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  • 13 Sep 2019 12:43 | Katharina Toeppe-Hudy (Administrator)

    Title Image: Valencia Old City, 2018. Left: Original Image; Right: Image after Post-Processing

    By Dr. Thorsten S. Beck

    13. September 2019


    Note: Due to copyright restrictions the images in this article do not come from photo competitions, but show digitally enhanced images provided by the author. All of the post-processing shown here is in accordance with what photo competition guidelines suggest.

    The credibility of images today is in crisis. Where images claim to document an event unadulterated, reliability plays a major role, as in journalistic or documentary practice, for example. But doubts about the reliability of images are growing. For years there have been cases of blatant photo manipulation at major photo competitions.

    Prominent Cases Attract Public Attention

    The winner of the World Wildlife Photographer of the Year Award 2016, Marcio Cabral, was disqualified because he was accused of staging the photo. To this day, the photographer claims his innocence. The winner of the African Photographer of the Year Award 2019, Björn Persson, was also disqualified because he had "corrected" the ear of an elephant, the winner of the Nikon Small Award 2016, Chay Yu Wei, had to endure mockery on the Internet in addition to the disqualification. Also memorable is the debate about Paul Hansen's "Gaza Burial" of 2013, which brought the photographer the first prize in the World Press Photo Award as well as a debate about the integrity of the image that continues to this day. These are just a few examples, but the question arises as to how serious the problem of inappropriate image manipulation in journalistic photo contests actually is, how seriously it is taken by the organizers and what is being done to prevent such cases in the future?

    Fig. 2. Soccer Field by Night, Spain, 2018. Left: Original Image; Right: Image after Post-Processing

    Image Manipulations Shake World Press Photo Award

    Since the public debate on Paul Hansen's "Gaza burial" in 2013, there has been a growing awareness that a certain number of images in the competition could be manipulated. In 2015 the World Press Photo Award (WPPA) published a press release and indicated that more than 20% of the entries in the last preliminary round were inappropriately manipulated and therefore had to be disqualified. In the Sports category no third price could be awarded due to the high number of disqualifications. There was basically no image left to win the third prize. The competition office then took steps to combat such forms of falsification. In addition, WPPA in 2018 provided a detailed documentation on how competition entries are analyzed and how inappropriate images are discovered in the competition. Transparency thus is regarded a means of combating the widespread loss of confidence.

    Fig. 3. Musician, Spain, 2018. Left: Original Image; Right: Image after Post-Processing

    Combating Inappropriate Manipulations

    With the WPPA the rule (Entry Rule 11) applies that “the content of a picture can not be altered by adding, rearranging, reversing, distorting or removing people and/or objects from within the frame.” (Technical Report, p. 12). However, the cropping of images is permitted within certain limits and disturbing artifacts (such as dust) may also subsequently be removed from the image. All submissions will be reviewed by professional image analysts to determine any irregularities. In case of anomalies, photographers are obliged to submit the RAW file of the image so that the original and edited versions can be compared. For 2018, World Press Photo documents that 304 requests for the RAW files were made and that consequently 3 participants withdrew their submission. Apart from that, a thorough investigation led to further consequences: "From the 301 entries that were requested 159 were checked because they then remained in the later rounds, and of those 20 entries were excluded because they violated entry rule 11.“ (Technical Report, p. 13). In addition, it was determined for the 2018 competition that all forms of cloning should be disqualified. Another rule (Entry Rule 12) states that changes in contrast and color must not cause regions in the image to become obscured, or colors in the image to differ from the original colors. However, the competition committee is obviously aware that the boundary here is not easy to draw, especially since all images are basically processed to a certain degree: "All images are processed, but the line is drawn at those instances where there is darkening or lightening of area of an image so that material content is obscured and in effect removed.“ (Technical Report, p. 13). Four further entries were disqualified in 2018 due to a violation of these rules. 

