Fabrication means inventing data or results that were never actually produced by an experiment or test. Falsification means manipulating real research materials, equipment, or processes, or altering/omitting genuine data, so that the record no longer accurately reflects what happened. Both are treated as equally serious under the ORI’s FFP standard.

    Is image manipulation considered research misconduct?

    Yes — inappropriate image manipulation is treated as a form of falsification under COPE guidance. Enhancing, removing, moving, or adding specific image features is prohibited; uniform brightness or contrast adjustments applied equally to an image and its controls are acceptable, provided nothing is obscured or misrepresented.

    What is an example of research misconduct in engineering?

    Documented examples include the Kobe Steel falsified materials-certification scandal, the Ranga Dias room-temperature superconductivity fabrication finding at the University of Rochester, and falsified precast-concrete inspection paperwork reported through ASCE channels. Each involved misrepresenting physical-material or structural-test data rather than clinical or behavioural data.

    For research administrators, the implication is practical: engineering integrity offices should maintain re-testing and sample-retention protocols alongside the statistical and plagiarism-detection tools built for biomedical and social-science misconduct. A materials sample or calibration log retained past publication is often the single most decisive piece of evidence in resolving an engineering allegation — a resource with no direct biomedical equivalent once a clinical study has closed. As more engineering journals adopt COPE’s flowcharts and licensing boards sharpen data-retention expectations, expect engineering cases to be resolved increasingly through re-testable physical evidence rather than statistical inference alone.

  • Stem Cell Research Scientific Misconduct Legacy

    Stem cell research scientific misconduct is best defined by two landmark cases: Hwang Woo-suk’s fabricated human cloning papers (South Korea, 2004-2006) and the STAP cell falsification scandal (Japan, 2014). Both involved fabricated data published in top journals, both were exposed through failed replication and image forensics, and both reshaped how institutions oversee stem cell research integrity today.

    Research misconduct is formally defined as fabrication, falsification, or plagiarism in proposing, performing, or reviewing research, or in reporting results. In stem cell science specifically, stem cell research scientific misconduct has produced two of the most consequential fraud cases in modern science, each triggering journal retractions, criminal or disciplinary proceedings, and lasting changes to how laboratories, journals, and funders verify extraordinary claims.

    What counts as stem cell research scientific misconduct?

    Stem cell research scientific misconduct covers fabricated data, falsified images, and unethical procurement of biological materials in studies involving embryonic, induced pluripotent, or somatic-cell-derived stem cell lines. The field is unusually exposed to this risk because of intense media attention, national prestige stakes, and the technical difficulty of independently verifying claims about pluripotency or successful cloning.

    Both cases examined here meet the formal definition applied by journals and integrity offices: invented results presented as genuine experimental findings, subsequently confirmed by institutional investigation and retracted from the scientific record.

    How did the Hwang Woo-suk cloning fraud unravel?

    Hwang Woo-suk, a veterinary scientist at Seoul National University, published two papers in Science in 2004 and 2005 claiming to have created the first cloned human embryonic stem cell lines through somatic cell nuclear transfer, including patient-matched lines for individuals with injuries or disease. The claims made Hwang a national figure in South Korea and were treated as a milestone toward personalised regenerative medicine.

    The case collapsed on two fronts simultaneously. First, journalists and whistleblowers within Hwang’s own laboratory raised concerns that junior female researchers had donated their own eggs for the experiments, a practice that breached informed-consent norms because of the coercive power dynamics involved. Second, a Seoul National University investigative panel examined the underlying data in December 2005 and January 2006 and found that none of the eleven claimed patient-specific stem cell lines existed; the single verified cell line was later shown to have arisen through parthenogenesis rather than cloning.

    • Science formally retracted both papers in January 2006.
    • Hwang was dismissed from his university post and indicted for fraud, embezzlement of research funds, and bioethical violations.
    • A South Korean court convicted him in 2009 and imposed a two-year suspended prison sentence, a verdict the South Korean Supreme Court upheld on appeal in 2014.

    What happened in the STAP cell scandal?

