This paper presents a novel dataset of blockchain-based decentralized autonomous organization (DAO) governance activity, unified across the six major DAO platforms. This dataset is the first of its scale and completeness, featuring 85K DAOs, 330K proposals and 66M votes. The release is based on a prior 2023 census, which is updated and expanded by systemizing heterogeneous outputs into a common schema with three tables (deployments, proposals, votes), adding fields, fixing inconsistencies, and documenting provenance for every row. We describe the data sources and retrieval pipelines, the integration and validation steps, and known limitations. To illustrate reusability, we benchmark the updated dataset against the 2023 DAO census and outline directions for further cross-domain analyses, including governance participation trends, power concentration, and connections between DAO and open-source software (OSS) communities. The dataset and code are all publicly available and open-licensed. We aim to lower the barrier to cross-chain governance studies and enable replication of earlier findings, facilitating future evidence-based DAO research.

The advent of blockchain technology has paved the way for numerous innovations in online governance, with decentralized autonomous organizations (DAOs) emerging as a prominent development, often referred to as “digital jurisdictions.” Despite experiencing remarkable growth, currently boasting nearly 7M users and $18 billion in assets, DAOs remain relatively underexplored in the existing literature, particularly from an empirical perspective. This study presents a comprehensive framework comprising 15 dimensions to categorize DAOs based on their operational domain, purpose, scope, voting process, and utilization of crypto-tokens. By applying this categorization schema to 40 DAO communities hosted on the Aragon platform, encompassing over 423 000 participants and managing treasuries worth $960M, we shed light on the prevailing characteristics of these DAOs. Contrary to assertions made by blockchain enthusiasts, our analysis reveals that DAOs predominantly operate in financial and technological domains, primarily offering blockchain-based services. Additionally, our investigation into their governance structure exposes limitations in terms of democratic participation, as decision-making power typically correlates with the number of tokens owned by the voter, resembling plutocracies rather than true democracies. We believe these findings will facilitate researchers’ comprehension of this innovative form of governance and aid practitioners in designing future DAOs with greater effectiveness. Furthermore, our analysis can be replicated on other platforms or at different time periods to validate and contrast our conclusions.

Blockchain technology has emerged as a new paradigm to build decentralized systems which do not require a central authority. It is most popular for enabling Bitcoin and other crypto-currencies. However, blockchain applications span beyond Finance, and recently it has been applied to decentralized governance. Blockchain-enabled "Decentralized Autonomous Organizations" (DAOs) have emerged as a new form of collective governance, in which communities may organize themselves relying on decentralized infrastructure. In this article, we introduce the concept of DAO and review the main software platforms that offer DAO creation as a service, which simplifies the use of DAOs to non-blockchain experts; namely: Aragon, DAOstack, DAOhaus and Colony. These platforms will be compared by showing their key features. Finally, we will review the available visualisation tools for DAOs, and we will introduce our open-source tool to plot DAOs activity, DAO-Analyzer. We will illustrate its potential with the case of the DAO Genesis Alpha, which is the main DAO of the DAOstack project.

Commons-based peer production communities can be analyzed with the help of social network analysis. However, since they are fluid organizations that change over time, the time dimension needs to be taken into account.

Understanding the Wikisphere phenomenon is undoubtedly of great interest. Most of the studies focus in Wikipedia and its generalization to other wikis requires an enormous amount of work in terms of selecting and retrieving the data. To facilitate the analysis of other wikis we developed a set of tools to collect and create a census of Wikia, one of the largest and most diverse repository of wikis, which hosts more than 300,000 wikis. In this work, we carry out a preliminary quantitative analysis of the census, emphasizing on the differences between active and inactive wikis. Additionally, we provide the wiki research community with the census and the scripts employed to retrieve the data, facilitating others to reproduce or reuse it.

It is widely accepted that peer production communities show a high level of inequality in the level of participation. Typically, we can observe a power law in the distribution of contributions. However, we argue that participation inequality, and specifically its evolution over time, has been understudied. Previous research has concentrated on large successful projects (e.g. Wikipedia), leaving a gap regarding small or mid-size communities. The wiki ecosystem is highly diverse, and so it may be the participation distribution in the communities and its evolution. The aim of this work is twofold: (1) To show a novel webtool, WikiChron, for the analysis of wiki evolution, with a focus on inequality metrics; (2) To provide relevant cases of comparative analysis that show the diversity of the wiki ecosystem, with examples that are counter-intuitive or contradict existing literature, while reflecting the limits of single-metric studies. We aim to open lines of wiki research which require the use of multiple metrics to study participation inequality.

Abstract Variability is inherent in human language as different people make different choices when facing the same communicative act. In Natural Language Processing, variability is a challenge. It hinders some tasks such as evaluation of generated expressions, while it constitutes an interesting resource to achieve naturalness and to avoid repetitiveness. In this work, we present a methodological approach to study the influence of lexical variability. We apply this approach to TUNA, a corpus of referring expression lexicalizations, in order to study the use of different lexical choices. First, we reannotate the TUNA corpus with new information about lexicalization, and then we analyze this reannotation to study how people lexicalize referring expressions. The results show that people tend to be consistent when generating referring expressions. But at the same time, different people also share certain preferences.

In this work, we are proposing a trading system where fuzzy logic is applied not only for defining the trading rules, but also for managing the capital to invest. In fact, two fuzzy decision support systems are developed. The first one uses fuzzy logic to design the trading rules and to apply the stock market technical indicators. The second one enhances this fuzzy trading system adding a fuzzy strategy to manage the capital to trade. Additionally, a new technical market indicator that produces short and long entry signals is introduced. It is based on the moving average convergence divergence indicator. Its parameters have been optimized by genetic algorithms. The proposals are compared to a classical nonfuzzy version of the proposed trading systems and to the buy-and-hold strategy. Results favor our fuzzy trading system in the two markets considered, NASDAQ100 and EUROSTOXX. Conclusions suggest that the use of fuzzy logic for capital management is promising and deserves further exploration.

This paper presents a new collection based on DBpedia and INEX for evaluating semantic search performance. The proposed corpus is used to calculate the impact of considering document's structure on the retrieval performance of the Lucene and BM25 ranking functions. Results show that BM25 outperforms Lucene in all the considered metrics and that there is room for future improvements, which may be obtained using a hybrid approach combining both semantic technology and information retrieval ranking functions.

Object-oriented software design requires a combination of abilities that can not be easily transferred to the students in lecture sessions. Based on our experience, this paper details how to increase student comprehension of design patterns through a project-based approach that consists in the development of a family of abstract strategy games in an incremental way. In this approach, the application resulting from the previous assignment is refined using refactoring and design patterns to complete the next assignment. Our approach is illustrated by the development of a family of n-in-a-row games in a CS3 course.