    Rules in Other Photo Awards

    Other photo competitions have also formulated rules to protect against the inappropriate manipulation of images. The Pictures of the Year Competition of the Missouri School of Journalism, for example, gives the instruction: "Color images should replicate what the human eye experiences.  Flagrant pre- or post-production effects that use excessive tonal aberrations, textures, vignettes, or other artistic manipulations will be disqualified. Examples of prohibited techniques include the exaggerated use of color saturation, contrast or burning and dodging methods.“ At first glance this manual seems sufficiently clear, but at second sight it raises a number of questions. The spectrum of what the human eye can perceive is certainly not easy to generalize and it could be asked what exactly is meant by "excessive tonal aberrations", or "exaggerated color saturation"? People may most likely hold different views on what the terms „excessive“, and „exaggerated“ imply. However, where rules leave room for interpretation, no binding regulating effect can be expected. The Kuala Lumpur International Photo Award is similarly vague in its guidelines for image manipulation. It prescribes: "Commercial and advertising images, heavy digitally manipulated or post-processed, HDR (High Dynamic Range) or digitally montaged images are not permissible. eg. heavy vignetting, extreme darkening of skies, toning, filter effects. In-camera multiple exposures are allowed." Again, the question here is how the participants interpret terms like “minimal" or "heavy". In addition, however, it is demanded that "In all categories, minimal digital enhancement is advised, subject to basic darkroom techniques e.g. levels, curves, dodging, burning, minimal sharpening, colour correction. Photographs can be Black and White or Colour, in any format. Photographs must not have any borders, keyline or film edges, name, personal logos or watermarks. Scanned film images must be free of dust marks and borders." Here, too, there is plenty of room for interpretation and speculation. It is precisely this vagueness of the guidelines that opens the door to cross-border manipulation.

    The National Geographic Travel Photo Contest formulates it more clearly: "Elements of the picture cannot be altered in any manner, including the following prohibited changes: adding, removing, rearranging, inversing, or distorting or removing subjects and/or objects from the image. Additionally, the following alterations, but not limited to, are not allowed: removal of dirt, bubbles, debris. Cloning or altering pixels, in any manner, is not permitted. Composite imagery is not acceptable." However, participants cannot not learn on the competition page how the examination of the pictures is carried out in detail. Instead, the unedited RAW/JPG files must be submitted for the images selected for the prize. Apart from the mentioned set of instructions and the policy of submitting an unaltered source file, it would be interesting to find out if and how many pictures were disqualified in the past due to inappropriate manipulations. But numbers are rarely available and transparency about internal workflows and statistics are rarely made visible for the wider public.

    Fig. 4. German Unification Day, 2018. Left: Original Image; Right: Image after Post-Processing

    Room for Interpretation

    The rules for the Travel Photographer of the Year Award are also kind of vague. Here it reads: "You may use digital manipulation to optimise an image, and you may crop an image, but you are not permitted to add or remove key elements of the composition. Brightness, contrast, colour balance can all be adjusted. Dust spots and smaller elements etc. can be retouched. Images can be sharpened before printing. Manipulations which could realistically be achieved in a darkroom will be accepted, but the judges have the discretion to reject any image which has been, in their opinion, over- manipulated, removing the integrity of the original image." The problem with these rules is that they basically motivate the author to optimize the image. A clear definition of where the line between reasonable and inappropriate manipulation runs looks different.

    The British Wildlife Photography Award sets out in its rules, which manipulations are allowed and which are inappropriate. Contestants may remove disruptive artifacts, adjust contrasts and tonal values, change shadows, colors and saturation, but „physical changes to the scene e.g. adding or removing objects, trees, animals, plants, people, items of civilization or stripping in sky from another image etc.", as well as "digital collages, sandwich shots and composites" are prohibited.

    Fig. 5. Platform in Pottenstein, Germany, 2019. Left: Original Image; Right: Image after Post-Processing

    In order to verify the authenticity of what the photograph shows, the organizers of the Wildlife Photographer of the Year Award require participants to supply a range of 'before' and 'after' original files for any image that requires further authentication. A number of digital manipulations are simultaneously considered permissible as long as they do not violate the fundamental principle that "they do not deceive the viewer or misrepresent the reality of nature". The actual problem that the scope of permissible manipulation is left to the judgement of the individual photographer cannot be solved in such manner. Digital manipulation is thus at least partially transformed into an ethical question, and a clear definition of the boundary between appropriate and inappropriate manipulation is left to the discretion of the rules. The uncertainty as to whether the authenticity of the images can be ensured at all by rules already resonates in the formulations: "The following digital adjustments – but not limited to these – are not allowed: adding, moving or removing objects, animals or parts of animals, plants, people etc; the removal of dirt, highlights, backscatter, bubbles, debris and similar; composites, painting the foreground / painting out the background.“ The addition "but not limited to these" makes a dilemma visible. The rules may set a framework, but may not be complete under all circumstances. 

