424,805 research outputs found

    SciKGTeX - A LATEX Package to Semantically Annotate Contributions in Scientific Publications

    Get PDF
    The continuously increasing output of published research makes the work of researchers harder as it becomes impossible to keep track of and compare the most recent advances in a field. Scientific knowledge graphs have been proposed as a solution to structure the content of research publications in a machine-readable way and enable more efficient, computer-assisted workflows for many research activities. Crowdsourcing approaches are used frequently to build and maintain such scientific knowledge graphs. Researchers are motivated to contribute to these crowdsourcing efforts as they want their work to be included in the knowledge graphs and benefit from applications built on top of them. To contribute to scientific knowledge graphs, researchers need simple and easy-to-use solutions to generate new knowledge graph elements and establish the practice of semantic representations in scientific communication. In this thesis, I present SciKGTeX, a LATEX package to semantically annotate scientific contributions at the time of document creation. The LATEX package allows authors of scientific publications to mark the main contributions such as the background, research problem, method, results and conclusion of their work directly in LATEX source files. The package then automatically embeds them as metadata into the generated PDF document. In addition to the package, I document a user evaluation with 26 participants which I conducted to assess the usability and feasibility of the solution. The analysis of the evaluation results shows that SciKGTeX is highly usable with a score of 79 out of 100 on the System Usability Scale. Furthermore, the study showed that the functionalities of the package can be picked up very quickly by the study participants which only needed 7 minutes on average to annotate the main contributions on a sample abstract of a published paper. SciKGTeX demonstrates a new way to generate structured metadata for the key contributions of research publications and embed them into PDF files at the time of document creation

    Encoding models for scholarly literature

    Get PDF
    We examine the issue of digital formats for document encoding, archiving and publishing, through the specific example of "born-digital" scholarly journal articles. We will begin by looking at the traditional workflow of journal editing and publication, and how these practices have made the transition into the online domain. We will examine the range of different file formats in which electronic articles are currently stored and published. We will argue strongly that, despite the prevalence of binary and proprietary formats such as PDF and MS Word, XML is a far superior encoding choice for journal articles. Next, we look at the range of XML document structures (DTDs, Schemas) which are in common use for encoding journal articles, and consider some of their strengths and weaknesses. We will suggest that, despite the existence of specialized schemas intended specifically for journal articles (such as NLM), and more broadly-used publication-oriented schemas such as DocBook, there are strong arguments in favour of developing a subset or customization of the Text Encoding Initiative (TEI) schema for the purpose of journal-article encoding; TEI is already in use in a number of journal publication projects, and the scale and precision of the TEI tagset makes it particularly appropriate for encoding scholarly articles. We will outline the document structure of a TEI-encoded journal article, and look in detail at suggested markup patterns for specific features of journal articles

    Communication and re-use of chemical information in bioscience.

    Get PDF
    The current methods of publishing chemical information in bioscience articles are analysed. Using 3 papers as use-cases, it is shown that conventional methods using human procedures, including cut-and-paste are time-consuming and introduce errors. The meaning of chemical terms and the identity of compounds is often ambiguous. valuable experimental data such as spectra and computational results are almost always omitted. We describe an Open XML architecture at proof-of-concept which addresses these concerns. Compounds are identified through explicit connection tables or links to persistent Open resources such as PubChem. It is argued that if publishers adopt these tools and protocols, then the quality and quantity of chemical information available to bioscientists will increase and the authors, publishers and readers will find the process cost-effective.An article submitted to BiomedCentral Bioinformatics, created on request with their Publicon system. The transformed manuscript is archived as PDF. Although it has been through the publishers system this is purely automatic and the contents are those of a pre-refereed preprint. The formatting is provided by the system and tables and figures appear at the end. An accommpanying submission, http://www.dspace.cam.ac.uk/handle/1810/34580, describes the rationale and cultural aspects of publishing , abstracting and aggregating chemical information. BMC is an Open Access publisher and we emphasize that all content is re-usable under Creative Commons Licens

    Further clarifications about the success-index

    Get PDF
    The aim of this brief communication is to reply to a letter by Kosmulski (Journal of Informetrics 6(3):368-369, 2012), which criticizes a recent indicator called "success-index". The most interesting features of this indicator, presented in Franceschini et al. (Scientometrics, in press), are: (i) allowing the selection of an "elite" subset from a set of publications and (ii) implementing the field-normalization at the level of an individual publication. We show that the Kosmulski's criticism is unfair and inappropriate, as it is the result of a misinterpretation of the indicato

    Citation analysis of Canadian psycho-oncology and supportive care researchers

    Get PDF
    Purpose: The purpose of this study was to conduct a historical review of psycho-oncology and supportive care research in Canada using citation analysis and to review the clinical impact of the research conducted by the most highly cited researchers. Methods: The lifetime journal publication records of 109 psycho-oncology and supportive care researchers in Canada were subject to citation analysis using the Scopus database, based on citations since 1996 of articles deemed relevant to psychosocial oncology and supportive care, excluding selfcitations. Three primary types of analysis were performed for each individual: the number of citations for each journal publication, a summative citation count of all published articles, and the Scopus h-index. Results: The top 20 psycho-oncology/supportive care researchers for each of five citation categories are presented: the number of citations for all publications; the number of citations for first-authored publications; the most highly cited first-authored publications; the Scopus h-index for all publications; and the Scopus h-index for first-authored publications. The three most highly cited Canadian psychooncology researchers are Dr. Kerry Courneya (University of Alberta), Dr. Lesley Degner, (University of Manitoba), and Dr. Harvey Chochinov (University of Manitoba). Conclusions: Citation analysis is useful for examining the research performance of psycho-oncology and supportive care researchers and identifying leaders among the

    The distorted mirror of Wikipedia: a quantitative analysis of Wikipedia coverage of academics

    Get PDF
    Activity of modern scholarship creates online footprints galore. Along with traditional metrics of research quality, such as citation counts, online images of researchers and institutions increasingly matter in evaluating academic impact, decisions about grant allocation, and promotion. We examined 400 biographical Wikipedia articles on academics from four scientific fields to test if being featured in the world's largest online encyclopedia is correlated with higher academic notability (assessed through citation counts). We found no statistically significant correlation between Wikipedia articles metrics (length, number of edits, number of incoming links from other articles, etc.) and academic notability of the mentioned researchers. We also did not find any evidence that the scientists with better WP representation are necessarily more prominent in their fields. In addition, we inspected the Wikipedia coverage of notable scientists sampled from Thomson Reuters list of "highly cited researchers". In each of the examined fields, Wikipedia failed in covering notable scholars properly. Both findings imply that Wikipedia might be producing an inaccurate image of academics on the front end of science. By shedding light on how public perception of academic progress is formed, this study alerts that a subjective element might have been introduced into the hitherto structured system of academic evaluation.Comment: To appear in EPJ Data Science. To have the Additional Files and Datasets e-mail the corresponding autho
    • …
    corecore