    In January 2014, RIKEN researcher Haruko Obokata and co-authors published two papers in Nature describing “stimulus-triggered acquisition of pluripotency” (STAP) — a claim that ordinary adult cells could be reprogrammed into a stem-cell-like state simply by exposing them to stress, such as a weak acid bath. The method promised a dramatically simpler alternative to existing induced pluripotent stem cell techniques.

    Independent laboratories worldwide were unable to replicate the results, and close scrutiny of the published figures revealed duplicated and manipulated images alongside plagiarised text from earlier work. RIKEN convened a formal investigation committee, which in April 2014 found Obokata guilty of falsification and fabrication. Nature retracted both papers in July 2014, and a subsequent verification experiment — conducted by Obokata herself under RIKEN supervision — failed to reproduce STAP cells by the end of that year, at which point she resigned.

    The human cost was severe. Obokata’s supervisor and senior co-author, RIKEN Center for Developmental Biology (CDB) deputy director Yoshiki Sasai, died by suicide in August 2014 amid the fallout. Waseda University revoked Obokata’s doctorate in 2015 after she failed to correct the thesis within a set deadline, and RIKEN CDB itself was dissolved and reorganised into the RIKEN Center for Biosystems Dynamics Research in 2018, partly in response to the reputational damage.

    How do the two cases compare?

    Despite occurring a decade apart and in different countries, the Hwang and STAP cases share a common failure pattern: extraordinary claims, inadequate internal verification before publication, and exposure driven by replication failure rather than routine peer review.

    Factor Hwang Woo-suk case STAP cell case
    Journal Science (2004, 2005) Nature (2014)
    Claimed method Cloned human embryonic stem cells via somatic cell nuclear transfer Pluripotency induced by external stress (acid bath)
    Detection trigger Whistleblower reports plus egg-donation ethics concerns Failed replication plus image duplication analysis
    Institutional finding Seoul National University panel, Dec 2005-Jan 2006 RIKEN investigation committee, April 2014
    Retraction date January 2006 July 2014
    Consequence for researcher 2009 conviction, two-year suspended sentence Resignation; doctorate revoked 2015

    What reforms followed these scandals?

    Both scandals functioned as forcing events for the wider research integrity infrastructure, well beyond the two institutions directly involved.

    • Image-forensics screening became standard practice at major journals after 2014, with publishers adopting software-assisted duplication and manipulation detection for every submitted figure, not just those flagged by reviewers.
    • The Committee on Publication Ethics (COPE) expanded its guidance on image manipulation and data fabrication, giving journal editors a shared, referenceable framework for handling suspected figure manipulation as research misconduct.
    • Institutional oversight of extraordinary claims tightened, with more stem cell laboratories requiring independent, blinded replication of headline results before submission.
    • Retraction Watch, founded in 2010, has since built a public database that made both cases — and thousands of subsequent retractions — searchable and citable as case-study evidence for research misconduct case studies used in training and policy work.
    • Egg-donation and biospecimen ethics protocols were tightened across stem cell research consent frameworks following direct scrutiny of the coercive donation practices in the Hwang case.

    Frequently asked questions

    What are the problems with stem cell research?

    Beyond the underlying ethical debate over embryo use, stem cell research carries elevated misconduct risk because pluripotency and cloning claims are technically hard to verify quickly, media and funding pressure reward speed over replication, and image-based evidence is easy to manipulate before independent scrutiny occurs.

    What is the controversy with stem cell research?

    The scientific controversy extends beyond embryo ethics to research integrity: the Hwang and STAP cases showed that landmark claims in prestigious journals could be entirely fabricated, undermining public trust and forcing funders and institutions to demand stronger pre-publication verification.

    Is stem cell research banned in the UK?

    No. The United Kingdom permits regulated human embryonic stem cell research under the Human Fertilisation and Embryology Authority framework, one of the more permissive regimes in Europe, though all work remains subject to licensing and ethical review distinct from the misconduct issues in the Hwang and STAP cases.

    What does this legacy mean for research integrity oversight?

    For research administrators, publishers, and funders, the enduring lesson of these two cases is structural, not personal: misconduct was caught by replication failure and whistleblowing, not by peer review at the point of publication. Institutional research integrity offices, journal editorial teams, and funder due-diligence processes now build in image screening, raw-data deposition requirements, and independent replication checkpoints specifically because peer review alone did not catch either fraud before publication.

