Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Graphics
Title: Research Articles in Simplified HTML: a Web-first format for HTML-based scholarly articles
Abstract: <jats:sec><jats:title>Purpose</jats:title><jats:p>This paper introduces the Research Articles in Simplified HTML (or RASH), which is a Web-first format for writing HTML-based scholarly papers; it is accompanied by the RASH Framework, a set of tools for interacting with RASH-based articles. The paper also presents an evaluation that involved authors and reviewers of RASH articles submitted to the SAVE-SD 2015 and SAVE-SD 2016 workshops.</jats:p></jats:sec><jats:sec><jats:title>Design</jats:title><jats:p>RASH has been developed aiming to: be easy to learn and use; share scholarly documents (and embedded semantic annotations) through the Web; support its adoption within the existing publishing workflow.</jats:p></jats:sec><jats:sec><jats:title>Findings</jats:title><jats:p>The evaluation study confirmed that RASH is ready to be adopted in workshops, conferences, and journals and can be quickly learnt by researchers who are familiar with HTML.</jats:p></jats:sec><jats:sec><jats:title>Research Limitations</jats:title><jats:p>The evaluation study also highlighted some issues in the adoption of RASH, and in general of HTML formats, especially by less technically savvy users. Moreover, additional tools are needed, e.g., for enabling additional conversions from/to existing formats such as OpenXML.</jats:p></jats:sec><jats:sec><jats:title>Practical Implications</jats:title><jats:p>RASH (and its Framework) is another step towards enabling the definition of formal representations of the meaning of the content of an article, facilitating its automatic discovery, enabling its linking to semantically related articles, providing access to data within the article in actionable form, and allowing integration of data between papers.</jats:p></jats:sec><jats:sec><jats:title>Social Implications</jats:title><jats:p>RASH addresses the intrinsic needs related to the various users of a scholarly article: researchers (focussing on its content), readers (experiencing new ways for browsing it), citizen scientists (reusing available data formally defined within it through semantic annotations), publishers (using the advantages of new technologies as envisioned by the Semantic Publishing movement).</jats:p></jats:sec><jats:sec><jats:title>Value</jats:title><jats:p>RASH helps authors to focus on the organisation of their texts, supports them in the task of semantically enriching the content of articles, and leaves all the issues about validation, visualisation, conversion, and semantic data extraction to the various tools developed within its Framework.</jats:p></jats:sec>
Extracted statement — Semantic representation: RASH
Research field: Graphics
Title: Towards a Linked-Data based Visualization Wizard
Abstract: Datasets published in the LOD cloud are recommended to follow some best practice in order to be 4-5 stars Linked Data compliant. They can often be consumed and accessed by different means such as API access, bulk download or as linked data fragments, but most of the time, a SPARQL endpoint is also provided. While the LOD cloud keeps growing, having a quick glimpse of those datasets is getting harder and there is a need to develop new methods enabling to detect automatically what an arbitrary dataset is about and to recommend visualizations for data samples. We consider that "a visualization is worth a million triples", and in this paper, we propose a novel approach that mines the content of datasets and automatically generates visualizations. Our approach is directly based on the usage of SPARQL queries that will detect the important categories of a dataset and that will specifically consider the properties used by the objects which have been interlinked via owl:sameAs links. We then propose to associate type of visualization for those categories. We have implemented this approach into a so-called Linked Data Vizualization Wizard (LDVizWiz).
Extracted statement — implementation: LDVizWiz
Research field: Graphics
Title: LODWheel - JavaScript-based Visualization of RDF Data.
Abstract: Visualizing Resource Description Framework (RDF) data to support decision-making processes is an important and challenging aspect of consuming Linked Data. With the recent development of JavaScript libraries for data visualization, new opportunities for Web-based visualization of Linked Data arise. This paper presents an extensive evaluation of JavaScript-based libraries for visualizing RDF data. A set of criteria has been devised for the evaluation and 15 major JavaScript libraries have been analyzed against the criteria. The two JavaScript libraries with the highest score in the evaluation acted as the basis for developing LODWheel (Linked Open Data Wheel) - a prototype for visualizing Linked Open Data in graphs and charts - introduced in this paper. This way of visualizing RDF data leads to a great deal of challenges related to data-categorization and connecting data resources together in new ways, which are discussed in this paper.
