The development of electron and scanning probe microscopy has resulted in spectacular images of the internal structure and composition of matter with nanometer, molecular, and atomic resolution. This advancement has been aided by computer-assisted microscope operation, data acquisition, and analysis. At the turn of the twenty-first century, advances in imaging technology facilitated the availability of high-veracity information about structure and function. From a hardware perspective, high-resolution imaging techniques can now resolve atomic positions to approximately picometer precision, allowing for quantitative measurements of individual bond lengths and angles. Functional imaging frequently generates multidimensional data sets that contain partial or complete information about desired properties as a function of multiple parameters such as time, temperature, or other external stimuli. Electron microscopy is undergoing a paradigm shift from the traditional model of producing only a few micrographs to the current state where many images and spectra can be digitally recorded into a new mode and very large volumes of data including movies, ptychographic, and multi-dimensional series can be rapidly obtained.

Unsupervised multivariate statistical techniques can be employed to investigate salient image features in high-dimensional microscopy data by applying big data methods to the high-dimensional microscopy data. Surprisingly, despite the algorithm's purely statistical nature, which requires no prior knowledge about the material, any coexisting phases, or any differentiating structures, k-means clustering reveals domain differentiation. While this is a trivial illustration, it demonstrates how useful physical and structural information can be extracted regardless of the sample or instrumental modality. Additionally, human interpretation of these types of results may be required. However, the algorithm's open nature and widespread availability enable broad collaborations and exploratory work that are necessary for efficient electron microscopy data analysis. Historically, the obstacles to accessing the entire dataset included the limitations imposed by the detector acquisition speed; the data storage requirements, and the data processing, synthesis, and visualization required to extract useful information. In recent years, data acquisition and storage technologies have advanced significantly meaning it is now possible to rapidly capture and store large multidimensional data sets. However, the inherent complexity and scarcity of mathematical tools for visualizing and analyzing these data are exacerbated by the data's transient information content. Expectations about the available information may be influenced by fundamental assumptions about the image formation process.

This Special Issue aims to collate cutting-edge research on microscopy and its biomedical applications. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Scanning probe microscopies
• Data acquisition and micro-level analytics
• High-veracity information analysis
• Multidimensional data analysis
• Unsupervised multivariate
• High-resolution analysis
• Big data and methodology in the field of micrographs
• Visualization and analysis on intervention