    Two decades after Hwang and more than a decade after STAP, both cases remain the reference points cited whenever a stem cell claim looks too clean, too fast, or too convenient — a durable legacy for a field whose credibility depends on distinguishing genuine breakthroughs from fabricated ones.

  • Image Manipulation as Research Misconduct

    Image manipulation as research misconduct means altering a figure — micrograph, blot, gel, or scan — so it misrepresents the underlying data; under the US Office of Research Integrity (ORI) and most institutional policies this falls under falsification, one of the three FFP misconduct categories. Forensic screening tools now flag duplication, splicing, and, increasingly, AI-generated fabrication before publication.

    Image manipulation is the alteration of a scientific image — through cloning, splicing, selective erasure, or generative synthesis — in a way that changes the scientific meaning of the data it depicts. Not every edit is misconduct: adjustments to brightness, contrast, or colour balance applied uniformly across an entire image are generally acceptable, provided they do not obscure, eliminate, or misleadingly enhance specific features. The distinction was first codified by Mike Rossner and Kenneth Yamada in a widely cited 2004 Journal of Cell Biology editorial, which remains the reference framework cited by UKRIO, ORI, and most publisher guidelines today.

    What Counts as Image Manipulation in Research Misconduct?

    Research-integrity bodies distinguish acceptable image processing from misconduct by asking a single question: does the resulting image still accurately represent the original data? Acceptable adjustments are applied uniformly, disclosed, and do not change scientific meaning. Unacceptable manipulations — the kind that constitute misconduct — include:

    • Cloning or duplicating a band, cell, or region within the same image or across different figures without disclosure
    • Splicing separate gel or blot lanes together and presenting them as one continuous exposure
    • Selectively erasing or adding features (bands, cells, particles) to support a claimed result
    • Non-uniform adjustment of brightness, contrast, or colour that obscures or exaggerates specific data points
    • Reusing an image from an unrelated experiment and relabelling it as a different condition

    ORI’s own reference guidance, distributed as an infographic to US research institutions, sets out these categories explicitly and has become the de facto training standard cited by UK and European research-integrity offices, including the UK Research Integrity Office (UKRIO).

    Why Do Research-Integrity Bodies Treat Manipulated Images as Misconduct?

    Image manipulation is classified as falsification, not fabrication, when an underlying experiment did take place but its visual record has been altered to misrepresent the result. The distinction matters for investigation and sanction, but the practical effect is the same: the published record no longer reflects what was actually observed.

    The scale of the problem is well documented. A landmark 2016 study in mBio by Elisabeth Bik, Arturo Casadevall, and Ferric Fang screened 20,621 papers published between 1995 and 2014 and found problematic figures in 3.8% of them, with roughly one in twenty-five showing duplication and about 0.3% showing clear evidence of deliberate manipulation rather than honest error. That single study reframed image screening from a niche editorial concern into a routine publisher workflow requirement.

    How Do Forensic Screening Tools Detect Fabricated or Duplicated Images?

    Detection now runs on three layers: manual visual review, software-assisted forensic analysis, and, most recently, AI-based classifiers trained to spot synthetic content. Each layer catches different manipulation types.

    Detection layer What it catches Typical method
    Visual/manual review Obvious splicing, mismatched lighting, repeated backgrounds Trained editor or reviewer inspection
    Software-assisted forensics Cloned regions, inconsistent noise patterns, hidden splice lines Contrast/histogram enhancement in tools such as ImageJ; error-level and JPEG-artefact analysis
    AI-based screening Cross-figure and cross-manuscript duplication, rotated/mirrored reuse, synthetic image artefacts Commercial platforms such as Proofig and ImageTwin, integrated via the STM Integrity Hub

    The International Association of Scientific, Technical and Medical Publishers (STM) launched its Integrity Hub in 2022 specifically so member publishers could share signals — including image-duplication flags — across manuscripts before they reach peer review, rather than each journal screening in isolation. The Committee on Publication Ethics (COPE) publishes a companion flowchart for what an editor should do once a screening tool raises a suspected-manipulation flag, covering author correspondence, raw-data requests, and escalation to institutional investigation.

