Wikidata introduced support for lexicographic data in 2018.Here we describe the lexicographic part of Wikidata as well as experiences with setting up lexemes for the Danish language.We note various possible annotations for lexemes as well as discuss various choices made.

Wikipedia may be the best‐developed attempt thus far to gather all human knowledge in one place. Its accomplishments in this regard have made it a point of inquiry for researchers from different fields of knowledge. A decade of research has thrown light on many aspects of the Wikipedia community, its processes, and its content. However, due to the variety of fields inquiring about Wikipedia and the limited synthesis of the extensive research, there is little consensus on many aspects of Wikipedia's content as an encyclopedic collection of human knowledge. This study addresses the issue by systematically reviewing 110 peer‐reviewed publications on Wikipedia content, summarizing the current findings, and highlighting the major research trends. Two major streams of research are identified: the quality of Wikipedia content (including comprehensiveness, currency, readability, and reliability) and the size of Wikipedia. Moreover, we present the key research trends in terms of the domains of inquiry, research design, data source, and data gathering methods. This review synthesizes scholarly understanding of Wikipedia content and paves the way for future studies.

Hundreds of scholarly studies have investigated various aspects of Wikipedia. Although a number of literature reviews have provided overviews of this vast body of research, none has specifically focused on the readers of Wikipedia and issues concerning its readership. In this systematic literature review, we review 99 studies to synthesize current knowledge regarding the readership of Wikipedia and provide an analysis of research methods employed. The scholarly research has found that Wikipedia is popular not only for lighter topics such as entertainment but also for more serious topics such as health and legal information. Scholars, librarians, and students are common users, and Wikipedia provides a unique opportunity for educating students in digital literacy. We conclude with a summary of key findings, implications for researchers, and implications for the Wikipedia community.

We provide a method for mass meta-analysis in a neuroinformatics database containing stereotaxic Talairach coordinates from neu- roimaging experiments. Database labels are used to group the in- dividual experiments, e.g., according to cognitive function, and the consistent pattern of the experiments within the groups are de- termined. The method voxelizes each group of experiments via a kernel density estimation, forming probability density volumes. The values in the probability density volumes are compared to null-hypothesis distributions generated by resamplings from the entire unlabeled set of experiments, and the distances to the null- hypotheses are used to sort the voxels across groups of experi- ments. This allows for mass meta-analysis, with the construction of a list with the most prominent associations between brain ar- eas and group labels. Furthermore, the method can be used for functional labeling of voxels. 1 Introduction Neuroimaging experimenters usually report their results in the form of 3- dimensional coordinates in the standardized stereotaxic Talairach system [1]. Auto- mated meta-analytic and information retrieval methods are enabled when such data are represented in databases such as the BrainMap DBJ ([2], www.brainmapdbj.org) or the Brede database [3]. Example methods include outlier detection [4] and iden- tification of similar volumes [5]. Apart from the stereotaxic coordinates, the databases record details of the exper- imental situation, e.g., the behavioral domain and the scanning modality. In the Brede database the main annotation is the so-called "external components"1 which are heuristically organized in a simple ontology: A directed graph (more specifically, a causal network) with the most general components as the roots of the graph, e.g., 1External components might be thought of as "cognitive components" or simply "brain functions", but they are more general, e.g., they also incorporate neuroreceptors. The components are called "external" since they are external variables to the brain image. WOEXT: 41 Cold pain WOEXT: 40 WOEXT: 261 Pain Thermal pain WOEXT: 69 Hot pain Figure 1: The external components around "thermal pain" with "pain" as the parent of "thermal pain" and "cold pain" and "hot pain" as children. "hot pain" is a child of "thermal pain" that in turn is a child of "pain" (see Figure 1). The simple ontology is setup from information typically found in the introduction section of scientific articles, and it is compared with the Medical Subject Headings ontology of the National Library of Medicine. The ontology is stored in a simple XML file. The Brede database is organized, like the BrainMap DBJ, on different levels with scientific papers on the highest level. Each scientific paper contains one or more "experiments", which each in turn contains one or more locations. The individual experiments are typically labeled with an external component. The experiments that are labeled with the same external component form a group, and the distribu- tion of locations within the group become relevant: If a specific external component is localized to a specific brain region, then the locations associated with the external component should cluster in Talairach space. We will describe a meta-analytic method that identifies important associations be- tween external components and clustered Talairach coordinates. We have previously modeled the relation between Talairach coordinates and neuroanatomical terms [4, 6] and the method that we propose here can be seen as an extension describing the relationship between Talairach coordinates and, e.g., cognitive components.

Game sites on the World Wide Web draw people from around the world with specialized interests, skills, and knowledge. Data from the games often reflects the players' expertise and will to win. We extract probabilistic forecasts from data obtained from three online games: the Hollywood Stock Exchange (HSX), the Foresight Exchange (FX), and the Formula One Pick Six (F1P6) competition. We find that all three yield accurate forecasts of uncertain future events. In particular, prices of so-called "movie stocks" on HSX are good indicators of actual box office returns. Prices of HSX securities in Oscar, Emmy, and Grammy awards correlate well with observed frequencies of winning. FX prices are reliable indicators of future developments in science and technology. Collective predictions from players in the F1 competition serve as good forecasts of true race outcomes. In some cases, forecasts induced from game data are more reliable than expert opinions. We argue that web games naturally attract well-informed and well-motivated players, and thus offer a valuable and oft-overlooked source of high-quality data with significant predictive value.

We analyze the persistence of information on the web, looking at the percentage of invalid URLs contained in academic articles within the CiteSeer (ResearchIndex) database.The number of URLs contained in the papers has increased from an average of 0.06 in 1993 to 1.6 in 1999.We found that a significant percentage of URLs are now invalid, ranging from 23% for 1999 articles, to 53% for 1994.We also found that for almost all of the invalid URLs, it was possible to locate the information (or highly related information) in an alternate location, primarily with the use of search engines.However, the ability to relocate missing information varied according to search experience and effort expended.Citation practices suggest that more information may be lost in the future unless these practices are improved.We discuss persistent URL standards and their usage, and give recommendations for citing URLs in research articles as well as for finding the new location of invalid URLs.

We apply and discuss generalizable Gaussian mixture (GGM) models for text mining. The model automatically adapts model complexity for a given text representation. We show that the generalizability of these models depends on the dimensionality of the representation and the sample size. We discuss the relation between supervised and unsupervised learning in the test data. Finally, we implement a novelty detector based on the density model.

Modeling the haemodynamic response in functional magnetic resonance (fMRI) experiments is an important aspect of the analysis of functional neuroimages. This has been done in the past using parametric response function, from a limited family. In this contribution, we adopt a semi-parametric approach based on finite impulse response (FIR) filters. In order to cope with the increase in the number of degrees of freedom, we introduce a Gaussian process prior on the filter parameters. We show how to carry on the analysis by incorporating prior knowledge on the filters, optimizing hyper-parameters using the evidence framework, or sampling using a Markov Chain Monte Carlo (MCMC) approach. We present a comparison of our model with standard haemodynamic response kernels on simulated data, and perform a full analysis of data acquired during an experiment involving visual stimulation.