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This study aimed to provide scientific evidence and strategic suggestions for coastline management and engineering practices through analysis of the characteristics and temporal and spatial variations of wind and waves. We selected a public dataset from National Marine Data Center (NMDC) pertaining to wind and waves, subsequently performing feature selection through an analysis of the correlations among the various elements within the datasets. Furthermore, we conducted a thorough analysis and discussion of the data that was missing from these datasets, ensuring a comprehensive understanding of their limitations and potential implications. After conducting thorough statistics and analysis of the measured data pertaining to waves and winds, we studied the temporal and spatial variations observed in coastal waves. This paper provided insights into how these waves evolve and change over different time periods and locations.

[This corrects the article DOI: 10.1371/journal.pone.0201195.].

doi: 10.1038/nature19104 Corrigendum: Holocene shifts in the assembly of plant and animal communities implicate human impacts S. Kathleen Lyons, Kathryn L. Amatangelo, Anna K. Behrensmeyer, Antoine Bercovici, Jessica L. Blois, Matt Davis, William A. DiMichele, Andrew Du, Jussi T. Eronen, J. Tyler Faith, Gary R. Graves, Nathan Jud, conrad Labandeira, cindy V. Looy, brian McGill, Joshua H. Miller, David Patterson, Silvia Pineda-Munoz, Richard Potts, Brett Riddle, Rebecca Terry, Anikó Tóth, Werner Ulrich, Amelia Villaseñor, Scott Wing, Heidi Anderson, John Anderson, Donald Waller & Nicholas J. Gotelli Nature 529, 80-83 (2016); doi:10.1038/nature16447 It has come to our attention that in this Letter, there were some errors in the categorization of some of the modern datasets (R. Telford et al., personal communication).

The ColorChecker dataset is the most widely used dataset for evaluating and benchmarking illuminant-estimation algorithms. Although it is distributed with a 3-fold cross-validation partitioning, no procedure is defined on how to use it. In order to permit a fair comparison between illuminant-estimation algorithms, in this short correspondence we define a fair comparison procedure, showing that illuminant-estimation errors of state-of-the-art algorithms have been underestimated by up to 33%. We also compute the lower error bounds that can be reached on this dataset, which demonstrates that the existing algorithms have not yet reached their maximum performance potential.

The evaluation of clustering effects has been an important issue for a long time. How to effectively evaluate the clustering results of clustering algorithms is the key to the problem. The clustering effect evaluation is generally divided into internal clustering effect evaluation and external clustering effect evaluation. This paper focuses on the internal clustering effect evaluation, and proposes an improved index based on the Silhouette index and the Calinski-Harabasz index: Peak Weight Index (PWI). PWI combines the characteristics of Silhouette index and Calinski-Harabasz index, and takes the peak value of the two indexes as the impact point and gives appropriate weight within a certain range. Silhouette index and Calinski-Harabasz index will help improve the fluctuation of clustering results in the data set. Through the simulation experiments on four self-built influence data sets and two real data sets, it will prove that the PWI has excellent evaluation of clustering results.

The sharing and re-use of data has become a cornerstone of modern science. Multiple platforms now allow quick and easy data sharing. So far, however, data publishing models have not accommodated on-going scientific improvements in data: for many problems, datasets continue to grow with time -- more records are added, errors fixed, and new data structures are created. In other words, datasets, like scientific knowledge, advance with time. We therefore suggest that many datasets would be usefully published as a series of versions, with a simple naming system to allow users to perceive the type of change between versions. In this article, we argue for adopting the paradigm and processes for versioned data, analogous to software versioning. We also introduce a system called Versioned Data Delivery and present tools for creating, archiving, and distributing versioned data easily, quickly, and cheaply. These new tools allow for individual research groups to shift from a static model of data curation to a dynamic and versioned model that more naturally matches the scientific process.

Automatic facial expression recognition is essential for many potential applications. Thus, having a clear overview on existing datasets that have been investigated within the framework of face expression recognition is of paramount importance in designing and evaluating effective solutions, notably for neural networks-based training. In this survey, we provide a review of more than eighty facial expression datasets, while taking into account both macro- and micro-expressions. The proposed study is mostly focused on spontaneous and in-the-wild datasets, given the common trend in the research is that of considering contexts where expressions are shown in a spontaneous way and in a real context. We have also provided instances of potential applications of the investigated datasets, while putting into evidence their pros and cons. The proposed survey can help researchers to have a better understanding of the characteristics of the existing datasets, thus facilitating the choice of the data that best suits the particular context of their application.