    Against this background and with the aim of strengthening the credibility of photography as a whole, it would make sense for the organizers of photo competitions not only to be concerned about legitimizing the authenticity of the submitted images, but also to take further steps towards transparency. This would include publicly documenting the proportion of inappropriate manipulations disqualified by the competition (see World Press Photo Award) and detailing the decisions made by a jury in individual cases. Developing a new, critical approach to the medium of photography also means - as David Campell has said - being even more aware of its artificial character than before and avoiding dimensions such as 'truth,' 'objectivity' and 'reality'. It is basically not bad that mistrust of digital images is growing - as long as it strengthens vigilance and a critical view of visual information. The open question, however, is how far mistrust can grow before a critical perspective turns into an expression of resignation. A world in which images seem more and more dramatic and astonishing, but in which the trustworthiness of the images becomes more and more uncertain, runs the risk of becoming cynical.

  • 9 May 2019 14:00 | Michael Seadle (Administrator)

    VroniPlag has posted an analysis of the dissertation of Franziska Giffey (geb. Süllke) with the claim that they have documented plagiarism on 76 of the 205 pages with content or 37.1%.

    "Bisher (8. Mai 2019, 09:20:57 (UTC+2)) wurden auf 76 von 205 Seiten Plagiatsfundstellen dokumentiert. Dies entspricht einem Anteil von 37.1% aller Seiten. Davon enthalten 11 Seiten 50%-75% Plagiatstext und 1 Seiten mehr als 75% Plagiatstext."

    "Up to now (8 May 2019 at 09:20:57 (UTC+2)) 76 instances of plagiarism out of 205 pages have been documented. This means 37.1% of all pages. Of those, 11 pages have 50%-75% plagiarism and 1 page more than 75%." [my translation] (VroniPlag, 2019)

    The figures on VroniPlag are misleading, because they give the impression that 37.1% of the whole content had plagiarism, rather than that problems (according to their definition) occurred on 37.1% of individual pages, regardless of whether just a few lines were involved. In fact the overall percentage is significantly lower by VroniPlag's own standards, if one uses the percentages linked to their own colour-coding:

    • Black is up to 50%

    • Dark Red is 50% to 75%

    • Red is 75% to 100%

    If one multiplies the number of pages in each of the colours times the maximum percentage, the results are as follows:

    • 64 pages are coloured black: 64 * 50% (the maximal value for black) = 32 pages worth of possible plagiarism.

    • 11 pages are coloured dark red: 11 * 75% (the maximal value for dark red) = 8.3 pages worth of possible plagiarism.

    • 1 page is coloured red: 1 * 100% (the maximal value for red) = 1 page worth of possible plagiarism.

    • The total for all three colours using the maximum percentages is: 41.3 pages or 20.1% of the 205 pages with content.

    Since the percentages associated with the colours are maximum values, the midpoint may give a more accurate picture:

    • 64 pages are coloured black: 64 * 25% (the midpoint between 0 and 50%) = 16 pages worth of possible plagiarism.

    • 11 pages are coloured dark red: 11 * 62.5% (the midpoint between 50% and 75%) = 6.9 pages worth of possible plagiarism.

    • 1 page is coloured red: 1 * 87.5% (the midpoint between 75% and 100%) = .9 page worth of possible plagiarism

    • The total for all three colours using the midpoint percentages is: 23.8 pages or 11.6% of the 205 pages with content.

    The VroniPlag figures need to be understood in context. VroniPlag's mission is to find plagiarism and they put the worst possible interpretation on their results. There is in fact a big difference between 37.1% and 11.6%. One could argue that 11.6% is still too much -- if all of the marked passages were genuine plagiarism -- but the numbers need to be presented in a more balanced and less misleading way, which VroniPlag fails to do. Systems like iThenticate give a percentage of words, not of pages with any potential plagiarism.

    VroniPlag publishes their criteria for plagiarism, which is commendable, but their criteria reflect rigid rules that are not universal in academic practice. The rules also take no account of legitimate choices about which source to cite or about the context within a work. In a literature review, for example, it is almost impossible not to reuse words from the articles being discussed.

    A set of standards that measures the number of overlapping words in a particular spatial context (that is, the number of words in a sentence or paragraph that overlap with another text) gives a more nuanced and more accurate view. An example can be found in my book "Quantifying Research Integrity". The results of this kind of "greyscale analysis" are not designed for capturing headlines, but for judging fairly.