Extracted statement — implementation: LODWheel
Research field: Graphics
Title: LODWheel – JavaScript-based Visualization of RDF Data
Abstract: Visualizing Resource Description Framework (RDF) data to support decision-making processes is an important and challenging aspect of consuming Linked Data. With the recent development of JavaScript libraries for data visualization, new opportunities for Web-based visualization of Linked Data arise. This paper presents an extensive evaluation of JavaScript-based libraries for visualizing RDF data. A set of criteria has been devised for the evaluation and 15 major JavaScript libraries have been analyzed against the criteria. The two JavaScript libraries with the highest score in the evaluation acted as the basis for developing LODWheel (Linked Open Data Wheel) - a prototype for visualizing Linked Open Data in graphs and charts - introduced in this paper. This way of visualizing RDF data leads to a great deal of challenges related to data-categorization and connecting data resources together in new ways, which are discussed in this paper.
Extracted statement — implementation: LODWheel
Research field: Immunology and Infectious Disease
Title: Safety and Immunogenicity of Two RNA-Based Covid-19 Vaccine Candidates
Abstract: Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the resulting disease, coronavirus disease 2019 (Covid-19), have spread to millions of persons worldwide. Multiple vaccine candidates are under development, but no vaccine is currently available. Interim safety and immunogenicity data about the vaccine candidate BNT162b1 in younger adults have been reported previously from trials in Germany and the United States. Methods In an ongoing, placebo-controlled, observer-blinded, dose-escalation, phase 1 trial conducted in the United States, we randomly assigned healthy adults 18 to 55 years of age and those 65 to 85 years of age to receive either placebo or one of two lipid nanoparticle–formulated, nucleoside-modified RNA vaccine candidates: BNT162b1, which encodes a secreted trimerized SARS-CoV-2 receptor–binding domain; or BNT162b2, which encodes a membrane-anchored SARS-CoV-2 full-length spike, stabilized in the prefusion conformation. The primary outcome was safety (e.g., local and systemic reactions and adverse events); immunogenicity was a secondary outcome. Trial groups were defined according to vaccine candidate, age of the participants, and vaccine dose level (10 μg, 20 μg, 30 μg, and 100 μg). In all groups but one, participants received two doses, with a 21-day interval between doses; in one group (100 μg of BNT162b1), participants received one dose. Results A total of 195 participants underwent randomization. In each of 13 groups of 15 participants, 12 participants received vaccine and 3 received placebo. BNT162b2 was associated with a lower incidence and severity of systemic reactions than BNT162b1, particularly in older adults. In both younger and older adults, the two vaccine candidates elicited similar dose-dependent SARS-CoV-2–neutralizing geometric mean titers, which were similar to or higher than the geometric mean titer of a panel of SARS-CoV-2 convalescent serum samples. Conclusions The safety and immunogenicity data from this U.S. phase 1 trial of two vaccine candidates in younger and older adults, added to earlier interim safety and immunogenicity data regarding BNT162b1 in younger adults from trials in Germany and the United States, support the selection of BNT162b2 for advancement to a pivotal phase 2–3 safety and efficacy evaluation. (Funded by BioNTech and Pfizer; ClinicalTrials.gov number, NCT04368728.)
Extracted statement — Vaccine Name: BNT162b2
Research field: Immunology and Infectious Disease
Title: Phase 1 Assessment of the Safety and Immunogenicity of an mRNA- Lipid Nanoparticle Vaccine Candidate Against SARS-CoV-2 in Human Volunteers
Abstract: There is an urgent need for vaccines to counter the COVID-19 pandemic due to infections with severe acute respiratory syndrome coronavirus (SARS-CoV-2). Evidence from convalescent sera and preclinical studies has identified the viral Spike (S) protein as a key antigenic target for protective immune responses. We have applied an mRNA-based technology platform, RNActive, to develop CVnCoV which contains sequence optimized mRNA coding for a stabilized form of S protein encapsulated in lipid nanoparticles (LNP). Following demonstration of protective immune responses against SARS-CoV-2 in animal models we performed a dose-escalation phase 1 study in healthy 18-60 year-old volunteers. This interim analysis shows that two doses of CVnCoV ranging from 2 g to 12 g per dose, administered 28 days apart were safe. No vaccine-related serious adverse events were reported. There were dose-dependent increases in frequency and severity of solicited systemic adverse events, and to a lesser extent of local reactions, but the majority were mild or moderate and transient in duration. Immune responses when measured as IgG antibodies against S protein or its receptor-binding domain (RBD) by ELISA, and SARS-CoV-2-virus neutralizing antibodies measured by micro-neutralization, displayed dose-dependent increases. Median titers measured in these assays two weeks after the second 12 g dose were comparable to the median titers observed in convalescent sera from COVID-19 patients. Seroconversion (defined as a 4-fold increase over baseline titer) of virus neutralizing antibodies two weeks after the second vaccination occurred in all participants who received 12 g doses. Preliminary results in the subset of subjects who were enrolled with known SARS-CoV-2 seropositivity at baseline show that CVnCoV is also safe and well tolerated in this population, and is able to boost the pre-existing immune response even at low dose levels. Based on these results, the 12 g dose is selected for further clinical investigation, including a phase 2b/3 study that will investigate the efficacy, safety, and immunogenicity of the candidate vaccine CVnCoV.
