Manipulation and Misconduct in the Handling of Image Data
Manipulation and Misconduct in the Handling of Image Data
复制标题
图像数据处理中的操纵和不当行为
DOI：
10.1104/pp.113.900471
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发表时间：
2013
期刊：
影响因子：
7.4
通讯作者：
Blatt M
中科院分区：
文献类型：
--
作者：
Blatt M
在过去的几年里，少数著名的科学欺诈案件已经出现在普通媒体上。更多的不恰当数据处理的例子出现在几乎每一本科学期刊的编辑部。这些使编辑的注意力集中在不适当的数据处理和欺诈性的图像处理上。植物细胞和植物生理学也不例外。二十年前，图像处理的实用性意味着什么是可以接受的，什么是不可以接受的，界限是明确的；所需的暗室技能对不适当地操纵图像数据构成了重大的技术障碍，特别是在没有欺骗意图的情况下进行的操纵，只是为了“清理”图像。今天的道德界限和四分之一世纪前一样清晰，但随着数字图像采集、存储和处理的出现，许多不适当操作的技术障碍几乎消失了。Adobe Photoshop于1990年在Macintosh上推出，在1992年在PC上推出；它的广泛应用，以及在过去十年中对数字格式的广泛接受，大大简化了图像准备的任务。它们还意味着，操纵图像所需的技能要少得多。事实上，数字格式带来的一个常见问题是，许多科学家无意中操纵了他们的图像数据，往往会导致重要信息的丢失，以使他们的数据看起来尽可能好。《细胞生物学杂志》在过去十年中进行了一项详细的研究，值得称赞的是，它公开分享了这一信息。研究发现，在接受发表的文章中，10%的文章包含对图像数据的不当操纵，这违反了期刊政策，即使它们没有改变从数据中得出的结论(国际管理和技术编辑协会，2013年)。令人惊讶的是，大量的作者似乎没有意识到他们对图像数据的处理不当，在许多情况下，他们没有意识到他们的行为的伦理问题和后果。作为编辑，我们如何维护出版的道德标准？作为科学家，我们如何教育我们的学生并支持我们的同龄人理解在处理图像数据时什么是(以及什么不是)可接受的做法？必须认识到，数字图像是数据，实际上是数字数据的数组，必须如此对待。作为科学家，我们假设图像不会以任何影响视觉印象的方式进行更改；图像(数据阵列)中的定量和定性关系必须保持。如果更改了这些关系，则必须对这些更改进行充分的记录和解释。这些期望背后有两个明确的原则：(1)我们期望科学报告的诚实和透明，(2)我们希望作为作者的科学家了解处理图像数据的后果，以确保任何转换在数量上都是严格的，并符合道德标准。为了满足这些期望，有几个简单的规则可以遵循(Rossner和Yamada，2004；North，2006；Cromey，2010)：1.作为良好实验室实践的一部分，原始图像数据必须完好无损地保存和存档，不得更改。数字图像的处理应该在图像数据文件的副本上进行，而不是在原件上。保留原始图像数据很重要，因为它们可以作为比较最终图像的标准，并确保在处理过程中出错的情况下进行恢复。我们建议将图像数据保存在…中
The past few years have seen a small number of celebrated cases of scientific fraud that have found their way into the general media. Many more examples of inappropriate data handling come across the editorial desks of virtually every scientific journal. These have focused editors’ attention on inappropriate data handling and fraudulent image manipulation. The Plant Cell and Plant Physiology are no exceptions. Two decades ago, the practicalities of image handling meant that the boundaries were well-defined between what was acceptable and what was not; the darkroom skills needed posed a significant technical barrier to inappropriate manipulation of image data, particularly manipulation done without the intention to deceive but simply to “clean up” the image. The ethical boundaries are as clear-cut today as they were a quarter century ago, but many of the technical barriers to inappropriate manipulation have all but disappeared with the advent of digital image acquisition, storage, and handling. Adobe Photoshop was introduced in 1990 for Macintosh and in 1992 for PCs; its widespread application, and the broader acceptance of digital formats during this past decade, have simplified greatly the tasks of image preparation. They also mean that much less skill is needed to manipulate images. Indeed, a common problem arising from digital formats is that many scientists inadvertently manipulate their image data, often in ways that result in the loss of important information, to make their data look as good as possible. The Journal of Cell Biology carried out a detailed study over the past decade and, commendably, has shared this information publicly. The study found that 10% of articles accepted for publication included inappropriate manipulations of image data that contravened journal policy, even if they did not alter the conclusions drawn from the data (International Society of Managing and Technical Editors, 2013). A surprisingly large number of the authors appeared unaware that they had handled image data inappropriately and, in many cases, were not conscious of the ethical issues and consequences of their actions. As editors, how do we maintain ethical standards in publishing? And, as scientists, how do we educate our students and support our peers to understand what is (and what is not) acceptable practice when handling image data? It is essential to recognize that digital images are data, in fact arrays of numerical data, and must be treated as such. As scientists, we assume that images will not have been altered in any way that affects the visual impression; the quantitative and qualitative relationships within images (data arrays) must be maintained. If these relationships are altered, then such alterations must be fully documented and explained. There are two defining principles behind these expectations:(1) We expect honesty and transparency in scientific reporting, and (2) We expect the scientist, as author, to understand the consequences of processing image data to ensure that any transformations are quantitatively rigorous and comply with ethical standards. There are a few simple rules to follow in meeting these expectations (Rossner and Yamada, 2004; North, 2006; Cromey, 2010):1. Raw image data must be saved and archived intact and without alteration as part of good laboratory practice. Processing of digital images should be done on a copy of the image data file, not on the original. Retaining raw image data is important because they serve as the standard against which the final image can be compared, and they ensure a route for recovery should a mistake be made during processing. We recommend that image data are saved …