Unraveling the NFT economy: A comprehensive collection of Non-Fungible Token transactions and metadata.

Non-Fungible Tokens (NFTs) have emerged as the most representative application of blockchain technology in recent years, fostering the development of the Web3. Nonetheless, while the interest in NFTs rapidly boomed, creating unprecedented fervour in traders and creators, the demand for highly representative and up-to-date data to shed light on such an intriguing yet complex domain mostly remained unmet. To pursue this objective, we introduce a large collection of NFT transactions and associated metadata that correspond to trading operations between 2021 and 2023. Our developed dataset is the most extensive and representative in the NFT landscape to date, as it contains more than 70 M transactions performed by more than 6 M users across 36.3 M NFTs and 281 K collections. Moreover, this dataset boasts a wealth of metadata, including encoded textual descriptions and multimedia content, thus being suitable for a plethora of tasks relevant to database systems, AI, data science, Web and network science fields. This dataset represents a unique resource for researchers and industry practitioners to delve into the inner workings of NFTs through a multitude of perspectives, paving the way for unprecedented opportunities across multiple research fields.

Drivers of social influence in the Twitter migration to Mastodon.

The migration of Twitter users to Mastodon following Elon Musk's acquisition presents a unique opportunity to study collective behavior and gain insights into the drivers of coordinated behavior in online media. We analyzed the social network and the public conversations of about 75,000 migrated users and observed that the temporal trace of their migrations is compatible with a phenomenon of social influence, as described by a compartmental epidemic model of information diffusion. Drawing from prior research on behavioral change, we delved into the factors that account for variations of the effectiveness of the influence process across different Twitter communities. Communities in which the influence process unfolded more rapidly exhibit lower density of social connections, higher levels of signaled commitment to migrating, and more emphasis on shared identity and exchange of factual knowledge in the community discussion. These factors account collectively for 57% of the variance in the observed data. Our results highlight the joint importance of network structure, commitment, and psycho-linguistic aspects of social interactions in characterizing grassroots collective action, and contribute to deepen our understanding of the mechanisms that drive processes of behavior change of online groups.

The new abnormal: Identifying and ranking anomalies in the land trade market.

Large-scale national and transnational commercial land transactions, or Large-Scale Land Acquisitions (LSLAs), have been gaining a lot of academic attention since the late 2000s and since the reported rush for land, resulting in turn from an increase in demand for arable land. If many data exist to characterize land deals, the analysis of investment networks remain limited and predominantly portrays power asymmetries between countries from the Global North investing in the Global South. The aim of this work is to perform a deeper investigation on the land trade market, specifically focusing on cases that do not follow such narratives. For instance, almost 25% of the countries included in the transnational land trade network do not follow a strict investor/target dichotomy, thus being characterized by a double role, i.e., they both acquire and cede land in the transnational context. In order to globally acknowledge for what was currently considered as abnormal cases, we model open access data about LSLAs extracted from the Land Matrix Initiative (LMI) open-access database into a network graph, and adapt an eigenvector based centrality method originally conceived for online social networks, namely LurkerRank, to identify and rank anomalous profiles in the land trade market. We take into account three different network snapshots: a multi-sector network (including all the transnational deals in the LMI database), and three networks referring to specific investment sectors (agriculture,mines and biofuels). Experimental results show that emerging economies (e.g., China and Malaysia) play a central role in the land trade market, by creating alternative dynamics that escape the classic North/South one. Our analyses also show how African countries that are often seen as targets of land trade transactions in a specific sector, may often acquire foreign land in the context of investments in the same sector (i.e., Zimbabwe for biofuels and the Democratic Republic of Congo for the mining sector).

Correction: Structural and spectral properties of generative models for synthetic multilayer air transportation networks.

[This corrects the article DOI: 10.1371/journal.pone.0258666.].

Structural and spectral properties of generative models for synthetic multilayer air transportation networks.

To understand airline transportation networks (ATN) systems we can effectively represent them as multilayer networks, where layers capture different airline companies, the nodes correspond to the airports and the edges to the routes between the airports. We focus our study on the importance of leveraging synthetic generative multilayer models to support the analysis of meaningful patterns in these routes, capturing an ATN's evolution with an emphasis on measuring its resilience to random or targeted attacks and considering deliberate locations of airports. By resorting to the European ATN and the United States ATN as exemplary references, in this work, we provide a systematic analysis of major existing synthetic generation models for ATNs, specifically ANGEL, STARGEN and BINBALL. Besides a thorough study of the topological aspects of the ATNs created by the three models, our major contribution lays on an unprecedented investigation of their spectral characteristics based on Random Matrix Theory and on their resilience analysis based on both site and bond percolation approaches. Results have shown that ANGEL outperforms STARGEN and BINBALL to better capture the complexity of real-world ATNs by featuring the unique properties of building a multiplex ATN layer by layer and of replicating layers with point-to-point structures alongside hub-spoke formations.

MaSDA: a system for analyzing mass spectrometry data.

Mass spectrometry (MS) approaches have been recently coupled with advanced data analysis techniques in order to enable clinicians to discover useful knowledge from MS data. However, effectively and efficiently handling and analyzing MS data requires to take into account a number of issues. In particular, the huge dimensionality and the variety of noisy factors present in MS data require careful preprocessing and modeling phases in order to make them amenable to the further analysis. In this paper we present MaSDA, a system performing advanced analysis on MS data. MaSDA has the following main features: (i) it implements an approach of MS data representation that exploits a model based on low dimensional, dense time series; (ii) it provides a wide set of MS preprocessing operations which are accomplished by means of a user-friendly graphical tool; (iii) it embeds a number of tools implementing various tasks of data mining and knowledge discovery, in order to assist the user in taking critical clinical decisions. Our system has been experimentally tested on several publicly available datasets, showing effectiveness and efficiency in supporting advanced analysis of MS data.

Beneficial effects of iodized salt prophylaxis on thyroid volume in an iodine deficient area of southern Italy.

OBJECTIVE AND SUBJECTS: Goitre prevalence in school-age children is an indicator of the severity of iodine deficiency disorders (IDD) in an endemic area. The aims of the present study were (i) to provide ultrasound thyroid volume (TV) reference values in a healthy population of school-children aged 11-14 year living in iodine-sufficient areas of Calabria region (ii) to assess both goitre prevalence and urinary iodine (UI) concentration in all children aged 11-14 year from four mildly iodine-deficient areas in which we have carried out a program of salt iodization and (iii) to evaluate the efficacy of the iodoprophylaxis in an adult population living in a small village of the same endemic area. DESIGN: Cross-sectional and prospective studies. METHODS: TV was assessed by ultrasonography and iodine intake was estimated by measuring iodine excretion in spot urine samples. Results We provided the ultrasound normal reference values as a function of age and body surface area, which displayed significant differences from those recommended by the World Health Organization. By adopting local criteria, the prevalence of goitre in children ranged from 23.4% to 27.7% normalized for age and body surface area, respectively, while the UI excretion was < 100 microg/l in 38% of subjects studied. In an adult population living in the same endemic area, goitre prevalence was lowest in the 18-27-year-old age group, and increased progressively with age. CONCLUSION: We propose for the first time local reference ultrasound values for TV in a population of 11-14-year-old school-children that should be used for monitoring IDDs and have demonstrated the beneficial effects of iodoprophylaxis in consistent with reduced goitre prevalence in children and in the young adult population studied.