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Societies worldwide have witnessed growing rifts separating advocates and opponents of vaccinations and other COVID-19 countermeasures. With the rollout of vaccination campaigns, the European German-speaking region (Germany, Austria, and Switzerland) initially exhibited a noticeably low vaccination uptake compared to other European regions. Later, uptake increased. It remains unclear which factors contributed to these changes. This study aimed to shed light on the intricacies of vaccine hesitancy among the German-speaking population and the possible dynamics between policy changes and public concerns using web discourse data. These insights are valuable for policymakers tasked with making far-reaching decisions-policies need to effectively curb the spread of the virus and at the same time respect fundamental civil liberties and minimize undesired consequences. This study drew on data from Twitter (subsequently rebranded X). We used a hybrid pipeline to detect and analyze 191,750 German-language vaccination-related tweets using a semiautomatic seed list generation approach, topic modeling, sentiment analysis, and a minimum of social scientific domain knowledge to evaluate the discourse about vaccinations in light of the COVID-19 pandemic. We further analyzed the evolution of public attention during different phases of the pandemic and in relation to policy changes to identify potential drivers of shifts in public attention. The discourse concerning vaccinations was associated with more negative sentiments than the general discourse on German-speaking Twitter (47,159/191,750, 24.59% vs 1,758,776/12,297,163, 14.3% predominantly negative tweets, respectively). The relative frequencies of the discussed themes fluctuated heavily (eg, safety and side effects was the most dominant theme in wave 3 [1,611/9,179, 17.55%] but ranked 6th in wave 5 [428/4,865, 8.8%], and effectiveness of vaccinations ranked 7th in wave 3 [711/9,179, 7.75%] and 2nd in wave 5 [831/4,865, 17.08%]). In wave 3, vaccines were authorized, and vaccinations were suspended and resumed due to safety concerns. Later, policies were implemented that restricted the freedom of unvaccinated citizens. Change points in attention aligned better with policy actions than with pandemic phases. During the later phases, vaccination uptake increased (wave 2: 5.6%, wave 3: 47%, and wave 5: 74% compared to 30%, 62%, and 78%, respectively, in the United Kingdom), and so did the attention to freedom and civil liberties (wave 2: 1,139/6,595, 17.27%; wave 5: 1,403/4,865, 28.84%). Substantially increasing negative and stronger sentiments were expressed. Our analyses suggest potential interactions among policies, public attention to different topics, and associated sentiments. While vaccination uptake increased, our findings indicate that citizens' doubts and concerns did not decrease and that, rather than being fully persuaded, they remained skeptical. This study showcases that monitoring web discourse can provide valuable insights for data-driven policymaking in highly dynamic contexts such as the COVID-19 pandemic.

Science across all disciplines has become increasingly data-driven, leading to additional needs with respect to software for collecting, processing and analysing data. Thus, transparency about software used as part of the scientific process is crucial to understand provenance of individual research data and insights, is a prerequisite for reproducibility and can enable macro-analysis of the evolution of scientific methods over time. However, missing rigor in software citation practices renders the automated detection and disambiguation of software mentions a challenging problem. In this work, we provide a large-scale analysis of software usage and citation practices facilitated through an unprecedented knowledge graph of software mentions and affiliated metadata generated through supervised information extraction models trained on a unique gold standard corpus and applied to more than 3 million scientific articles. Our information extraction approach distinguishes different types of software and mentions, disambiguates mentions and outperforms the state-of-the-art significantly, leading to the most comprehensive corpus of 11.8 M software mentions that are described through a knowledge graph consisting of more than 300 M triples. Our analysis provides insights into the evolution of software usage and citation patterns across various fields, ranks of journals, and impact of publications. Whereas, to the best of our knowledge, this is the most comprehensive analysis of software use and citation at the time, all data and models are shared publicly to facilitate further research into scientific use and citation of software.

Web service (WS) orchestration can be considered as a fundamental concept in service-oriented architectures (SOA), as well as in spatial data infrastructures (SDI). In recent years in SOA, advanced solutions were developed, such as realizing orchestrated web services on the basis of already existing more fine-granular web services by using standardized notations and existing orchestration engines. Even if the concepts can be mapped to the field of SDI, on a conceptual level the implementations target different goals. As a specialized form of a common web service, an Open Geospatial Consortium (OGC) web service (OWS) is optimized for a specific purpose. On the technological level, web services depend on standards like the Web Service Description Language (WSDL) or the Simple Object Access Protocol (SOAP). However OWS are different. Consequently, a new concept for OWS orchestration is needed that works on the interface provided by OWS. Such a concept is presented in this work. The major component is an orchestration engine integrated in a Web Processing Service (WPS) server that uses a domain specific language (DSL) for workflow description. The developed concept is the base for the realization of new functionality, such as workflow testing, and workflow optimization.

Societies worldwide have witnessed growing rifts separating advocates and opponents of vaccinations and other COVID-19 countermeasures. With the rollout of vaccination campaigns, German-speaking regions exhibited much lower vaccination uptake than other European regions. While Austria, Germany, and Switzerland (the DACH region) caught up over time, it remains unclear which factors contributed to these changes. Scrutinizing public discourses can help shed light on the intricacies of vaccine hesitancy and inform policy-makers tasked with making far-reaching decisions: policies need to effectively curb the spread of the virus while respecting fundamental civic liberties and minimizing undesired consequences. This study draws on Twitter data to analyze the topics prevalent in the public discourse. It further maps the topics to different phases of the pandemic and policy changes to identify potential drivers of change in public attention. We use a hybrid pipeline to detect and analyze vaccination-related tweets using topic modeling, sentiment analysis, and a minimum of social scientific domain knowledge to analyze the discourse about vaccinations in the light of the COVID-19 pandemic in the DACH region. We show that skepticism regarding the severity of the COVID-19 virus and towards efficacy and safety of vaccines were among the prevalent topics in the discourse on Twitter but that the most attention was given to debating the theme of freedom and civic liberties. Especially during later phases of the pandemic, when implemented policies restricted the freedom of unvaccinated citizens, increased vaccination uptake could be observed. At the same time, increasingly negative and polarized sentiments emerge in the discourse. This suggests that these policies might have effectively attenuated vaccination hesitancy but were not successfully dispersing citizens' doubts and concerns.

Knowledge about software used in scientific investigations is important for several reasons, for instance, to enable an understanding of provenance and methods involved in data handling. However, software is usually not formally cited, but rather mentioned informally within the scholarly description of the investigation, raising the need for automatic information extraction and disambiguation. Given the lack of reliable ground truth data, we present SoMeSci (Software Mentions in Science) a gold standard knowledge graph of software mentions in scientific articles. It contains high quality annotations (IRR: \\(=.82\\)) of 3756 software mentions in 1367 PubMed Central articles. Besides the plain mention of the software, we also provide relation labels for additional information, such as the version, the developer, a URL or citations. Moreover, we distinguish between different types, such as application, plugin or programming environment, as well as different types of mentions, such as usage or creation. To the best of our knowledge, SoMeSci is the most comprehensive corpus about software mentions in scientific articles, providing training samples for Named Entity Recognition, Relation Extraction, Entity Disambiguation, and Entity Linking. Finally, we sketch potential use cases and provide baseline results.