Responsible data management is a multifaceted topic involving standards within the research community regarding research design and the sharing of data as well as the collection, selection, analysis and interpretation of data. Transparency in the manipulation of images is increasingly important in order to avoid misrepresentation of research findings, and research oversight is also critical in helping to assure the integrity of the research process. Intellectual property issues both unite and divide academe and industry in their approaches to data management. (...) Central to the realization and promulgation of responsible data management is clear and careful communication of standards and expectations within the research community to trainees as well as among colleagues. These topics are examined and explored in depth in a special issue of Science and Engineering Ethics on responsible data management. (shrink)

The field of scientific image integrity presents a challenging research bottleneck given the lack of available datasets to design and evaluate forensic techniques. The sensitivity of data also creates a legal hurdle that restricts the use of real-world cases to build any accessible forensic benchmark. In light of this, there is no comprehensive understanding on the limitations and capabilities of automatic image analysis tools for scientific images, which might create a false sense of data integrity. To mitigate this issue, we (...) present an extendable open-source algorithm library that reproduces the most common image forgery operations reported by the research integrity community: duplication, retouching, and cleaning. We create a large scientific forgery image benchmark with enriched ground truth using this library and realistic scientific images. All figures within the benchmark are synthetically doctored using images collected from creative commons sources. While collecting the source images, we ensured that the they did not present any suspicious integrity problems. Because of the high number of retracted papers due to image duplication, this work evaluates the state-of-the-art copy-move detection methods in the proposed dataset, using a new metric that asserts consistent match detection between the source and the copied region. All evaluated methods had a low performance in this dataset, indicating that scientific images might need a specialized copy-move detector. The dataset and source code are available at https://github.com/phillipecardenuto/rsiil. (shrink)

This article looks at the positioning of the aesthetic in microscopy to understand how it can be both side-lined and deployed. It considers the boundary between the pictorial and the notational in current microscopy practice and speculates on a space of mutual relation. Microscopy’s dual threads of capture for data analysis and capture for publication reveal complicated relationships and conflicted stances, reflective of a broader iconoclastic tendency in microscopy where the image as enacted perception is erased while the notation generated (...) and carried by these images is preserved. This article seeks to suggest a space in which phenomena are situated, materially embedded and emergent through imaging practices. I ground my position on experimental imaging of bacterial iridescence to consider the entanglement of microscopy’s living image with its processes of making-seen. Microscopy is instrumental in supporting descriptive-explanatory claims on biological life yet is also implicated in the generation of novel, hybrid phenomena and sensibilities. Exploring both tendencies exposes a microscopy of permeable membranes and suggests alternative orientations through microscopy practice and its images. (shrink)

Modifying images for scientific publication is now quick and easy due to changes in technology. This has created a need for new image processing guidelines and attitudes, such as those offered to the research community by Doug Cromey (Cromey 2010). We suggest that related changes in technology have simplified the task of detecting misconduct for journal editors as well as researchers, and that this simplification has caused a shift in the responsibility for reporting misconduct. We also argue that the concept (...) of best practices in image processing can serve as a general model for education in best practices in research. (shrink)

The quality and authenticity of images is essential for data presentation, especially in the life sciences. Questionable images may often be a first indicator for questionable results, too. Therefore, a tool that uses mathematical methods to detect suspicious images in large image archives can be a helpful instrument to improve quality assurance in publications. As a first step towards a systematic screening tool, especially for journal editors and other staff members who are responsible for quality assurance, such as laboratory supervisors, (...) we propose a basic classification of image manipulation. Based on this classification, we developed and explored some simple algorithms to detect copied areas in images. Using an artificial image and two examples of previously published modified images, we apply quantitative methods such as pixel-wise comparison, a nearest neighbor and a variance algorithm to detect copied-and-pasted areas or duplicated images. We show that our algorithms are able to detect some simple types of image alteration, such as copying and pasting background areas. The variance algorithm detects not only identical, but also very similar areas that differ only by brightness. Further types could, in principle, be implemented in a standardized scanning routine. We detected the copied areas in a proven case of image manipulation in Germany and showed the similarity of two images in a retracted paper from the Kato labs, which has been widely discussed on sites such as pubpeer and retraction watch. (shrink)

Photography has become a pervasive component of contemporary communication. Recent technological advances in creating and manipulating images have provided renewed impetus to decades-long debates on use of photographs in science. With increase in the potential for inappropriate image manipulation, fears about misrepresentation have heightened concern among journal editors and scholars about the 'accuracy' of published images. We discuss how science has responded to growing concerns surrounding falsification and inaccuracy of photography. We document progress in implementing a variety of complementary approaches (...) to addressing the problem. These include digital forensics, photo member checking and the implementation of codes of ethics to enhance the veracity of published photographs in science research. We conclude by acknowledging the irreducible conflict likely to remain between use of the photograph as a creative work of art and employment of this medium as a source of information in the progress of science. (shrink)