On community structure validation in real networks

Community structure is a commonly observed feature of real networks. The term refers to the presence in a network of groups of nodes (communities) that feature high internal connectivity, but are poorly connected between each other. Whereas the issue of community detection has been addressed in several works, the problem of validating a partition of nodes as a good community structure for a real network has received considerably less attention and remains an open issue. We propose a set of indices for community structure validation of network partitions that are based on an hypothesis testing procedure that assesses the distribution of links between and within communities. Using both simulations and real data, we illustrate how the proposed indices can be employed to compare the adequacy of different partitions of nodes as community structures in a given network, to assess whether two networks share the same or similar community structures, and to evaluate the performance of different network clustering algorithms.

Multi-Purpose Dataset of Webpages and Its Content Blocks: Design and Structure Validation

The need for automated data extraction is continuously growing due to the constant addition of information to the worldwide web. Researchers are developing new data extraction methods to achieve increased performance compared to existing methods. Comparing algorithms to evaluate their performance is vital when developing new solutions. Different algorithms require different datasets to test their performance due to the various data extraction approaches. Currently, most datasets tend to focus on a specific data extraction approach. Thus, they generally lack the data that may be useful for other extraction methods. That leads to difficulties when comparing the performance of algorithms that are vastly different in their approach. We propose a dataset of web page content blocks that includes various data points to counter this. We also validate its design and structure by performing block labeling experiments. Web developers of varying experience levels labeled multiple websites presented to them. Their labeling results were stored in the newly proposed dataset structure. The experiment proved the need for proposed data points and validated dataset structure suitability for multi-purpose dataset design.

Distortions, deviations and alternative facts: reliability in crystallography

This feature article is derived from the author's presentation of the Lonsdale lecture at the BCA Spring Meeting in 2018. One of the research results for which Kathleen Lonsdale is best known was her 1929 demonstration that the benzene ring in crystalline hexamethylbenzene is planar and has essentially hexagonal symmetry, resolving decades of dispute among organic chemists. More recent crystallographic studies of hexamethylbenzene have shown that there are actually small deviations from planarity. Such deviations for aromatic compounds may be due to electronic, steric, and/or intermolecular factors. Some substituted benzene molecules display remarkably large deviations, both from a planar ring structure and from regular hexagonal angular geometry around the ring. Starting from this specific connection with Kathleen Lonsdale's research, a number of stories are recounted of structural distortions and deviations from expected results and explanations that have been suggested for them, across a wide range of chemical topics including macrocycles, metal clusters, unusual coordination geometry and isomerism. On the way we find genuine surprises and results that have led to new understanding, but also examples of poor experiments, misinterpretation of data, scientific bias and preconceived ideas, incompetence and even deliberate fraud. Some aspects of structure validation are discussed. While showcasing some interesting research in its own right, this account also serves an educational purpose.

Leveraging glycomics data in glycoprotein 3D structure validation with Privateer

The heterogeneity, mobility and complexity of glycans in glycoproteins have been, and currently remain, significant challenges in structural biology. These aspects present unique problems to the two most prolific techniques: X-ray crystallography and cryo-electron microscopy. At the same time, advances in mass spectrometry have made it possible to get deeper insights on precisely the information that is most difficult to recover by structure solution methods: the full-length glycan composition, including linkage details for the glycosidic bonds. The developments have given rise to glycomics. Thankfully, several large scale glycomics initiatives have stored results in publicly available databases, some of which can be accessed through API interfaces. In the present work, we will describe how the Privateer carbohydrate structure validation software has been extended to harness results from glycomics projects, and its use to greatly improve the validation of 3D glycoprotein structures.

Leveraging glycomics data in glycoprotein 3D structure validation with Privateer

The heterogeneity, mobility and complexity of glycans in glycoproteins have been, and currently remain, significant challenges in structural biology. Those aspects present unique problems to the two most prolific techniques: X-ray crystallography and cryo-electron microscopy. At the same time, advances in mass spectrometry have made it possible to get deeper insights on precisely the information that is most difficult to recover by structure solution methods: full-length glycan composition, including linkage details for the glycosidic bonds. These developments have given rise to glycomics. Thankfully, several large scale glycomics initiatives have stored results in publicly-available databases, some of which can be accessed through API interfaces. In the present work, we will describe how the Privateer carbohydrate structure validation software has been extended to harness results from glycomics projects, and its use to greatly improve the validation of 3D glycoprotein structures.

Development and validation of a research instrument for measuring the presence of democratic school leadership characteristics

The main purpose of this article is to describe the process of development and validation of a research instrument for measuring the presence of democratic school leadership characteristics. The instrument has been developed and validated in the city of Zagreb and Zagreb County in Croatia in four phases: theoretical construction; expert validation ( N = 6 experts); pilot research ( N = 77 teachers); and instrument structure validation through the main research ( N = 651 teachers). The expert validation and pilot research took place in 2018. The main research was conducted in January and February 2019. According to Carmines and Zeller (1979), the results of the factor analysis, which show that the instrument is measuring a single phenomenon, indicate that all four hypotheses have been fulfilled. More specifically, several aspects of the extracted (i.e. unrotated) factor matrix support the following four hypotheses: the (unrotated) factor matrix of all 32 items explains a total of 66.793% of the variance; subsequent components demonstrate the fairly equal proportions of the remaining variance except for a gradual decrease; all items have high factor loadings on the first component (from .603 to .861); and all items have higher loadings on the first than on the second component.