COnVIDa: COVID-19 multidisciplinary data collection and dashboard.

Since the first reported case in Wuhan in late 2019, COVID-19 has rapidly spread worldwide, dramatically impacting the lives of millions of citizens. To deal with the severe crisis resulting from the pandemic, worldwide institutions have been forced to make decisions that profoundly affect the socio-economic realm. In this sense, researchers from diverse knowledge areas are investigating the behavior of the disease in a rush against time. In both cases, the lack of reliable data has been an obstacle to carry out such tasks with accuracy. To tackle this challenge, COnVIDa (https://convida.inf.um.es) has been designed and developed as a user-friendly tool that easily gathers rigorous multidisciplinary data related to the COVID-19 pandemic from different data sources. In particular, the pandemic expansion is analyzed with variables of health nature, but also social ones, mobility, etc. Besides, COnVIDa permits to smoothly join such data, compare and download them for further analysis. Due to the open-science nature of the project, COnVIDa is easily extensible to any other region of the planet. In this way, COnVIDa becomes a data facilitator for decision-making processes, as well as a catalyst for new scientific researches related to this pandemic.

Twitter social bots: The 2019 Spanish general election data.

The term social bots refer to software-controlled accounts that actively participate in the social platforms to influence public opinion toward desired directions. To this extent, this data descriptor presents a Twitter dataset collected from October 4th to November 11th, 2019, within the context of the Spanish general election. Starting from 46 hashtags, the collection contains almost eight hundred thousand users involved in political discussions, with a total of 5.8 million tweets. The proposed data descriptor is related to the research article available at [1]. Its main objectives are: i) to enable worldwide researchers to improve the data gathering, organization, and preprocessing phases; ii) to test machine-learning-powered proposals; and, finally, iii) to improve state-of-the-art solutions on social bots detection, analysis, and classification. Note that the data are anonymized to preserve the privacy of the users. Throughout our analysis, we enriched the collected data with meaningful features in addition to the ones provided by Twitter. In particular, the tweets collection presents the tweets' topic mentions and keywords (in the form of political bag-of-words), and the sentiment score. The users' collection includes one field indicating the likelihood of one account being a bot. Furthermore, for those accounts classified as bots, it also includes a score that indicates the affinity to a political party and the followers/followings list.

BEHACOM - a dataset modelling users' behaviour in computers.

This paper details the methodology and approach conducted to monitor the behaviour of twelve users interacting with their computers for fifty-five consecutive days without preestablished indications or restrictions. The generated dataset, called BEHACOM, contains for each user a set of features that models, in one-minute time windows, the usage of computer resources such as CPU or memory, as well as the activities registered by applications, mouse and keyboard. It has to be stated that the collected data have been treated in a privacy-preserving way during each phase of the collection and analysis. Together with the features and their explanation, we also detail the software used to gather and process the data. Finally, this article describes the data distribution of the BEHACOM dataset.