Extracted statement — Vaccine Name: CVnCoV
Research field: Immunology and Infectious Disease
Title: An mRNA Vaccine against SARS-CoV-2 — Preliminary Report
Abstract: Abstract Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019 and spread globally, prompting an international effort to accelerate development of a vaccine. The candidate vaccine mRNA-1273 encodes the stabilized prefusion SARS-CoV-2 spike protein. Methods We conducted a phase 1, dose-escalation, open-label trial including 45 healthy adults, 18 to 55 years of age, who received two vaccinations, 28 days apart, with mRNA-1273 in a dose of 25 μg, 100 μg, or 250 μg. There were 15 participants in each dose group. Results After the first vaccination, antibody responses were higher with higher dose (day 29 enzyme-linked immunosorbent assay anti–S-2P antibody geometric mean titer [GMT], 40,227 in the 25-μg group, 109,209 in the 100-μg group, and 213,526 in the 250-μg group). After the second vaccination, the titers increased (day 57 GMT, 299,751, 782,719, and 1,192,154, respectively). After the second vaccination, serum-neutralizing activity was detected by two methods in all participants evaluated, with values generally similar to those in the upper half of the distribution of a panel of control convalescent serum specimens. Solicited adverse events that occurred in more than half the participants included fatigue, chills, headache, myalgia, and pain at the injection site. Systemic adverse events were more common after the second vaccination, particularly with the highest dose, and three participants (21%) in the 250-μg dose group reported one or more severe adverse events. Conclusions The mRNA-1273 vaccine induced anti–SARS-CoV-2 immune responses in all participants, and no trial-limiting safety concerns were identified. These findings support further development of this vaccine. (Funded by the National Institute of Allergy and Infectious Diseases and others; mRNA-1273 ClinicalTrials.gov number, NCT04283461).
Extracted statement — Vaccine Name: mRNA-1273
Research field: Information Science
Title: AUPress: A Comparison of an Open Access University Press with Traditional Presses
Abstract: This study is a comparison of AUPress with three other traditional (non-open access) Canadian university presses. The analysis is based on the rankings that are correlated with book sales on Amazon.com and Amazon.ca. Statistical methods include the sampling of the sales ranking of randomly selected books from each press. The results of one-way ANOVA analyses show that there is no significant difference in the ranking of printed books sold by AUPress in comparison with traditional university presses. However, AUPress, can demonstrate a significantly larger readership for its books as evidenced by the number of downloads of the open electronic versions.
Extracted statement — statistical_methods: ANOVA
Research field: Information Science
Title: Persistent Identification of Instruments
Abstract: Instruments play an essential role in creating research data. Given the importance of instruments and associated metadata to the assessment of data quality and data reuse, globally unique, persistent and resolvable identification of instruments is crucial. The Research Data Alliance Working Group Persistent Identification of Instruments (PIDINST) developed a community-driven solution for persistent identification of instruments which we present and discuss in this paper. Based on an analysis of 10 use cases, PIDINST developed a metadata schema and prototyped schema implementation with DataCite and ePIC as representative persistent identifier infrastructures and with HZB (Helmholtz-Zentrum Berlin fur Materialien und Energie) and BODC (British Oceanographic Data Centre) as representative institutional instrument providers. These implementations demonstrate the viability of the proposed solution in practice. Moving forward, PIDINST will further catalyse adoption and consolidate the schema by addressing new stakeholder requirements.
Extracted statement — Used by: BODC
Research field: Information Science
Title: DataCite: Lessons Learned on Persistent Identifiers for Research Data
Abstract: Data are the infrastructure of science and they serve as the groundwork for scientific pursuits. Data publication has emerged as a game-changing breakthrough in scholarly communication. Data form the outputs of research but also are a gateway to new hypotheses, enabling new scientific insights and driving innovation. And yet stakeholders across the scholarly ecosystem, including practitioners, institutions, and funders of scientific research are increasingly concerned about the lack of sharing and reuse of research data. Across disciplines and countries, researchers, funders, and publishers are pushing for a more effective research environment, minimizing the duplication of work and maximizing the interaction between researchers. Availability, discoverability, and reproducibility of research outputs are key factors to support data reuse and make possible this new environment of highly collaborative research. An interoperable e-infrastructure is imperative in order to develop new platforms and services for to data publication and reuse. DataCite has been working to establish and promote methods to locate, identify and share information about research data. Along with service development, DataCite supports and advocates for the standards behind persistent identifiers (in particular DOIs, Digital Object Identifiers) for data and other research outputs. Persistent identifiers allow different platforms to exchange information consistently and unambiguously and provide a reliable way to track citations and reuse. Because of this, data publication can become a reality from a technical standpoint, but the adoption of data publication and data citation as a practice by researchers is still in its early stages. Since 2009, DataCite has been developing a series of tools and services to foster the adoption of data publication and citation among the research community. Through the years, DataCite has worked in a close collaboration with interdisciplinary partners on these issues and we have gained insight into the development of data publication workflows. This paper describes the types of different actions and the lessons learned by DataCite.
