Ten simple rules on writing clean and reliable open-source scientific software.

Functional, usable, and maintainable open-source software is increasingly essential to scientific research, but there is a large variation in formal training for software development and maintainability. Here, we propose 10 "rules" centered on 2 best practice components: clean code and testing. These 2 areas are relatively straightforward and provide substantial utility relative to the learning investment. Adopting clean code practices helps to standardize and organize software code in order to enhance readability and reduce cognitive load for both the initial developer and subsequent contributors; this allows developers to concentrate on core functionality and reduce errors. Clean coding styles make software code more amenable to testing, including unit tests that work best with modular and consistent software code. Unit tests interrogate specific and isolated coding behavior to reduce coding errors and ensure intended functionality, especially as code increases in complexity; unit tests also implicitly provide example usages of code. Other forms of testing are geared to discover erroneous behavior arising from unexpected inputs or emerging from the interaction of complex codebases. Although conforming to coding styles and designing tests can add time to the software development project in the short term, these foundational tools can help to improve the correctness, quality, usability, and maintainability of open-source scientific software code. They also advance the principal point of scientific research: producing accurate results in a reproducible way. In addition to suggesting several tips for getting started with clean code and testing practices, we recommend numerous tools for the popular open-source scientific software languages Python, R, and Julia.

Segmentation of nearly isotropic overlapped tracks in photomicrographs using successive erosions as watershed markers.

INTRODUCTION: Procedures for measuring and counting tracks are time-consuming and involve practical problems. The precision of automatic counting methods is not satisfactory yet; the major challenges are distinguishing tracks and material defects, identifying small tracks and defects of similar size, and detecting overlapping tracks. MATERIALS AND METHODS: Here, we address the overlapping tracks issue using the algorithm Watershed Using Successive Erosions as Markers (WUSEM), which combines the watershed transform, morphological erosions and labeling to separate regions in photomicrographs. We tested this method in two data sets of diallyl phthalate (DAP) photomicrographs and compared the results when counting manually and using the classic watershed and H-watershed transforms. RESULTS: The mean automatic/manual efficiency counting ratio when using WUSEM in the test data sets is 0.97 ± 0.11. CONCLUSION: WUSEM shows reliable results when used in photomicrographs presenting almost isotropic objects. Also, diameter and eccentricity criteria may be used to increase the reliability of this method.

Jansen-MIDAS: A multi-level photomicrograph segmentation software based on isotropic undecimated wavelets.

Image segmentation, the process of separating the elements within a picture, is frequently used for obtaining information from photomicrographs. Segmentation methods should be used with reservations, since incorrect results can mislead when interpreting regions of interest (ROI). This decreases the success rate of extra procedures. Multi-Level Starlet Segmentation (MLSS) and Multi-Level Starlet Optimal Segmentation (MLSOS) were developed to be an alternative for general segmentation tools. These methods gave rise to Jansen-MIDAS, an open-source software. A scientist can use it to obtain several segmentations of hers/his photomicrographs. It is a reliable alternative to process different types of photomicrographs: previous versions of Jansen-MIDAS were used to segment ROI in photomicrographs of two different materials, with an accuracy superior to 89%.

Segmentation of scanning electron microscopy images from natural rubber samples with gold nanoparticles using starlet wavelets.

Electronic microscopy has been used for morphology evaluation of different materials structures. However, microscopy results may be affected by several factors. Image processing methods can be used to correct and improve the quality of these results. In this article, we propose an algorithm based on starlets to perform the segmentation of scanning electron microscopy images. An application is presented in order to locate gold nanoparticles in natural rubber membranes. In this application, our method showed accuracy greater than 85% for all test images. Results given by this method will be used in future studies, to computationally estimate the density distribution of gold nanoparticles in natural rubber samples and to predict reduction kinetics of gold nanoparticles at different time periods.