    How Is AI-Generated Fabrication Changing Image-Integrity Screening in 2026?

    Duplication-detection algorithms work by matching pixel regions against other images in a database or manuscript. That approach struggles against a newer threat: images generated wholesale by diffusion or generative-adversarial models, which contain no duplicated pixels to match because every pixel is synthetic. A fabricated Western blot or flow-cytometry plot produced this way can pass a same-image duplication check while still depicting an experiment that never happened.

    This is the gap existing FFP and paper-mill guidance largely predates. Screening vendors are responding by adding generative-artefact detectors — models trained to spot the statistical fingerprints diffusion models leave behind (unnatural noise distributions, repeating micro-textures, implausible optical consistency) rather than searching for copies. Retraction Watch has tracked a rising number of retractions citing AI-generated or “nonsensical” figures since 2023, a trend distinct from — and additive to — the classic clone-and-splice cases the 2016 Bik study catalogued. Institutions and publishers now need two separate detection pipelines: similarity-matching for reused images, and artefact/statistical analysis for wholly synthetic ones.

    What Happens During a Research Misconduct Investigation Into Images?

    Once a screening tool or reviewer flags a suspected image, most institutions follow a two-stage process: an initial inquiry to establish whether the allegation has substance, followed by a formal investigation if it does. Investigators typically request the original, unprocessed image files, any laboratory notebooks describing acquisition, and metadata showing capture date and editing history. Research administration offices coordinating these inquiries generally work to institutional timelines rather than journal timelines, since a retraction outcome depends on the institution’s finding, not the publisher’s screening flag alone.

    Outcomes range from an author-issued correction (where the error was inadvertent and does not affect conclusions) through to retraction and a formal misconduct finding recorded against the researcher, which can trigger funder debarment or employment consequences depending on jurisdiction.

    Answer-First Questions

    What is image manipulation in research?

    Image manipulation in research is the alteration of a digital scientific image — through cloning, splicing, selective erasure, or software adjustment — in a way that changes what the image communicates about the underlying data. Uniform, disclosed adjustments to brightness or contrast are acceptable; selective, undisclosed changes that alter scientific meaning are not.

    What are some examples of research misconduct?

    Research misconduct is generally defined as fabrication, falsification, or plagiarism (FFP). Examples include inventing data that was never collected, splicing unrelated gel lanes into one figure, duplicating a microscopy image to represent two different conditions, and presenting another researcher’s text or data as one’s own.

    What are the negative impacts of image manipulation?

    Manipulated images can misdirect an entire research field, waste replication effort and funding, and — in biomedical contexts — inform clinical decisions based on results that never occurred. A single high-profile retraction linked to fabricated figures can also delay legitimate follow-on research for years while the record is corrected.

    What is an example of image manipulation in a published paper?

    A commonly documented example is lane splicing: joining gel or blot lanes from different experiments and presenting the composite as a single continuous exposure without a dividing line or disclosure, so the figure implies all samples were run and imaged together when they were not.

    What Are the Implications for Institutions and Publishers?

    Publishers integrating image screening into submission workflows (via STM Integrity Hub member tools) shift detection earlier, before peer review rather than after publication, which reduces the volume of post-publication corrections research administration offices must manage. For institutions, the practical implication is that image-integrity training now needs two tracks: the long-established Rossner–Yamada rules on acceptable processing, and newer guidance on recognising signs of wholly synthetic, AI-generated figures, which look different from spliced or cloned ones and are not caught by the same tools.

    Where Image-Integrity Screening Is Heading

    Image manipulation will keep sitting inside the falsification arm of research misconduct policy, but the detection toolkit is bifurcating: similarity-matching tools such as Proofig and ImageTwin remain effective against duplication and splicing, while a newer generation of generative-artefact detectors is needed for AI-synthesised figures that contain no copied pixels at all. Institutions, journals, and funders that treat these as one problem risk missing the category their existing tools cannot see.

    Research administrators overseeing integrity policy and investigations can find further framework context in CASRAI’s research administration resources.