    Seadle, Michael. December 2016. Quantifying Research Integrity. Morgan Claypool. Available online.

    VroniPlag, 2019. Eine kritische Auseinandersetzung mit der Dissertation von Dr. Franziska Giffey (geb. Süllke): Europas Weg zum Bürger - Die Politik der Europäischen Kommission zur Beteiligung der Zivilgesellschaft. Available online.

  • 4 Apr 2019 01:08 | Michael Seadle (Administrator)

    The US Federal Trade Commission has announced "a $50 million court judgment against Omics International of Hyderabad, India, and its owner, Srinubabu Gedela."  (New York Times, 3 April 2019). 

  • 2 Apr 2019 15:51 | Thorsten Beck (Administrator)

    How to Detect Image Manipulations Part V: The Subtraction Tool in ImageJ

    As already outlined in Part IV of this series, the image editing program ImageJ, developed by Wayne Rasband under the auspices of the United States National Institute of Health, offers a wide range of image editing possibilities, as well as a wealth of tools and extensions that can be used for measuring and analyzing images.


    One of the obvious challenges when screening scientific images is to reliably identify elements that have been added subsequently. In the following experiment we are going to test the capacities and limitations of the subtraction operation in the Image Calculator – one of the tools available in ImageJ. We find this tool in the drop-down menu under ‘Process’ (see Fig. 1).

    Fig. 1. Image Calculator in ImageJ

    To test how effective the Image Calculator is, we think of a scenario in which an element has been copied into an image. The Image Calculator should be able to identify this very element. I generated the following three examples to use them for this test.


    In the first example (see Fig. 2 for the original image), one of the individual bees has been copied, moved and pasted into the image (see Fig. 3). Compared to the original image, it becomes clear how difficult it is to perceive this manipulation with the naked eye. Even the trained eye has difficulties to clearly identify such copied and re-used picture elements when there are many of the same kind.

    Fig. 2. Original Image

    Fig. 3. Image with bee added.

    In example 2 we apply this basic operation to an electrophoresis image to simulate a scenario in scientific research. An image element from the original image is duplicated and reused several times (see Fig. 4). Here, too, we will test how effectively the Image Calculator can help to identify and highlight this manipulation.

    Fig. 4. Electrophoresis Image (Original on the left, and altered version on the right.)

    In the third and last example (see Fig. 5 for the original image), one of the elements is removed from the photo using the Clone Stamp in Photoshop (and overwritten with background texture). Another element is copied and used to replace the removed element (see Fig. 6). The resulting image will then also be analyzed with the help of the ImageJ subtraction tool in the Image Calculator.

    Fig. 5. Original Image

    Fig. 6. Manipulated Version: Image element deleted with Photoshop Clone Stamp and pliers (Pos. 4 on the right) copied and pasted.


    In order to analyze the test images, a reference image is required as a source for comparison for each of the examples. Here are some instructions of the steps that must be performed one after the other in the program: First, the source image and the manipulated version are dragged and dropped onto the ImageJ tool bar. Both images are now displayed automatically and the Image Calculator opens. To determine the difference between both versions, the operation 'Subtract' (the original version is subtracted from the manipulated version) is selected (see Fig. 7). The result image clearly reveals the difference that was so difficult to see with the naked eye (see Fig. 8).

    Fig. 7. Image Calculator Menu

    Fig. 8. Subtraction Result of Bee Example Image

    We proceed similarly with the second example. Here, too, the operation produces a quite clear result. The subsequently added blots are clearly displayed, while all identical image information remains black (see Fig. 9).

    Fig. 9. Subtraction Result of Electrophoresis Example Image

    Somewhat less clear, but still clear enough, is the result of the third experiment. The Image Calculator shows both copied and moved image element as well as the cloned region and the image information that is actually covered by the image background (see Fig. 10).

    Fig. 10. Subtraction Result of Tool Example Image


    The Image Calculator in ImageJ reliably determines the differences between non-manipulated source images and manipulated versions – upon visual inspection. Especially for images with elements that are somewhat difficult to clearly identify and analyze for the naked eye, this tool appears to be a good help.

