  The World Wide Web is not only one of the most important platforms of
communication and information at present, but also an area of growing interest
for scientific research. This motivates a lot of work and projects that require
large amounts of data. However, there is no dataset that integrates the
parameters and visual appearance of Web pages, because its collection is a
costly task in terms of time and effort. With the support of various computer
tools and programming scripts, we have created a large dataset of 49,438 Web
pages. It consists of visual, textual and numerical data types, includes all
countries worldwide, and considers a broad range of topics such as art,
entertainment, economy, business, education, government, news, media, science,
and environment, covering different cultural characteristics and varied design
preferences. In this paper, we describe the process of collecting, debugging
and publishing the final product, which is freely available. To demonstrate the
usefulness of our dataset, we expose a binary classification model for
detecting error Web pages, and a multi-class Web subject-based categorization,
both problems using convolutional neural networks.
