108,874 research outputs found
Surfacing the deep data of taxonomy
Taxonomic databases are perpetuating approaches to citing literature that may have been appropriate before the Internet, often being little more than digitised 5 × 3 index cards. Typically the original taxonomic literature is either not cited, or is represented in the form of a (typically abbreviated) text string. Hence much of the “deep data” of taxonomy, such as the original descriptions, revisions, and nomenclatural actions are largely hidden from all but the most resourceful users. At the same time there are burgeoning efforts to digitise the scientific literature, and much of this newly available content has been assigned globally unique identifiers such as Digital Object Identifiers (DOIs), which are also the identifier of choice for most modern publications. This represents an opportunity for taxonomic databases to engage with digitisation efforts. Mapping the taxonomic literature on to globally unique identifiers can be time consuming, but need be done only once. Furthermore, if we reuse existing identifiers, rather than mint our own, we can start to build the links between the diverse data that are needed to support the kinds of inference which biodiversity informatics aspires to support. Until this practice becomes widespread, the taxonomic literature will remain balkanized, and much of the knowledge that it contains will linger in obscurity
Discovering Rehabilitation trends in Spain: A bibliometric analysis
The main purpose of this study is to offer an overview of the rehabilitation research area in Spain from 1970 to 2018 through a bibliometric analysis. Analysis of performance and a co-word science mapping analysis were conducted to highlight the topics covered. The software tool SciMAT was used to analyse the themes concerning their performance and impact measures. A total of 3,564 documents from the Web of Science were retrieved. Univ Deusto, Univ Rey Juan Carlos and Basque Foundation for Science are the institutions with highest relative priority. The most important research themes are IntellectualDisability, Neck-Pain and Pain
Embedding-based Scientific Literature Discovery in a Text Editor Application
Each claim in a research paper requires all relevant prior knowledge to be
discovered, assimilated, and appropriately cited. However, despite the
availability of powerful search engines and sophisticated text editing
software, discovering relevant papers and integrating the knowledge into a
manuscript remain complex tasks associated with high cognitive load. To define
comprehensive search queries requires strong motivation from authors,
irrespective of their familiarity with the research field. Moreover, switching
between independent applications for literature discovery, bibliography
management, reading papers, and writing text burdens authors further and
interrupts their creative process. Here, we present a web application that
combines text editing and literature discovery in an interactive user
interface. The application is equipped with a search engine that couples
Boolean keyword filtering with nearest neighbor search over text embeddings,
providing a discovery experience tuned to an author's manuscript and his
interests. Our application aims to take a step towards more enjoyable and
effortless academic writing.
The demo of the application (https://SciEditorDemo2020.herokuapp.com/) and a
short video tutorial (https://youtu.be/pkdVU60IcRc) are available online
What country, university or research institute, performed the best on COVID-19? Bibliometric analysis of scientific literature
In this article, we conduct data mining to discover the countries,
universities and companies, produced or collaborated the most research on
Covid-19 since the pandemic started. We present some interesting findings, but
despite analysing all available records on COVID-19 from the Web of Science
Core Collection, we failed to reach any significant conclusions on how the
world responded to the COVID-19 pandemic. Therefore, we increased our analysis
to include all available data records on pandemics and epidemics from 1900 to
2020. We discover some interesting results on countries, universities and
companies, that produced collaborated most the most in research on pandemic and
epidemics. Then we compared the results with the analysing on COVID-19 data
records. This has created some interesting findings that are explained and
graphically visualised in the article
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
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