    The procedure presented here, however, is an idealized scenario. The tool will only produce such impressive results if the original version of the modified image is available and can be compared with the manipulated image. But that is rarely the case in real-world scenarios. Even if the journal instructions would require authors to submit the original data of their images along with the publication, a small change to the manipulated image is enough to significantly reduce the efficiency of the Image Calculator. As soon as the dimensions of the image are not identical, the program calculates a completely different result:

    Fig. 11. Subtraction Result with Cropped Reference Image

    In summary, the ImageJ subtraction tool has delivered surprisingly clear and comprehensible results, but facing the limitation that these were artificial conditions.

    Given the precondition that original and reference image are both available (and not cropped or stretched) for analysis, the program delivers consistent and convincing results. It goes without saying that reality tends to be somewhat more complicated than hand-made test conditions.

    More tools will be evaluated soon – please visit HEADT.EU for upcoming posts.

    ©HEADT CENTRE 2019

  • 6 Dec 2018 13:55 | Melanie Rügenhagen (Administrator)

    In this video, Prof. Michael Seadle gives a short introduction to our exhibition “How Trustworthy? An Exhibtion on Negligence, Fraud and Measuring Integrity”.

  • 19 Oct 2018 13:52 | Melanie Rügenhagen (Administrator)
    On the evening of 18 October 2018, we opened the exhibition HOW TRUSTWORTHY? – An Exhibition on Negligence, Fraud, and Measuring Integrity

    in the Jacob-und-Wilhelm-Grimm-Zentrum, the university library of Humboldt-Universität zu Berlin (HU Berlin). Short welcoming speeches by Prof. Dr. Degkwitz (director of the university library), Prof. Dr. Graßhoff (vice dean of the Faculty of Humanities) as well as Prof. Seadle, PhD (Senior Researcher at HU Berlin and Principal Investigator of the HEADT Centre) introduced the urgency of the topic.

    Impressions from setting up the exhibition. Photo credit: Thorsten Beck.

    The exhibition is a joint project of the HEADT Centre and the Berlin School of Library and Information Science at Humboldt-Universität zu Berlin and shows that both human error and deliberate manipulation can compromise the integrity of scholarly findings.

    Interested people can visit the exhibition until 17 December 2018 in the foyer of the library at no charge. The English version of the text can be accessed at any time on our website (here).

  • 10 Oct 2018 13:46 | Melanie Rügenhagen (Administrator)

    On 9 October, Elsevier Connect published the HEADT Centre article Combating image misuse in science: new Humboldt database provides “missing link” by Dr, Thorsten Beck. The article is free to access. You are welcome to read it here!

  • 21 Sep 2018 13:49 | Thorsten Beck (Administrator)

    The goal of this exhibition is to increase awareness about research integrity. The exhibition highlights areas where both human errors and intentional manipulation have resulted in damage to careers, and it serves as a learning tool.

    Cover Image: Eagle Nebula, M 16, Messier 16 // NASA, ESA / Hubble and the Hubble Heritage Team (2015) // Original picture in color, greyscale edited © HEADT Centre 2018

    The exhibition has four parts. One deals with image manipulation and falsification. Another addresses data problems, including human error and fabrication. A third is about plagiarism, fake journals and censorship. The last section covers detection and the nuanced analysis needed to distinguish the grey zones between minor problems and negligence in case of fraud.

    October 18 – December 17, 2018Opening on October 18, 6 pm
    in the foyer of the Jacob-und-Wilhelm-Grimm-Zentrum of Humboldt-Universität zu Berlin

    Geschwister-Scholl-Straße 1/3, 10117 Berlin

    Admission to the exhibition is free.

    An exhibition of the HEADT Centre (Humboldt-Elsevier Advanced Data & Text Center)
    in cooperation with the Berlin School of Library and Information Science
    at Humboldt-Universität zu Berlin

    Cover Image: Eagle Nebula, M 16, Messier 16
    False colored space photography ( 2015)
    NASA, ESA / Hubble and the Hubble Heritage Team
    Original picture in color, greyscale edited

  • 13 Jul 2018 12:00 | Melanie Rügenhagen (Administrator)

    We have published a new HEADT Centre video that introduces how the HEADT Centre is contributing to research on similarity search at Humboldt-Universität zu Berlin. The video is available here:

  • 13 Jun 2018 11:28 | Michael Seadle (Administrator)

    NOTE: Melanie Rügenhagen was co-author of this entry.