Extracted statement — uses identifier system: DOI
Research field: Information Science
Title: Persistent Identification of Instruments
Abstract: Instruments play an essential role in creating research data. Given the importance of instruments and associated metadata to the assessment of data quality and data reuse, globally unique, persistent and resolvable identification of instruments is crucial. The Research Data Alliance Working Group Persistent Identification of Instruments (PIDINST) developed a community-driven solution for persistent identification of instruments which we present and discuss in this paper. Based on an analysis of 10 use cases, PIDINST developed a metadata schema and prototyped schema implementation with DataCite and ePIC as representative persistent identifier infrastructures and with HZB (Helmholtz-Zentrum Berlin fur Materialien und Energie) and BODC (British Oceanographic Data Centre) as representative institutional instrument providers. These implementations demonstrate the viability of the proposed solution in practice. Moving forward, PIDINST will further catalyse adoption and consolidate the schema by addressing new stakeholder requirements.
Extracted statement — Used by: HZB
Research field: Information Science
Title: Microsoft Academic Graph: When experts are not enough
Abstract: <jats:p> An ongoing project explores the extent to which artificial intelligence (AI), specifically in the areas of natural language processing and semantic reasoning, can be exploited to facilitate the studies of science by deploying software agents equipped with natural language understanding capabilities to read scholarly publications on the web. The knowledge extracted by these AI agents is organized into a heterogeneous graph, called Microsoft Academic Graph (MAG), where the nodes and the edges represent the entities engaging in scholarly communications and the relationships among them, respectively. The frequently updated data set and a few software tools central to the underlying AI components are distributed under an open data license for research and commercial applications. This paper describes the design, schema, and technical and business motivations behind MAG and elaborates how MAG can be used in analytics, search, and recommendation scenarios. How AI plays an important role in avoiding various biases and human induced errors in other data sets and how the technologies can be further improved in the future are also discussed. </jats:p>
Extracted statement — Database: MAG
Research field: Information Science
Title: The MIPS mammalian protein–protein interaction database
Abstract: SUMMARY The MIPS mammalian protein-protein interaction database (MPPI) is a new resource of high-quality experimental protein interaction data in mammals. The content is based on published experimental evidence that has been processed by human expert curators. We provide the full dataset for download and a flexible and powerful web interface for users with various requirements.
Extracted statement — Database: MIPS
Research field: Information Science
Title: A Hybrid Knowlegde-Based Approach for Recommending Massive Learning Activities
Abstract: In recent years, the development of recommender systems has attracted increased interest in several domains, especially in e-learning. Massive Open Online Courses have brought a revolution. However, deficiency in support and personalization in this context drive learners to lose their motivation and leave the learning process. To overcome this problem we focus on adapting learning activities to learners' needs using a recommender system.This paper attempts to provide an introduction to different recommender systems for e-learning settings, as well as to present our proposed recommender system for massive learning activities in order to provide learners with the suitable learning activities to follow the learning process and maintain their motivation. We propose a hybrid knowledge-based recommender system based on ontology for recommendation of e-learning activities to learners in the context of MOOCs. In the proposed recommendation approach, ontology is used to model and represent the knowledge about the domain model, learners and learning activities.
Extracted statement — keywords: MOOC
Research field: Information Science
Title: A Hybrid Knowlegde-Based Approach for Recommending Massive Learning Activities
Abstract: In recent years, the development of recommender systems has attracted increased interest in several domains, especially in e-learning. Massive Open Online Courses have brought a revolution. However, deficiency in support and personalization in this context drive learners to lose their motivation and leave the learning process. To overcome this problem we focus on adapting learning activities to learners' needs using a recommender system.This paper attempts to provide an introduction to different recommender systems for e-learning settings, as well as to present our proposed recommender system for massive learning activities in order to provide learners with the suitable learning activities to follow the learning process and maintain their motivation. We propose a hybrid knowledge-based recommender system based on ontology for recommendation of e-learning activities to learners in the context of MOOCs. In the proposed recommendation approach, ontology is used to model and represent the knowledge about the domain model, learners and learning activities.
Extracted statement — Development in: OWL