    Replication is difficult to apply to qualitative studies in so far as it means recreating the exact conditions of the original study — a condition that is often impossible in the real world. The key question then becomes: “how close to the original must a replication be to validate an original experiment?” (Seadle, 2018)

    This question is particularly important because of the widespread belief that only quantitative research is replicable. Leppink (2017) writes:

    “Unfortunately, the heuristic of equating a qualitative–quantitative distinction with that of a multiple–single truths distinction is closely linked with the popular belief that replication research has relevance for quantitative research only. In fact, the usefulness of replication research has not rarely been narrowed down even further to repeating randomised controlled experiments.” (Leppink, 2017)

    Dennis and Valacich (2014) suggest three categories for replication studies, only one of which is “exact” (see the column from 23 May 2018). The conceptual and methodological categories are both relevant to qualitative research, because the participants and the context can vary as long as the replication tests the inherent goals and concepts, as well as the methodological framework of the original. In other words, successful qualitative replications can provide a confirmation of the hypotheses at a higher level of generalisation. Even when the specific contexts change. What matters is that the concepts and outcomes remain constant. As Polit and Beck (2010) write:

    “If concepts, relationships, patterns, and successful interventions can be confirmed in multiple contexts, varied times, and with different types of people, confidence in their validity and applicability will be strengthened.” (Polit & Beck, 2010)

    These authors support the use of replication in qualitative research, and argue that replication is the best way to confirm the results of a study:

    “Knowledge does not come simply by testing a new theory, using a new instrument, or inventing a new construct (or, worse, giving an inventive label to an old construct). Knowledge grows through confirmation. Many theses and dissertations would likely have a bigger impact on nursing practice if they were replications that yielded systematic, confirmatory evidence—or if they revealed restrictions on generalized conclusions.” (Polit & Beck, 2010)

    How can one ensure that the evidence is systematic? Leppink (2017) suggests that researchers in all kinds of studies have to decide when they no longer need more data in order to answer their research question and calls this concept saturation.

    It is important to remember that qualitative research normally does not generalise about results beyond the community involved in the samples, which sets a very limited and specific context for the research question. At some point researchers need to decide when their question is answered, stop their inquiries, and come to a conclusion. Leppink (2017) writes:

    “If saturation was achieved, one might expect that a replication of the study with a very similar group of participants would result in very similar findings. If the replication study leads to substantially different findings, this would provide evidence against the saturation assumption made by the researchers in the initial study.”

    Saturation means that the answer to a research question is complete, and becomes a core element of the “systematic, confirmatory evidence” (Polit & Beck, 2010) for analyzing validity. It can also help to provide metrics by uncovering the degree to which a study may be flawed or even intentionally manipulated.

    Nonetheless there are barriers. While a range of studies based on the same concepts and methodology can lead to insights about whether a phenomenon is true, not knowing exactly how the original researchers conducted their studies may make replication impossible (Leppink, 2017). This makes describing the methodology particularly important.

    None of this is easy. Replication studies remain a stepchild in the world of academic publishing. Gleditsch and Janz (2016) write about efforts to encourage replicating their own research area (international relations):

    “Nevertheless, progress has been slow, and many journals still have no policy on replication or fail to follow up in practice.”

    The problem is simple. There is no fame to be gained in showing that someone else’s ideas and conclusions are in fact correct, and it is hardly surprising that ambitious researchers avoid doing replications, especially for qualitative research, where the risk of failing is high and succeeding only makes readers think that the original study was done well.


    Gleditsch, Nils Petter, and Nicole Janz. 2016. “Replication in International Relations.” International Studies Perspectives, ekv003. Available online.

    Polit, Denise F., and Cheryl Tatano Beck. 2010. “Generalization in Quantitative and Qualitative Research: Myths and Strategies.” International Journal of Nursing Studies 47 (11): 1451–58. Available online.

    Michael Seadle. 2018. “Replication Testing.” Column on Information Integrity 2/2018. Published on 23 May 2018. Available online.

    Leppink, Jimmie. 2017. “Revisiting the Quantitative–Qualitative-Mixed Methods Labels: Research Questions, Developments, and the Need for Replication.” Journal of Taibah University Medical Sciences 12 (2). Elsevier B.V.: 97–101. Available online.

    Dennis, Alan R, and Joseph S Valacich. 2014. “A Replication Manifesto.” AIS Transactions on Replication Research 1 (1): 1–5.

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